41 KiB
41 KiB
应用简介12
MathVerify Pro智能数学题目分析与验证系统，是用于高中数学题目分析的工具，主要是通过把试卷切割成一小题一小题的题目，并通过AI分析题目所涉及的知识点和方法，再通过人工验证，一方面来确定AI的分析正确与否；另一方面，可以把这些数学题的正确的知识点方法存储起来，用于后期知识库、知识图谱的使用，以及AI Agent或数学模型的使用。
MathVerify Pro智能数学题目分析与验证系统分为前端和后端两部分。 前端又分为：试卷分析和题目验证两部分。参考前端代码和前端部分的README.md
后端应用程序的工作流程：
试卷分析过程：
- 从前端接收一张或多张试卷的图片（PNG或JPG）；
- 保存到本地文件夹；
- 逐张发送给
分割试卷API，把整张试卷分割成一张张的题目图片（简称小题图片）； - 逐张发送每张小题图片到
ocr api； - 把ocr的结果，发送到
题目分析api； - 把小题图片（需转换为base64）、ocr结果的文本内容、题目分析api，存入数据库；暂时存到sqlite3中；
- 每一小题存储完成后，就发送到前端，用于显示题目和分析结果；
- 一张卷子完成以后，发送给前端一个单张卷子任务完成的通知；
- 所有卷子都完成以后，发送给前端，所有任务完成的通知。
题目验证过程：
- 前端从数据库中（通过接口）读取未验证的题目，每次10条；
- 前端更新修改后的数据，并修改标记位为已验证。后端更新完成后，发送通知。
涉及到的外部接口
外部接口，是后端应用程序请求的外部api，不是提供给前端直接使用的api。后端请求了这些api以后还要进行处理后，再提供给前端使用。
1. 分割试卷API:
请求:
curl --location --request POST 'https://ekt1.suzhou.edu.cn/autocut/detect' \
--header 'Accept: */*' \
--header 'Host: ekt1.suzhou.edu.cn' \
--header 'Connection: keep-alive' \
--header 'Content-Type: multipart/form-data; boundary=--------------------------687058231513459413708701' \
--form 'image=@"/Users/robertmaxwell/code/zstp/AutoCut/pic.png"' \
--form 'enable_error_detection="false"'
返回:
{
"data": {
"crop_results": {
"cropped_count": 14,
"cropped_images": [
{
"bbox": [
970,
69,
1909,
246
],
"filename": "question_001.jpg",
"question_id": 1,
"vertical_id": 1
},
{
"bbox": [
41,
107,
970,
253
],
"filename": "question_002.jpg",
"question_id": 2,
"vertical_id": 2
},
{
"bbox": [
970,
245,
1909,
479
],
"filename": "question_003.jpg",
"question_id": 3,
"vertical_id": 3
},
{
"bbox": [
41,
254,
970,
394
],
"filename": "question_004.jpg",
"question_id": 4,
"vertical_id": 4
},
{
"bbox": [
41,
395,
970,
506
],
"filename": "question_005.jpg",
"question_id": 5,
"vertical_id": 5
},
{
"bbox": [
970,
480,
1909,
661
],
"filename": "question_006.jpg",
"question_id": 6,
"vertical_id": 6
},
{
"bbox": [
41,
507,
970,
655
],
"filename": "question_007.jpg",
"question_id": 7,
"vertical_id": 7
},
{
"bbox": [
41,
656,
970,
820
],
"filename": "question_008.jpg",
"question_id": 8,
"vertical_id": 8
},
{
"bbox": [
970,
662,
1909,
904
],
"filename": "question_009.jpg",
"question_id": 9,
"vertical_id": 9
},
{
"bbox": [
41,
821,
970,
968
],
"filename": "question_010.jpg",
"question_id": 10,
"vertical_id": 10
},
{
"bbox": [
970,
905,
1909,
1183
],
"filename": "question_011.jpg",
"question_id": 11,
"vertical_id": 11
},
{
"bbox": [
41,
969,
970,
1111
],
"filename": "question_012.jpg",
"question_id": 12,
"vertical_id": 12
},
{
"bbox": [
41,
1112,
970,
1364
],
"filename": "question_013.jpg",
"question_id": 13,
"vertical_id": 13
},
{
"bbox": [
970,
1184,
1909,
1364
],
"filename": "question_014.jpg",
"question_id": 14,
"vertical_id": 14
}
],
"mode": "all_questions",
"zip_url": "/download/20251125_082929_f1975f5c"
},
"error_count": 8,
"error_details": [
{
"error_boxes": [
{
"bbox": [
1772,
678,
44,
44
],
"class_name": "cuo",
"confidence": 0.7618222236633301
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
970,
662,
939,
242
]
}
},
{
"error_boxes": [
{
"bbox": [
1209,
163,
31,
32
],
"class_name": "cuo",
"confidence": 0.7555574178695679
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
970,
69,
939,
177
]
}
},
{
"error_boxes": [
{
"bbox": [
1225,
533,
46,
44
],
"class_name": "cuo",
"confidence": 0.7304249405860901
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
970,
480,
939,
181
]
}
},
{
"error_boxes": [
{
"bbox": [
797,
827,
40,
42
],
"class_name": "cuo",
"confidence": 0.7287607192993164
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
41,
821,
929,
147
]
}
},
{
"error_boxes": [
{
"bbox": [
1299,
363,
33,
33
],
"class_name": "cuo",
"confidence": 0.7262661457061768
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
970,
245,
939,
234
]
}
},
{
"error_boxes": [
{
"bbox": [
1226,
1028,
36,
36
],
"class_name": "cuo",
"confidence": 0.7171130776405334
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
970,
905,
939,
278
]
}
},
{
"error_boxes": [
{
"bbox": [
814,
1116,
36,
37
],
"class_name": "cuo",
"confidence": 0.7121084332466125
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
41,
1112,
929,
252
]
}
},
{
"error_boxes": [
{
"bbox": [
793,
127,
39,
30
],
"class_name": "xie",
"confidence": 0.5237091183662415
}
],
"match_method": "中心点包含",
"question_box": {
"bbox": [
41,
107,
929,
146
]
}
}
],
"error_symbol_count": 8,
"question_count": 14,
"session_id": "20251125_082929_f1975f5c",
"visualization_images": {
"error_symbols": "/visualizations/20251125_082929_f1975f5c/error_symbols.jpg",
"matched_errors": "/visualizations/20251125_082929_f1975f5c/matched_errors.jpg",
"questions": "/visualizations/20251125_082929_f1975f5c/questions.jpg"
}
},
"success": true
}
被切割后的图片拼接方式:
https://ekt1.suzhou.edu.cn/autocut/files/<session_id>/<filename>
如上图,则被切割了14个图片:
https://ekt1.suzhou.edu.cn/autocut/files/20251125_082929_f1975f5c/question_001.jpg
https://ekt1.suzhou.edu.cn/autocut/files/20251125_082929_f1975f5c/question_002.jpg
https://ekt1.suzhou.edu.cn/autocut/files/20251125_082929_f1975f5c/question_003.jpg
…………
…………
https://ekt1.suzhou.edu.cn/autocut/files/20251125_082929_f1975f5c/question_014.jpg
question_id 也需要记录。其他字段可忽略.
返回错误包括:只支持图片格式: { "message": "不支持的文件格式", "success": false }
字段缺失或错误: { "message": "没有上传图片文件", "success": false }
2. ocr api：
注意：这是一个ai模型接口，可能会存在一些不可预知的错误，需要做好错误处理，比如，返回的内容 "content"字段中，有可能带上markdown标记，也有可能不带。模型有可能因超时或其他原因，返回错误。请合理处理错误。
输入：上面的切割试卷api所生成的url； 输出：markdown形式的带有数学公式的题目文本内容。 大模型api地址：https://ekt1.suzhou.edu.cn/llm/v1/chat/completions openai兼容格式 模型名称：qwen-vl，是一个VL模型。 上传需要先把url解析成base64。模型不能直接识别url。 设定温度为0； 设定提示词是：
你是一名数学文字识别专家。你的任务是，将图片中的文字，严格转换为markdown格式的文字；如图中有数学公式，你必须使用$或者$$进行包裹，生成markdown可解析的latex格式的数学公式；你无需输出其他任何解释性文字，只需严格输出图片中的文字。
输入示例：
POST: https://ekt1.suzhou.edu.cn/llm/v1/chat/completions
HEADER: Authonrization: Bearer sk-PqY5sI1od5Hbl6hVsniLDp7G0DOOBlUzE8E22p8HdVSTM5Rs
{
"model": "qwen-vl",
"messages": [
{
"role": "system",
"content": "你是一名数学图片文字识别专家。"
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "你的任务是，将图片中的文字，严格转换为markdown格式的文字；如图中有数学公式，你必须使用$或者$$进行包裹，生成markdown可解析的latex格式的数学公式；你无需输出其他任何解释性文字，只需严格输出图片中的文字。"
},
{
"type": "image_url",
"image_url": {
"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABfYAAAGCCAIAAAAt4wW+AAAACXBIWXMAAAsTAAALEwEAm==CC"
}
}
]
}
],
"temperature": 0
}
输出格式：
{
"id": "chatcmpl-08cc11398014472fa86bd8cbdb2ded8b",
"object": "chat.completion",
"created": 1764146440,
"model": "qwen-vl",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "12. 已知集合 $A=\\{1, 2, 4, 8\\}$, $B=\\{z|z=xy, x \\in A, y \\in A\\}$, 则下列四个结论正确的是 ( $\\not A B$ )\n\nA. $A \\nsubseteq B$\n\nB. $\\{1, 4, 16, 64\\} \\subseteq B$\n\nC. 集合 $B$ 中元素的个数为 8\n\nD. 集合 $B$ 的真子集的个数为 127",
"refusal": null,
"annotations": null,
"audio": null,
"function_call": null,
"tool_calls": [],
"reasoning_content": null
},
"logprobs": null,
"finish_reason": "stop",
"stop_reason": null
}
],
"service_tier": null,
"system_fingerprint": null,
"usage": {
"prompt_tokens": 841,
"total_tokens": 972,
"completion_tokens": 131,
"prompt_tokens_details": null
},
"prompt_logprobs": null,
"kv_transfer_params": null
}
"content": 这里面的文字就是ocr的结果。
3. 题目分析api：
-
请求方式: POST
-
请求URL:
https://aimaster.apps.k8s.aiscenes.cn/dev-api/aimaster/scenario/open/apihttps://aimaster.apps.k8s.aiscenes.cn/为域名/dev-api/ 是测试环境, 目前只有测试环境
/aimaster/scenario/open/api 为固定后缀
-
request:
header参数说明:
| 字段名 | 类型 | 说明 |
|---|---|---|
| Authorization | String | token登录获取,给固定值Bearer wo54FKODEaPi9ip3NbjbE0/2YtIom1oyQXvlvqsLEpc= |
样例:
Authorization:Bearer wo54FKODEaPi9ip3NbjbE0/2YtIom1oyQXvlvqsLEpc=
body参数说明:
| 字段名 | 类型 | 说明 |
|---|---|---|
| chatId | String | 对话主键,uuid自己生成,同一个id表示一次对话 |
| scenarioInfoId | String | 场景中心主键 |
| latestMessageContent | String | 用户最后一次提问 |
| params | Object | 应用的自定义参数, |
样例:
{
"latestMessageContent": "分析这道数学题：f(x)=2x^2的单调性",
"chatId": "706e73c3694146f2b703d0da2c4cd95b",
"params": {
"userId": ""
},
"scenarioInfoId": "e6e822e47fa24ca380f8d2d09eb114a7"
}
-
response
- 流式传输：采用
data:前缀+JSON对象的分块传输格式
样例:
data: {"id":"AI Master","choices":[{"delta":{"content":null,"function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741340356,"model":"ai-master","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null,"step":null,"agent_step":"Agent Start","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Start","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"@","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"企业","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"变","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"更","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"助手","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":" 企业","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"迁","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: {"id":"chatcmpl-B8PGrW9oyk1P3EiKUHwffLhIxpVg6","choices":[{"delta":{"content":"址","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1741343465,"model":"gpt-4o-2024-11-20","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":"fp_b705f0c291","usage":null,"step":"AI Starting","agent_step":"Agent Starting","agent_id":"d9bdca85-6dce-4dc9-833a-9b3c15a4603b","agent_name":"企业办事指南","function":null} data: 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你需要把流式的response 合并。
这个接口的返回的格式是：
{ "input_problem_summary": "简要概括题目（不超过20字）", "analysis": { "knowledges": [ "题目所涉及到的知识点1", "题目所涉及到的知识点2" ], "methods": [ "题目所涉及到的解题方法1", "题目所涉及到的解题方法2" ] } }
response example：
{
"input_problem_summary": "已知对数函数含参单调性求参数范围",
"analysis": {
"knowledges": [
"导数及其应用 - 导数在研究函数中的应用 - 利用导数研究函数的单调性",
"基本初等函数 - 对数函数 - 对数函数的导数"
],
"methods": [
"转化与化归思想",
"分离参数法",
"不等式恒成立问题求解"
]
}
}
注意：虽然prompt已经严格要求输出的内容是json格式，但是有可能AI会增加前面或后面的一些解释，也有可能加入markdown的json代码头，或者直接以{}开头，或者以引号开头。你需要预处理这些内容， 以使存储的和给到前端的数据是正确的json格式。
涉及到的存储
使用sqlite3 ，直接存储；（知识点、方法，使用sqlite3 的JSON 存储，示例：存入时存为 ["apple", "banana", "cherry"]，ORM 映射为 List/Array）
存储：切割后的照片base64、原始照片的sessionId、切割后照片的questionId、题目的markdown格式文本（ocr后）、题目的知识点json、题目的方法json，是否verified， updated 知识点（如有）；updated 方法（如有），以及其他必要字段如日期等；
与前端通信接口
- 上传接口POST，用于接收前端照片，可同时传多张；
- 后端主动发送的接口：发送的小题内容、单张卷子完成通知、全部任务完成通知；
- 前端获取数据库中题目的接口GET
- 前端更新数据库的接口POST或PUT
- 后端发送更新成功的通知接口
后端开发注意事项
- 使用GO GIN开发；
- 如果前端有不适合的或不匹配的情况，均以这个文档为准，请修改前端代码以匹配这个文档；
- 前端目前使用了大量的模拟数据，请直接修改前端代码，以使用后端的正式api；
- 调试请使用 chrome devtools mcp进行；
- 请务必前后端联调成功、测试完整后，才算完成你的工作；
- 所有外部接口中的key，都是可以用的，请先不要考虑安全性，请直接使用。我的任务是跑通。