init: KG_ICH 项目初始化
- data/: 非遗地理编码数据（GIS shapefile + CSV） - dofile/kg_project/: 知识图谱构建代码（纳入主仓库） - dofile/visulization/: 可视化数据与路线图 - officefile/: 文献、草稿、bib 文档 - officefile/latex/: Overleaf 同步目录（独立管理，不纳入） - output/: 输出目录 - logs/: 日志目录
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"""
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数据预处理脚本 - 读取黑龙江非遗Excel数据
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"""
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import pandas as pd
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import json
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from pathlib import Path
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from typing import Dict, List, Any
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import logging
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class ExcelDataReader:
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"""Excel数据读取器"""
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def __init__(self, excel_path: str):
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"""
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初始化数据读取器
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Args:
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excel_path: Excel文件路径
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"""
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self.excel_path = Path(excel_path)
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self.data = None
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self.logger = self._setup_logger()
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def _setup_logger(self):
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"""设置日志"""
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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return logging.getLogger(__name__)
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def read_excel(self) -> pd.DataFrame:
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"""
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读取Excel文件
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Returns:
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DataFrame: 数据框
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"""
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try:
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self.logger.info(f"开始读取Excel文件: {self.excel_path}")
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# 读取Excel文件
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self.data = pd.read_excel(self.excel_path)
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self.logger.info(f"成功读取 {len(self.data)} 行数据")
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self.logger.info(f"列名: {list(self.data.columns)}")
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# 显示前5行
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self.logger.info("\n前5行数据:")
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self.logger.info(self.data.head())
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return self.data
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except Exception as e:
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self.logger.error(f"读取Excel文件失败: {str(e)}")
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raise
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def analyze_data(self) -> Dict[str, Any]:
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"""
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分析数据
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Returns:
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Dict: 分析结果
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"""
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if self.data is None:
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raise ValueError("请先读取Excel文件")
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analysis = {
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"total_records": len(self.data),
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"columns": list(self.data.columns),
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"column_types": {col: str(dtype) for col, dtype in self.data.dtypes.items()},
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"missing_values": self.data.isnull().sum().to_dict(),
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"statistics": {}
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}
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# 分析类别分布
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if '类别' in self.data.columns:
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category_counts = self.data['类别'].value_counts()
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analysis['category_distribution'] = category_counts.to_dict()
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# 分析级别分布
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if '项目级别' in self.data.columns:
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level_counts = self.data['项目级别'].value_counts()
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analysis['level_distribution'] = level_counts.to_dict()
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# 分析地域分布
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if '项目申报单位/地区' in self.data.columns:
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location_counts = self.data['项目申报单位/地区'].value_counts()
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analysis['location_distribution'] = location_counts.head(20).to_dict()
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# 传承人覆盖率
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if '代表性传承人' in self.data.columns:
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has_inheritor = self.data['代表性传承人'].notna().sum()
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analysis['inheritor_coverage'] = {
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"total": len(self.data),
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"has_inheritor": int(has_inheritor),
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"coverage_rate": float(has_inheritor / len(self.data) * 100)
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}
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return analysis
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def clean_data(self) -> pd.DataFrame:
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"""
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清洗数据
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Returns:
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DataFrame: 清洗后的数据
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"""
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if self.data is None:
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raise ValueError("请先读取Excel文件")
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self.logger.info("开始清洗数据")
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# 去除空行
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original_len = len(self.data)
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self.data = self.data.dropna(how='all')
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self.logger.info(f"去除空行: {original_len} -> {len(self.data)}")
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# 填充缺失值
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for col in self.data.columns:
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if self.data[col].dtype == 'object':
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self.data[col] = self.data[col].fillna('')
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else:
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self.data[col] = self.data[col].fillna(0)
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# 去重（基于项目名称、项目批次、项目保护单位、代表性传承人四个字段）
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dedup_cols = ['项目名称', '项目批次', '项目保护单位', '代表性传承人']
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existing_cols = [col for col in dedup_cols if col in self.data.columns]
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if existing_cols:
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before_dedup = len(self.data)
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self.data = self.data.drop_duplicates(subset=existing_cols, keep='first')
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duplicate_count = before_dedup - len(self.data)
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dedup_rate = (duplicate_count / before_dedup * 100) if before_dedup > 0 else 0
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self.logger.info(f"去重（基于{len(existing_cols)}个字段: {', '.join(existing_cols)}）: {before_dedup} -> {len(self.data)} (删除{duplicate_count}条，去重率{dedup_rate:.1f}%)")
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return self.data
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def convert_to_kg_format(self) -> List[Dict[str, Any]]:
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"""
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转换为知识图谱格式
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Returns:
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List[Dict]: 知识图谱节点列表
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"""
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if self.data is None:
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raise ValueError("请先读取Excel文件")
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self.logger.info("转换为知识图谱格式")
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kg_nodes = []
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for idx, row in self.data.iterrows():
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try:
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# 创建非遗项目节点
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project_node = {
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"id": f"ICH-{idx:04d}",
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"type": "ICH_Project",
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"properties": {
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"project_id": f"ICH-{idx:04d}",
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"name": str(row.get('项目名称', '')),
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"level": str(row.get('项目级别', '')),
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"batch": str(row.get('批次号', '')),
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"category": str(row.get('类别', '')),
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"declaration_area": str(row.get('项目申报单位/地区', '')),
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"description": str(row.get('备注', '')),
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"source_row": idx + 2 # Excel行号（从2开始，第一行是表头）
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}
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}
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kg_nodes.append(project_node)
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except Exception as e:
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self.logger.warning(f"转换第{idx}行数据失败: {str(e)}")
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continue
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self.logger.info(f"成功转换 {len(kg_nodes)} 个节点")
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return kg_nodes
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def extract_inheritors(self) -> List[Dict[str, Any]]:
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"""
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提取传承人信息
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Returns:
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List[Dict]: 传承人节点列表
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"""
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if self.data is None:
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raise ValueError("请先读取Excel文件")
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self.logger.info("提取传承人信息")
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inheritors = []
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inheritor_id = 0
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for idx, row in self.data.iterrows():
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inheritor_names = row.get('代表性传承人', '')
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if pd.isna(inheritor_names) or not str(inheritor_names).strip():
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continue
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# 处理多个传承人（用顿号分隔）
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names = str(inheritor_names).replace('、', ',').replace('，', ',').split(',')
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for name in names:
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name = name.strip()
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if not name:
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continue
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inheritor_id += 1
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inheritor_node = {
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"id": f"INH-{inheritor_id:04d}",
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"type": "Inheritor",
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"properties": {
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"inheritor_id": f"INH-{inheritor_id:04d}",
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"name": name,
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"project_name": str(row.get('项目名称', '')),
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"source_row": idx + 2
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}
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}
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inheritors.append(inheritor_node)
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self.logger.info(f"提取了 {len(inheritors)} 个传承人")
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return inheritors
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def extract_relations(self) -> List[Dict[str, Any]]:
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"""
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提取关系
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Returns:
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List[Dict]: 关系列表
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"""
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if self.data is None:
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raise ValueError("请先读取Excel文件")
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self.logger.info("提取关系")
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relations = []
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relation_id = 0
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for idx, row in self.data.iterrows():
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project_id = f"ICH-{idx:04d}"
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# 项目-类别关系
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category = row.get('类别', '')
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if pd.notna(category) and str(category).strip():
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relation_id += 1
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relations.append({
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"id": f"REL-{relation_id:04d}",
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"type": "belongs_to",
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"from": project_id,
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"to": f"CAT-{str(category)}",
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"properties": {}
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})
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# 项目-传承人关系
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inheritor_names = row.get('代表性传承人', '')
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if pd.notna(inheritor_names) and str(inheritor_names).strip():
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names = str(inheritor_names).replace('、', ',').replace('，', ',').split(',')
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for name in names:
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name = name.strip()
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if name:
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relation_id += 1
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relations.append({
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"id": f"REL-{relation_id:04d}",
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"type": "has_inheritor",
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"from": project_id,
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"to": f"INH-{name}", # 简化处理
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"properties": {}
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})
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self.logger.info(f"提取了 {len(relations)} 个关系")
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return relations
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def save_analysis_report(self, output_path: str):
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"""
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保存分析报告
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Args:
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output_path: 输出路径
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"""
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analysis = self.analyze_data()
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(analysis, f, indent=2, ensure_ascii=False)
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self.logger.info(f"分析报告已保存到: {output_path}")
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def save_kg_data(self, nodes: List[Dict], relations: List[Dict], output_dir: str):
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"""
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保存知识图谱数据
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Args:
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nodes: 节点列表
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relations: 关系列表
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output_dir: 输出目录
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"""
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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# 保存节点
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nodes_file = output_path / "nodes.json"
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with open(nodes_file, 'w', encoding='utf-8') as f:
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json.dump(nodes, f, indent=2, ensure_ascii=False)
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self.logger.info(f"节点已保存到: {nodes_file}")
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# 保存关系
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relations_file = output_path / "relations.json"
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with open(relations_file, 'w', encoding='utf-8') as f:
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json.dump(relations, f, indent=2, ensure_ascii=False)
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self.logger.info(f"关系已保存到: {relations_file}")
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def main():
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"""主函数"""
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# 配置
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excel_path = r"E:\Project\2026_KG_ICH\data\黑龙江国家级和省级非遗名单.xlsx"
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output_dir = r"E:\Project\2026_KG_ICH\dofile\kg_project\output"
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# 创建读取器
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reader = ExcelDataReader(excel_path)
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# 读取数据
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reader.read_excel()
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# 分析数据
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print("\n" + "="*60)
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print("数据分析")
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print("="*60)
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analysis = reader.analyze_data()
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print(f"\n总记录数: {analysis['total_records']}")
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print(f"\n类别分布:")
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for cat, count in analysis.get('category_distribution', {}).items():
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print(f" {cat}: {count}")
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print(f"\n传承人覆盖率: {analysis.get('inheritor_coverage', {}).get('coverage_rate', 0):.1f}%")
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# 保存分析报告
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Path(output_dir).mkdir(parents=True, exist_ok=True)
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reader.save_analysis_report(f"{output_dir}/data_analysis.json")
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# 清洗数据
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print("\n" + "="*60)
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print("数据清洗")
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print("="*60)
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reader.clean_data()
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# 转换为知识图谱格式
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print("\n" + "="*60)
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print("转换为知识图谱格式")
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print("="*60)
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nodes = reader.convert_to_kg_format()
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inheritors = reader.extract_inheritors()
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relations = reader.extract_relations()
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# 合并所有节点
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all_nodes = nodes + inheritors
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print(f"\n节点统计:")
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print(f" 非遗项目节点: {len(nodes)}")
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print(f" 传承人节点: {len(inheritors)}")
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print(f" 总节点数: {len(all_nodes)}")
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print(f" 关系数: {len(relations)}")
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# 保存知识图谱数据
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reader.save_kg_data(all_nodes, relations, output_dir)
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print("\n" + "="*60)
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print("处理完成！")
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print("="*60)
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if __name__ == '__main__':
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main()
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