AcidMap/scripts/import_assessment.py

"""
评价数据导入脚本
@description: 从Excel文件读取Assessment评价数据并导入到Assessment表
"""

import os
import sys
import pandas as pd
import logging
from datetime import datetime

# 添加项目根目录到Python路径
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from app.database import engine, SessionLocal
from app.models.assessment import Assessment  # 确保已创建Assessment模型

# 设置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)


class AssessmentDataImporter:
    """
    评价数据导入器

    @description: 从Excel文件读取评价数据并导入到Assessment表
    """

    def __init__(self, excel_path, sheet_name='Assessment'):
        """
        初始化导入器

        @param {str} excel_path - Excel文件路径
        @param {str} sheet_name - Sheet名称，默认为'Assessment'
        """
        self.excel_path = excel_path
        self.sheet_name = sheet_name

        # 用地类型映射
        self.land_use_mapping = {
            '旱地': 0.0,
            '水田': 1.0,
            '水浇地': 2.0
        }

        # 定义必需列
        self.required_columns = [
            'Farmland_ID',
            'Sample_ID',
            'Type',
            'IDW_2023SP_Cd',
            'IDW_2023SP_pH',
            'SOM_IDW',
            'safety_production_threshold',
            'pollution_risk_screening_value'
        ]

    def read_excel_data(self):
        """
        读取Excel文件数据

        @returns: DataFrame 读取的数据
        """
        try:
            logger.info(f"开始读取Excel文件: {self.excel_path}")
            logger.info(f"Sheet名称: {self.sheet_name}")

            # 检查文件是否存在
            if not os.path.exists(self.excel_path):
                raise FileNotFoundError(f"Excel文件不存在: {self.excel_path}")

            # 读取Excel文件
            df = pd.read_excel(self.excel_path, sheet_name=self.sheet_name)

            logger.info(f"成功读取数据，共 {len(df)} 行")
            logger.info(f"数据列: {list(df.columns)}")

            # 显示前几行数据供确认
            logger.info("前5行数据预览:")
            logger.info(df.head().to_string())

            return df

        except Exception as e:
            logger.error(f"读取Excel文件失败: {str(e)}")
            raise

    def validate_data(self, df):
        """
        验证数据格式和完整性

        @param {DataFrame} df - 要验证的数据
        @returns: DataFrame 验证后的数据
        """
        try:
            logger.info("开始验证数据...")

            # 检查必需的列是否存在
            missing_columns = [col for col in self.required_columns if col not in df.columns]

            if missing_columns:
                raise ValueError(f"缺少必需的列: {missing_columns}")

            # 检查Farmland_ID和Sample_ID是否重复
            duplicates = df.duplicated(subset=['Farmland_ID', 'Sample_ID'])
            if duplicates.any():
                dup_rows = df[duplicates]
                logger.warning(f"发现 {len(dup_rows)} 条重复记录(基于Farmland_ID和Sample_ID)")
                logger.info("重复记录示例:\n" + dup_rows.head().to_string())
                # 删除重复行，保留第一个出现的
                df = df.drop_duplicates(subset=['Farmland_ID', 'Sample_ID'], keep='first')
                logger.info(f"删除重复记录后剩余 {len(df)} 行数据")

            # 转换数值类型
            numeric_columns = [
                'IDW_2023SP_Cd',
                'IDW_2023SP_pH',
                'SOM_IDW',
                'safety_production_threshold',
                'pollution_risk_screening_value'
            ]

            for col in numeric_columns:
                if col in df.columns:
                    # 尝试转换为数值类型
                    df[col] = pd.to_numeric(df[col], errors='coerce')

                    # 检查空值
                    if df[col].isnull().any():
                        invalid_rows = df[df[col].isnull()]
                        logger.warning(f"列 {col} 中有无效值，行号: {list(invalid_rows.index)}")

                        # 标记为无效但保留行，稍后处理
                        df[f'{col}_valid'] = ~df[col].isnull()

            # 转换Farmland_ID和Sample_ID为整数
            for col in ['Farmland_ID', 'Sample_ID']:
                if col in df.columns:
                    # 首先转换为浮点类型，再尝试转整数
                    df[col] = pd.to_numeric(df[col], errors='coerce').fillna(-1)
                    df[col] = df[col].astype(int)

                    # 检查无效值
                    if (df[col] < 0).any():
                        invalid_rows = df[df[col] < 0]
                        logger.warning(f"列 {col} 中有无效值，行号: {list(invalid_rows.index)}")
                        df[f'{col}_valid'] = (df[col] >= 0)

            # 用地类型转换
            if 'Type' in df.columns:
                # 尝试直接转换为数值
                df['Type_Numeric'] = pd.to_numeric(df['Type'], errors='coerce')

                # 处理无法转换的类型
                unknown_types = df[df['Type_Numeric'].isnull()]['Type'].unique()
                if len(unknown_types) > 0:
                    logger.info(f"发现未知用地类型: {unknown_types}, 尝试映射...")
                    # 使用映射转换
                    df['Type_Mapped'] = df['Type'].map(self.land_use_mapping)

                    # 合并两种转换方式
                    df['Final_Type'] = df['Type_Numeric'].fillna(df['Type_Mapped'])
                else:
                    df['Final_Type'] = df['Type_Numeric']

                # 检查是否还有无效值
                if df['Final_Type'].isnull().any():
                    invalid_rows = df[df['Final_Type'].isnull()]
                    logger.warning(f"列 Type 中有无法识别的值，行号: {list(invalid_rows.index)}")
                    logger.info("为无效值设置默认值0.0(旱地)")
                    df['Final_Type'] = df['Final_Type'].fillna(0.0)

            logger.info(f"数据验证完成，有效数据 {len(df)} 行")
            return df

        except Exception as e:
            logger.error(f"数据验证失败: {str(e)}")
            raise

    def create_assessment_object(self, row):
        """
        创建评价数据对象

        @param {pd.Series} row - 数据行
        @returns: Assessment 对象
        """
        try:
            return Assessment(
                farmland_id=row['Farmland_ID'],
                sample_id=row['Sample_ID'],
                type=row['Final_Type'],
                idw_2023sp_cd=row['IDW_2023SP_Cd'],
                idw_2023sp_ph=row['IDW_2023SP_pH'],
                som_idw=row['SOM_IDW'],
                safety_production_threshold=row['safety_production_threshold'],
                pollution_risk_screening_value=row['pollution_risk_screening_value']
            )
        except KeyError as e:
            logger.warning(f"创建对象时缺少必要字段: {str(e)}")
            return None
        except Exception as e:
            logger.warning(f"创建Assessment对象失败: {str(e)}")
            return None

    def import_data(self, df):
        """
        将数据导入到数据库

        @param {DataFrame} df - 要导入的数据
        """
        try:
            logger.info("开始导入数据到数据库...")

            # 创建数据库会话
            db = SessionLocal()

            try:
                # 检查现有数据量
                existing_count = db.query(Assessment).count()
                logger.info(f"数据库中现有评价数据记录: {existing_count} 条")

                # 批量创建对象并导入
                total_rows = len(df)
                imported_count = 0
                skipped_count = 0
                invalid_count = 0

                # 分批处理数据
                for i, row in df.iterrows():
                    try:
                        # 检查是否有效行（所有关键字段都有效）
                        is_valid = True
                        for col in self.required_columns:
                            if f'{col}_valid' in row and not row[f'{col}_valid']:
                                is_valid = False
                                break

                        if not is_valid:
                            invalid_count += 1
                            logger.debug(f"跳过无效行 {i}: 存在无效值")
                            continue

                        # 创建Assessment对象
                        assessment = self.create_assessment_object(row)
                        if not assessment:
                            skipped_count += 1
                            continue

                        # 添加到会话
                        db.add(assessment)
                        imported_count += 1

                        # 每50条提交一次
                        if imported_count % 50 == 0:
                            db.commit()
                            logger.info(f"已导入 {imported_count}/{total_rows} 条数据")

                    except Exception as e:
                        logger.warning(f"导入行 {i} 时出错: {str(e)}")
                        skipped_count += 1
                        db.rollback()

                # 提交剩余数据
                db.commit()

                # 更新统计信息
                new_count = db.query(Assessment).count()
                added_count = new_count - existing_count

                logger.info(f"评价数据导入完成!")
                logger.info(f"成功导入: {imported_count} 条")
                logger.info(f"跳过无效数据: {invalid_count} 条")
                logger.info(f"处理失败: {skipped_count} 条")
                logger.info(f"数据库中新增加: {added_count} 条记录")
                logger.info(f"数据库总记录: {new_count} 条")

            except Exception as e:
                db.rollback()
                logger.error(f"数据导入失败，已回滚: {str(e)}")
                raise
            finally:
                db.close()

        except Exception as e:
            logger.error(f"数据导入过程失败: {str(e)}")
            raise

    def run_import(self):
        """
        执行完整的导入流程
        """
        try:
            logger.info("=" * 60)
            logger.info("开始评价数据导入流程")
            logger.info("=" * 60)

            # 1. 读取Excel数据
            df = self.read_excel_data()

            # 2. 验证数据
            df = self.validate_data(df)

            # 3. 导入数据
            self.import_data(df)

            logger.info("=" * 60)
            logger.info("评价数据导入流程完成!")
            logger.info("=" * 60)

        except Exception as e:
            logger.error(f"导入流程失败: {str(e)}")
            raise


def main():
    """
    主函数
    """
    # Excel文件路径
    excel_path = r"D:\destkop\数据库对应数据.xlsx"  # 根据实际路径修改
    sheet_name = "Assessment"  # 确保Excel中有这个sheet

    try:
        # 创建导入器并执行导入
        importer = AssessmentDataImporter(excel_path, sheet_name)
        importer.run_import()

    except Exception as e:
        logger.error(f"程序执行失败: {str(e)}")
        sys.exit(1)


if __name__ == "__main__":
    main()