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日常更新 

from mac mini(m1)
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liubiren 2025-12-17 02:14:56 +08:00
parent ef3699973a
commit 16d41d8fae
4 changed files with 66 additions and 46 deletions
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utils/caches.db
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票据理赔自动化/SQLite.db → 票据理赔自动化/database.db
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110
票据理赔自动化/main.py
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@ -55,7 +55,7 @@ if __name__ == "__main__":
return ZenEngine({"loader": loader}).get_decision(rule_path.as_posix()) return ZenEngine({"loader": loader}).get_decision(rule_path.as_posix())
# 影像件识别使能 # 影像件识别使能
recognition_enable = rule_engine(Path("rules/影像件识别使能.json")) recognize_enable = rule_engine(Path("rules/影像件识别使能.json"))
class MasterData(SQLiteClient): class MasterData(SQLiteClient):
"""主数据""" """主数据"""
@ -65,7 +65,7 @@ if __name__ == "__main__":
初始化主数据 初始化主数据
""" """
# 初始化SQLite客户端 # 初始化SQLite客户端
super().__init__(database="SQLite.db") super().__init__(database="database.db")
try: try:
with self: with self:
@ -465,45 +465,42 @@ if __name__ == "__main__":
if not (response.get("status") == 200 and response.get("code") == 0): if not (response.get("status") == 200 and response.get("code") == 0):
raise RuntimeError("请求深圳快瞳通用文本识别接口发生异常") raise RuntimeError("请求深圳快瞳通用文本识别接口发生异常")
boxes = [] blocks = []
for box in response["data"]: for block in response["data"]:
# noinspection PyTypeChecker # noinspection PyTypeChecker
boxes.append( blocks.append(
[ [
numpy.float64( int(block["itemPolygon"]["x"]), # 文本块左上角的X坐标
box["itemPolygon"]["x"] int(block["itemPolygon"]["y"]), # 文本块左上角的Y坐标
), # 文本标注框左上角的X坐标 int(block["itemPolygon"]["height"]), # 文本块左上角的高度
numpy.float64( block["value"], # 文本块的文本内容
box["itemPolygon"]["y"]
), # 文本标注框左上角的Y坐标
numpy.float64(
box["itemPolygon"]["height"]
), # 文本标注框左上角的高度
box["value"], # 文本标注框的文本
] ]
) )
# 按照文本标注框的Y坐标升序（先从上到下） # 使用俄罗斯方块方法整理文本块，先按照文本块的Y坐标升序（从上到下）
boxes.sort(key=lambda x: x[1]) blocks.sort(key=lambda x: x[1])
rows = [] lines = []
for idx, box in enumerate(boxes[1:]): for idx, block in enumerate(blocks[1:]):
if idx == 0: if idx == 0:
row = [boxes[0]] line = [blocks[0]]
continue continue
# 若文本标注框的Y坐标与当前行的最后一个文本标注框的Y坐标差值小于阈值则归为同一行，否则结束当前行（分行） # 若当前文本块的Y坐标和当前文本行的平均Y坐标差值小于阈值则归为同一文本行，否则另起一文本行（分行）
if box[1] - row[-1][1] < row[-1][2] * 0.5: if (
row.append(box) block[1] - numpy.array([e[1] for e in line]).mean()
< numpy.array([e[2] for e in line]).mean()
):
line.append(block)
else: else:
rows.append(row) lines.append(line)
row = [box] line = [block]
rows.append(row) lines.append(line)
boxes = [] blocks = []
for row in rows: for line in lines:
boxes.extend( blocks.extend(
[re.sub(r"\s", "", x[3]) for x in sorted(row, key=lambda x: x[0])] [re.sub(r"\s", "", x[3]) for x in sorted(line, key=lambda x: x[0])]
) # 按照文本标注框的X坐标升序（再从左到右）并去除所有空字符 ) # 按照文本块的X坐标升序（从左到右）并去除文本块的文本内容中所有空字符
return "\n".join(boxes) # 整合 return "\n".join(blocks)
# TODO: 后续添加居民身份证(国徽面)和居民身份证(头像面)合并 # TODO: 后续添加居民身份证(国徽面)和居民身份证(头像面)合并
# noinspection PyShadowingNames # noinspection PyShadowingNames
@ -608,9 +605,8 @@ if __name__ == "__main__":
:param image: 影像件 :param image: 影像件
:return: 空 :return: 空
""" """
# 方法1：先使用深圳快瞳通用文本识别再使用硅基流动中大语言模型结构化，可行但是需要请求二次
# 方法2：使用硅基流动中支持OCR的大语言模型
# 请求硅基流动的大语言模型接口 # 请求硅基流动的大语言模型接口
# noinspection PyTypeChecker
response = http_client.post( response = http_client.post(
url="https://api.siliconflow.cn/v1/chat/completions", url="https://api.siliconflow.cn/v1/chat/completions",
headers={ headers={
@ -618,18 +614,41 @@ if __name__ == "__main__":
"Content-Type": "application/json; charset=utf-8", "Content-Type": "application/json; charset=utf-8",
}, },
json={ json={
"model": "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", # 通过从DeepSeek-R1-0528模型蒸馏思维链接至Qwen3-8B-Base获得的模型 "model": (model := "THUDM/GLM-4.1V-9B-Thinking"),
"messages": [{"role": "user", "content": ""}], "messages": [
"max_tokens": 10240, # 生成文本最大令牌数 {
"temperature": 0.2, "role": "user",
"top_p": 0.5, "content": [
"top_k": 20, {
"frequency_penalty": 0.0, "type": "text",
"thinking_budget": 1, "text": (
text := "请以JSON字符串的形式输出识别结果"
),
}, },
guid=md5(prompt.encode("utf-8")).hexdigest().upper(), {
"type": "image_url",
"image_url": {
"url": f"data:image/{image["影像件格式"]};base64,{image["影像件BASE64编码"]}"
},
},
],
}
],
"max_tokens": 8192,
"temperature": 0.95,
"top_p": 0.7,
"top_k": 2,
"frequency_penalty": 1.1,
"thinking_budget": 8192,
},
guid=md5((model + text + image["影像件唯一标识"]).encode("utf-8"))
.hexdigest()
.upper(),
) )
print(response)
exit()
recognition = ( recognition = (
json.loads(match.group("json")) json.loads(match.group("json"))
if ( if (
@ -648,6 +667,7 @@ if __name__ == "__main__":
# 根据保险分公司匹配结构化识别文本方法 # 根据保险分公司匹配结构化识别文本方法
match insurer_company: match insurer_company:
# 中银保险有限公司
case _ if insurer_company.startswith("中银保险有限公司"): case _ if insurer_company.startswith("中银保险有限公司"):
boc_application_recognize(image) boc_application_recognize(image)
@ -1382,12 +1402,12 @@ if __name__ == "__main__":
) )
# 影像件识别使能检查，若影像件不识别则跳过 # 影像件识别使能检查，若影像件不识别则跳过
if not recognition_enable.evaluate( if not recognize_enable.evaluate(
{ {
"insurer_company": insurer_company, "insurer_company": insurer_company,
"image_type": image["影像件类型"], "image_type": image["影像件类型"],
} }
)["result"]["recognition_enable"]: )["result"]["recognize_enable"]:
return return
# 根据影像件类型匹配影像件识别方法 # 根据影像件类型匹配影像件识别方法

2
票据理赔自动化/rules/影像件识别使能.json
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@ -139,7 +139,7 @@
{ {
"id": "a2fc744f-930d-43e0-b5cf-824a5928c7f1", "id": "a2fc744f-930d-43e0-b5cf-824a5928c7f1",
"name": "是否识别", "name": "是否识别",
"field": "recognition_enable" "field": "recognize_enable"
} }
], ],
"hitPolicy": "first", "hitPolicy": "first",