software copyright
This commit is contained in:
jiahong
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# 自然写手写识别与AI分析引擎软件 V1.0
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# 作文批改接口模块 - AI作文评分与批改建议服务
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"""
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作文批改API接口
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提供AI作文评分、多维度分析（结构/语法/内容/修辞）、批改建议生成等功能
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支持小学至初中阶段作文批改，基于大语言模型与NLP分析管道
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"""
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import time
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import json
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import logging
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import hashlib
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import re
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from typing import List, Dict, Optional, Tuple
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from dataclasses import dataclass, field
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from enum import Enum
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from fastapi import APIRouter, HTTPException, Depends
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from pydantic import BaseModel, Field, validator
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logger = logging.getLogger(__name__)
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# ==================== 数据模型定义 ====================
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class EssayReviewRequest(BaseModel):
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"""作文批改请求"""
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text: str = Field(..., min_length=10, max_length=5000, description="作文OCR识别文本")
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title: Optional[str] = Field(None, description="作文题目")
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grade: int = Field(3, ge=1, le=9, description="年级(1-9)")
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genre: str = Field("narrative", description="文体类型: narrative/argumentative/expository/descriptive")
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max_score: int = Field(100, description="满分值")
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student_id: Optional[str] = Field(None, description="学生ID")
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assignment_id: Optional[str] = Field(None, description="作业ID")
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enable_suggestions: bool = Field(True, description="是否生成修改建议")
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@validator('genre')
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def validate_genre(cls, v):
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valid_genres = ['narrative', 'argumentative', 'expository', 'descriptive']
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if v not in valid_genres:
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raise ValueError(f'文体类型必须为: {valid_genres}')
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return v
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class SentenceError(BaseModel):
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"""句子级错误标注"""
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sentence: str = Field(..., description="原始句子")
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error_type: str = Field(..., description="错误类型")
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suggestion: str = Field(..., description="修改建议")
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position: int = Field(..., description="句子在原文中的位置索引")
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class EssayScoreDetail(BaseModel):
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"""作文各维度评分详情"""
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structure: float = Field(..., description="结构分")
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grammar: float = Field(..., description="语法分")
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content: float = Field(..., description="内容分")
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rhetoric: float = Field(..., description="修辞分")
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handwriting: Optional[float] = Field(None, description="书写分（如有）")
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# ==================== 文本分析工具 ====================
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class TextAnalyzer:
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"""
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文本分析工具类
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提供基础的中文文本分析功能：分句、词频统计、句式分析等
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"""
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# 中文句末标点
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SENTENCE_ENDINGS = {'。', '！', '？', '……', '；'}
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# 中文段落标识
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PARAGRAPH_INDENT = ' '
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@staticmethod
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def split_sentences(text: str) -> List[str]:
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"""将文本分割为句子列表"""
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sentences = []
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current = ""
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for char in text:
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current += char
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if char in TextAnalyzer.SENTENCE_ENDINGS:
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if current.strip():
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sentences.append(current.strip())
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current = ""
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if current.strip():
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sentences.append(current.strip())
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return sentences
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@staticmethod
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def split_paragraphs(text: str) -> List[str]:
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"""将文本分割为段落列表"""
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# 按换行符分割，过滤空段落
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paragraphs = [p.strip() for p in text.split('\n') if p.strip()]
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return paragraphs
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@staticmethod
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def count_characters(text: str) -> Dict[str, int]:
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"""统计文本字符数"""
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chinese_count = sum(1 for c in text if '\u4e00' <= c <= '\u9fff')
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punctuation_count = sum(1 for c in text if c in '，。！？、；：""''（）《》……—')
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total_count = len(text.replace(' ', '').replace('\n', ''))
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return {
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"total": total_count,
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"chinese": chinese_count,
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"punctuation": punctuation_count
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}
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@staticmethod
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def detect_rhetoric(text: str) -> List[Dict]:
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"""
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检测修辞手法使用情况
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识别常见修辞：比喻、排比、拟人、夸张等
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"""
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rhetorics = []
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# 比喻检测：包含"像...一样"、"如同"、"仿佛"等关键词
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simile_patterns = [
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r'像.{2,10}一样', r'如同.{2,10}', r'仿佛.{2,10}',
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r'好像.{2,10}', r'犹如.{2,10}', r'宛如.{2,10}'
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]
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for pattern in simile_patterns:
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matches = re.finditer(pattern, text)
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for m in matches:
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rhetorics.append({
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"type": "simile", "name": "比喻",
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"text": m.group(), "position": m.start()
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})
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# 排比检测：连续出现相似句式结构
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sentences = TextAnalyzer.split_sentences(text)
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for i in range(len(sentences) - 2):
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s1, s2, s3 = sentences[i], sentences[i+1], sentences[i+2]
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# 简化判断：三个连续句子长度相近且首字相同
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if (abs(len(s1) - len(s2)) < 5 and abs(len(s2) - len(s3)) < 5 and
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len(s1) > 5 and s1[0] == s2[0] == s3[0]):
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rhetorics.append({
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"type": "parallelism", "name": "排比",
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"text": f"{s1}{s2}{s3}", "position": text.find(s1)
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})
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# 拟人检测：非人事物使用人的动作词
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personification_patterns = [
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r'[风雨雪花树草月阳光河水山].{0,3}[笑哭唱跳跑走说叫]',
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r'[风雨雪花树草月阳光河水山].{0,3}[温柔轻轻悄悄]'
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]
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for pattern in personification_patterns:
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matches = re.finditer(pattern, text)
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for m in matches:
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rhetorics.append({
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"type": "personification", "name": "拟人",
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"text": m.group(), "position": m.start()
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})
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return rhetorics
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# ==================== 作文评分引擎 ====================
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class EssayScoringEngine:
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"""
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作文评分引擎
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基于多维度分析管道对作文进行综合评分
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评分维度：结构(25%)、语法(25%)、内容(30%)、修辞(20%)
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"""
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# 各年级期望字数范围
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EXPECTED_LENGTH = {
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1: (50, 150), 2: (100, 250), 3: (200, 400),
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4: (300, 500), 5: (350, 600), 6: (400, 700),
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7: (500, 800), 8: (600, 900), 9: (600, 1000)
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}
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# 评分维度权重配置
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DIMENSION_WEIGHTS = {
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"structure": 0.25,
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"grammar": 0.25,
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"content": 0.30,
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"rhetoric": 0.20
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}
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def __init__(self):
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self._text_analyzer = TextAnalyzer()
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self._error_patterns = self._load_error_patterns()
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logger.info("作文评分引擎初始化完成")
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def _load_error_patterns(self) -> List[Dict]:
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"""加载常见语法错误模式库"""
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return [
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{"pattern": r"的的", "type": "repetition", "msg": "重复用字'的的'"},
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{"pattern": r"了了", "type": "repetition", "msg": "重复用字'了了'"},
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{"pattern": r"因为.{5,50}因为", "type": "logic", "msg": "重复使用'因为'，建议精简"},
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{"pattern": r"然后.{3,20}然后.{3,20}然后", "type": "style", "msg": "过度使用'然后'连接"},
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{"pattern": r"非常非常", "type": "repetition", "msg": "重复使用'非常'"},
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{"pattern": r"[，]{3,}", "type": "punctuation", "msg": "连续使用多个逗号，建议使用句号断句"},
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]
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def score_structure(self, text: str, grade: int) -> Tuple[float, List[str]]:
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"""
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评估文章结构（满分100）
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检查：段落划分、开头结尾完整性、字数是否达标、层次是否清晰
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"""
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comments = []
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score = 100.0
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paragraphs = self._text_analyzer.split_paragraphs(text)
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char_stats = self._text_analyzer.count_characters(text)
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# 段落数评估（期望3-8段）
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if len(paragraphs) < 2:
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score -= 25
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comments.append("文章缺少段落划分，建议分段书写使结构更清晰")
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elif len(paragraphs) < 3:
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score -= 10
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comments.append("段落较少，建议增加过渡段落")
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# 字数评估
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expected = self.EXPECTED_LENGTH.get(grade, (300, 600))
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if char_stats["chinese"] < expected[0]:
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deficit = expected[0] - char_stats["chinese"]
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score -= min(30, deficit // 10)
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comments.append(f"字数偏少（{char_stats['chinese']}字），该年级建议{expected[0]}-{expected[1]}字")
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elif char_stats["chinese"] > expected[1] * 1.5:
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score -= 5
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comments.append("字数偏多，建议精简语句突出重点")
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# 开头结尾评估
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if paragraphs:
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first_para = paragraphs[0]
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last_para = paragraphs[-1]
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if len(first_para) < 15:
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score -= 10
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comments.append("开头过于简短，建议丰富开篇引入")
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if len(last_para) < 10:
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score -= 10
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comments.append("结尾过于简短，建议加强收束呼应主题")
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return max(0, score), comments
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def score_grammar(self, text: str) -> Tuple[float, List[SentenceError]]:
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"""
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评估语法正确性（满分100）
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检查：常见语病、标点使用、词语搭配
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"""
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errors = []
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score = 100.0
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# 使用预定义的错误模式进行匹配检测
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for ep in self._error_patterns:
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matches = re.finditer(ep["pattern"], text)
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for m in matches:
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errors.append(SentenceError(
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sentence=m.group(),
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error_type=ep["type"],
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suggestion=ep["msg"],
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position=m.start()
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))
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score -= 5 # 每个语法错误扣5分
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# 检查句子长度（过长的句子可能有语病）
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sentences = self._text_analyzer.split_sentences(text)
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for i, s in enumerate(sentences):
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if len(s) > 80:
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errors.append(SentenceError(
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sentence=s[:30] + "...",
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error_type="long_sentence",
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suggestion="句子过长，建议拆分为多个短句以提高可读性",
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position=text.find(s)
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))
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score -= 3
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return max(0, score), errors
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def score_content(self, text: str, title: Optional[str], genre: str, grade: int) -> Tuple[float, List[str]]:
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"""
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评估内容质量（满分100）
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检查：主题相关性、内容丰富度、逻辑连贯性、情感表达
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"""
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comments = []
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score = 85.0 # 基础分（内容难以精确量化，给予较高基础分）
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char_stats = self._text_analyzer.count_characters(text)
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sentences = self._text_analyzer.split_sentences(text)
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# 内容丰富度：通过不同词汇的数量粗略评估
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unique_chars = set(c for c in text if '\u4e00' <= c <= '\u9fff')
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vocab_richness = len(unique_chars) / max(char_stats["chinese"], 1)
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if vocab_richness > 0.6:
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score += 10
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comments.append("词汇丰富，用词多样化")
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elif vocab_richness < 0.3:
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score -= 10
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comments.append("词汇较为单一，建议使用更丰富的词语表达")
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# 逻辑连贯性：检查是否使用连接词
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connectors = ['因此', '所以', '但是', '然而', '首先', '其次', '最后', '总之',
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'不仅', '而且', '虽然', '但', '因为', '于是']
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used_connectors = [c for c in connectors if c in text]
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if len(used_connectors) >= 3:
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score += 5
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comments.append("逻辑衔接词使用恰当，行文连贯")
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elif len(used_connectors) == 0 and len(sentences) > 5:
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score -= 5
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comments.append("缺少逻辑连接词，建议增加过渡衔接使行文更连贯")
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# 情感表达评估
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emotion_words = ['开心', '快乐', '高兴', '感动', '难过', '伤心', '惊讶',
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'温暖', '幸福', '骄傲', '担心', '紧张']
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used_emotions = [w for w in emotion_words if w in text]
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if used_emotions:
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score += 3
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comments.append("有恰当的情感表达，增强了文章感染力")
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return min(100, max(0, score)), comments
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def score_rhetoric(self, text: str, grade: int) -> Tuple[float, List[str]]:
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"""
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评估修辞运用（满分100）
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检查：修辞手法的使用数量和质量
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"""
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comments = []
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score = 70.0 # 基础分
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rhetorics = self._text_analyzer.detect_rhetoric(text)
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# 根据检测到的修辞数量加分
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rhetoric_types = set(r["type"] for r in rhetorics)
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if len(rhetoric_types) >= 3:
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score += 25
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comments.append(f"修辞手法运用丰富，使用了{len(rhetoric_types)}种修辞手法")
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elif len(rhetoric_types) >= 1:
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score += 15
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used_names = set(r["name"] for r in rhetorics)
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comments.append(f"使用了{'、'.join(used_names)}等修辞手法")
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else:
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comments.append("建议适当使用比喻、排比等修辞手法增强表达效果")
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# 高年级对修辞有更高要求
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if grade >= 5 and len(rhetoric_types) < 2:
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score -= 10
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comments.append("该年级建议至少使用2种以上修辞手法")
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return min(100, max(0, score)), comments
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def review_essay(self, request: EssayReviewRequest) -> Dict:
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"""
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综合批改作文，返回总分和各维度分析结果
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"""
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start_time = time.time()
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# 各维度独立评分
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struct_score, struct_comments = self.score_structure(request.text, request.grade)
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grammar_score, grammar_errors = self.score_grammar(request.text)
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content_score, content_comments = self.score_content(
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request.text, request.title, request.genre, request.grade)
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rhetoric_score, rhetoric_comments = self.score_rhetoric(request.text, request.grade)
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# 按权重计算总分，并映射到满分值
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weighted_score = (
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struct_score * self.DIMENSION_WEIGHTS["structure"] +
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grammar_score * self.DIMENSION_WEIGHTS["grammar"] +
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content_score * self.DIMENSION_WEIGHTS["content"] +
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rhetoric_score * self.DIMENSION_WEIGHTS["rhetoric"]
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)
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total_score = round(weighted_score / 100 * request.max_score, 1)
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# 字数统计
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char_stats = TextAnalyzer.count_characters(request.text)
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# 生成综合评语
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overall_comment = self._generate_overall_comment(
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total_score, request.max_score, struct_comments,
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content_comments, rhetoric_comments
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)
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elapsed = (time.time() - start_time) * 1000
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result = {
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"total_score": total_score,
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"max_score": request.max_score,
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"dimensions": {
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"structure": round(struct_score / 100 * request.max_score * self.DIMENSION_WEIGHTS["structure"], 1),
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"grammar": round(grammar_score / 100 * request.max_score * self.DIMENSION_WEIGHTS["grammar"], 1),
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"content": round(content_score / 100 * request.max_score * self.DIMENSION_WEIGHTS["content"], 1),
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"rhetoric": round(rhetoric_score / 100 * request.max_score * self.DIMENSION_WEIGHTS["rhetoric"], 1),
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},
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"character_count": char_stats,
|
||||
"overall_comment": overall_comment,
|
||||
"structure_analysis": struct_comments,
|
||||
"content_analysis": content_comments,
|
||||
"rhetoric_analysis": rhetoric_comments,
|
||||
"grammar_errors": [e.dict() for e in grammar_errors] if request.enable_suggestions else [],
|
||||
"inference_time_ms": round(elapsed, 2)
|
||||
}
|
||||
return result
|
||||
|
||||
def _generate_overall_comment(self, score: float, max_score: int,
|
||||
struct_comments: List, content_comments: List,
|
||||
rhetoric_comments: List) -> str:
|
||||
"""生成综合评语"""
|
||||
ratio = score / max_score
|
||||
if ratio >= 0.9:
|
||||
prefix = "优秀！"
|
||||
elif ratio >= 0.75:
|
||||
prefix = "良好。"
|
||||
elif ratio >= 0.6:
|
||||
prefix = "中等。"
|
||||
else:
|
||||
prefix = "需要加强。"
|
||||
|
||||
suggestions = []
|
||||
if struct_comments:
|
||||
suggestions.append(struct_comments[0])
|
||||
if content_comments:
|
||||
suggestions.append(content_comments[0])
|
||||
if rhetoric_comments:
|
||||
suggestions.append(rhetoric_comments[0])
|
||||
|
||||
return f"{prefix}{'；'.join(suggestions[:3])}"
|
||||
|
||||
|
||||
# ==================== API路由定义 ====================
|
||||
|
||||
router = APIRouter(prefix="/api/v1", tags=["作文批改"])
|
||||
_scoring_engine = EssayScoringEngine()
|
||||
|
||||
|
||||
@router.post("/essay/review")
|
||||
async def review_essay(request: EssayReviewRequest):
|
||||
"""
|
||||
AI作文评分与批改接口
|
||||
POST /api/v1/essay/review
|
||||
输入作文OCR识别文本，返回综合评分、各维度分析和修改建议
|
||||
"""
|
||||
try:
|
||||
result = _scoring_engine.review_essay(request)
|
||||
|
||||
# 审计日志记录
|
||||
logger.info(
|
||||
f"作文批改完成: score={result['total_score']}/{request.max_score}, "
|
||||
f"student={request.student_id}, assignment={request.assignment_id}, "
|
||||
f"chars={result['character_count']['chinese']}, time={result['inference_time_ms']}ms"
|
||||
)
|
||||
return {"code": 200, "msg": "success", "data": result}
|
||||
except Exception as e:
|
||||
logger.error(f"作文批改异常: {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=f"作文批改服务异常: {str(e)}")
|
||||
Reference in New Issue
Block a user