algorithm_platform/common/database_dml.py

from pymongo import MongoClient, UpdateOne
import pandas as pd
from scripts.regsetup import description
from sqlalchemy import create_engine
import pickle
from io import BytesIO
import joblib
import h5py
import tensorflow as tf

def get_data_from_mongo(args):
    mongodb_connection = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/"
    mongodb_database = args['mongodb_database']
    mongodb_read_table = args['mongodb_read_table']
    query_dict = {}
    if 'timeBegin' in args.keys():
        timeBegin = args['timeBegin']
        query_dict.update({"$gte": timeBegin})
    if 'timeEnd' in args.keys():
        timeEnd = args['timeEnd']
        query_dict.update({"$lte": timeEnd})

    client = MongoClient(mongodb_connection)
    # 选择数据库（如果数据库不存在，MongoDB 会自动创建）
    db = client[mongodb_database]
    collection = db[mongodb_read_table]  # 集合名称
    if len(query_dict) != 0:
        query = {"dateTime": query_dict}
        cursor = collection.find(query)
    else:
        cursor = collection.find()
    data = list(cursor)
    df = pd.DataFrame(data)
    # 4. 删除 _id 字段（可选）
    if '_id' in df.columns:
        df = df.drop(columns=['_id'])
    client.close()
    return df


def get_df_list_from_mongo(args):
    mongodb_connection,mongodb_database,mongodb_read_table = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['mongodb_read_table'].split(',')
    df_list = []
    client = MongoClient(mongodb_connection)
    # 选择数据库（如果数据库不存在，MongoDB 会自动创建）
    db = client[mongodb_database]
    for table in mongodb_read_table:
        collection = db[table]  # 集合名称
        data_from_db = collection.find()  # 这会返回一个游标（cursor）
        # 将游标转换为列表，并创建 pandas DataFrame
        df = pd.DataFrame(list(data_from_db))
        if '_id' in df.columns:
            df = df.drop(columns=['_id'])
        df_list.append(df)
    client.close()
    return df_list

def insert_data_into_mongo(res_df, args):
    """
    插入数据到 MongoDB 集合中，可以选择覆盖、追加或按指定的 key 进行更新插入。

    参数:
    - res_df: 要插入的 DataFrame 数据
    - args: 包含 MongoDB 数据库和集合名称的字典
    - overwrite: 布尔值，True 表示覆盖，False 表示追加
    - update_keys: 列表，指定用于匹配的 key 列，如果存在则更新，否则插入 'col1','col2'
    """
    mongodb_connection = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/"
    mongodb_database = args['mongodb_database']
    mongodb_write_table = args['mongodb_write_table']
    overwrite = 1
    update_keys = None
    if 'overwrite' in args.keys():
        overwrite = int(args['overwrite'])
    if 'update_keys' in args.keys():
        update_keys = args['update_keys'].split(',')

    client = MongoClient(mongodb_connection)
    db = client[mongodb_database]
    collection = db[mongodb_write_table]

    # 覆盖模式：删除现有集合
    if overwrite:
        if mongodb_write_table in db.list_collection_names():
            collection.drop()
            print(f"Collection '{mongodb_write_table}' already exists, deleted successfully!")

    # 将 DataFrame 转为字典格式
    data_dict = res_df.to_dict("records")  # 每一行作为一个字典

    # 如果没有数据，直接返回
    if not data_dict:
        print("No data to insert.")
        return

    # 如果指定了 update_keys，则执行 upsert（更新或插入）
    if update_keys and not overwrite:
        operations = []
        for record in data_dict:
            # 构建查询条件，用于匹配要更新的文档
            query = {key: record[key] for key in update_keys}
            operations.append(UpdateOne(query, {'$set': record}, upsert=True))

        # 批量执行更新/插入操作
        if operations:
            result = collection.bulk_write(operations)
            print(f"Matched: {result.matched_count}, Upserts: {result.upserted_count}")
    else:
        # 追加模式：直接插入新数据
        collection.insert_many(data_dict)
        print("Data inserted successfully!")


def get_data_fromMysql(params):
    mysql_conn = params['mysql_conn']
    query_sql = params['query_sql']
    #数据库读取实测气象
    engine = create_engine(f"mysql+pymysql://{mysql_conn}")
    # 定义SQL查询
    env_df = pd.read_sql_query(query_sql, engine)
    return env_df


def insert_pickle_model_into_mongo(model, args):
    mongodb_connection, mongodb_database, mongodb_write_table, model_name = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/", \
    args['mongodb_database'], args['mongodb_write_table'], args['model_name']
    client = MongoClient(mongodb_connection)
    db = client[mongodb_database]
    # 序列化模型
    model_bytes = pickle.dumps(model)
    model_data = {
        'model_name': model_name,
        'model': model_bytes,  # 将模型字节流存入数据库
    }
    print('Training completed!')

    if mongodb_write_table in db.list_collection_names():
        db[mongodb_write_table].drop()
        print(f"Collection '{mongodb_write_table} already exist, deleted successfully!")
    collection = db[mongodb_write_table]  # 集合名称
    collection.insert_one(model_data)
    print("model inserted successfully!")


def insert_h5_model_into_mongo(model,feature_scaler_bytes,target_scaler_bytes ,args):
    mongodb_connection,mongodb_database,scaler_table,model_table,model_name = ("mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",
                                args['mongodb_database'],args['scaler_table'],args['model_table'],args['model_name'])
    client = MongoClient(mongodb_connection)
    db = client[mongodb_database]
    if scaler_table in db.list_collection_names():
        db[scaler_table].drop()
        print(f"Collection '{scaler_table} already exist, deleted successfully!")
    collection = db[scaler_table]  # 集合名称
    # Save the scalers in MongoDB as binary data
    collection.insert_one({
        "feature_scaler": feature_scaler_bytes.read(),
        "target_scaler": target_scaler_bytes.read()
    })
    print("scaler_model inserted successfully!")
    if model_table in db.list_collection_names():
        db[model_table].drop()
        print(f"Collection '{model_table} already exist, deleted successfully!")
    model_table = db[model_table]
    # 创建 BytesIO 缓冲区
    model_buffer = BytesIO()
    # 将模型保存为 HDF5 格式到内存 (BytesIO)
    model.save(model_buffer, save_format='h5')
    # 将指针移到缓冲区的起始位置
    model_buffer.seek(0)
    # 获取模型的二进制数据
    model_data = model_buffer.read()
    # 将模型保存到 MongoDB
    model_table.insert_one({
        "model_name": model_name,
        "model_data": model_data
    })
    print("模型成功保存到 MongoDB！")

def insert_trained_model_into_mongo(model ,args):
    mongodb_connection,mongodb_database,model_table,model_name = ("mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",
                                args['mongodb_database'],args['model_table'],args['model_name'])

    gen_time, params_json, descr = args['gen_time'], args['params'], args['descr']
    client = MongoClient(mongodb_connection)
    db = client[mongodb_database]
    if model_table in db.list_collection_names():
        db[model_table].drop()
        print(f"Collection '{model_table} already exist, deleted successfully!")
    model_table = db[model_table]
    # 创建 BytesIO 缓冲区
    model_buffer = BytesIO()
    # 将模型保存为 HDF5 格式到内存 (BytesIO)
    model.save(model_buffer, save_format='h5')
    # 将指针移到缓冲区的起始位置
    model_buffer.seek(0)
    # 获取模型的二进制数据
    model_data = model_buffer.read()
    # 将模型保存到 MongoDB
    model_table.insert_one({
        "model_name": model_name,
        "model_data": model_data,
        "gen_time": gen_time,
        "params": params_json,
        "descr": descr
    })
    print("模型成功保存到 MongoDB！")

def insert_scaler_model_into_mongo(feature_scaler_bytes, args):
    mongodb_connection,mongodb_database,scaler_table,model_table,model_name = ("mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",
                                args['mongodb_database'],args['scaler_table'],args['model_table'],args['model_name'])
    client = MongoClient(mongodb_connection)
    db = client[mongodb_database]
    if scaler_table in db.list_collection_names():
        db[scaler_table].drop()
        print(f"Collection '{scaler_table} already exist, deleted successfully!")
    collection = db[scaler_table]  # 集合名称
    # Save the scalers in MongoDB as binary data
    collection.insert_one({
        "feature_scaler": feature_scaler_bytes.read(),
    })
    print("scaler_model inserted successfully!")


def get_h5_model_from_mongo(args):
    mongodb_connection,mongodb_database,model_table,model_name = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['model_table'],args['model_name']
    client = MongoClient(mongodb_connection)
    # 选择数据库（如果数据库不存在，MongoDB 会自动创建）
    db = client[mongodb_database]
    collection = db[model_table]  # 集合名称

     # 查询 MongoDB 获取模型数据
    model_doc = collection.find_one({"model_name": model_name})
    if model_doc:
        model_data = model_doc['model_data']  # 获取模型的二进制数据
        # 将二进制数据加载到 BytesIO 缓冲区
        model_buffer = BytesIO(model_data)
        # 从缓冲区加载模型
         # 使用 h5py 和 BytesIO 从内存中加载模型
        with h5py.File(model_buffer, 'r') as f:
            model = tf.keras.models.load_model(f)
        print(f"{model_name}模型成功从 MongoDB 加载！")
        client.close()
        return model
    else:
        print(f"未找到model_name为 {model_name} 的模型。")
        client.close()
        return None


def get_scaler_model_from_mongo(args):
    mongodb_connection, mongodb_database, scaler_table, = ("mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",
                                                           args['mongodb_database'], args['scaler_table'])
    client = MongoClient(mongodb_connection)
    # 选择数据库（如果数据库不存在，MongoDB 会自动创建）
    db = client[mongodb_database]
    collection = db[scaler_table]  # 集合名称
    # Retrieve the scalers from MongoDB
    scaler_doc = collection.find_one()
    # Deserialize the scalers

    feature_scaler_bytes = BytesIO(scaler_doc["feature_scaler"])
    feature_scaler = joblib.load(feature_scaler_bytes)
    target_scaler_bytes = BytesIO(scaler_doc["target_scaler"])
    target_scaler = joblib.load(target_scaler_bytes)
    return feature_scaler,target_scaler