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commit , wait to tune api

xiaopzhang 1 miesiąc temu
rodzic
7fb01e3d13
commit
a6cc53b698
40 zmienionych plików z 3877 dodań i 32 usunięć
Zunifikowany widok Pokaż statystyki zmian
  1. 0 0
      0103basic_rule
  2. 5 5
      JD_DanWeiGJ.csv
  3. 17 0
      README
  4. 6 3
      Service.js
  5. 0 0
      basic_rule
  6. 764 0
      cuoshi_corpus.jsonl
  7. 74 0
      cuoshi_corpus.txt
  8. 5 5
      de/tj/JD_DanWeiGJ.csv
  9. 5 0
      dedata.py
  10. 56 0
      dianceng.py
  11. 83 0
      extra.py
  12. 71 0
      fallback.py
  13. 1 0
      gangjiegouhuansuan
  14. 59 0
      huansuan.py
  15. 90 0
      huansuan0103.py
  16. 49 0
      huansuan0105.py
  17. 62 0
      huansuan0106.py
  18. 36 0
      huansuan0108.py
  19. 37 0
      huansuan0110.py
  20. 38 0
      jieheceng.py
  21. 0 0
      label_name
  22. 45 0
      lingji.py
  23. 0 0
      menchuang_rule
  24. 7 0
      menchuangfallback.py
  25. 0 0
      name_label
  26. 0 0
      nantong_rule
  27. 35 0
      postprocess.py
  28. 197 0
      postprocess0101.py
  29. 252 0
      postprocess0103.py
  30. 50 0
      postprocess0104.py
  31. 92 0
      postprocess0105.py
  32. 376 0
      postprocess0106.py
  33. 174 0
      postprocess0108.py
  34. 268 0
      postprocess0109.py
  35. 260 0
      postprocess0110.py
  36. 302 0
      postprocess0111.py
  37. 65 0
      postprocess0117.py
  38. 3 0
      server.js
  39. 292 19
      tasks.py
  40. 1 0
      zhaoping_rule

Plik diff jest za duży
+ 0 - 0
0103basic_rule


+ 5 - 5
JD_DanWeiGJ.csv
Wyświetl plik 

@@ -860,12 +860,12 @@
 
 596,597,4-98,基础垫层 道碴垫层,,,85.71,1.27,46.2,11.87,,5.7,,,133.18,150.75,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,597
 
 596,597,4-98,基础垫层 道碴垫层,,,85.71,1.27,46.2,11.87,,5.7,,,133.18,150.75,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,597
 
 597,598,4-99,基础垫层 碎石 干铺,,,110.46,1.27,46.2,11.87,,5.7,,,157.93,175.5,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,598
 
 597,598,4-99,基础垫层 碎石 干铺,,,110.46,1.27,46.2,11.87,,5.7,,,157.93,175.5,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,598
 
 598,599,4-100,基础垫层 碎石 灌石灰黏土浆,,,121.2,1.27,65.45,16.68,,8.01,,,187.92,212.61,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,599
 
 598,599,4-100,基础垫层 碎石 灌石灰黏土浆,,,121.2,1.27,65.45,16.68,,8.01,,,187.92,212.61,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,599
 
-599,600,4-101,M2.5基础垫层 碎石 灌砂浆,,,161.59,9.12,65.45,18.64,,8.95,,,236.16,263.75,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,600
 
+599,600,4-101,基础垫层 碎石 灌M2.5混合砂浆,,,161.59,9.12,65.45,18.64,,8.95,,,236.16,263.75,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,600
 
 600,601,4-102,基础垫层 碎砖 干铺,,,60.68,1.27,43.89,11.29,,5.42,,,105.84,122.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,601
 
 600,601,4-102,基础垫层 碎砖 干铺,,,60.68,1.27,43.89,11.29,,5.42,,,105.84,122.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,601
 
 601,602,4-103,基础垫层 碎砖 灌石灰黏土浆,,,79.13,1.27,65.45,16.68,,8.01,,,145.85,170.54,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,602
 
 601,602,4-103,基础垫层 碎砖 灌石灰黏土浆,,,79.13,1.27,65.45,16.68,,8.01,,,145.85,170.54,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,602
 
-602,603,4-104,M2.5基础垫层 碎砖 灌砂浆,,,98.96,6.42,65.45,17.97,,8.62,,,170.83,197.42,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,603
 
+602,603,4-104,基础垫层 碎砖 灌M2.5混合砂浆,,,98.96,6.42,65.45,17.97,,8.62,,,170.83,197.42,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,603
 
 603,604,4-105,基础垫层 毛石 干铺,,,112.52,1.27,45.43,11.68,,5.6,,,159.22,176.5,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,604
 
 603,604,4-105,基础垫层 毛石 干铺,,,112.52,1.27,45.43,11.68,,5.6,,,159.22,176.5,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,604
 
-604,605,4-106,M2.5基础垫层 毛石 灌砂浆,,,141.97,6.67,76.23,20.73,,9.95,,,224.87,255.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,605
 
+604,605,4-106,基础垫层 毛石 灌M2.5混合砂浆,,,141.97,6.67,76.23,20.73,,9.95,,,224.87,255.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,605
 
 605,606,4-107,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1,,,123.06,1.27,53.9,13.79,,6.62,,,178.23,198.64,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,606
 
 605,606,4-107,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1,,,123.06,1.27,53.9,13.79,,6.62,,,178.23,198.64,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,606
 
 606,607,4-108,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1.5,,,118.32,1.27,53.9,13.79,,6.62,,,173.49,193.9,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,607
 
 606,607,4-108,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1.5,,,118.32,1.27,53.9,13.79,,6.62,,,173.49,193.9,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,607
 
 607,608,4-109,基础垫层 碎石(道碴)和砂(石屑) 压路机 1:1,,,141.6,8.57,48.51,14.27,,6.85,,,198.68,219.8,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,608
 
 607,608,4-109,基础垫层 碎石(道碴)和砂(石屑) 压路机 1:1,,,141.6,8.57,48.51,14.27,,6.85,,,198.68,219.8,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,608
 
@@ -2986,11 +2986,11 @@
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1947
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1947
 
 1947,1948,13-5,毛石 干铺垫层,,,112.4,1.06,47.56,12.16,,5.83,,,161.02,179.01,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 1947,1948,13-5,毛石 干铺垫层,,,112.4,1.06,47.56,12.16,,5.83,,,161.02,179.01,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1948
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1948
 
-1948,1949,13-6,M2.5毛石 灌浆垫层,,,141.03,6.33,76.26,20.65,,9.91,,,223.62,254.18,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
+1948,1949,13-6,毛石 灌M2.5混合砂浆垫层,,,141.03,6.33,76.26,20.65,,9.91,,,223.62,254.18,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1949
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1949
 
 1949,1950,13-7,碎砖 干铺垫层,,,57.75,1.06,41.0,10.52,,5.05,,,99.81,115.38,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 1949,1950,13-7,碎砖 干铺垫层,,,57.75,1.06,41.0,10.52,,5.05,,,99.81,115.38,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1950
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1950
 
-1950,1951,13-8,M2.5碎砖 灌浆垫层,,,87.6,6.21,61.5,16.93,,8.13,,,155.31,180.37,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
+1950,1951,13-8,碎砖 灌M2.5混合砂浆垫层,,,87.6,6.21,61.5,16.93,,8.13,,,155.31,180.37,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1951
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1951
 
 1951,1952,13-9,碎石 干铺垫层,,,110.46,1.06,43.46,11.13,,5.34,,,154.98,171.45,,,m3,,,,,00000245,,,000001;000246;000247;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1952
 
 1951,1952,13-9,碎石 干铺垫层,,,110.46,1.06,43.46,11.13,,5.34,,,154.98,171.45,,,m3,,,,,00000245,,,000001;000246;000247;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1952
 
 1952,1953,13-10,道碴 干铺垫层,,,85.71,1.06,64.78,16.46,,7.9,,,151.55,175.91,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1953
 
 1952,1953,13-10,道碴 干铺垫层,,,85.71,1.06,64.78,16.46,,7.9,,,151.55,175.91,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1953

+ 17 - 0
README
Wyświetl plik 

@@ -1,2 +1,19 @@
 
 celery -A tasks worker --loglevel=info
 
 celery -A tasks worker --loglevel=info
 
 ZAI 29b9cd63fa5f4666bf01aaf9f7c0302e.8NqXiixA3oAZgq1o
 
 ZAI 29b9cd63fa5f4666bf01aaf9f7c0302e.8NqXiixA3oAZgq1o
 
+HUNYUAN sk-Sshjopx8L4M035UbwEs3Wb8OjhwI6HgkvJHjbSRWqdpzyzth
 
+BAIDU bce-v3/ALTAK-K98CKmtc7tJlL9YI0CUPb/48d71a9d697244aac26c76b626bfd6cba2178800
 
+SF sk-qurpsudpuorcunpfandrxapbyqaauthpxdhrdhsxdnklctgo
 
+wuwen sk-oxwdksnnrfkkbgcq
 
+对于回填土，定额套用的套路是这样的
 
+首先。要考虑土的来源。
 
+一种情况是，不需要运土
 
+另一种情况是，土是从其他地方挖运过来的，需要机械作业。
 
+机械作业的话，有两种可能的方式
 
+一种是推土机推土
 
+另一种是挖掘机挖土加上自卸汽车运土。
 
+有了土之后，腰考虑填土
 
+填土可以是机械填，可以是人工填，可以给给个比例。
 
+比如人工20%机械80%
 
+当然，你可以选择全是人工。考虑到机械施展不开。
 
+但是一般情况下，基坑回填是不能全是机械没有人工的
 
+

+ 6 - 3
Service.js
Wyświetl plik 

@@ -2181,14 +2181,17 @@ class Service{
 
         }
 
         }
 
     }
 
     }
 
 
 
-    danxiangdinge(row) {
 
-        let bh = danxiangdinge_index(this.cache);
 
+    danxiangdinge(row, maxN, extra) {
 
+        //let bh = danxiangdinge_index(this.cache);
 
+	     let result =  (maxN + 1).toString();
 
+        result = result.padStart(5, '0');
 
+        let bh= "D".concat(result);
 
         
         
         if (this.cache.filter(x=>x['key'] == row).length > 0) {
 
         if (this.cache.filter(x=>x['key'] == row).length > 0) {
 
             this.updateFootprint(row);
 
             this.updateFootprint(row);
 
             let selected = this.cache.filter(x=>x['key'] == row)[0];
 
             let selected = this.cache.filter(x=>x['key'] == row)[0];
 
             let danwei = selected["单位"];
 
             let danwei = selected["单位"];
 
-            let mingcheng = selected['名称'];
 
+            let mingcheng = extra;//selected['名称'];
 
             let children = selected["_children"];
 
             let children = selected["_children"];
 
             children.push({
 
             children.push({
 
                 '操作': '',
 
                 '操作': '',

Plik diff jest za duży
+ 0 - 0
basic_rule


+ 764 - 0
cuoshi_corpus.jsonl
Wyświetl plik 

@@ -0,0 +1,764 @@
 
+{"id":"0","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e30m"}
 
+{"id":"1","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e40m"}
 
+{"id":"2","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e50m"}
 
+{"id":"3","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e60m"}
 
+{"id":"4","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e70m"}
 
+{"id":"5","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e80m"}
 
+{"id":"6","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e90m"}
 
+{"id":"7","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e100m"}
 
+{"id":"8","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e110m"}
 
+{"id":"9","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e120m"}
 
+{"id":"10","text":"\u7b2c\u5341\u4e5d\u7ae0  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39\u7528 19.1  \u5efa\u7b51\u7269\u8d85\u9ad8\u589e\u52a0\u8d39 \u5efa\u7b51\u7269\u6a90\u53e3\u9ad8\u5ea6 20m(7\u5c42)\uff5e130m"}
 
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+{"id":"740","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u5761\u5ea6\u5927\u4e8e10\u00b0\u7684\u659c\u73b0\u6d47\u677f \u539a\u5ea650cm\u5185 \u7ec4\u5408\u94a2\u6a21\u677f"}
 
+{"id":"741","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u5761\u5ea6\u5927\u4e8e10\u00b0\u7684\u659c\u73b0\u6d47\u677f \u539a\u5ea650cm\u5185 \u590d\u5408\u6a21\u677f"}
 
+{"id":"742","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea610cm\u5185 \u7ec4\u5408\u94a2\u6a21\u677f"}
 
+{"id":"743","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea610cm\u5185 \u590d\u5408\u6728\u6a21\u677f"}
 
+{"id":"744","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea620cm\u5185 \u7ec4\u5408\u94a2\u6a21\u677f"}
 
+{"id":"745","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea620cm\u5185 \u590d\u5408\u6728\u6a21\u677f"}
 
+{"id":"746","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea630cm\u5185 \u7ec4\u5408\u94a2\u6a21\u677f"}
 
+{"id":"747","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea630cm\u5185 \u590d\u5408\u6728\u6a21\u677f"}
 
+{"id":"748","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea650cm\u5185 \u7ec4\u5408\u94a2\u6a21\u677f"}
 
+{"id":"749","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u677f \u9636\u68af\u6559\u5ba4\u3001\u4f53\u80b2\u770b\u53f0\u677f \u539a\u5ea650cm\u5185 \u590d\u5408\u6728\u6a21\u677f"}
 
+{"id":"750","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u5176\u4ed6 \u73b0\u6d47\u783c\u95e8\u7a97\u67f1\u6728\u6a21\u677f"}
 
+{"id":"751","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u5176\u4ed6 \u6df7\u51dd\u571f\u9762\u5b89\u88c5\u783c\u590d\u5408\u4fdd\u6e29(XPS)\u677f\uff08\u4f5c\u6c38\u4e45\u6027\u6a21\u677f\uff09"}
 
+{"id":"752","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u6df7\u51dd\u571f\u3001\u7816\u5e95\u80ce\u6a21\u53ca\u7816\u4fa7\u6a21 \u516b\u4e94\u7816\u4fa7\u6a21"}
 
+{"id":"753","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u6df7\u51dd\u571f\u3001\u7816\u5e95\u80ce\u6a21\u53ca\u7816\u4fa7\u6a21 \u516b\u4e94\u534a\u7816\u4fa7\u6a21"}
 
+{"id":"754","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e00\u7ae0 \u6a21\u677f\u5de5\u7a0b \u73b0\u6d47\u6784\u4ef6 \u6df7\u51dd\u571f\u3001\u7816\u5e95\u80ce\u6a21\u53ca\u7816\u4fa7\u6a21 \u51f8\u51fa\u6574\u677f\u57fa\u7840\u4e0b\u8868\u9762\u5f27\u5f62\u53cd\u6881\u7816\u4fa7\u6a21"}
 
+{"id":"755","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e09\u7ae0 \u5efa\u7b51\u5de5\u7a0b\u5782\u76f4\u8fd0\u8f93 \u4ec5\u91c7\u7528\u5854\u5f0f\u8d77\u91cd\u673a\u65bd\u5de5 \u7816\u6df7\u7ed3\u6784 \u6a90\u53e3\u9ad8\u5ea6\uff08\u5c42\u6570\uff09\u4ee5\u5185 20m(6)"}
 
+{"id":"756","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e09\u7ae0 \u5efa\u7b51\u5de5\u7a0b\u5782\u76f4\u8fd0\u8f93 \u4ec5\u91c7\u7528\u5854\u5f0f\u8d77\u91cd\u673a\u65bd\u5de5 \u7816\u6df7\u7ed3\u6784 \u6a90\u53e3\u9ad8\u5ea6\uff08\u5c42\u6570\uff09\u4ee5\u5185 30m(7~10)"}
 
+{"id":"757","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e09\u7ae0 \u5efa\u7b51\u5de5\u7a0b\u5782\u76f4\u8fd0\u8f93 \u4ec5\u91c7\u7528\u5854\u5f0f\u8d77\u91cd\u673a\u65bd\u5de5 \u73b0\u6d47\u6846\u67b6 \u6a90\u53e3\u9ad8\u5ea6\uff08\u5c42\u6570\uff09\u4ee5\u5185 20m(6)"}
 
+{"id":"758","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2016 \u7b2c\u4e8c\u5341\u4e09\u7ae0 \u5efa\u7b51\u5de5\u7a0b\u5782\u76f4\u8fd0\u8f93 \u4ec5\u91c7\u7528\u5854\u5f0f\u8d77\u91cd\u673a\u65bd\u5de5 \u73b0\u6d47\u6846\u67b6 \u6a90\u53e3\u9ad8\u5ea6\uff08\u5c42\u6570\uff09\u4ee5\u5185 40m(7~13)"}
 
+{"id":"759","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2022 \u9ad8\u5927\u652f\u6a21\u6a21\u677f\u652f\u6491\u589e\u52a0\u8d39 \u9ad8\u652f\u6a21\u652f\u6491 \u589e\u52a0\u8d39 10\u7c73\u4ee5\u5185"}
 
+{"id":"760","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2022 \u9ad8\u5927\u652f\u6a21\u6a21\u677f\u652f\u6491\u589e\u52a0\u8d39 \u9ad8\u652f\u6a21\u652f\u6491 \u589e\u52a0\u8d39 12\u7c73\u4ee5\u5185"}
 
+{"id":"761","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2022 \u9ad8\u5927\u652f\u6a21\u6a21\u677f\u652f\u6491\u589e\u52a0\u8d39 \u9ad8\u652f\u6a21\u652f\u6491 \u589e\u52a0\u8d39 15\u7c73\u4ee5\u5185"}
 
+{"id":"762","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2022 \u9ad8\u5927\u652f\u6a21\u6a21\u677f\u652f\u6491\u589e\u52a0\u8d39 \u9ad8\u652f\u6a21\u652f\u6491 \u589e\u52a0\u8d39 18\u7c73\u4ee5\u5185"}
 
+{"id":"763","text":"\u5357\u901a\u8865\u5145\u5b9a\u989d \u5357\u901a\u8865\u5145\u5b9a\u989d2024 \u627f\u63d2\u578b\u76d8\u6263\u5f0f\u9ad8\u5927\u652f\u6a21\u6a21\u677f\u652f\u6491\u67b6 \u76d8\u6263\u5f0f\u94a2\u7ba1\u652f\u6491 \u652f\u6491\u9ad8\u5ea6\u226416\u7c73"}

+ 74 - 0
cuoshi_corpus.txt
Wyświetl plik 

@@ -0,0 +1,74 @@
 
+大型机械进退场及安拆费 履带式起重机15t以内 场外运输费用
 
+大型机械进退场及安拆费 履带式起重机15t以内 组装拆卸费
 
+大型机械进退场及安拆费 履带式起重机30t以内 场外运输费用
 
+大型机械进退场及安拆费 履带式起重机30t以内 组装拆卸费
 
+大型机械进退场及安拆费 履带式起重机50t以内 场外运输费用
 
+大型机械进退场及安拆费 履带式起重机50t以内 组装拆卸费
 
+大型机械进退场及安拆费 履带式起重机70t以内 场外运输费用
 
+大型机械进退场及安拆费 履带式起重机70t以内 组装拆卸费
 
+大型机械进退场及安拆费 轮胎式起重机25t以内 场外运输费用
 
+大型机械进退场及安拆费 轮胎式起重机25t以内 组装拆卸费
 
+大型机械进退场及安拆费 轮胎式起重机40t以内 场外运输费用
 
+大型机械进退场及安拆费 轮胎式起重机40t以内 组装拆卸费
 
+大型机械进退场及安拆费 轮胎式起重机60t以内 场外运输费用
 
+大型机械进退场及安拆费 轮胎式起重机60t以内 组装拆卸费
 
+大型机械进退场及安拆费 强夯机械 场外运输费用
 
+大型机械进退场及安拆费 强夯机械 组装拆卸费
 
+大型机械进退场及安拆费 柴油打桩机5t以内 场外运输费用
 
+大型机械进退场及安拆费 柴油打桩机5t以内 组装拆卸费
 
+大型机械进退场及安拆费 柴油打桩机5t以外 场外运输费用
 
+大型机械进退场及安拆费 柴油打桩机5t以外 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩机900kN 场外运输费用
 
+大型机械进退场及安拆费 静力压桩机900kN 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩机1200kN 场外运输费用
 
+大型机械进退场及安拆费 静力压桩机1200kN 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩机1600kN 场外运输费用
 
+大型机械进退场及安拆费 静力压桩机1600kN 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩机2000kN 场外运输费用
 
+大型机械进退场及安拆费 静力压桩机2000kN 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩机3000kN 场外运输费用
 
+大型机械进退场及安拆费 静力压桩机3000kN 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩机4000kN 场外运输费用
 
+大型机械进退场及安拆费 静力压桩机4000kN 组装拆卸费
 
+大型机械进退场及安拆费 塔式起重机630kNm以内 场外运输费用
 
+大型机械进退场及安拆费 塔式起重机630kNm以内 组装拆卸费
 
+大型机械进退场及安拆费 塔式起重机800kNm以内 场外运输费用
 
+大型机械进退场及安拆费 塔式起重机800kNm以内 组装拆卸费
 
+大型机械进退场及安拆费 塔式起重机1500kNm以内 场外运输费用
 
+大型机械进退场及安拆费 塔式起重机1500kNm以内 组装拆卸费
 
+大型机械进退场及安拆费 塔式起重机2500kNm以内 场外运输费用
 
+大型机械进退场及安拆费 塔式起重机2500kNm以内 组装拆卸费
 
+大型机械进退场及安拆费 自升式塔式起重机 场外运输费用
 
+大型机械进退场及安拆费 自升式塔式起重机 组装拆卸费
 
+大型机械进退场及安拆费 施工电梯75m 场外运输费用
 
+大型机械进退场及安拆费 施工电梯75m 组装拆卸费
 
+大型机械进退场及安拆费 施工电梯100m 场外运输费用
 
+大型机械进退场及安拆费 施工电梯100m 组装拆卸费
 
+大型机械进退场及安拆费 施工电梯200m以内 场外运输费用
 
+大型机械进退场及安拆费 施工电梯200m以内 组装拆卸费
 
+大型机械进退场及安拆费 混凝土搅拌站 场外运输费用
 
+大型机械进退场及安拆费 混凝土搅拌站 组装拆卸费
 
+大型机械进退场及安拆费 潜水钻孔机 场外运输费用
 
+大型机械进退场及安拆费 潜水钻孔机 组装拆卸费
 
+大型机械进退场及安拆费 转盘钻孔机(回转式工程钻机) 场外运输费用
 
+大型机械进退场及安拆费 转盘钻孔机(回转式工程钻机) 组装拆卸费
 
+大型机械进退场及安拆费 汽车式钻机(直径1000-2000以内) 场外运输费用
 
+大型机械进退场及安拆费 汽车式钻机(直径1000-2000以内) 组装拆卸费
 
+大型机械进退场及安拆费 沥青摊铺机12t以内(或带自动找平) 场外运输费用
 
+大型机械进退场及安拆费 沥青摊铺机12t以内(或带自动找平) 组装拆卸费
 
+大型机械进退场及安拆费 沥青摊铺机24t以内(或带自动找平) 场外运输费用
 
+大型机械进退场及安拆费 沥青摊铺机24t以内(或带自动找平) 组装拆卸费
 
+大型机械进退场及安拆费 桅杆式起重机5t 场外运输费用
 
+大型机械进退场及安拆费 桅杆式起重机5t 组装拆卸费
 
+大型机械进退场及安拆费 桅杆式起重机10t 场外运输费用
 
+大型机械进退场及安拆费 桅杆式起重机10t 组装拆卸费
 
+大型机械进退场及安拆费 桅杆式起重机15t 场外运输费用
 
+大型机械进退场及安拆费 桅杆式起重机15t 组装拆卸费
 
+大型机械进退场及安拆费 桅杆式起重机40t 场外运输费用
 
+大型机械进退场及安拆费 桅杆式起重机40t 组装拆卸费
 
+大型机械进退场及安拆费 深层搅拌机 场外运输费用
 
+大型机械进退场及安拆费 深层搅拌机 组装拆卸费
 
+大型机械进退场及安拆费 三轴搅拌桩机 场外运输费
 
+大型机械进退场及安拆费 三轴搅拌桩机 组装拆卸费
 
+大型机械进退场及安拆费 静力压桩（液压）机 场外运输费用
 
+大型机械进退场及安拆费 静力压桩（液压）机 组装拆卸费

+ 5 - 5
de/tj/JD_DanWeiGJ.csv
Wyświetl plik 

@@ -860,12 +860,12 @@
 
 596,597,4-98,基础垫层 道碴垫层,,,85.71,1.27,46.2,11.87,,5.7,,,133.18,150.75,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,597
 
 596,597,4-98,基础垫层 道碴垫层,,,85.71,1.27,46.2,11.87,,5.7,,,133.18,150.75,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,597
 
 597,598,4-99,基础垫层 碎石 干铺,,,110.46,1.27,46.2,11.87,,5.7,,,157.93,175.5,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,598
 
 597,598,4-99,基础垫层 碎石 干铺,,,110.46,1.27,46.2,11.87,,5.7,,,157.93,175.5,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,598
 
 598,599,4-100,基础垫层 碎石 灌石灰黏土浆,,,121.2,1.27,65.45,16.68,,8.01,,,187.92,212.61,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,599
 
 598,599,4-100,基础垫层 碎石 灌石灰黏土浆,,,121.2,1.27,65.45,16.68,,8.01,,,187.92,212.61,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,599
 
-599,600,4-101,M2.5基础垫层 碎石 灌砂浆,,,161.59,9.12,65.45,18.64,,8.95,,,236.16,263.75,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,600
 
+599,600,4-101,基础垫层 碎石 灌M2.5混合砂浆,,,161.59,9.12,65.45,18.64,,8.95,,,236.16,263.75,,,m3,,,,,00000082,,,000085;002028,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,600
 
 600,601,4-102,基础垫层 碎砖 干铺,,,60.68,1.27,43.89,11.29,,5.42,,,105.84,122.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,601
 
 600,601,4-102,基础垫层 碎砖 干铺,,,60.68,1.27,43.89,11.29,,5.42,,,105.84,122.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,601
 
 601,602,4-103,基础垫层 碎砖 灌石灰黏土浆,,,79.13,1.27,65.45,16.68,,8.01,,,145.85,170.54,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,602
 
 601,602,4-103,基础垫层 碎砖 灌石灰黏土浆,,,79.13,1.27,65.45,16.68,,8.01,,,145.85,170.54,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,602
 
-602,603,4-104,M2.5基础垫层 碎砖 灌砂浆,,,98.96,6.42,65.45,17.97,,8.62,,,170.83,197.42,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,603
 
+602,603,4-104,基础垫层 碎砖 灌M2.5混合砂浆,,,98.96,6.42,65.45,17.97,,8.62,,,170.83,197.42,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,603
 
 603,604,4-105,基础垫层 毛石 干铺,,,112.52,1.27,45.43,11.68,,5.6,,,159.22,176.5,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,604
 
 603,604,4-105,基础垫层 毛石 干铺,,,112.52,1.27,45.43,11.68,,5.6,,,159.22,176.5,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,604
 
-604,605,4-106,M2.5基础垫层 毛石 灌砂浆,,,141.97,6.67,76.23,20.73,,9.95,,,224.87,255.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,605
 
+604,605,4-106,基础垫层 毛石 灌M2.5混合砂浆,,,141.97,6.67,76.23,20.73,,9.95,,,224.87,255.55,,,m3,,,,,00000082,,,000085,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,605
 
 605,606,4-107,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1,,,123.06,1.27,53.9,13.79,,6.62,,,178.23,198.64,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,606
 
 605,606,4-107,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1,,,123.06,1.27,53.9,13.79,,6.62,,,178.23,198.64,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,606
 
 606,607,4-108,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1.5,,,118.32,1.27,53.9,13.79,,6.62,,,173.49,193.9,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,607
 
 606,607,4-108,基础垫层 碎石(道碴)和砂(石屑) 电动夯实机 1:1.5,,,118.32,1.27,53.9,13.79,,6.62,,,173.49,193.9,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,607
 
 607,608,4-109,基础垫层 碎石(道碴)和砂(石屑) 压路机 1:1,,,141.6,8.57,48.51,14.27,,6.85,,,198.68,219.8,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,608
 
 607,608,4-109,基础垫层 碎石(道碴)和砂(石屑) 压路机 1:1,,,141.6,8.57,48.51,14.27,,6.85,,,198.68,219.8,,,m3,,,,,00000082,,,000390,,1,,,,,铺设垫层、找平、灌浆、夯实。,,,0,0.0,,,,608
 
@@ -2986,11 +2986,11 @@
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1947
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1947
 
 1947,1948,13-5,毛石 干铺垫层,,,112.4,1.06,47.56,12.16,,5.83,,,161.02,179.01,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 1947,1948,13-5,毛石 干铺垫层,,,112.4,1.06,47.56,12.16,,5.83,,,161.02,179.01,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1948
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1948
 
-1948,1949,13-6,M2.5毛石 灌浆垫层,,,141.03,6.33,76.26,20.65,,9.91,,,223.62,254.18,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
+1948,1949,13-6,毛石 灌M2.5混合砂浆垫层,,,141.03,6.33,76.26,20.65,,9.91,,,223.62,254.18,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1949
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1949
 
 1949,1950,13-7,碎砖 干铺垫层,,,57.75,1.06,41.0,10.52,,5.05,,,99.81,115.38,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 1949,1950,13-7,碎砖 干铺垫层,,,57.75,1.06,41.0,10.52,,5.05,,,99.81,115.38,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1950
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1950
 
-1950,1951,13-8,M2.5碎砖 灌浆垫层,,,87.6,6.21,61.5,16.93,,8.13,,,155.31,180.37,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
+1950,1951,13-8,碎砖 灌M2.5混合砂浆垫层,,,87.6,6.21,61.5,16.93,,8.13,,,155.31,180.37,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,"1．拌和、铺设、找平、夯实。
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1951
 
 2．调制砂浆、灌缝。",,,0,0.0,,,,1951
 
 1951,1952,13-9,碎石 干铺垫层,,,110.46,1.06,43.46,11.13,,5.34,,,154.98,171.45,,,m3,,,,,00000245,,,000001;000246;000247;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1952
 
 1951,1952,13-9,碎石 干铺垫层,,,110.46,1.06,43.46,11.13,,5.34,,,154.98,171.45,,,m3,,,,,00000245,,,000001;000246;000247;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1952
 
 1952,1953,13-10,道碴 干铺垫层,,,85.71,1.06,64.78,16.46,,7.9,,,151.55,175.91,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1953
 
 1952,1953,13-10,道碴 干铺垫层,,,85.71,1.06,64.78,16.46,,7.9,,,151.55,175.91,,,m3,,,,,00000245,,,000001;000246;002087,,1,,,,,铺设、找平、夯实。,,,0,0.0,,,,1953

+ 5 - 0
dedata.py
Wyświetl plik 

@@ -79,6 +79,10 @@ def read_singledexilie2(zhuanye: int, debh: str):
 
     ####14-227+[14-228]*-2
 
     ####14-227+[14-228]*-2
 
     ####3-409*1.2
 
     ####3-409*1.2
 
     ##print(r_debh)
 
     ##print(r_debh)
 
+    r_debh = r_debh.replace("(", "")
 
+    r_debh = r_debh.replace("（", "")
 
+    r_debh = r_debh.replace(")", "")
 
+    r_debh = r_debh.replace("）", "")
 
     if "[" in r_debh:
 
     if "[" in r_debh:
 
         position3 = r_debh.find("*")
 
         position3 = r_debh.find("*")
 
         coef = 1
 
         coef = 1
 
@@ -127,6 +131,7 @@ def read_singledexilie2(zhuanye: int, debh: str):
 
         else:
 
         else:
 
             result3["reverse"] = 'None'
 
             result3["reverse"] = 'None'
 
             return json.dumps(service.chaifen(result3, actual_zhuanye), ensure_ascii=False)
 
             return json.dumps(service.chaifen(result3, actual_zhuanye), ensure_ascii=False)
 
+        print("解析算式，系数为" + str(coef))
 
         result1_, result2_, rgde_, jxde_, clde_, bz_selected_, bz_selected2_, actual_zhuanye_ = service.getSingleDeXilie(zhuanye, debh)
 
         result1_, result2_, rgde_, jxde_, clde_, bz_selected_, bz_selected2_, actual_zhuanye_ = service.getSingleDeXilie(zhuanye, debh)
 
         if result1_:
 
         if result1_:
 
             util.mergerg(rgde, rgde_, coef)
 
             util.mergerg(rgde, rgde_, coef)

+ 56 - 0
dianceng.py
Wyświetl plik 

@@ -0,0 +1,56 @@
 
+example1="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+4、150厚粒径10-40卵石灌M2.5混合砂浆
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+answer1="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+4、垫层 150厚粒径10-40卵石灌M2.5混合砂浆 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+example2="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+2、250-300厚回填土或种植土
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+answer2="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+2、250-300厚回填土或种植土
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+def dianceng(A, aiclient, qwclient):
 
+    completion = qwclient.chat.completions.create(
 
+        model="Qwen/Qwen3-32B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "垫层是一种放置在地面或其他表面下方的材料层，其主要功能是增加支撑力、减少冲击或防止渗透。常见的垫层材料包括砂、碎石、泥土和聚合物等，具体选择取决于应用需求。垫层在建筑领域的应用广泛，包括路面、停车场、建筑物地基等，能够改善基层和土基的工作条件，防止水分渗透和冻土层形成。"},
 
+            {"role": "user", "content": "给定一段文字: " + A +   "\n 该段文字描述了包括一道或多道工序的散水、坡道的施工工作内容。其中有可能包括了垫层施工工序。请你阅读这段文字，判断是否存在垫层的工作内容。如果没有，请原封不动的返回给定的文字；如果有，请在垫层的工序上注明“垫层”两个字，其余则原封不动，并返回。 例如，给定一段文字：" + example1 +"\n请返回：" + answer1 + "\n 再例如，给定一段文字： " + example2 + "\n请返回： " + answer2},
 
+        ],
 
+        #extra_body={"thinking": {"type": "disabled"}},
 
+        extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "给定一段文字: " + json_string +   "\n 请抽取出文字中的结果"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return '\n'.join([x for x in json_string.split("\n") if '结果' not in x])
 
+    
+    
+

+ 83 - 0
extra.py
Wyświetl plik 

@@ -0,0 +1,83 @@
 
+import time
 
+from menchuangfallback import menchuangfallback
 
+def extra(
 
+       data, #data
 
+       aiclient,
 
+       qwclient,
 
+       menchuang_collection,
 
+       model):
 
+    if data['bianma'].startswith("0108"):
 
+        sentence=["特征描述：" + data['mc'] + "\n" + data['tz']]
 
+        embeddings = model.encode(sentence)
 
+        result = menchuang_collection.query(query_embeddings=embeddings, n_results=10)
 
+        print(result['documents'][0])
 
+        l = len([x for x in result['distances'][0] if x < 0.5])
 
+        if l < 2:
 
+            l = 2
 
+        completion = aiclient.chat.completions.create(
 
+            model="glm-4.5-flash",
 
+            messages=[
 
+                {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+                {"role": "user", "content": "特征描述往往比较具体，工作内容是对特征描述的主要关键的总结提炼。以下是一些特征描述以及对应的提炼的工作内容的例子。" + '\n\n'.join(result['documents'][0][:l]) + "给定一段特征描述，内容为" + data['mc'] +data['tz'] + "。请参照示例，给出提炼的工作内容. 注意，不需要输出特征描述，仅输出工作内容"},
 
+            ],
 
+            extra_body={"thinking": {"type": "disabled"}},
 
+        )
 
+        json_string = completion.choices[0].message.content
 
+        print(json_string)
 
+        answers = json_string.split("\n")
 
+        answers = [x for x in answers if ':' in x ]
 
+        answer2 = answers[0].split(":")[1].replace(" ", "")
 
+        return answer2
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：已知预应力高强混凝土管桩（PHC）代号定义为PHC-AAA(BB)CC-DDD-E1,E2,E3,E4,其中AAA代表管桩外径，BB代表管桩壁厚，CC表示型号，DDD表示混凝土强度等级，E1/E2/E3/E4表示分段桩长。例如，PHC-500(125)-AB-C80-9,7 表示外径500mm,壁厚125mm,型号AB，混凝土强度C80, 分段桩长分别为9米和7米，总桩长16米,施工时需要将两个分段接桩"},
 
+        {"role": "user", "content": "问题描述：  给定一段工作内容描述，内容为" + data['mc'] +data['tz'] + "。请判断内容中是否包含桩的代号，如果没有，请输出“无”,如果有，请输出代号"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个代号作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    
+    answers = json_string.split("\n")
 
+    answers = [x for x in answers if ':' in x ]
 
+    answers = [x for x in answers if not 'true' in x]
 
+    answers = [x for x in answers if not '是' in x]
 
+    print(answers)
 
+    if len(answers) == 0:
 
+        return "无"
 
+    answer2 = answers[0].split(":")[1].replace(" ", "")
 
+    return answer2
 
+
 
+def need_extra(
 
+       data, #data
 
+       aiclient,
 
+       qwclient,
 
+       result):
 
+    if data['bianma'].startswith("0108") and len(result) == 0:
 
+        return True
 
+    time.sleep(1)
 
+    completion = qwclient.chat.completions.create(
 
+        model="ZhipuAI/GLM-4.6",
 
+        #model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+        {"role": "user", "content": "问题描述：  给定一段工作内容描述，内容为" + data['mc'] +data['tz'] + "。请判断内容是否属于打桩、压桩。请回答是或者否"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if "是" in json_string:
 
+        return True
 
+    else:
 
+        return False

+ 71 - 0
fallback.py
Wyświetl plik 

@@ -0,0 +1,71 @@
 
+from menchuangfallback import menchuangfallback
 
+def fallback(A, B, aiclient, qwclient, menchuang_collection, model):
 
+    if B['bianma'].startswith("0109"):
 
+        if B['bianma'].startswith("010902") and '非固化' in B['tz'] and '沥青防水涂料' in B['tz']:##屋面防水
 
+            return ['第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)'] ##需要换
 
+        if B['bianma'].startswith("010902") and '高聚物' in B['tz'] and '改性沥青防水涂料' in B['tz']:##屋面防水
 
+            return ['第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)'] ##需要换
 
+        if B['bianma'].startswith("010904") and '高聚物' in B['tz'] and '改性沥青防水涂料' in B['tz']:##屋面防水
 
+            return ['第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)'] ##需要换
 
+        if B['bianma'].startswith("010904") and '非固化' in B['tz'] and '沥青防水涂料' in B['tz']:##屋面防水
 
+            return ['第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)'] ##需要换
 
+        #menchuangfallback(A, B, aiclient, qwclient, menchuang_collection, model)
 
+        
+    if B['bianma'].startswith("0110"):
 
+        result = []
 
+        if '界面剂' in B['tz']:##屋面防水
 
+            result.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂 混凝土面') ##需要换
 
+        if '玻纤网' in B['tz']:##屋面防水
 
+            result.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 墙面耐碱玻纤网格布 一层') ##需要换
 
+        if '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 墙面耐碱玻纤网格布 一层' in result and '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 抗裂砂浆抹面4mm (网格布)' not in result:
 
+            result.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 抗裂砂浆抹面4mm (网格布)')
 
+        return result ##需要换
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定定额选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        model="Qwen/Qwen3-32B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "问题描述： 给定一条工程量清单: " + B['label'] + " " + B['mc'] + " " + B['tz'] +   ",清单描述了包括一道或多道工序的工作内容" + "，".join(options) + "。请筛选出恰当的定额选项，使得定额选项匹配上清单所描述的工作内容。如果清单描述有多道工作内容，则你可能需要筛选出多个选项。选项中可能有意思相同或相近的选项，对于相同或相近的一组选项，你要挑选出其中最恰当的选项，排除掉其余的选项，以防止重复套定额。请返回一个数组。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "disabled"}},
 
+        extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer

+ 1 - 0
gangjiegouhuansuan
Wyświetl plik 

@@ -0,0 +1 @@
 
+{"mc": ["\u94a2\u7f51\u67b6 \u87ba\u6813\u7403\u8282\u70b9", "\u94a2\u7f51\u67b6 \u710a\u63a5\u7403\uff08\u677f\uff09\u8282\u70b9", "\u94a2\u5c4b\u67b6 \u95e8\u5f0f\u521a\u67b6", "\u94a2\u5c4b\u67b6 \u8f7b\u94a2\u5c4b\u67b6", "\u94a2\u6258\u67b6", "\u94a2\u6841\u67b6", "\u76f8\u8d2f\u8282\u70b9\u94a2\u7ba1\u6841\u67b6", "\u5b9e\u8179\u5f0f\u94a2\u67f1 H\u578b", "\u7a7a\u8179\u5f0f\u94a2\u67f1 \u7bb1\u578b", "\u7a7a\u8179\u5f0f\u94a2\u67f1 \u683c\u6784\u578b", "\u94a2\u7ba1\u67f1", "\u5b9e\u8179\u5f0f\u94a2\u6881 H\u578b", "\u7a7a\u8179\u5f0f\u94a2\u6881 \u7bb1\u578b", "\u7a7a\u8179\u5f0f\u94a2\u6881 \u683c\u6784\u578b", "\u94a2\u540a\u8f66\u6881", "\u6c34\u5e73\u94a2\u652f\u6491", "\u7ad6\u5411\u94a2\u652f\u6491", "\u94a2\u62c9\u6761", "\u94a2\u6aa9\u6761 \u70ed\u8f67H\u578b", "\u94a2\u6aa9\u6761 \u9ad8\u9891\u710a\u63a5\u53e3\u578b", "\u94a2\u6aa9\u6761 \u51b7\u5f2fCZ\u578b", "\u94a2\u5929\u7a97\u67b6", "\u94a2\u6321\u98ce\u67b6", "\u94a2\u5899\u67b6 \u70ed\u8f67H\u578b", "\u94a2\u5899\u67b6 \u9ad8\u9891\u710a\u63a5\u53e3\u578b", "\u94a2\u5899\u67b6 \u51b7\u5f2fCZ\u578b", "\u94a2\u5e73\u53f0", "\u94a2\u8d70\u9053", "\u94a2\u68af", "\u94a2\u62a4\u680f"], "alpha": [17.19, 15.24, 35.56, 52.85, 37.15, 26.2, 15.48, 12.12, 4.3, 16.25, 4.85, 16.1, 4.61, 16.25, 17.16, 37.4, 16.04, 44.34, 49.33, 26.3, 74.43, 52.28, 48.26, 35.84, 26.3, 74.43, 45.03, 43.05, 37.77, 54.07]}

+ 59 - 0
huansuan.py
Wyświetl plik 

@@ -0,0 +1,59 @@
 
+from huansuan0103 import callzaihuansuan0103_1
 
+from huansuan0103 import callzaihuansuan0103_3
 
+from huansuan0103 import callzaihuansuan0103_2
 
+from huansuan0105 import callzaihuansuan0105_1
 
+from huansuan0105 import callzaihuansuan0105_2
 
+from huansuan0106 import callzaihuansuan0106_1
 
+from huansuan0106 import callzaihuansuan0106_2
 
+from huansuan0106 import callzaihuansuan0106_3
 
+from huansuan0108 import callzaihuansuan0108_1
 
+from huansuan0110 import callzaihuansuan0110_1
 
+
 
+def callzaihuansuan(bianma, label, A,B,C, aiclient, qwclient):
 
+    if bianma.startswith("0103"):
 
+        if '送桩 桩长' in label or '送桩  桩长' in label:
 
+            return callzaihuansuan0103_1(bianma, A, B, C, aiclient)
 
+        elif '电焊接桩' in label:
 
+            return callzaihuansuan0103_3(bianma, A, B, C, aiclient, qwclient)
 
+        else:
 
+            return callzaihuansuan0103_2(bianma, A, B, C, aiclient, qwclient)
 
+    if bianma.startswith("0110"):
 
+        print(label)
 
+        print(A)
 
+        if '找坡' in label and 'm2' in A:##混凝土找坡的单位是m3
 
+            return callzaihuansuan0110_1(bianma, label, A, B, C, aiclient, qwclient)
 
+    if bianma.startswith("0105"):
 
+        print(label)
 
+        print(A)
 
+        if '现浇水平挑檐' in label and 'm3' in A:
 
+            return """
 
+            {
 
+            "answer": "A=B"
 
+            }    
+            """
 
+        elif '垫层' in label and 'm2' in A:##垫层的单位是m3
 
+            return callzaihuansuan0105_1(bianma, label, A, B, C, aiclient, qwclient)
 
+        elif '成品不锈钢盖板安装' in label and '套' in A:##垫层的单位是m3
 
+            return callzaihuansuan0105_2(bianma, label, A, B, C, aiclient, qwclient)
 
+        elif '油膏' in label and 'm2' in A and '散水' in C:##伸缩缝的单位是10m
 
+            return """
 
+                 {
 
+                    "answer": "A=0.18*B"
 
+                 }
 
+            """
 
+    if bianma.startswith("0106"):
 
+        print(A)
 
+        print(B)
 
+        if 't' in A and 'm2' in B and '10m2' not in B and '100m2' not in B:
 
+            return callzaihuansuan0106_1(bianma, label, A, B, C, aiclient, qwclient)
 
+        if 't' in A and '10m2' in B:
 
+            return callzaihuansuan0106_2(bianma, label, A, B, C, aiclient, qwclient)
 
+        if 't' in A and '100m2' in B:
 
+            return callzaihuansuan0106_3(bianma, label, A, B, C, aiclient, qwclient)
 
+    if bianma.startswith("0108"):
 
+        if '樘' in A and 'm2' in B:
 
+            return callzaihuansuan0108_1(bianma, label, A, B, C, aiclient, qwclient)
 
+
 
+    return """
 
+       {"answer": "0"}
 
+    """

+ 90 - 0
huansuan0103.py
Wyświetl plik 

@@ -0,0 +1,90 @@
 
+
 
+def callzaihuansuan0103_1(bianma, A,B,C, aiclient):
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：已知预应力高强混凝土管桩（PHC）型号定义为PHC-AAA(BB)CC-DDD-E1,E2,E3,其中AAA代表管桩外径，BB代表管桩壁厚，CC表示型号，DDD表示混凝土强度等级，E1/E2/E3表示分段桩长。例如，PHC-500(125)-AB-C80-9,7表示外径500mm,壁厚125mm,型号AB，混凝土强度C80,分段桩长分别为9米和7米，总桩长16米"},
 
+            {"role": "user", "content": " 背景知识：送桩 是指当桩顶设计标高低于地面时,需要把桩顶打入到设计标高。计算时，送桩长度为自桩顶面到自然地坪高度另加0.5米,送桩系数为送桩长度除以总桩长。例如，某桩总长10米，送桩长度2米，则送桩系数为2米除以10米等于0.2"},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3。给定一段工作内容描述，内容为" + C + "，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。若A表示长度米，B表示体积立方米，且工作内容涉及送桩，则可以计算桩的截面积(单位为平方米)，作为换算系数coefficient_a，再计算送桩系数，作为换算系数coefficient_b, 最终返回A=coefficient_a*coefficient_b*B。例如，工作内容涉及桩的截面积是0.1平方米，送桩系数为0.1，则返回A=0.01*B。工作内容涉及桩的截面积为0.2，送桩系数为0.2，则返回A=0.04*B。若不符合上述情况，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+
 
+def callzaihuansuan0103_2(bianma, A,B,C, aiclient, qwclient):
 
+    print("0103_2")
 
+    print(A)
 
+    print(B)
 
+    print(C)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        #model="qwen3-14b",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：已知预应力高强混凝土管桩（PHC）型号定义为PHC-AAA(BB)CC-DDD-E1,E2,E3,其中AAA代表管桩外径，BB代表管桩壁厚，CC表示型号，DDD表示混凝土强度等级，E1/E2/E3表示分段桩长。例如，PHC-500(125)-AB-C80-9,7表示外径500mm,壁厚125mm,型号AB，混凝土强度C80,分段桩长分别为9米和7米，总桩长16米"},
 
+            {"role": "user", "content": " 背景知识：送桩 是指当桩顶设计标高低于地面时,需要把桩顶打入到设计标高。计算时，送桩长度为自桩顶面到自然地坪高度另加0.5米"},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3, 表示数目的符号“个”。给定一段工作内容描述，内容为" + C + "，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。请先辨别出上述个单位表示什么，然后解决下面的问题。如果经过辨别后，你认为A表示长度米，B表示体积立方米，且工作内容涉及打桩、压桩，则可以计算桩的截面积(单位为平方米)，作为换算系数，返回A=coefficient*B。例如，工作内容涉及桩的截面积是0.1平方米，则返回A=0.1*B。工作内容涉及桩的截面积为0.2，则返回A=0.2*B。若A不表示长度米，或者B不表示体积立方米，或工作内容不符合上述情况，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B, 再比如，A表示长度，B表示数目（个），则返回A<>B"},
 
+        ],
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，表达式中不含有圆周率π, 请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+
 
+def callzaihuansuan0103_3(bianma, A,B,C, aiclient, qwclient):
 
+    print("0103_3")
 
+    print(A)
 
+    print(B)
 
+    print(C)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        #model="qwen3-14b",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：已知预应力高强混凝土管桩（PHC）型号定义为PHC-AAA(BB)CC-DDD-E1,E2,E3,其中AAA代表管桩外径，BB代表管桩壁厚，CC表示型号，DDD表示混凝土强度等级，E1/E2/E3表示分段桩长。例如，PHC-500(125)-AB-C80-9,7表示外径500mm,壁厚125mm,型号AB，混凝土强度C80,分段桩长分别为9米和7米，总桩长16米"},
 
+            {"role": "user", "content": " 背景知识：接桩，可以将多节管桩连续打入地基，直至达到设计深度.焊接是一种接桩的方法。两节桩接桩，有一个接头；三节桩焊接，有两个接头；以此类推；可以定义接桩系数，为单位长度的接头个数。例如，桩总长10米，有接头1个，则接桩系数为0.1；再例如，桩总长20米，有接头4个，则接桩系数为0.2"},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3, 表示数目的符号“个”。给定一段工作内容描述，内容为" + C + "，该工作需要进行接桩，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。请先辨别出上述个单位表示什么，然后解决下面的问题。如果经过辨别后，你认为A表示长度米，B表示接头个数，且工作内容涉及接桩，则可以计算接桩系数，作为换算系数，返回A=coefficient*B。例如，工作内容涉及桩的接桩系数为0.1，则返回A=0.1*B。工作内容涉及桩的接桩系数为0.2，则返回A=0.2*B。若A不表示长度米，或者B不表示接头个数，或工作内容不符合上述情况，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B"},
 
+        ],
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+

+ 49 - 0
huansuan0105.py
Wyświetl plik 

@@ -0,0 +1,49 @@
 
+
 
+def callzaihuansuan0105_1(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：厚度可以用单位米（m）来衡量，可以用单位厘米（cm）来衡量，可以用单位毫米（mm）来衡量；如果没有明确单位，默认指毫米"},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3。给定一段工作内容描述，内容为" + C + "，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工序描述， 内容为" + label + ",它属于前述工作内容的一部分。它的计量单位为" + B + "，记作B。若A表示面积，B表示体积，且工序涉及垫层，则可以从工作内容中识别出垫层的厚度(米)，作为换算系数coefficient，最终返回A=coefficient*B。例如，工作内容涉及垫层的厚度是0.1米，则返回A=0.1*B。工作内容涉及垫层的厚度为200mm，则换算为0.2米，返回A=0.2*B。若不符合上述情况，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+
 
+def callzaihuansuan0105_2(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：长度可以用单位米（m）来衡量，可以用单位厘米（cm）来衡量，可以用单位毫米（mm）来衡量；如果没有明确单位，默认指毫米"},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3，也有比较宽泛的数量单位，例如套。给定一段工作内容描述，内容为" + C + "，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工序描述， 内容为" + label + ",它属于前述工作内容的一部分。它的计量单位为" + B + "，记作B。若A表示套，B表示面积，且工序涉及盖板，则可以从工作内容中识别出盖板的尺寸，计算他的面积（不考虑厚度），作为换算系数coefficient，最终返回A=coefficient*B。例如，工作内容涉及盖板是长方形，面积1平方米，B是1平方米，则返回A=1*B。如果B是10平方米（m2），则返回A=0.1*B。若不符合上述情况，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+

+ 62 - 0
huansuan0106.py
Wyświetl plik 

@@ -0,0 +1,62 @@
 
+import re
 
+with open('gangjiegouhuansuan','r') as f:
 
+    content = f.read()
 
+import json
 
+obj = json.loads(content)
 
+options=[]
 
+for i in range(len(obj['mc'])):
 
+    options.append('给定选项A'+str(i)+', 内容为'+obj['mc'][i])
 
+options = ','.join(options)
 
+def callzaihuansuan0106_0(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+        {"role": "user", "content": "问题描述：  给定一段工作内容描述，内容为" + C + "，" + options +  "，请选出最匹配工作内容的选项并输出。例如如果你觉得A11选项最匹配，请输出A11"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        return json_string
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A10的表达式作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    lines = json_string.split('\n')
 
+    lines = [x for x in lines if ':' in x]
 
+    line = lines[0].split(':')[1]
 
+    line = line.replace('\'', '')
 
+    line = line.replace('\"', '')
 
+    matched_letters = re.findall(r'[a-zA-Z0-9]', line)
 
+    return ''.join(matched_letters)
 
+    
+def callzaihuansuan0106_1(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    t =  callzaihuansuan0106_0(bianma, label, A,B,C, aiclient, qwclient)
 
+    t=t.replace('A', '')
 
+    t = int(t)
 
+    t = obj['alpha'][t]
 
+    return "{" + "\n" + "answer: A=" + str(t) + "*B\n}"
 
+def callzaihuansuan0106_2(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    t =  callzaihuansuan0106_0(bianma, label, A,B,C, aiclient, qwclient)
 
+    t=t.replace('A', '')
 
+    t = int(t)
 
+    t = obj['alpha'][t]/10
 
+    return "{" + "\n" + "answer: A=" + str(t) + "*B\n}"
 
+
 
+def callzaihuansuan0106_3(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    t =  callzaihuansuan0106_0(bianma, label, A,B,C, aiclient, qwclient)
 
+    t=t.replace('A', '')
 
+    t = int(t)
 
+    t = obj['alpha'][t]/100
 
+    return "{" + "\n" + "answer: A=" + str(t) + "*B\n}"
 
+
 
+    
+

+ 36 - 0
huansuan0108.py
Wyświetl plik 

@@ -0,0 +1,36 @@
 
+import re
 
+with open('gangjiegouhuansuan','r') as f:
 
+    content = f.read()
 
+import json
 
+obj = json.loads(content)
 
+options=[]
 
+for i in range(len(obj['mc'])):
 
+    options.append('给定选项A'+str(i)+', 内容为'+obj['mc'][i])
 
+options = ','.join(options)
 
+def callzaihuansuan0108_1(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        #model="qwen3-14b",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km, 表示数目的符号“个”,对于门窗安装而言，数量词可以用樘.给定一段工作内容描述，内容为" + C + "，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。请先辨别出上述个单位表示什么，然后解决下面的问题。如果经过辨别后，你认为A表示樘，B表示面积，且工作内容涉及门窗洞口尺寸，则可以计算洞口面积(单位为平方米)，作为换算系数，返回A=coefficient*B。例如，工作内容涉及门洞口面积是0.1平方米，则返回A=0.1*B。如果B表示10m2, 则返回A=0.01*B。若A不表示樘，或者B不表示面积，或工作内容没有给出洞口尺寸，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B, 再比如，A表示长度，B表示数目（个），则返回A<>B"},
 
+        ],
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，表达式中不含有圆周率π, 请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+
 
+

+ 37 - 0
huansuan0110.py
Wyświetl plik 

@@ -0,0 +1,37 @@
 
+import re
 
+with open('gangjiegouhuansuan','r') as f:
 
+    content = f.read()
 
+import json
 
+obj = json.loads(content)
 
+options=[]
 
+for i in range(len(obj['mc'])):
 
+    options.append('给定选项A'+str(i)+', 内容为'+obj['mc'][i])
 
+options = ','.join(options)
 
+def callzaihuansuan0110_1(bianma, label, A,B,C, aiclient, qwclient):##C->tz
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        #model="qwen3-14b",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "在施工上下文中，描述厚度，不加计量单位，则默认指的是毫米（mm）。例如，找坡厚度30，指的是30mm厚"},
 
+        {"role": "user", "content": "问题描述：  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示平方米的符号m2, 表示立方米的符号m3.给定一段工作内容描述，内容为" + C + "，给定其工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。请先辨别出上述个单位表示什么，然后解决下面的问题。如果经过辨别后，你认为A表示面积，B表示体积，且工作内容涉及混凝土找坡，则可以以找坡厚度(米)作为换算系数，返回A=coefficient*B。例如，工作内容涉及找坡厚度30mm，则返回A=0.03*B。若A不表示面积，或者B不表示体积，或工作内容没有给出找坡厚度，则返回A<>B。例如，A表示长度，B表示面积，则返回A<>B。再例如，A表示重量，B表示体积，则返回A<>B, 再比如，A表示长度，B表示数目（个），则返回A<>B"},
 
+        ],
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，表达式中不含有圆周率π, 请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+
 
+

+ 38 - 0
jieheceng.py
Wyświetl plik 

@@ -0,0 +1,38 @@
 
+example1="""
 
+1、用于生活泵房地面 做法详见图纸设计及相关图集规范
 
+2、10厚600x600防滑地砖,DTG砂浆擦缝
 
+3、30厚DS M15砂浆(1:3干硬性水泥砂浆)结合层,表面撒水泥粉
 
+4、最薄处30厚C20细石混凝土找坡层,向排水沟、集水坑找1%坡,随打随抹平,立管根部用DS M15砂浆(1:3水泥砂浆)抹小八字角
 
+5、含设备基础顶面及侧面
 
+"""
 
+answer1="""
 
+1、用于生活泵房地面 做法详见图纸设计及相关图集规范
 
+2、10厚600x600防滑地砖,DTG砂浆擦缝
 
+4、最薄处30厚C20细石混凝土找坡层,向排水沟、集水坑找1%坡,随打随抹平,立管根部用DS M15砂浆(1:3水泥砂浆)抹小八字角
 
+5、含设备基础顶面及侧面
 
+"""
 
+example2="""
 
+1、用于消防水池、集水坑井地面及侧壁、排水沟地面及侧壁 做法详见图纸设计及相关图集规范
 
+2、20厚DS M20砂浆(1:2.5水泥砂浆)保护层
 
+3、15厚1:3水泥砂浆找平
 
+"""
 
+answer2="""
 
+1、用于消防水池、集水坑井地面及侧壁、排水沟地面及侧壁 做法详见图纸设计及相关图集规范
 
+2、20厚DS M20砂浆(1:2.5水泥砂浆)保护层
 
+3、15厚1:3水泥砂浆找平
 
+"""
 
+def jieheceng(A, aiclient, qwclient):
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="ZhipuAI/GLM-4.5",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "给定一段文字: " + A +   "\n 该段文字描述了包括一道或多道工序的工作内容。其中有可能一道或多道工作内容含有“结合层”字样。请你阅读这段文字，判断是否存在“结合层”工序。如果没有，请原封不动的返回给定的文字；如果有，请将含有“结合层”字样的一道或多道工作内容删除，返回剩余的工作内容，剩余的工作内容请原封不动的返回。 例如，给定一段文字：" + example1 +"\n请返回：" + answer1 + "\n 再例如，给定一段文字： " + example2 + "\n请返回： " + answer2},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+

Plik diff jest za duży
+ 0 - 0
label_name


+ 45 - 0
lingji.py
Wyświetl plik 

@@ -0,0 +1,45 @@
 
+example1="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+2、250-300厚回填土或种植土（另计）
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+4、150厚粒径10-40卵石灌M2.5混合砂浆
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+answer1="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+4、150厚粒径10-40卵石灌M2.5混合砂浆
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+example2="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+2、250-300厚回填土或种植土
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+4、150厚粒径10-40卵石灌M2.5混合砂浆
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+answer2="""
 
+散水 1、种植散水 做法详见图纸设计及图集12J003-6A/A2
 
+2、250-300厚回填土或种植土
 
+3、60厚C20细石混凝土，随打随磨，撒1：1水泥砂子压实赶光
 
+4、150厚粒径10-40卵石灌M2.5混合砂浆
 
+5、素土夯实，向外坡3%-5%
 
+6、与墙交接处聚乙烯泡沫塑料及密封膏嵌缝
 
+"""
 
+def lingji(A, aiclient, qwclient):
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "给定一段文字: " + A +   "\n 该段文字描述了包括一道或多道工序的工作内容。其中有可能一道或多道工作内容被指定为（另计）。请你阅读这段文字，判断是否存在被指定为（另计）的工作内容。如果没有，请原封不动的返回给定的文字；如果有，请将另计的一道或多道工作内容删除，返回剩余的工作内容，剩余的工作内容请原封不动的返回。 例如，给定一段文字：" + example1 +"\n请返回：" + answer1 + "\n 再例如，给定一段文字： " + example2 + "\n请返回： " + answer2},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+

Plik diff jest za duży
+ 0 - 0
menchuang_rule


+ 7 - 0
menchuangfallback.py
Wyświetl plik 

@@ -0,0 +1,7 @@
 
+def menchuangfallback(A, B, aiclient, qwclient, menchuang_collection, model):
 
+    sentence=["特征描述：" + B['mc'] + "\n" + B['tz']]
 
+    embeddings = model.encode(sentence) 
+    result = menchuang_collection.query(query_embeddings=embeddings, n_results=25)
 
+    print(result['documents'][0])
 
+    print(result['distances'][0])
 
+

Plik diff jest za duży
+ 0 - 0
name_label


Plik diff jest za duży
+ 0 - 0
nantong_rule


+ 35 - 0
postprocess.py
Wyświetl plik 

@@ -0,0 +1,35 @@
 
+from postprocess0103 import postprocess0103
 
+from postprocess0106 import postprocess0106
 
+from postprocess0105 import postprocess0105
 
+from postprocess0104 import postprocess0104
 
+from postprocess0101 import postprocess0101
 
+from postprocess0117 import postprocess0117
 
+from postprocess0108 import postprocess0108
 
+from postprocess0109 import postprocess0109
 
+from postprocess0110 import postprocess0110
 
+from postprocess0111 import postprocess0111
 
+def postprocess(selected, data, aiclient, qwclient, sfclient, label_name, name_dw):
 
+    if len(selected) == 0:
 
+        return []
 
+    if data['bianma'].startswith("0103"):
 
+        return postprocess0103(selected, data, aiclient)
 
+    elif data['bianma'].startswith("0117"):
 
+        return postprocess0117(selected, data, aiclient)
 
+    elif data['bianma'].startswith("0101"):
 
+        return postprocess0101(selected, data, aiclient)
 
+    elif data['bianma'].startswith("0104"):
 
+        return postprocess0104(selected, data, aiclient)
 
+    elif data['bianma'].startswith("0105"):
 
+        return postprocess0105(selected, data, aiclient, qwclient, label_name, name_dw)
 
+    elif data['bianma'].startswith("0106"):
 
+        return postprocess0106(selected, data, aiclient, qwclient, label_name, name_dw)
 
+    elif data['bianma'].startswith("0108"):
 
+        return postprocess0108(selected, data, aiclient, qwclient, label_name, name_dw)
 
+    elif data['bianma'].startswith("0109"):
 
+        return postprocess0109(selected, data, aiclient, qwclient, label_name, name_dw)
 
+    elif data['bianma'].startswith("0110"):
 
+        return postprocess0110(selected, data, aiclient, qwclient, label_name, name_dw)
 
+    elif data['bianma'].startswith("0111"):
 
+        return postprocess0111(selected, data, aiclient, qwclient, sfclient, label_name, name_dw)
 
+    else: 
+        return selected

+ 197 - 0
postprocess0101.py
Wyświetl plik 

@@ -0,0 +1,197 @@
 
+import json
 
+def huitianfilter(A, #options
 
+       B, #data
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：回填方一般不使用单轮车或者双轮车运输"},
 
+            {"role": "user", "content": " 背景知识：室内回填、房心回填一般不使用压路机、挖掘机、推土机、单轮车、双轮车"},
 
+            {"role": "user", "content": " 背景知识：回填方时，挖掘机挖土与自卸汽车运土需要配套使用，也就是说，如果选择两者之一，另一个也不许选择。如果不选择两者之一，则另一个也不能选择"},
 
+            {"role": "user", "content": " 背景知识：回填方时，挖掘机挖土与推土机推土二者著能选择其一，不能同时选择"},
 
+            {"role": "user", "content": "问题描述： 给定一段回填方工作内容: " + B['mc'] + " " + B['tz'] +   "," + "，".join(options) + "。请筛选出恰当的选项，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    return answer
 
+def aifilter(A, #options
 
+       B, #data
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：如果工作内容中没有明确要求使用人工挖土石方或者人工运土石方，则去掉给定选项中的人工挖土石方、运土石方选项"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['mc'] + " " + B['tz'] +   "," + "，".join(options) + "。请做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    return answer
 
+def ai(A, #options
 
+       B, #data
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：平整场地可以使用人工的方法，也可以使用机械的方法，机械的方法更经济"},
 
+        {"role": "user", "content": "问题描述： " +   "，".join(options) + "。请从上述选项中选择一更经济的选项，并返回代号。例如，如果A选项最恰当，请返回A"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if 'A' in json_string:
 
+            return A[0]
 
+        if 'B' in json_string:
 
+            return A[1]
 
+        if 'C' in json_string:
 
+            return A[2]
 
+        if 'D' in json_string:
 
+            return A[3]
 
+        if 'E' in json_string:
 
+            return A[4]
 
+        if 'F' in json_string:
 
+            return A[5]
 
+        if 'G' in json_string:
 
+            return A[6]
 
+        if 'H' in json_string:
 
+            return A[7]
 
+        if 'I' in json_string:
 
+            return A[8]
 
+        if 'J' in json_string:
 
+            return A[9]
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A或者B或者C的表达式作为答案，请将该最终答案输出.例如，文字提到答案为A，请输出A；文字提到答案是B，请输出B"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answers = json_string.split("\n")
 
+    answers = [x for x in answers if ':' in x]
 
+    print(answers)
 
+    if len(answers) == 0:
 
+        return A[0]
 
+    answer2 = answers[0].split(":")[1].replace(" ", "")
 
+    if 'A' in answer2:
 
+        return A[0]
 
+    if 'B' in answer2:
 
+        return A[1]
 
+    if 'C' in answer2:
 
+        return A[2]
 
+    if 'D' in answer2:
 
+        return A[3]
 
+    if 'E' in answer2:
 
+        return A[4]
 
+    if 'F' in answer2:
 
+        return A[5]
 
+    if 'G' in answer2:
 
+        return A[6]
 
+    if 'H' in answer2:
 
+        return A[7]
 
+    if 'I' in answer2:
 
+        return A[8]
 
+    if 'J' in answer2:
 
+        return A[9]
 
+
 
+def tihuan(selected):
 
+    hit = False
 
+    for entry in selected:
 
+        if '自动平地机' in entry:
 
+            hit = True
 
+    if hit:
 
+        left = [x for x in selected if '自动平地机' not in x]
 
+        left.append('第一章  土、石方工程 1.2  机械土、石方 1.2.9  平整场地、碾压 平整场地(厚300mm以内) 推土机(kW以内) 75')
 
+        return left
 
+    else:
 
+        return selected
 
+def postprocess0101(selected, data, aiclient):
 
+    if data['bianma'].startswith("010101001"):##平整场地
 
+        if len(selected) > 1:
 
+            return tihuan([ai(selected, data, aiclient)])
 
+        else:
 
+            return tihuan(selected)
 
+    elif data['bianma'].startswith("010103"):##回填
 
+        return huitianfilter(selected, data, aiclient)
 
+    else:
 
+        return aifilter(selected, data, aiclient)

+ 252 - 0
postprocess0103.py
Wyświetl plik 

@@ -0,0 +1,252 @@
 
+import json
 
+with open("0103basic_rule", "r") as f:
 
+    content = f.read()
 
+
 
+rule = json.loads(content)
 
+def aifilter(A, #options
 
+       B, #data
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 处理要求：如果清单描述的工作内容是管桩清理，则去除给定选项中的管桩填芯的选项; 如果清单描述的工作内容不是管桩清理，则不做处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工程量清单描述: " + B['mc'] + " " + B['tz'] +   "," + "，".join(options) + "。请根据处理要求，处理选项，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终结果以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    return answer
 
+def associate_jiezhuang(answer):
 
+    hit = False
 
+    for entry in answer:
 
+        if entry in ['第三章  桩基工程 3.1  打桩工程 3.1.5  电焊接桩 电焊接桩 方桩包角钢','第三章  桩基工程 3.1  打桩工程 3.1.5  电焊接桩 电焊接桩 方桩包钢板','第三章  桩基工程 3.1  打桩工程 3.1.5  电焊接桩 电焊接桩 螺栓+电焊']:
 
+            hit = True
 
+            return answer
 
+    if not hit:
 
+        return answer + ['第三章  桩基工程 3.1  打桩工程 3.1.5  电焊接桩 电焊接桩 螺栓+电焊']
 
+
 
+def associate(answer):
 
+    hit = False
 
+    for entry in answer:
 
+        if entry in rule['3.1.1.1']:
 
+            hit = "3.1.1.2"
 
+        elif entry in rule['3.1.2.1']:
 
+            hit = "3.1.2.2"
 
+        elif entry in rule['3.1.3.1']:
 
+            hit = "3.1.3.2"
 
+        elif entry in rule["3.1.4.1"]:
 
+            hit = "3.1.4.2"
 
+    if hit:
 
+        already = False
 
+        for entry in answer:
 
+            if entry in rule[hit]:
 
+                already = True
 
+        if not already:
 
+            return answer + [rule[hit][0]]
 
+        else:
 
+            return answer
 
+    else:
 
+        return answer
 
+
 
+
 
+def jiezhuang(
 
+       data, #data
 
+       aiclient):
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：已知预应力高强混凝土管桩（PHC）型号定义为PHC-AAA(BB)CC-DDD-E1,E2,E3,E4,其中AAA代表管桩外径，BB代表管桩壁厚，CC表示型号，DDD表示混凝土强度等级，E1/E2/E3/E4表示分段桩长。例如，PHC-500(125)-AB-C80-9,7 表示外径500mm,壁厚125mm,型号AB，混凝土强度C80, 分段桩长分别为9米和7米，总桩长16米,施工时需要将两个分段接桩;再例如，PHC-500(125)-AB-C80-9 表示外径500mm,壁厚125mm,型号AB，混凝土强度C80, 为一整段桩,长9米, 施工时不需要接桩"},
 
+            {"role": "user", "content": " 背景知识：在施工过程中，若管桩长度不足，则需在原有管桩基础上连接新的管桩，这一过程即为接桩。 通过接桩，可以将多节管桩连续打入地基，直至达到设计深度"},
 
+        {"role": "user", "content": "问题描述：  给定一段工作内容描述，内容为" + data['mc'] +data['tz'] + "。请判断完成该工作内容是否需要接桩。请回答是或者否"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if "是" in json_string and "否" not in json_string:
 
+        return True
 
+    if "是" not in json_string and "否" in json_string:
 
+        return False
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if "是" in json_string and "否" not in json_string:
 
+        return True
 
+    if "是" not in json_string and "否" in json_string:
 
+        return False
 
+def songzhuang(
 
+       data, #data
 
+       aiclient):
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：送桩 是指当桩顶设计标高低于地面时,需要把桩顶打入到设计标高。"},
 
+        {"role": "user", "content": "问题描述：  给定一段工作内容描述，内容为" + data['mc'] +data['tz'] + "。请判断完成该工作内容是否需要送桩。请回答是或者否"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if "是" in json_string and "否" not in json_string:
 
+        return True
 
+    if "是" not in json_string and "否" in json_string:
 
+        return False
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if "是" in json_string and "否" not in json_string:
 
+        return True
 
+    if "是" not in json_string and "否" in json_string:
 
+        return False
 
+def ai(A, #options
 
+       B, #data
 
+       C, #entry
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：已知预应力高强混凝土管桩（PHC）型号定义为PHC-AAA(BB)CC-DDD-E1,E2,E3,E4,其中AAA代表管桩外径，BB代表管桩壁厚，CC表示型号，DDD表示混凝土强度等级，E1/E2/E3/E4表示分段桩长。例如，PHC-500(125)-AB-C80-9,7 表示外径500mm,壁厚125mm,型号AB，混凝土强度C80, 分段桩长分别为9米和7米，总桩长16米"},
 
+        {"role": "user", "content": "问题描述：  给定一段工作内容描述，内容为" + B['mc'] +B['tz'] + "，" + "，".join(options) + "。请从上述选项中选择（总）桩长最恰当的一个选项，并返回代号,如果无法得出总桩长，则默认桩长10米。例如，如果A选项最恰当，请返回A; "},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    answer2 = completion.choices[0].message.content
 
+    print(answer2)
 
+    if len(answer2) < 4:
 
+        if 'A' in answer2:
 
+            return A[0]
 
+        if 'B' in answer2:
 
+            return A[1]
 
+        if 'C' in answer2:
 
+            return A[2]
 
+        if 'D' in answer2:
 
+            return A[3]
 
+        if 'E' in answer2:
 
+            return A[4]
 
+        if 'F' in answer2:
 
+            return A[5]
 
+        if 'G' in answer2:
 
+            return A[6]
 
+        if 'H' in answer2:
 
+            return A[7]
 
+        if 'I' in answer2:
 
+            return A[8]
 
+        if 'J' in answer2:
 
+            return A[9]
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + answer2 + ",其中给出了一个类似于A或者B或者C的表达式作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answers = json_string.split("\n")
 
+    answers = [x for x in answers if ':' in x]
 
+    print(answers)
 
+    if len(answers) == 0:
 
+        return C
 
+    answer2 = answers[0].split(":")[1].replace(" ", "")
 
+    if 'A' in answer2:
 
+        return A[0]
 
+    if 'B' in answer2:
 
+        return A[1]
 
+    if 'C' in answer2:
 
+        return A[2]
 
+    if 'D' in answer2:
 
+        return A[3]
 
+    if 'E' in answer2:
 
+        return A[4]
 
+    if 'F' in answer2:
 
+        return A[5]
 
+    if 'G' in answer2:
 
+        return A[6]
 
+    if 'H' in answer2:
 
+        return A[7]
 
+    if 'I' in answer2:
 
+        return A[8]
 
+    if 'J' in answer2:
 
+        return A[9]
 
+def select(options, data, entry, aiclient):
 
+    if len([x for x in options if '桩长在' in x]) == len(options):
 
+        return ai(options, data, entry, aiclient)
 
+    else:
 
+        return entry
 
+def postprocess0103(selected, data, aiclient):
 
+    if jiezhuang(data, aiclient):
 
+        selected = associate_jiezhuang(selected)
 
+    if songzhuang(data, aiclient):
 
+        selected = associate(selected)
 
+    correct=[]
 
+    for entry in selected:
 
+        options = []
 
+        for item in rule:
 
+            l = rule[item]
 
+            if entry in l:
 
+                options = l
 
+        if len(options) > 0:
 
+            correct.append(select(options, data, entry, aiclient))
 
+        else:
 
+            correct.append(entry)
 
+    return aifilter(correct, data, aiclient)

+ 50 - 0
postprocess0104.py
Wyświetl plik 

@@ -0,0 +1,50 @@
 
+import json
 
+def aifilter(A, #options
 
+       B, #data
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 处理要求：如果工作内容中没有明确提出砌贴砖，则去除选项中的砌贴砖选项"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +   "," + "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    return answer
 
+def postprocess0104(selected, data, aiclient):
 
+    return aifilter(selected, data, aiclient)

+ 92 - 0
postprocess0105.py
Wyświetl plik 

@@ -0,0 +1,92 @@
 
+import json
 
+with open('nantong_rule', 'r') as f:
 
+    content = f.read()
 
+import json
 
+obj=json.loads(content)
 
+from fallback import fallback
 
+def aifilter(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-z1-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中同时出现现浇板式雨棚（雨蓬）选项和现浇水平板(平板)选项，则二者只能选一个，且优先选择出现更早的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中同时出现多个钢丝网选项，则只能选一个，且优先选择出现更早的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：钢盖板，钢篦子一般指不锈钢材质。如果选项中同时出现不锈钢盖板选项和铸铁盖板选项，则只能选一种，且优先选择与工作内容描述更接近的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中同时出现混凝土散水选项和混凝土（砼）整体面层选项，则二者只能选一个，且优先选择出现更早的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有模板工程选项，但是工作内容中没有明确提出包含模板工程，则去除选项中的模板工程选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有锯缝选项，但是工作内容中没有明确提出需要锯缝（道路伸缩缝），则去除选项中的锯缝选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有檩条选项，但是工作内容中没有涉及檩条，则去除选项中的檩条选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有加浆选项，但是工作内容中没有明确提出需要加浆，则去除选项中的加浆选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有混凝土垫层选项，但是工作内容中明确描述垫层做法是碎石垫层，则去除选项中的混凝土垫层选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有混凝土垫层选项，但是工作内容中不涉及垫层，则去除选项中的混凝土垫层选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有细石混凝土（细石砼）找平层选项，但是工作内容没有明确表述细石混凝土找平层，则去除选项中的细石混凝土找平层选项"},
 
+            {"role": "user", "content": " 特殊处理要求：抱框柱是柱的一种，与门框不相同。如果选项中有门框选项，但是工作内容中没有明确提出门框，则去除选项中的门框选项"},
 
+            {"role": "user", "content": " 特殊处理要求：不需考虑工作内容中的混凝土强度等级与选项中的混凝土强度等级的差异"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": " 重要提示：混凝土找平层与混凝土面层是不同的概念，不得混淆"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，写出推理过程，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "enabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def postprocess0105(selected, data, aiclient, qwclient, label_name, name_dw):
 
+    name=[label_name[x] for x in selected]
 
+    dw=[name_dw[x] for x in name]
 
+    if len(selected) == 1:
 
+        t1 = selected
 
+    else:   
+        t1 = aifilter(selected, data, aiclient, qwclient, dw)
 
+    #t2 = fallback(t1, data, aiclient, qwclient)
 
+    result = []
 
+    for entry in t1:
 
+        if entry in obj['7.8']:##钢盖板
 
+            result.append(obj['nantong7.8'][0])
 
+        else:
 
+            result.append(entry)
 
+    return result

+ 376 - 0
postprocess0106.py
Wyświetl plik 

@@ -0,0 +1,376 @@
 
+tuliaos=[
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 0.5小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 1小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 1.5小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 2小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 厚型 2小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 厚型 2.5小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 厚型 3小时',
 
+]
 
+tuliao_options=[]
 
+tuliao_letters='ABCDEFG'
 
+for i in range(len(tuliaos)):
 
+    tuliao_options.append('给定选项'+tuliao_letters[i]+', 内容为' + tuliaos[i])
 
+import json
 
+with open('nantong_rule', 'r') as f:
 
+    content = f.read()
 
+import json
 
+obj=json.loads(content)
 
+from fallback import fallback
 
+def fanghuotuliao(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "问题描述： " + ','.join(options) + "。请问选项中是否有防火涂料相关选项，请回答是或者否"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if '是' in json_string:
 
+            return True
 
+        else:
 
+            return False
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的判断作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if '否' in json_string:
 
+        return False
 
+    return True
 
+def fanghuo(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz']  + "。请问工作内容是否提及防火要求，请回答是或者否"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if '是' in json_string:
 
+            return True
 
+        else:
 
+            return False
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的判断作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if '否' in json_string:
 
+        return False
 
+    return True
 
+def tuliaofilter(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "问题描述： 给定一段涉及防火涂料的工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(tuliao_options) + "。请选出最恰当的选项。请返回类似A或者B或者C这样的结果"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if 'A' in json_string:
 
+            return tuliaos[0]
 
+        if 'B' in json_string:
 
+            return tuliaos[1]
 
+        if 'C' in json_string:
 
+            return tuliaos[2]
 
+        if 'D' in json_string:
 
+            return tuliaos[3]
 
+        if 'E' in json_string:
 
+            return tuliaos[4]
 
+        if 'F' in json_string:
 
+            return tuliaos[5]
 
+        if 'G' in json_string:
 
+            return tuliaos[6]
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A或者B的字母作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if 'A' in json_string:
 
+        return tuliaos[0]
 
+    if 'B' in json_string:
 
+        return tuliaos[1]
 
+    if 'C' in json_string:
 
+        return tuliaos[2]
 
+    if 'D' in json_string:
 
+        return tuliaos[3]
 
+    if 'E' in json_string:
 
+        return tuliaos[4]
 
+    if 'F' in json_string:
 
+        return tuliaos[5]
 
+    if 'G' in json_string:
 
+        return tuliaos[6]
 
+def aifilter3(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有含有“零星钢构件制作”字样的选项，并且同时选项中有含有“铁件制作”字样的选项，则二者只能选一，且优先选择出现靠前的选项"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter1(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有晒衣架制作安装的选项，但是工作内容中不涉及晒衣架，则去除选项中的晒衣架制作安装的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有龙骨钢骨架制作安装的选项，但是工作内容中不涉及龙骨钢骨架，则去除选项中的龙骨钢骨架的选项"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter2(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有含有“零星钢构件制作”字样的选项，并且选项中没有其他具体的钢构件选项，则不做任何处理"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有含有“零星钢构件制作”字样的选项，并且选项中有其他具体的钢构件选项（例如CZ轻钢檩条），则去除选项中的含有“零星钢构件制作”字样的选项"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        
+        answer=[]
 
+        if 'A' in json_string: 
+            answer.append(A[0])
 
+        if 'B' in json_string: 
+            answer.append(A[1])
 
+        if 'C' in json_string: 
+            answer.append(A[2])
 
+        if 'D' in json_string: 
+            answer.append(A[3])
 
+        if 'E' in json_string: 
+            answer.append(A[4])
 
+        if 'F' in json_string: 
+            answer.append(A[5])
 
+        if 'G' in json_string: 
+            answer.append(A[6])
 
+        if 'H' in json_string: 
+            answer.append(A[7])
 
+        if 'I' in json_string: 
+            answer.append(A[8])
 
+        if 'J' in json_string: 
+            answer.append(A[9])
 
+       
+        
+
 
+
 
+        return answer
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def postprocess0106_(selected, data, aiclient, qwclient, label_name, name_dw):
 
+    if len(selected) == 1:
 
+        return selected
 
+    return aifilter3(aifilter2(aifilter1(selected, data, aiclient, qwclient, name_dw), data, aiclient, qwclient, name_dw), data, aiclient, qwclient, name_dw)
 
+def postprocess0106(selected, data, aiclient, qwclient, label_name, name_dw):
 
+    selected =  postprocess0106_(selected, data, aiclient, qwclient, label_name, name_dw)
 
+    hit = fanghuo(selected, data, aiclient, qwclient, name_dw)
 
+    if hit:
 
+        tuliao = fanghuotuliao(selected, data, aiclient, qwclient, name_dw)
 
+        if not tuliao:
 
+            selected.append(tuliaofilter(selected, data, aiclient, qwclient, name_dw))
 
+        return selected
 
+    else:
 
+        return selected

+ 174 - 0
postprocess0108.py
Wyświetl plik 

@@ -0,0 +1,174 @@
 
+tuliaos=[
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 0.5小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 1小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 1.5小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 薄型 2小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 厚型 2小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 厚型 2.5小时',
 
+'第十七章  油漆、涂料、裱糊工程 17.1  油漆、涂料 17.1.2  金属面油漆 17.1.2.3  防火涂料 金属面防火涂料 厚型 3小时',
 
+]
 
+tuliao_options=[]
 
+tuliao_letters='ABCDEFG'
 
+for i in range(len(tuliaos)):
 
+    tuliao_options.append('给定选项'+tuliao_letters[i]+', 内容为' + tuliaos[i])
 
+import json
 
+with open('nantong_rule', 'r') as f:
 
+    content = f.read()
 
+import json
 
+obj=json.loads(content)
 
+from fallback import fallback
 
+def aifilter1(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-z1-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：去掉所有含有“五金配件”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：去掉所有含有“金属面油漆”字样的选项"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "enabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter2(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容的描述没有明确提及无框，则去掉含有“无框”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：防火门与放火卷帘门是完全不同的产品。如果工作内容的描述没有明确提及防火卷帘门，则去掉防火卷帘门的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：防火门与铝合金门是完全不同的产品。如果工作内容的描述是防火门，则去掉铝合金门的选项"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        
+        answer=[]
 
+        if 'A' in json_string: 
+            answer.append(A[0])
 
+        if 'B' in json_string: 
+            answer.append(A[1])
 
+        if 'C' in json_string: 
+            answer.append(A[2])
 
+        if 'D' in json_string: 
+            answer.append(A[3])
 
+        if 'E' in json_string: 
+            answer.append(A[4])
 
+        if 'F' in json_string: 
+            answer.append(A[5])
 
+        if 'G' in json_string: 
+            answer.append(A[6])
 
+        if 'H' in json_string: 
+            answer.append(A[7])
 
+        if 'I' in json_string: 
+            answer.append(A[8])
 
+        if 'J' in json_string: 
+            answer.append(A[9])
 
+       
+        
+
 
+
 
+        return answer
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def postprocess0108(selected, data, aiclient, qwclient, label_name, name_dw):
 
+    prime = aifilter1(selected, data, aiclient, qwclient, name_dw)
 
+    if len(prime) == 0:
 
+        return []
 
+    
+    prime = aifilter2(prime, data, aiclient, qwclient, name_dw)
 
+    if len(prime) == 0:
 
+        return []
 
+    return selected

+ 268 - 0
postprocess0109.py
Wyświetl plik 

@@ -0,0 +1,268 @@
 
+import json
 
+with open('zhaoping_rule', 'r') as f:
 
+    content = f.read()
 
+import json
 
+obj=json.loads(content)
 
+with open('name_label', 'r') as f:
 
+    content = f.read()
 
+import json
 
+name_label=json.loads(content)
 
+baohuceng = ['10-74', '10-75', '10-77', '10-78', '10-80', '10-81', '10-83', '10-84', '10-86', '10-87', '10-90']
 
+from fallback import fallback
 
+def aifilter3(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJK"
 
+    for i in range(len(baohuceng)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+name_label[baohuceng[i]] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 重要提示：匹配保护层做法时，优先考虑材料的匹配性，比如，特征中描述是水泥砂浆，则优先选择水泥砂浆选项而不是防水砂浆选项"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，其中包含了屋面保护层的做法。".join(options) + "。请根据工作内容中保护层的描述，选择最匹配的选项并返回结果。例如，如果C选项最匹配，请返回C"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A或者B的选项作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(name_label[baohuceng[0]])
 
+    if 'B' in json_string:
 
+        answer.append(name_label[baohuceng[1]])
 
+    if 'C' in json_string:
 
+        answer.append(name_label[baohuceng[2]])
 
+    if 'D' in json_string:
 
+        answer.append(name_label[baohuceng[3]])
 
+    if 'E' in json_string:
 
+        answer.append(name_label[baohuceng[4]])
 
+    if 'F' in json_string:
 
+        answer.append(name_label[baohuceng[5]])
 
+    if 'G' in json_string:
 
+        answer.append(name_label[baohuceng[6]])
 
+    if 'H' in json_string:
 
+        answer.append(name_label[baohuceng[7]])
 
+    if 'I' in json_string:
 
+        answer.append(name_label[baohuceng[8]])
 
+    if 'J' in json_string:
 
+        answer.append(name_label[baohuceng[9]])
 
+    if 'K' in json_string:
 
+        answer.append(name_label[baohuceng[10]])
 
+    return answer
 
+def aifilter4(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-z1-air",
 
+        #model="Qwen/Qwen3-14B",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：石油沥青，沥青马蹄脂，渗透结晶防水材料等都是防水涂料。"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中有多个防水涂料的选项，则只能选择一个防水涂料选项，且优先选择渗透结晶防水材料；与防水涂料无关的选项全部保留"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "enabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter1(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-z1-flash",
 
+        model="Qwen/Qwen3-8B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：去掉所有含有“干铺卷材”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到玻纤网格布，则去掉所有含有“玻纤网格布”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述的是屋面刚性层，则去掉所有含有卷材字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述没有明确的“加浆抹光”字样，则去掉所有含有“加浆抹光”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述没有单独的一道“素水泥浆”工序，则去掉所有含有“素水泥浆”字样的选项"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "enabled"}},
 
+        extra_body={"enable_thinking": True},
 
+        stream=True
 
+    )
 
+    done_thinking = False
 
+    json_string=""
 
+    thinking_json_string=""
 
+    for chunk in completion:
 
+        thinking_chunk = chunk.choices[0].delta.reasoning_content
 
+        answer_chunk = chunk.choices[0].delta.content
 
+        if thinking_chunk != '':
 
+            thinking_json_string = thinking_json_string +  thinking_chunk
 
+        elif answer_chunk != '':
 
+            if not done_thinking:
 
+                done_thinking = True
 
+            json_string = json_string + answer_chunk
 
+    #json_string = completion.choices[0].message.content
 
+    print(thinking_json_string)
 
+    print(json_string)
 
+    if len([x for x in json_string if x != ',' and x != '[' and x != ']' and x != ' ' and (x < 'A' or x > 'M')]) < 5:
 
+        answer=[]
 
+        if 'A' in json_string and len(A) > 0:
 
+            answer.append(A[0])
 
+        if 'B' in json_string and len(A) > 1:
 
+            answer.append(A[1])
 
+        if 'C' in json_string and len(A) > 2:
 
+            answer.append(A[2])
 
+        if 'D' in json_string and len(A) > 3:
 
+            answer.append(A[3])
 
+        if 'E' in json_string and len(A) > 4:
 
+            answer.append(A[4])
 
+        if 'F' in json_string and len(A) > 5:
 
+            answer.append(A[5])
 
+        if 'G' in json_string and len(A) > 6:
 
+            answer.append(A[6])
 
+        if 'H' in json_string and len(A) > 7:
 
+            answer.append(A[7])
 
+        if 'I' in json_string and len(A) > 8:
 
+            answer.append(A[8])
 
+        if 'J' in json_string and len(A) > 9:
 
+            answer.append(A[9])
 
+        return answer
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    if 'I' in json_string:
 
+        answer.append(A[8])
 
+    if 'J' in json_string:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter2(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    hit_wumian = False
 
+    for entry in A:
 
+        if entry in obj['wumian']:
 
+            hit_wumian=True
 
+
 
+    hit_loumian = False
 
+    loumian_entry = ''
 
+    for entry in A:
 
+        if entry in obj['loumian']:
 
+            hit_loumian=True
 
+            loumian_entry = entry
 
+    if hit_wumian and hit_loumian:
 
+        return [x for x in A if x != loumian_entry]
 
+    return A
 
+
 
+
 
+def postprocess0109(selected, data, aiclient, qwclient, label_name, name_dw):
 
+    prime = aifilter1(selected, data, aiclient, qwclient, name_dw)
 
+    if data['bianma'].startswith("010902") and '高聚物' in data['tz'] and '改性沥青防水涂料' in data['tz']:##屋面防水
 
+        if '第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)' not in prime:
 
+            prime.append('第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)') ##需要换
 
+    if data['bianma'].startswith("010902") and '非固化' in data['tz'] and '沥青防水涂料' in data['tz']:##屋面防水
 
+        if '第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)' not in prime:
 
+            prime.append('第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)') ##需要换
 
+    prime = aifilter2(prime, data, aiclient, qwclient, name_dw)##找平层去重
 
+    prime = aifilter4(prime, data, aiclient, qwclient, name_dw)##沥青去重
 
+    if data['bianma'].startswith("010902") and '保护层' in data['tz']:##屋面防水保护层
 
+        l = len([x for x in prime if '刚性防水屋面' in x])
 
+        if l==0:
 
+            answer = aifilter3(prime, data, aiclient, qwclient, name_dw)
 
+            prime.append(answer[0])
 
+    if '南通补充定额 南通补充定额2016 第十章 屋面及防水工程 干铺法施工水泥彩瓦屋面（砼屋面板上钉钢挂瓦条、顺水条）' in prime:
 
+        prime.append('南通补充定额 南通补充定额2016 第十章 屋面及防水工程 干铺法施工水泥彩瓦屋面（铺瓦）')
 
+
 
+    return prime

+ 260 - 0
postprocess0110.py
Wyświetl plik 

@@ -0,0 +1,260 @@
 
+import json
 
+with open('zhaoping_rule', 'r') as f:
 
+    content = f.read()
 
+import json
 
+obj=json.loads(content)
 
+with open('name_label', 'r') as f:
 
+    content = f.read()
 
+import json
 
+name_label=json.loads(content)
 
+baohuceng = ['10-74', '10-75', '10-77', '10-78', '10-80', '10-81', '10-83', '10-84', '10-86', '10-87', '10-90']
 
+from fallback import fallback
 
+def aifilter4(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-z1-flash",
 
+        model="Qwen/Qwen3-8B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：土建施工中，保温做法有很多种，包括保温砖，保温瓦，保温砂浆，保温板等。保温板有不同材料，比如聚苯乙烯泡沫板，聚苯乙烯挤塑板，聚氨酯保温板等"},
 
+            {"role": "user", "content": "问题描述：" + ",".join(options)  + "。请问选项中是否有保温板的选项？请回答是或者否"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "enabled"}},
 
+        extra_body={"enable_thinking": True},
 
+        stream=True
 
+    )
 
+    done_thinking = False
 
+    json_string=""
 
+    thinking_json_string=""
 
+    for chunk in completion:
 
+        thinking_chunk = chunk.choices[0].delta.reasoning_content
 
+        answer_chunk = chunk.choices[0].delta.content
 
+        if thinking_chunk != '':
 
+            thinking_json_string = thinking_json_string +  thinking_chunk
 
+        elif answer_chunk != '':
 
+            if not done_thinking:
 
+                done_thinking = True
 
+            json_string = json_string + answer_chunk
 
+    #json_string = completion.choices[0].message.content
 
+    print(thinking_json_string)
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if '否' in json_string:
 
+            return False
 
+        return True
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的判断，请将该判断输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if '否' in json_string:
 
+        return False
 
+    return True
 
+
 
+def aifilter3(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-z1-flash",
 
+        model="Qwen/Qwen3-8B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：土建施工中，保温做法有很多种，包括保温砖，保温瓦，保温砂浆，保温板等。保温板有不同材料，比如聚苯乙烯泡沫板，聚苯乙烯挤塑板，聚氨酯保温板, 岩棉保温板等"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] + "。请问工作内容的描述中有涉及保温板吗？请回答是或者否"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "enabled"}},
 
+        extra_body={"enable_thinking": True},
 
+        stream=True
 
+    )
 
+    done_thinking = False
 
+    json_string=""
 
+    thinking_json_string=""
 
+    for chunk in completion:
 
+        thinking_chunk = chunk.choices[0].delta.reasoning_content
 
+        answer_chunk = chunk.choices[0].delta.content
 
+        if thinking_chunk != '':
 
+            thinking_json_string = thinking_json_string +  thinking_chunk
 
+        elif answer_chunk != '':
 
+            if not done_thinking:
 
+                done_thinking = True
 
+            json_string = json_string + answer_chunk
 
+    #json_string = completion.choices[0].message.content
 
+    print(thinking_json_string)
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if '否' in json_string:
 
+            return False
 
+        return True
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的判断，请将该判断输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if '否' in json_string:
 
+        return False
 
+    return True
 
+
 
+def aifilter1(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-z1-flash",
 
+        model="Qwen/Qwen3-8B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到素水泥浆，则去掉所有含有“素水泥浆”字样的选项"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "enabled"}},
 
+        extra_body={"enable_thinking": True},
 
+        stream=True
 
+    )
 
+    done_thinking = False
 
+    json_string=""
 
+    thinking_json_string=""
 
+    for chunk in completion:
 
+        thinking_chunk = chunk.choices[0].delta.reasoning_content
 
+        answer_chunk = chunk.choices[0].delta.content
 
+        if thinking_chunk != '':
 
+            thinking_json_string = thinking_json_string +  thinking_chunk
 
+        elif answer_chunk != '':
 
+            if not done_thinking:
 
+                done_thinking = True
 
+            json_string = json_string + answer_chunk
 
+    #json_string = completion.choices[0].message.content
 
+    print(thinking_json_string)
 
+    print(json_string)
 
+    if len([x for x in json_string if x != ',' and x != '[' and x != ']' and x != ' ' and (x < 'A' or x > 'M')]) < 5:
 
+        answer=[]
 
+        if 'A' in json_string and len(A) > 0:
 
+            answer.append(A[0])
 
+        if 'B' in json_string and len(A) > 1:
 
+            answer.append(A[1])
 
+        if 'C' in json_string and len(A) > 2:
 
+            answer.append(A[2])
 
+        if 'D' in json_string and len(A) > 3:
 
+            answer.append(A[3])
 
+        if 'E' in json_string and len(A) > 4:
 
+            answer.append(A[4])
 
+        if 'F' in json_string and len(A) > 5:
 
+            answer.append(A[5])
 
+        if 'G' in json_string and len(A) > 6:
 
+            answer.append(A[6])
 
+        if 'H' in json_string and len(A) > 7:
 
+            answer.append(A[7])
 
+        if 'I' in json_string and len(A) > 8:
 
+            answer.append(A[8])
 
+        if 'J' in json_string and len(A) > 9:
 
+            answer.append(A[9])
 
+        return answer
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string and len(A) > 0:
 
+        answer.append(A[0])
 
+    if 'B' in json_string and len(A) > 1:
 
+        answer.append(A[1])
 
+    if 'C' in json_string and len(A) > 2:
 
+        answer.append(A[2])
 
+    if 'D' in json_string and len(A) > 3:
 
+        answer.append(A[3])
 
+    if 'E' in json_string and len(A) > 4:
 
+        answer.append(A[4])
 
+    if 'F' in json_string and len(A) > 5:
 
+        answer.append(A[5])
 
+    if 'G' in json_string and len(A) > 6:
 
+        answer.append(A[6])
 
+    if 'H' in json_string and len(A) > 7:
 
+        answer.append(A[7])
 
+    if 'I' in json_string and len(A) > 8:
 
+        answer.append(A[8])
 
+    if 'J' in json_string and len(A) > 9:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter2(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    hit_wumian = False
 
+    for entry in A:
 
+        if entry in obj['wumian']:
 
+            hit_wumian=True
 
+
 
+    hit_loumian = False
 
+    loumian_entry = ''
 
+    for entry in A:
 
+        if entry in obj['loumian']:
 
+            hit_loumian=True
 
+            loumian_entry = entry
 
+    if hit_wumian and hit_loumian:
 
+        return [x for x in A if x != loumian_entry]
 
+    return A
 
+
 
+
 
+def postprocess0110(selected, data, aiclient, qwclient, label_name, name_dw):
 
+    ban = aifilter3(selected, data, aiclient, qwclient, name_dw)
 
+    if ban:
 
+        ban2 = aifilter4(selected, data, aiclient, qwclient, name_dw)
 
+        if not ban2:
 
+            if '墙面' in data['mc']:
 
+                selected.append('第十一章  保温、隔热、防腐工程 11.1  保温、隔热工程 11.1.2  墙、柱、天棚及其它 外墙外保温  聚苯乙烯挤塑板 厚度25mm 混凝土墙面')
 
+            else:
 
+                selected.append('第十一章  保温、隔热、防腐工程 11.1  保温、隔热工程 11.1.1  屋、楼地面 屋面、楼地面保温隔热 聚苯乙烯挤塑板(厚25mm)')
 
+    prime = aifilter1(selected, data, aiclient, qwclient, name_dw)
 
+    if '界面剂' in data['tz']:##保温
 
+        if len([x for x in prime if '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂' in x]) == 0:
 
+            prime.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂 混凝土面') ##需要换
 
+    if '玻纤网' in data['tz']:##保温
 
+        if '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 墙面耐碱玻纤网格布 一层' not in prime:
 
+            prime.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 墙面耐碱玻纤网格布 一层') ##需要换
 
+
 
+    return prime

+ 302 - 0
postprocess0111.py
Wyświetl plik 

@@ -0,0 +1,302 @@
 
+import time
 
+import json
 
+with open('zhaoping_rule', 'r') as f:
 
+    content = f.read()
 
+import json
 
+obj=json.loads(content)
 
+with open('name_label', 'r') as f:
 
+    content = f.read()
 
+import json
 
+name_label=json.loads(content)
 
+baohuceng = ['10-74', '10-75', '10-77', '10-78', '10-80', '10-81', '10-83', '10-84', '10-86', '10-87', '10-90']
 
+from fallback import fallback
 
+def aifilter3(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+              sfclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = sfclient.chat.completions.create(
 
+        model="THUDM/GLM-4-9B-0414",
 
+        #model="glm-4.5-flash",
 
+        #model="Qwen/Qwen3-8B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": "问题描述： 细石混凝土内配钢丝网片是一种常见的施工工艺。给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] + "。请问工作内容的描述中有该施工工艺吗？请回答有或者没有"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": True},
 
+        #stream=True
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if len(json_string) < 4:
 
+        if '没有' in json_string:
 
+            return False
 
+        return True
 
+    completion = sfclient.chat.completions.create(
 
+        #model="glm-4.5-flash",
 
+        model="THUDM/GLM-4-9B-0414",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个有或者没有的判断，请将该判断输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    if '没有' in json_string:
 
+        return False
 
+    return True
 
+
 
+def aifilter4(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-z1-flash",
 
+        model="Qwen/Qwen3-14B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：混凝土（砼）整体面层跟混凝土找平层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：混凝土（砼）整体面层跟混凝土垫层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：混凝土（砼）找平层跟混凝土垫层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：水泥砂浆找平层跟水泥砂浆面层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：水泥砂浆找平层跟水泥砂浆保护层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：“混凝土楼地面”施工是面层施工，跟“楼地面涂刷一遍901胶素水泥浆”是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中明确提到块料面层，比如地砖、石材块料等，则保留楼地面涂刷一遍901胶素水泥浆选项，去掉所有混凝土（砼）整体面层的选项"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "enabled"}},
 
+        extra_body={"enable_thinking": True},
 
+        stream=True
 
+    )
 
+    done_thinking = False
 
+    json_string=""
 
+    thinking_json_string=""
 
+    for chunk in completion:
 
+        thinking_chunk = chunk.choices[0].delta.reasoning_content
 
+        answer_chunk = chunk.choices[0].delta.content
 
+        if thinking_chunk != '':
 
+            thinking_json_string = thinking_json_string +  thinking_chunk
 
+        elif answer_chunk != '':
 
+            if not done_thinking:
 
+                done_thinking = True
 
+            json_string = json_string + answer_chunk
 
+    #json_string = completion.choices[0].message.content
 
+    print(thinking_json_string)
 
+    print(json_string)
 
+    if len([x for x in json_string if x != ',' and x != '[' and x != ']' and x != ' ' and (x < 'A' or x > 'M')]) < 5:
 
+        answer=[]
 
+        if 'A' in json_string and len(A) > 0:
 
+            answer.append(A[0])
 
+        if 'B' in json_string and len(A) > 1:
 
+            answer.append(A[1])
 
+        if 'C' in json_string and len(A) > 2:
 
+            answer.append(A[2])
 
+        if 'D' in json_string and len(A) > 3:
 
+            answer.append(A[3])
 
+        if 'E' in json_string and len(A) > 4:
 
+            answer.append(A[4])
 
+        if 'F' in json_string and len(A) > 5:
 
+            answer.append(A[5])
 
+        if 'G' in json_string and len(A) > 6:
 
+            answer.append(A[6])
 
+        if 'H' in json_string and len(A) > 7:
 
+            answer.append(A[7])
 
+        if 'I' in json_string and len(A) > 8:
 
+            answer.append(A[8])
 
+        if 'J' in json_string and len(A) > 9:
 
+            answer.append(A[9])
 
+        return answer
 
+    completion = qwclient.chat.completions.create(
 
+        model="ZhipuAI/GLM-4.5",
 
+        #model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string and len(A) > 0:
 
+        answer.append(A[0])
 
+    if 'B' in json_string and len(A) > 1:
 
+        answer.append(A[1])
 
+    if 'C' in json_string and len(A) > 2:
 
+        answer.append(A[2])
 
+    if 'D' in json_string and len(A) > 3:
 
+        answer.append(A[3])
 
+    if 'E' in json_string and len(A) > 4:
 
+        answer.append(A[4])
 
+    if 'F' in json_string and len(A) > 5:
 
+        answer.append(A[5])
 
+    if 'G' in json_string and len(A) > 6:
 
+        answer.append(A[6])
 
+    if 'H' in json_string and len(A) > 7:
 
+        answer.append(A[7])
 
+    if 'I' in json_string and len(A) > 8:
 
+        answer.append(A[8])
 
+    if 'J' in json_string and len(A) > 9:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter1(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i] )
 
+
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-z1-flash",
 
+        model="Qwen/Qwen3-14B",
 
+        #model="ernie-speed-128k",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：混凝土楼地面是面层，跟混凝土垫层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：混凝土（砼）整体面层跟混凝土找平层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：混凝土（砼）整体面层跟混凝土垫层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：混凝土（砼）找平层跟混凝土垫层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：水泥砂浆找平层跟水泥砂浆面层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 背景知识：水泥砂浆找平层跟水泥砂浆保护层是不同的施工步骤，不得混淆"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到踢脚线，则去掉所有踢脚线的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：去掉所有模板工程的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确涉及水泥砂浆面层，且没有提及水泥砂浆保护层，则去掉所有20mm水泥砂浆楼地面的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到素水泥浆，则去掉所有含有“素水泥浆”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到“加浆抹光”，则去掉所有含有“加浆抹光”字样的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到混凝土垫层，则去掉所有混凝土垫层的选项"},
 
+            {"role": "user", "content": " 特殊处理要求：如果选项中同时存在“冷轧带肋钢筋”选项和“抗裂基层 热镀锌钢丝网”选项，则去掉热镀锌钢丝网的选项"},
 
+            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
 
+            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        #extra_body={"thinking": {"type": "enabled"}},
 
+        extra_body={"enable_thinking": True},
 
+        stream=True
 
+    )
 
+    done_thinking = False
 
+    json_string=""
 
+    thinking_json_string=""
 
+    for chunk in completion:
 
+        thinking_chunk = chunk.choices[0].delta.reasoning_content
 
+        answer_chunk = chunk.choices[0].delta.content
 
+        if thinking_chunk != '':
 
+            thinking_json_string = thinking_json_string +  thinking_chunk
 
+        elif answer_chunk != '':
 
+            if not done_thinking:
 
+                done_thinking = True
 
+            json_string = json_string + answer_chunk
 
+    #json_string = completion.choices[0].message.content
 
+    print(thinking_json_string)
 
+    print(json_string)
 
+    if len([x for x in json_string if x != ',' and x != '[' and x != ']' and x != ' ' and (x < 'A' or x > 'M')]) < 5:
 
+        answer=[]
 
+        if 'A' in json_string and len(A) > 0:
 
+            answer.append(A[0])
 
+        if 'B' in json_string and len(A) > 1:
 
+            answer.append(A[1])
 
+        if 'C' in json_string and len(A) > 2:
 
+            answer.append(A[2])
 
+        if 'D' in json_string and len(A) > 3:
 
+            answer.append(A[3])
 
+        if 'E' in json_string and len(A) > 4:
 
+            answer.append(A[4])
 
+        if 'F' in json_string and len(A) > 5:
 
+            answer.append(A[5])
 
+        if 'G' in json_string and len(A) > 6:
 
+            answer.append(A[6])
 
+        if 'H' in json_string and len(A) > 7:
 
+            answer.append(A[7])
 
+        if 'I' in json_string and len(A) > 8:
 
+            answer.append(A[8])
 
+        if 'J' in json_string and len(A) > 9:
 
+            answer.append(A[9])
 
+        return answer
 
+    completion = qwclient.chat.completions.create(
 
+        #model="glm-4.5-flash",
 
+        model="ZhipuAI/GLM-4.5",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B]的数组作为结果，请将该最终结果输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+        #extra_body={"enable_thinking": False},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string and len(A) > 0:
 
+        answer.append(A[0])
 
+    if 'B' in json_string and len(A) > 1:
 
+        answer.append(A[1])
 
+    if 'C' in json_string and len(A) > 2:
 
+        answer.append(A[2])
 
+    if 'D' in json_string and len(A) > 3:
 
+        answer.append(A[3])
 
+    if 'E' in json_string and len(A) > 4:
 
+        answer.append(A[4])
 
+    if 'F' in json_string and len(A) > 5:
 
+        answer.append(A[5])
 
+    if 'G' in json_string and len(A) > 6:
 
+        answer.append(A[6])
 
+    if 'H' in json_string and len(A) > 7:
 
+        answer.append(A[7])
 
+    if 'I' in json_string and len(A) > 8:
 
+        answer.append(A[8])
 
+    if 'J' in json_string and len(A) > 9:
 
+        answer.append(A[9])
 
+    return answer
 
+def aifilter2(A, #options
 
+       B, #data
 
+       aiclient,
 
+             qwclient,
 
+             dw):
 
+    hit_wumian = False
 
+    for entry in A:
 
+        if entry in obj['wumian']:
 
+            hit_wumian=True
 
+
 
+    hit_loumian = False
 
+    loumian_entry = ''
 
+    for entry in A:
 
+        if entry in obj['loumian']:
 
+            hit_loumian=True
 
+            loumian_entry = entry
 
+    if hit_wumian and hit_loumian:
 
+        return [x for x in A if x != loumian_entry]
 
+    return A
 
+
 
+
 
+def postprocess0111(selected, data, aiclient, qwclient, sfclient, label_name, name_dw):
 
+    prime = aifilter1(selected, data, aiclient, qwclient, name_dw)
 
+    time.sleep(1)
 
+    wangpian = aifilter3(prime, data, aiclient, qwclient, sfclient, name_dw)
 
+    if not wangpian:
 
+        prime = aifilter4(prime, data, aiclient, qwclient, name_dw)
 
+
 
+    if '界面剂' in data['tz']:##保温
 
+        if len([ x for x in prime if '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂' in x]) == 0:
 
+            prime.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂 混凝土面') ##需要换
 
+    return prime

+ 65 - 0
postprocess0117.py
Wyświetl plik 

@@ -0,0 +1,65 @@
 
+import json
 
+with open("cuoshi_corpus.txt", "r") as f:
 
+    content = f.read()
 
+pairs = content.split("\n")
 
+pair_tuple = []
 
+i = 0
 
+while i < len(pairs):
 
+    pair_tuple.append([pairs[i], pairs[i+1]])
 
+    i = i + 2
 
+def aifilter(A, #options
 
+       B, #data
 
+       aiclient):
 
+    options=[]
 
+    letters = "ABCDEFGHIJKLMN"
 
+    for i in range(len(A)):
 
+        options.append("给定选项" + letters[i]+"，内容为"+A[i])
 
+
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+            {"role": "user", "content": " 背景知识：如果工作内容是土石方工程、土方工程等，那么不要能选用塔式起重机"},
 
+            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +   "," + "，".join(options) + "。请做出筛选，并返回结果。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
 
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = aiclient.chat.completions.create(
 
+        model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为答案，请将该最终答案输出"},                       
+        ],
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    answer=[]
 
+    if 'A' in json_string:
 
+        answer.append(A[0])
 
+    if 'B' in json_string:
 
+        answer.append(A[1])
 
+    if 'C' in json_string:
 
+        answer.append(A[2])
 
+    if 'D' in json_string:
 
+        answer.append(A[3])
 
+    if 'E' in json_string:
 
+        answer.append(A[4])
 
+    if 'F' in json_string:
 
+        answer.append(A[5])
 
+    if 'G' in json_string:
 
+        answer.append(A[6])
 
+    if 'H' in json_string:
 
+        answer.append(A[7])
 
+    return answer
 
+def postprocess0117(selected, data, aiclient):
 
+    correct=[]
 
+    for entry in selected:
 
+        correct.append(entry)
 
+        for item in pair_tuple:
 
+            if entry in item:
 
+                correct = correct + item
 
+    correct = list(set(correct))
 
+    return aifilter(correct, data, aiclient)

+ 3 - 0
server.js
Wyświetl plik 

@@ -51,6 +51,9 @@ app.post('/api/transform', (req, res) => {
 
        let r = Service.changguidinge(JSON.parse(result[i][2]), "1")
 
        let r = Service.changguidinge(JSON.parse(result[i][2]), "1")
 
        r = Service.updateShuliang(result[i][1], r[2])
 
        r = Service.updateShuliang(result[i][1], r[2])
 
    }
 
    }
 
+   if (data['extra'].length > 4) {
 
+        Service.danxiangdinge("1", data['n'], data['extra'])
 
+   }
 
    //console.log(Service.cache)
 
    //console.log(Service.cache)
 
    res.status(200).send(Service.cache);
 
    res.status(200).send(Service.cache);
 
 });
 
 });

+ 292 - 19
tasks.py
Wyświetl plik 

@@ -1,15 +1,26 @@
 
+import time
 
+from fallback import fallback
 
+from dianceng import dianceng
 
+from lingji import lingji
 
+from jieheceng import jieheceng
 
 from celery_app import celery_app
 
 from celery_app import celery_app
 
+from postprocess import postprocess
 
+from extra import extra, need_extra
 
+from huansuan import callzaihuansuan
 
 import json
 
 import json
 
 import dedata
 
 import dedata
 
 import chromadb
 
 import chromadb
 
 client = chromadb.HttpClient(host='47.101.198.30',port=8000)
 
 client = chromadb.HttpClient(host='47.101.198.30',port=8000)
 
 collection = client.get_or_create_collection(name="tj_de_bge")
 
 collection = client.get_or_create_collection(name="tj_de_bge")
 
+cuoshi_collection = client.get_or_create_collection(name="tj_cuoshi_bge")
 
+menchuang_collection = client.get_or_create_collection(name="tj_menchuang_bge")
 
 from FlagEmbedding import FlagModel
 
 from FlagEmbedding import FlagModel
 
-#model = FlagModel('/mnt/d/Develop/bge/test2_encoder_only_base_bge-large-zh-v1.5')
 
-model = FlagModel('/Users/zxp/Downloads/test2_encoder_only_base_bge-large-zh-v1.5')
 
+model = FlagModel('/mnt/d/Develop/bge/test2_encoder_only_base_bge-large-zh-v1.5')
 
+cuoshi_model = FlagModel('cuoshi_encoder_only_base_bge-large-zh-v1.5/cuoshi_encoder_only_base_bge-large-zh-v1.5')
 
 from sentence_transformers import CrossEncoder
 
 from sentence_transformers import CrossEncoder
 
-#ce = CrossEncoder('/mnt/d/Develop/celery/final')
 
-ce = CrossEncoder('/Users/zxp/Downloads/reranker')
 
+ce = CrossEncoder('/mnt/d/Develop/celery/final')
 
+cuoshi_ce = CrossEncoder('cuoshi_reranker/final')
 
+#ce = CrossEncoder('/Users/zxp/Downloads/reranker')
 
 with open("hunningtu_rule", "r") as f:
 
 with open("hunningtu_rule", "r") as f:
 
     content = f.read()
 
     content = f.read()
 
 obj = json.loads(content)
 
 obj = json.loads(content)
 
@@ -19,6 +30,9 @@ obj2 = json.loads(content)
 
 with open("basic_rule", "r") as f:
 
 with open("basic_rule", "r") as f:
 
     content = f.read()
 
     content = f.read()
 
 basic = json.loads(content)
 
 basic = json.loads(content)
 
+with open("menchuang_rule", "r") as f:
 
+    content = f.read()
 
+menchuang = json.loads(content)
 
 with open("incremental_rule", "r") as f:
 
 with open("incremental_rule", "r") as f:
 
     content = f.read()
 
     content = f.read()
 
 incremental = json.loads(content)
 
 incremental = json.loads(content)
 
@@ -31,27 +45,58 @@ name_label = json.loads(content)
 
 with open("name_dw", "r") as f:
 
 with open("name_dw", "r") as f:
 
     content = f.read()
 
     content = f.read()
 
 name_dw = json.loads(content)
 
 name_dw = json.loads(content)
 
-THRESHOLD=0.9####adjust it
 
+THRESHOLD=0.8####adjust it
 
 
 
 import os
 
 import os
 
 from openai import OpenAI
 
 from openai import OpenAI
 
 import requests
 
 import requests
 
 aiclient = OpenAI(
 
 aiclient = OpenAI(
 
-    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
 
     #api_key=os.getenv("DASHSCOPE_API_KEY"),
 
     #api_key=os.getenv("DASHSCOPE_API_KEY"),
 
     api_key=os.getenv("ZAI_API_KEY"),
 
     api_key=os.getenv("ZAI_API_KEY"),
 
     #base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 
     #base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 
     base_url="https://open.bigmodel.cn/api/paas/v4/",
 
     base_url="https://open.bigmodel.cn/api/paas/v4/",
 
 )
 
 )
 
+qwclient = OpenAI(
 
+    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
 
+    #api_key=os.getenv("DASHSCOPE_API_KEY"),
 
+    api_key=os.getenv("MS_API_KEY"),
 
+    #base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 
+    base_url="https://api-inference.modelscope.cn/v1/",
 
+)
 
+hyclient = OpenAI(
 
+    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
 
+    #api_key=os.getenv("DASHSCOPE_API_KEY"),
 
+    api_key=os.getenv("HY_API_KEY"),
 
+    #base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 
+    base_url="https://api.hunyuan.cloud.tencent.com/v1",
 
+)
 
+bdclient = OpenAI(
 
+    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
 
+    #api_key=os.getenv("DASHSCOPE_API_KEY"),
 
+    api_key=os.getenv("BD_API_KEY"),
 
+    #base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 
+    base_url="https://qianfan.baidubce.com/v2",
 
+)
 
+sfclient = OpenAI(
 
+    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
 
+    #api_key=os.getenv("DASHSCOPE_API_KEY"),
 
+    api_key=os.getenv("SF_API_KEY"),
 
+    #base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 
+    base_url="https://api.siliconflow.cn/v1",
 
+)
 
 
 
-def callzaidw(A,B):
 
+
 
+
 
+def callzaikuailiao(data):
 
+    time.sleep(1)
 
     completion = aiclient.chat.completions.create(
 
     completion = aiclient.chat.completions.create(
 
         # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
         # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
         model="glm-4.5-flash",
 
         model="glm-4.5-flash",
 
+        #model="ZhipuAI/GLM-4.5",
 
         #model="qwen3-4b",
 
         #model="qwen3-4b",
 
         messages=[
 
         messages=[
 
             {"role": "system", "content": "You are a helpful assistant."},
 
             {"role": "system", "content": "You are a helpful assistant."},
 
-        {"role": "user", "content": "  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3。给定一个工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。若两个单位相等，请返回A=B。若两个单位不相等，但是存在比例换算关系，请返回比例换算关系，例如A单位是m,B单位是10m, 则返回A=0.1*B。再例如，A单位是10m2,B单位是m2，则返回A=10*B。若两个单位不相等，且不存在比例换算关系，请返回A<>B，例如A单位是m,B单位是m2，一个表示长度，一个表示面积，不存在比例关系，则返回A<>B。 "},
 
+        {"role": "user", "content": " 楼地面有多种做法，包括整体面层，块料面层，木地板等。块料面层常见的有石材块料面板，缸砖，马赛克，假麻石块，地砖，橡胶塑料板等。 现在给定一工作内容如下： " + data['label'] + " " + data['mc'] + " " + data['tz'] + ", 计量单位为" + data['dw'] + ", 请问该工作内容中包括了块料面层施工吗？"},
 
         ],
 
         ],
 
         # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
         # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
         # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
         # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
@@ -60,10 +105,78 @@ def callzaidw(A,B):
 
     )
 
     )
 
     json_string = completion.choices[0].message.content
 
     json_string = completion.choices[0].message.content
 
     print(json_string)
 
     print(json_string)
 
+    time.sleep(1)
 
+    completion = sfclient.chat.completions.create(
 
+        # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
+        model="THUDM/GLM-4-9B-0414",
 
+        #model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了关于是否包括块料面层施工的判断，请将该判断输出。请输出是或者否"},                       
+        ],
 
+        # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
+        # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+def callzaiclarify(data):
 
     completion = aiclient.chat.completions.create(
 
     completion = aiclient.chat.completions.create(
 
         # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
         # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
-        #model="qwen3-4b",
 
         model="glm-4.5-flash",
 
         model="glm-4.5-flash",
 
+        #model="qwen3-4b",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+        {"role": "user", "content": "  管桩的清单工作内容描述可以分成两类，一类是打桩、压桩，属于桩基工程的一种，其工作内容计量的单位一般是米(m)或根。另一类是填芯，一般是向桩芯内浇混凝土，属于土建工程的一种，其配套的计量单位一般是立方米(m3)，即浇混凝土的体积量。现在给定一工作内容如下： " + data['label'] + " " + data['mc'] + " " + data['tz'] + ", 计量单位为" + data['dw'] + ", 请问该工作内容属于填芯吗？"},
 
+        ],
 
+        # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
+        # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    completion = sfclient.chat.completions.create(
 
+        # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
+        model="THUDM/GLM-4-9B-0414",
 
+        #model="glm-4.5-flash",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
+            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了关于是不是填芯的判断，请将该判断输出。请输出是或者否"},                       
+        ],
 
+        # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
+        # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    return json_string
 
+def callzaidw(A,B):
 
+    time.sleep(1)
 
+    completion = qwclient.chat.completions.create(
 
+        # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
+        #model="glm-4.5-flash",
 
+        model="ZhipuAI/GLM-4.5",
 
+        #model="qwen3-4b",
 
+        messages=[
 
+            {"role": "system", "content": "You are a helpful assistant."},
 
+        {"role": "user", "content": "  计量单位可以用名称或者符号表示，常用的符号包括表示米的符号m,表示千米的符号km,表示吨的符号t,表示千克的符号kg,表示平方米的符号m2,表示立方米的符号m3。也有计量单位很宽泛，比如“项”、“次”. 给定一个工作量计量单位，内容为" + A + "，记作A，再给定一个工作量计量单位，内容为" + B + "，记作B。若两个单位相等，请返回A=B。例如，“项”跟“次”是等价的，应返回A=B。若两个单位不相等，但是存在比例换算关系，请返回比例换算关系，例如A单位是m,B单位是10m, 则返回A=0.1*B。再例如，A单位是10m2,B单位是m2，则返回A=10*B。再例如，A单位是m3, B单位是1000m3，则返回A=0.001*B。若两个单位不相等，且不存在比例换算关系，请返回A<>B，例如A单位是m,B单位是m2，一个表示长度，一个表示面积，不存在比例关系，则返回A<>B。 "},
 
+        ],
 
+        # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
+        # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
+        #extra_body={"enable_thinking": False},
 
+        extra_body={"thinking": {"type": "disabled"}},
 
+    )
 
+    json_string = completion.choices[0].message.content
 
+    print(json_string)
 
+    time.sleep(1)
 
+    completion = qwclient.chat.completions.create(
 
+        # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
+        model="ZhipuAI/GLM-4.5",
 
+        #model="glm-4.5-flash",
 
         messages=[
 
         messages=[
 
             {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
             {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
 
             {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，请将该最终答案输出"},                       
             {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于A=B的表达式作为答案，请将该最终答案输出"},                       
@@ -85,7 +198,7 @@ def callzai(A,B,C):
 
         #model="qwen3-4b",
 
         #model="qwen3-4b",
 
         messages=[
 
         messages=[
 
             {"role": "system", "content": "You are a helpful assistant."},
 
             {"role": "system", "content": "You are a helpful assistant."},
 
-            {"role": "user", "content": " 给定一条主定额，内容为" + A + "，记作A，再给定一条配套定额，内容为" + B + "，记作B。可以通过简单的组合，来表达对应的工作内容的数量，比如A+B可以表达，主定额的工作量加上配套定额的工作量；再比如，A+B*2可以表达， 主定额的工作量加上两倍的配套定额的工作量；再比如，A+B*(-2)可以表达， 主定额减去两倍的配套定额的工作量；再比如，A可以表示，不使用配套定额，仅表示主定额的工作量。现在给你一条工程量清单，内容为" + C + "，该条清单包含了主定额描述的工作内容，但是数量并不一定一致。请你组合A与B，表示出清单描述的对应工作数量。请输出类似A+B的格式,如果清单里相应工作量的描述不明确，请输出A作为答案 "},
 
+            {"role": "user", "content": " 给定一条主定额，内容为" + A + "，记作A，再给定一条配套定额，内容为" + B + "，记作B。可以通过简单的组合，来表达对应的工作内容的数量，比如A+B可以表达，主定额的工作量加上配套定额的工作量；再比如，A+B*2可以表达， 主定额的工作量加上两倍的配套定额的工作量；再比如，A+B*(-2)可以表达， 主定额减去两倍的配套定额的工作量；再比如，A可以表示，不使用配套定额，仅表示主定额的工作量。现在给你一条工程量清单，内容为" + C + "，该条清单包含了主定额描述的工作内容，但是数量并不一定一致。请你组合A与B，表示出清单描述的对应工作数量。请输出类似A+B、A+B*2、A-B*2的格式,不要输出A+2*B、A-2*B的格式。如果清单里相应工作量的描述不明确，请输出A作为答案 "},
 
         ],
 
         ],
 
         # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
         # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
 
         # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
         # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
 
@@ -94,6 +207,8 @@ def callzai(A,B,C):
 
     )
 
     )
 
     json_string = completion.choices[0].message.content
 
     json_string = completion.choices[0].message.content
 
     print(json_string)
 
     print(json_string)
 
+    if len(json_string) < 4:
 
+        return json_string
 
     completion = aiclient.chat.completions.create(
 
     completion = aiclient.chat.completions.create(
 
         # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
         # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
 
         #model="qwen3-4b",
 
         #model="qwen3-4b",
 
@@ -127,18 +242,61 @@ def zuhe(input, content):
 
         option = incremental[input]
 
         option = incremental[input]
 
         for entry in option:
 
         for entry in option:
 
             answer = callzai(name_label[input], name_label[entry], content)
 
             answer = callzai(name_label[input], name_label[entry], content)
 
+            print("answer of callai for zuhe")
 
             print(answer)
 
             print(answer)
 
             if 'B' in answer:
 
             if 'B' in answer:
 
                 return transform(answer, input, entry)
 
                 return transform(answer, input, entry)
 
         return input
 
         return input
 
     else:
 
     else:
 
         return input
 
         return input
 
+def huansuan_highlevel(bianma, label, input,dw, tz):
 
+    time.sleep(1)
 
+    t = huansuan(input, dw)
 
+    if t == 0:
 
+        dw1 = input
 
+        dw1 = dw1.lower()
 
+        dw1 = dw1.replace("水平投影面积", "")
 
+        dw2 = name_dw[dw]
 
+        dw2 = dw2.lower()
 
+        dw2 = dw2.replace("水平投影面积", "")
 
+        answer = callzaihuansuan(bianma, label, dw1,dw2,tz, aiclient, qwclient)
 
+        answers = answer.split("\n")
 
+        answers = [x for x in answers if ':' in x and "A" in x]
 
+        answers = [x for x in answers if '=' in x or '<>' in x]
 
+        print("answer of callzaihuansuan")
 
+        print(answers)
 
+        if len(answers) == 0:
 
+            return 0
 
+        answer2 = answers[0].split(":")[1].replace(" ", "")
 
+        if "<>" in answer2:
 
+            return 0
 
+        answer2 = answer2.replace("\'", "")
 
+        answer2 = answer2.replace("\"", "")
 
+        answer2 = answer2.split("=")[1]
 
+        if answer2 == "B":
 
+            return 1
 
+        answer2=answer2.replace("B", "")
 
+        answer2=answer2.replace("*", "")
 
+        answer2=answer2.replace("x", "")
 
+        answer2=answer2.replace(",", "")
 
+        answer2=answer2.replace("，", "")
 
+        answer2=answer2.replace("×", "")
 
+        print(answer2)
 
+        return float(answer2)
 
+    else:
 
+        if '人工修边坡' in label:
 
+            return t * 0.1
 
+        else:
 
+            return t
 
+
 
 
 
 def huansuan(input, dw):
 
 def huansuan(input, dw):
 
     dw1 = input
 
     dw1 = input
 
     dw1 = dw1.lower()
 
     dw1 = dw1.lower()
 
+    dw1 = dw1.replace("水平投影面积", "")
 
     dw2 = name_dw[dw]
 
     dw2 = name_dw[dw]
 
     dw2 = dw2.lower()
 
     dw2 = dw2.lower()
 
+    dw2 = dw2.replace("水平投影面积", "")
 
     if dw1 == dw2:
 
     if dw1 == dw2:
 
         return 1
 
         return 1
 
     else:
 
     else:
 
@@ -148,30 +306,79 @@ def huansuan(input, dw):
 
         answers = [x for x in answers if '=' in x or '<>' in x]
 
         answers = [x for x in answers if '=' in x or '<>' in x]
 
         print(answers)
 
         print(answers)
 
         if len(answers) == 0:
 
         if len(answers) == 0:
 
-            return 1
 
+            return 0
 
         answer2 = answers[0].split(":")[1].replace(" ", "")
 
         answer2 = answers[0].split(":")[1].replace(" ", "")
 
         if "<>" in answer2:
 
         if "<>" in answer2:
 
             return 0
 
             return 0
 
         answer2 = answer2.replace("\'", "")
 
         answer2 = answer2.replace("\'", "")
 
         answer2 = answer2.replace("\"", "")
 
         answer2 = answer2.replace("\"", "")
 
+        answer2 = answer2.replace(",", "")
 
         answer2 = answer2.split("=")[1]
 
         answer2 = answer2.split("=")[1]
 
         if answer2 == "B":
 
         if answer2 == "B":
 
             return 1
 
             return 1
 
         answer2=answer2.replace("B", "")
 
         answer2=answer2.replace("B", "")
 
         answer2=answer2.replace("*", "")
 
         answer2=answer2.replace("*", "")
 
+        answer2=answer2.replace("x", "")
 
+        answer2=answer2.replace("×", "")
 
         print(answer2)
 
         print(answer2)
 
         return float(answer2)
 
         return float(answer2)
 
 
 
+def clarify(data):
 
+    data['tz'] = data['tz'].replace("水泥基防水涂料", "水泥基渗透结晶防水涂料")
 
+    data['tz'] = data['tz'].replace("无机保温砂浆", "无机轻集料保温砂浆")
 
+    data['tz'] = data['tz'].replace("JS防水涂料", "聚合物水泥防水涂料")
 
+    if data['bianma'].startswith("010301"):##打桩
 
+        print("clarify")
 
+        result = callzaiclarify(data)
 
+        if "是" in result:
 
+            data['mc'] = data['mc'] + '填芯'
 
+        return data, False
 
+    elif data['bianma'].startswith("010507001"): ##散水、坡道
 
+        data['tz'] = lingji(data['tz'], aiclient, qwclient)
 
+        data['tz'] = dianceng(data['tz'], aiclient, qwclient)
 
+        return data, False
 
+    elif data['bianma'].startswith("0109"): ##防水
 
+        data['tz'] = lingji(data['tz'], aiclient, qwclient)
 
+        data['tz'] = dianceng(data['tz'], aiclient, qwclient)
 
+        return data, False
 
+    elif data['bianma'].startswith("0111"):
 
+        data['tz'] = jieheceng(data['tz'], aiclient, qwclient)
 
+        result = callzaikuailiao(data)
 
+        if '是' in result:
 
+            return data, True
 
+        return data, False
 
+
 
+    else:
 
+        return data, False
 
+
 
 
 
 @celery_app.task
 
 @celery_app.task
 
 def process_data(data:dict)-> dict:
 
 def process_data(data:dict)-> dict:
 
+    placeholder, kuailiao = clarify(data)
 
     label = data['mc'] + ' ' + data['tz']
 
     label = data['mc'] + ' ' + data['tz']
 
+    if data['bianma'].startswith("0117"):
 
+        label = data['label'] + " " + data['mc'] + " " + data['tz']
 
     sentences = [label]
 
     sentences = [label]
 
-    embeddings = model.encode(sentences)
 
-    result = collection.query(query_embeddings=embeddings,n_results=25)      
+    if data['bianma'].startswith("0117"):
 
+        embeddings = cuoshi_model.encode(sentences)
 
+    else:
 
+        embeddings = model.encode(sentences)
 
+    if data['bianma'].startswith("0117"):
 
+        result = cuoshi_collection.query(query_embeddings=embeddings,n_results=25)      
+    else:
 
+        result = collection.query(query_embeddings=embeddings,n_results=25)      
+
 
     d = result['documents'][0]
 
     d = result['documents'][0]
 
     print(d)
 
     print(d)
 
-    ranks = ce.rank(label, d)
 
+    if data['bianma'].startswith("0117"):
 
+        ranks = cuoshi_ce.rank(label, d)
 
+    else:
 
+        ranks = ce.rank(label, d)
 
+    if data['bianma'].startswith("0117"):
 
+        cutoff = 0.6 
+    else:
 
+        cutoff = THRESHOLD
 
+    
     ranks = ranks[:10]
 
     ranks = ranks[:10]
 
     match = [("6.2.1.1","6.3.1.1"),
 
     match = [("6.2.1.1","6.3.1.1"),
 
     ("6.2.1.2", "6.3.1.2"),
 
     ("6.2.1.2", "6.3.1.2"),
 
@@ -252,17 +459,56 @@ def process_data(data:dict)-> dict:
 
     ("21.1.7","nantong21.1.7"),
 
     ("21.1.7","nantong21.1.7"),
 
     ("23.1.2","nantong23.1.2")
 
     ("23.1.2","nantong23.1.2")
 
     ]
 
     ]
 
+    match3=[('16.1', '16.2'), ('16.1.1', '16.2.1')]
 
     selected=[]
 
     selected=[]
 
     notselected=[]
 
     notselected=[]
 
+    #if data['bianma'].startswith('0108'): ##门窗
 
+    #    score = -1
 
+    #    for rank in ranks:
 
+    #        if label_name[d[rank['corpus_id']]].startswith('16-') and int(label_name[d[rank['corpus_id']]].split('-')[1]) < 308:
 
+    #            score = rank['score']
 
+    #            break
 
+    #    if score > -1 and score < cutoff:
 
+    #        cutoff = score - 0.1
 
+    #        if cutoff < 0.3:
 
+    #            cutoff = 0.3
 
+    if data['bianma'].startswith('0106'):##金属结构
 
+        score = -1
 
+        for rank in ranks:
 
+            if label_name[d[rank['corpus_id']]].startswith('7-') and label_name[d[rank['corpus_id']]] != '7-62' and label_name[d[rank['corpus_id']]] !='7-63':
 
+                score = rank['score']
 
+                break
 
+        if score > -1 and score < cutoff:
 
+            cutoff = score - 0.05
 
+            if cutoff < 0.3:
 
+                cutoff = 0.3
 
+    if data['bianma'].startswith('0111'):##楼地面
 
+        score = -1
 
+        for rank in ranks:
 
+            if '13.4' in d[rank['corpus_id']] and '块料面层' in d[rank['corpus_id']]:
 
+                score = rank['score']
 
+                break
 
+        if score > -1 and score < cutoff:
 
+            cutoff = score - 0.05
 
+            if cutoff < 0.3:
 
+                cutoff = 0.3
 
+    print("cutoff=" + str(cutoff))
 
     for entry in incremental:
 
     for entry in incremental:
 
         notselected = notselected + incremental[entry]
 
         notselected = notselected + incremental[entry]
 
     notselected=[name_label[x] for x in notselected]
 
     notselected=[name_label[x] for x in notselected]
 
     for rank in ranks:
 
     for rank in ranks:
 
-        if rank['score']<THRESHOLD:
 
+        
+        print(f"{rank['score']} {d[rank['corpus_id']]}")
 
+        if rank['score']<cutoff:
 
             continue
 
             continue
 
         if d[rank['corpus_id']] in notselected:
 
         if d[rank['corpus_id']] in notselected:
 
-            continue
 
-        print(f"{rank['score']} {d[rank['corpus_id']]}")
 
+            if d[rank['corpus_id']] != '盐城补充定额 盐城补充定额2018 平面立面及其它防水 涂刷油类 水泥基渗透结晶防水每增减0.5mm厚':
 
+                continue
 
+            else:
 
+                d[rank['corpus_id']]= '第十章  屋面及防水工程 10.2  平面立面及其它防水 10.2.1  涂刷油类 水泥基渗透结晶 防水材料 二～三遍(厚2mm)'
 
+                if d[rank['corpus_id']] in notselected:
 
+                    continue
 
+        print(f"select {rank['score']} {d[rank['corpus_id']]}")
 
         selected.append(d[rank['corpus_id']])
 
         selected.append(d[rank['corpus_id']])
 
         hunningtu_group = []
 
         hunningtu_group = []
 
         for entry in obj:
 
         for entry in obj:
 
@@ -273,6 +519,15 @@ def process_data(data:dict)-> dict:
 
                 if entry[0]==hunningtu_group[0] or entry[1] == hunningtu_group[0]:
 
                 if entry[0]==hunningtu_group[0] or entry[1] == hunningtu_group[0]:
 
                     notselected = notselected + obj[entry[0]]    
                     notselected = notselected + obj[entry[0]]    
                     notselected = notselected + obj[entry[1]]    
                     notselected = notselected + obj[entry[1]]    
+        menchuang_group = []
 
+        for entry in menchuang:
 
+            if d[rank['corpus_id']] in menchuang[entry]:
 
+                menchuang_group=[entry]
 
+        if len(menchuang_group) > 0:
 
+            for entry in match3:
 
+                if entry[0]==menchuang_group[0] or entry[1] == menchuang_group[0]:
 
+                    notselected = notselected + menchuang[entry[0]]    
+                    notselected = notselected + menchuang[entry[1]]    
         nantong_group = []
 
         nantong_group = []
 
         for entry in obj2:
 
         for entry in obj2:
 
             if d[rank['corpus_id']] in obj2[entry]:
 
             if d[rank['corpus_id']] in obj2[entry]:
 
@@ -286,8 +541,26 @@ def process_data(data:dict)-> dict:
 
             if d[rank['corpus_id']] in basic[entry]:
 
             if d[rank['corpus_id']] in basic[entry]:
 
                 notselected = notselected + basic[entry]
 
                 notselected = notselected + basic[entry]
 
         notselected = [x for x in notselected if x not in selected]
 
         notselected = [x for x in notselected if x not in selected]
 
-    result = [(label_name[x], huansuan(data['dw'], label_name[x])) for x in selected]  
+    selected = list(set(selected))
 
+    if len(selected) == 0:
 
+        candidates=[]
 
+        for rank in ranks:
 
+            candidates.append(d[rank['corpus_id']])
 
+        selected = fallback(candidates, data, aiclient, qwclient, menchuang_collection, model)
 
+    selected = postprocess(selected, data, aiclient, qwclient,sfclient, label_name, name_dw)
 
+    print("final selected")
 
+    print(selected)
 
+    result = [(label_name[x], huansuan_highlevel(data['bianma'], x, data['dw'], label_name[x], data['mc']+data['tz'])) for x in selected]  
+    print("after haunsuan")
 
+    print(result)
 
     result = [(zuhe(x[0], label), x[1]) for x in result]  
     result = [(zuhe(x[0], label), x[1]) for x in result]  
+    print("after zuhe")
 
+    print(result)
 
     result = [(x[0], x[1], dedata.read_singledexilie2(10, x[0])) for x in result]
 
     result = [(x[0], x[1], dedata.read_singledexilie2(10, x[0])) for x in result]
 
-    response = requests.post("http://localhost:3000/api/transform", json={'bianma': data['bianma'], 'mc': data['mc'], 'tz': data['tz'], 'dw': data['dw'], 'sl': data['sl'], 'result': result})
 
+    need = need_extra(data, aiclient, qwclient, result)
 
+    if need:
 
+        extra_info = extra(data, aiclient, qwclient, menchuang_collection, model)
 
+    else:
 
+        extra_info = "无"
 
+    response = requests.post("http://localhost:3000/api/transform", json={'bianma': data['bianma'], 'mc': data['mc'], 'tz': data['tz'], 'dw': data['dw'], 'sl': data['sl'], 'n': data['n'], "extra": extra_info, 'result': result})
 
     return {"result": response.json()}
 
     return {"result": response.json()}

+ 1 - 0
zhaoping_rule
Wyświetl plik 

@@ -0,0 +1 @@
 
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Niektóre pliki nie zostały wyświetlone z powodu dużej ilości zmienionych plików