297 lines
12 KiB
Python
297 lines
12 KiB
Python
from enum import Enum
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from typing import cast, Callable
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from rapidfuzz import process, fuzz
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from rapidfuzz.fuzz import WRatio
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import json
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from pypinyin import lazy_pinyin
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from constants import USELESS_COMPANY_WORDS, USELESS_PROJECT_WORDS
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# 数字转换表（1-20，常见数字）
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digit_to_chinese = {
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"1": "一", "2": "二", "3": "三", "4": "四", "5": "五",
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"6": "六", "7": "七", "8": "八", "9": "九", "10": "十",
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"11": "十一", "12": "十二", "13": "十三", "14": "十四",
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"15": "十五", "16": "十六", "17": "十七", "18": "十八",
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"19": "十九", "20": "二十"
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}
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def arabic_to_chinese_number(text):
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"""
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将文中阿拉伯数字转换为中文数字
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:param text: 输入文本
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:return: 转换后的文本
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"""
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cn_to_arabic = {'一': '1', '二': '2', '三': '3', '四': '4', '五': '5',
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'六': '6', '七': '7', '八': '8', '九': '9', '零': '0'}
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arabic_to_cn = {v: k for k, v in cn_to_arabic.items()} # 反向映射
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for num, cn in arabic_to_cn.items():
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text = text.replace(num, cn)
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return text
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def text_to_pinyin(text):
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"""将文本转换为拼音字符串"""
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return ''.join(lazy_pinyin(text))
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def load_standard_data(path):
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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def extract_number(text):
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"""
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提取项目部中的数字（支持阿拉伯数字和中文数字），并转换为统一格式（中文数字）。
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"""
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match = re.search(r'(第?[一二三四五六七八九十百千零\d]+)', text)
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if match:
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num_str = match.group(1).replace("第", "")
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if num_str.isdigit():
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return digit_to_chinese.get(num_str, num_str) # 阿拉伯数字转中文
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return num_str # 中文数字直接返回
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return None
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def fuzzy_match_and_filter(input_key, match_pool, mapping_dict, lower_score=70, high_score=85, top_k=3):
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"""
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对输入字符串在候选池中执行模糊匹配，并返回匹配程度高的映射原始值。
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:param input_key: 清洗后的用于匹配的关键词（如简化名或拼音）
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:param match_pool: 可用于匹配的候选集合（一般是映射表的 key）
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:param mapping_dict: 匹配项到标准原始名的映射字典
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:param lower_score: 匹配分数的下限，低于该分数视为无效
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:param high_score: 高置信度匹配分数，超过则直接返回所有等分结果
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:param top_k: 如果不满足高置信度，则返回前 top_k 个结果
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:return: 标准原始名的列表，可能为空
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"""
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match_results = process.extract(input_key, match_pool, scorer=cast(Callable, WRatio), limit=len(match_pool))
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high_conf_matches = [(m[0], m[1]) for m in match_results if m[1] >= lower_score]
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if not high_conf_matches:
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return []
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max_score = max(high_conf_matches, key=lambda x: x[1])
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best_matches = [m for m in high_conf_matches if m[1] == max_score[1]]
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if max_score[1] >= high_score:
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return [mapping_dict[m[0]] for m in best_matches]
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else:
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return [mapping_dict[m[0]] for m in high_conf_matches[:top_k]]
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def standardize_name(input_name, clean_func, simply_map, pinyin_map, lower_score=70, high_score=85):
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"""
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通用名称标准化函数，按中文 → 清洗 → 简化匹配 → 拼音匹配 的顺序进行处理。
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:param input_name: 用户输入的原始中文名
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:param clean_func: 清洗函数（针对不同实体类型，如工程名/公司名）
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:param simply_map: 简化后的名 → 原始标准名 映射
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:param pinyin_map: 拼音名 → 原始标准名 映射
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:param lower_score: 模糊匹配最低置信分数
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:param high_score: 模糊匹配高置信分数阈值
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:return: 标准名列表，可能为空
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"""
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simply_input = clean_func(input_name)
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result = fuzzy_match_and_filter(simply_input, list(simply_map.keys()), simply_map, lower_score, high_score)
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if result:
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return result
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# 拼音匹配
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pinyin_input = text_to_pinyin(simply_input)
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result = fuzzy_match_and_filter(pinyin_input, list(pinyin_map.keys()), pinyin_map, lower_score, high_score)
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return result
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def standardize_sub_company(input_name, simply_map, pinyin_map, lower_score=55, high_score=80):
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"""
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对用户输入的子公司名称进行标准化，返回最匹配的标准公司名列表。
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:param input_name: 原始中文子公司名
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:param simply_map: 清洗后的公司名 → 标准公司名映射
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:param pinyin_map: 洗后公司的拼音 → 标准公司名映射
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:param lower_score: 模糊匹配分数下限
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:param high_score: 高置信匹配分数阈值
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:return: 匹配的标准公司名列表
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"""
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return standardize_name(input_name, clean_useless_company_name, simply_map, pinyin_map, lower_score, high_score)
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def standardize_project_name(input_name, simply_map, pinyin_map, lower_score=70, high_score=90):
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"""
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对用户输入的项目名称进行标准化，返回最匹配的标准项目名列表。
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:param input_name: 原始中文项目名
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:param simply_map: 清洗后的项目名 → 标准项目名映射
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:param pinyin_map: 清洗后项目的拼音 → 标准项目名映射
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:param lower_score: 模糊匹配分数下限
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:param high_score: 高置信匹配分数阈值
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:return: 匹配的标准项目名列表
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"""
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return standardize_name(input_name, clean_useless_project_name, simply_map, pinyin_map, lower_score, high_score)
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#标准化项目部名
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def standardize_projectDepartment(standard_company, input_project, company_project_department_map, high_score=90):
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"""
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将口语化的公司名和项目部名转换为标准化名称。
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参数:
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standard_company (str): 标准化公司名。
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input_project (str): 用户输入的项目部名（可能是口语化或不完整的名称）。
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company_project_department_map (dict): 标准化的公司名和项目部名数据，格式为 {公司名: [项目部名1, 项目部名2, ...]}。
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pinyin_to_original_map:分公司拼音和分公司原始名的映射
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返回:
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tuple: (标准化公司名, 匹配的项目部名列表)。如果无法匹配，返回 (None, None)。
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"""
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try:
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# **2. 先尝试直接匹配最相似的项目名**
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project_match = process.extractOne(input_project, company_project_department_map[standard_company],
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scorer=cast(Callable, WRatio))
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print(f"项目部名称最相似：{project_match[0]},{project_match[1]}", flush=True)
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if project_match and project_match[1] >= high_score:
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return [project_match[0]] # 直接返回匹配的项目名
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# **3. 提取项目部的数字部分**
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query_number = extract_number(input_project)
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# **4. 过滤所有符合数字的项目部**
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matched_projects = []
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for project in company_project_department_map[standard_company]:
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project_number = extract_number(project)
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if query_number and query_number == project_number:
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matched_projects.append(project)
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return matched_projects
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except Exception as e:
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print(f"standardize_projectDepartment：{e}", flush=True)
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return None
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def multiple_standardize_single_name(origin_input_name, origin_name_list, pinyin_name_list=None,
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pinyin_to_original_map=None, lower_score=70, high_score=85, isArabicNumConv=False):
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"""
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使用拼音 + rapidfuzz 进行关键词模糊匹配，并返回原始的标准名
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:param origin_input_name: 口语化的名称（中文）
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:param origin_name_list: 关键词列表（中文）
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:pinyin_name_list: 关键词列表（拼音）
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:param pinyin_to_original_map: 拼音到原始标准名的映射
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:param lower_score: 低匹配分数阈值（默认70）
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:param high_score: 高匹配分数阈值（默认85）
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:return: 最匹配的原始关键词，或 None
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"""
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#First round, 原始标准名的匹配性查找，能找到直接返回
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if isArabicNumConv:
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origin_input_name = arabic_to_chinese_number(origin_input_name)
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match_results = process.extract(origin_input_name, origin_name_list, scorer=fuzz.token_sort_ratio,
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limit=len(origin_name_list))
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# 找到所有相似度 > 80 的匹配项
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original_high_confidence_matches = [(match[0], match[1]) for match in match_results if match[1] >= lower_score]
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print(f"standardize_pinyin_single_name 原始名匹配, high_confidence_matches:{original_high_confidence_matches[:3]}",
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flush=True)
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combined_low_confidence_matches = []
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if original_high_confidence_matches:
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origin_best_match = max(original_high_confidence_matches, key=lambda x: x[1], default=None)
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if origin_best_match and origin_best_match[1] > high_score:
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return [origin_best_match[0]]
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else:
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combined_low_confidence_matches = [match[0] for match in original_high_confidence_matches[:3]]
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else:
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if not pinyin_name_list or not pinyin_to_original_map:
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return None #
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return list(dict.fromkeys(combined_low_confidence_matches))
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def generate_project_prompt(matched_projects, original_name="", type="项目部名"):
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"""
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生成提示信息，用于让用户确认匹配的项目名或分公司名或项目名。
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参数:
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matched_projects (list): 匹配的项目或分公司名称列表。
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type (str): 提示信息的类型（例如 "项目名" 或 "分公司名"），默认值为 "项目名"。
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返回:
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str: 生成的提示信息。如果未找到匹配项，返回提示用户提供更准确信息的字符串。
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"""
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if not matched_projects:
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return f"<p>未找到匹配的<strong>{type}</strong>：{original_name}，请提供更准确的{type}信息。</p>"
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html_parts = [f"<p>您说的<strong>{type}</strong>可能是：</p>"]
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for idx, item in enumerate(matched_projects, start=1):
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html_parts.append(f"""
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<div class="project-entry">
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<text class="label"><strong>第{idx}个：</strong>{item}</text><br>
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</div>
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""")
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html_parts.append("<p>请确认您要选择哪一个？</p>")
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return "\n".join(html_parts)
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def load_standard_name(file_path: str):
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"""
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从指定文件中加载标准化的名称列表。
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参数:
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file_path (str): 文件路径，文件应包含标准化的名称列表，每行一个名称。
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返回:
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list: 从文件中读取的标准化名称列表。
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异常:
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FileNotFoundError: 如果文件不存在，抛出此异常。
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Exception: 如果读取文件时发生其他错误，抛出此异常。
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"""
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try:
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with open(file_path, 'r', encoding='utf-8') as file:
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lines = [line.strip() for line in file if line.strip()]
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return lines
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except FileNotFoundError:
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print(f"错误：文件 {file_path} 不存在", flush=True)
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raise FileNotFoundError(f"错误：文件 {file_path} 不存在")
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except Exception as e:
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print(f"读取文件时发生错误：{e}", flush=True)
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raise Exception(f"错误：文件 {file_path} 不存在")
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class CheckResult(Enum):
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NO_MATCH = 0 # 不符合检查条件
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MATCH_FOUND = 1 # 匹配到了值
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NEEDS_MORE_ROUNDS = 2 # 需要多轮
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class StandardType(Enum):
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#工程名检查
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PROJECT_CHECK = 0
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#项目名检查
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PROGRAM_CHECK = 1
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import re
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# 构建一个用于替换的正则表达式
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useless_project_words_pattern = re.compile("|".join(USELESS_PROJECT_WORDS))
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useless_company_words_pattern = re.compile("|".join(USELESS_COMPANY_WORDS))
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# 匹配所有数字、字母（含大小写）、特殊字符（包括空格、标点）
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project_symbols_pattern = re.compile(r"[A-Za-z0-9\s\W_]+")
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company_symbols_pattern = re.compile(r"[\s\W_]+")
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def clean_useless_project_name(name: str) -> str:
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# 去掉无意义词
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name = useless_project_words_pattern.sub("", name)
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# 去掉数字、字母、符号
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name = project_symbols_pattern.sub("", name)
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return name.strip()
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def clean_useless_company_name(name: str) -> str:
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# 去掉无意义词
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name = useless_company_words_pattern.sub("", name)
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name = company_symbols_pattern.sub("", name)
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return name.strip()
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