🎉 init(init):初始化仓库

2025-12-23 08:38:08 +08:00
parent 36226cc9fe
commit 066fe58f89
34 changed files with 3402 additions and 2 deletions
--- a/irrgiation/irrigateDecisionDemo.py
+++ b/irrgiation/irrigateDecisionDemo.py
@@ -0,0 +1,620 @@
+import statistics
+import uuid
+
+from collections import defaultdict
+from datetime import datetime, timedelta
+
+import pandas as pd
+
+from irrgiation.db_connect import query_postgresql_to_dataframe, dataframe_to_postgresql_batch,update_irrigation_data
+
+from irrgiation.weatherAndSoilDataRequest import getWeatherAndSoilData, loginAuth, dateToTimestamp, get_soil_data_dk1, \
+    getFutureWeather, weather_params, get_soil_data_dk2
+from irrgiation.dailyEvaporation import get_daily_kc, cal_et0_List, calculate_etc_List, export_etc_et0_date, \
+    cal_soil_fc_wp, estimate_mad_from_et, cal_stagebyDate, calculate_average_root_length,calculate_etc_List_new
+from irrgiation.mathuntils import day_of_year
+from irrgiation.soil_Ke import calculate_ke,calculate_D_ei
+from irrgiation.crop_growth_stages import crop_growth_stages
+
+#解析数据接口，计算均值、最大、最小值
+def analy_soil_data(data):
+    soil_result = {}
+    for key in data:
+        daily_data = defaultdict(lambda: defaultdict(list))
+        for metric, entries in data[key].items():
+            for entry in entries:
+                dt = datetime.strptime(entry["datetime"], "%Y-%m-%d %H:%M:%S")
+                date_key = dt.date()  # 按日期分组
+                value = float(entry["value"])
+                daily_data[date_key][metric].append(value)
+        result = {}
+        for date, metrics in daily_data.items():
+            date_stats = {}
+            for metric, values in metrics.items():
+                date_stats[metric] = {
+                    "max": max(values),
+                    "min": min(values),
+                    "mean": statistics.mean(values),
+                    'count': len(values),
+                    'sum': sum(values)
+                }
+            result[str(date)] = date_stats  # 将日期转为字符串作为键
+        soil_result[key] = result
+    return soil_result
+
+
+#获取地块6个传感器的土壤湿度均值
+def analy_soil_data_mean(soil_data):
+    result = defaultdict(lambda: {
+        "SOIL_MOISTURE_SURFACE": [],
+        "SOIL_MOISTURE_MIDDLE": [],
+        "SOIL_MOISTURE_BOTTOM": []
+
+    })
+    # 填充数据
+    for sensor, dates in soil_data.items():
+        for date, layers in dates.items():
+            result[date]["SOIL_MOISTURE_SURFACE"].append(layers["SOIL_MOISTURE_SURFACE"]["mean"])
+            result[date]["SOIL_MOISTURE_MIDDLE"].append(layers["SOIL_MOISTURE_MIDDLE"]["mean"])
+            result[date]["SOIL_MOISTURE_BOTTOM"].append(layers["SOIL_MOISTURE_BOTTOM"]["mean"])
+    # 转换为普通字典
+    final_result = dict(result)
+    result_soil = {}
+    for date, layers in final_result.items():
+        result_soil[date] = {
+            "SOIL_MOISTURE_SURFACE_MEAN": sum(layers["SOIL_MOISTURE_SURFACE"]) / len(layers["SOIL_MOISTURE_SURFACE"]),
+            "SOIL_MOISTURE_MIDDLE_MEAN": sum(layers["SOIL_MOISTURE_MIDDLE"]) / len(layers["SOIL_MOISTURE_MIDDLE"]),
+            "SOIL_MOISTURE_BOTTOM_MEAN": sum(layers["SOIL_MOISTURE_BOTTOM"]) / len(layers["SOIL_MOISTURE_BOTTOM"])
+        }
+
+    return final_result, result_soil
+
+
+def analy_soil_dataNight_mean(soil_data):
+    daily_means = defaultdict(lambda: defaultdict(list))
+    # 计算平均值
+    for sensor, measurements in soil_data.items():
+        for measure_type, dates in measurements.items():
+            for date, value in dates.items():
+                daily_means[date][measure_type].append(float(value))
+
+    # 计算并格式化结果
+    result = {}
+    for date, measures in daily_means.items():
+        result[date] = {
+            f"{measure_type}_MEAN": round(sum(values) / len(values), 2)
+            for measure_type, values in measures.items()
+        }
+
+    return result
+
+
+#获取地块6个传感器中土壤湿度最大值
+def analy_soil_data_max(soil_data):
+    result = defaultdict(lambda: {
+        "SOIL_MOISTURE_SURFACE": [],
+        "SOIL_MOISTURE_MIDDLE": [],
+        "SOIL_MOISTURE_BOTTOM": []
+
+    })
+    # 填充数据
+    for sensor, dates in soil_data.items():
+        for date, layers in dates.items():
+            result[date]["SOIL_MOISTURE_SURFACE"].append(layers["SOIL_MOISTURE_SURFACE"]["mean"])
+            result[date]["SOIL_MOISTURE_MIDDLE"].append(layers["SOIL_MOISTURE_MIDDLE"]["mean"])
+            result[date]["SOIL_MOISTURE_BOTTOM"].append(layers["SOIL_MOISTURE_BOTTOM"]["mean"])
+    # 转换为普通字典
+    final_result = dict(result)
+    result_soil = {}
+    for date, layers in final_result.items():
+        result_soil[date] = {
+            "SOIL_MOISTURE_SURFACE_MEAN": max(layers["SOIL_MOISTURE_SURFACE"]),
+            "SOIL_MOISTURE_MIDDLE_MEAN": max(layers["SOIL_MOISTURE_MIDDLE"]),
+            "SOIL_MOISTURE_BOTTOM_MEAN": max(layers["SOIL_MOISTURE_BOTTOM"])
+        }
+
+    return final_result, result_soil
+
+
+def get_soil_data_night_eyveryDay(data):
+    daily_data = {}
+    for sersor in data:
+        data_depth = {}
+        for key in data[sersor]:
+            target_time = (21, 59, 59)
+            result = {}  # 时、分、秒元组
+            daily_records = {}  # 临时存储每天的所有记录，用于找不到22点数据时获取最新记录
+            for record in data[sersor][key]:
+                try:
+                    dt = datetime.strptime(record["datetime"], "%Y-%m-%d %H:%M:%S")
+                    date_str = record["datetime"].split(" ")[0]  # 提取日期部分
+                    # 严格匹配 02:00:00
+                    # 提取时间部分（时、分、秒）
+                    current_time = (dt.hour, dt.minute, dt.second)
+                    # 存储每天的所有记录（按时间排序）
+                    if date_str not in daily_records:
+                        daily_records[date_str] = []
+                    daily_records[date_str].append((dt, record['value']))
+                    # 比较时间部分
+                    if current_time >= target_time:
+                        if date_str not in result:
+                           result[date_str] = record['value']
+                except:
+                    continue
+            # 对于没有22点数据的日期，取当天最新的一条数据
+            for date_str in daily_records:
+                if date_str not in result and daily_records[date_str]:
+                   daily_records[date_str].sort(key=lambda x: x[0])  # 按datetime排序
+                   result[date_str] = daily_records[date_str][-1][1]  # 取最后一个值
+            data_depth[key] = result
+        daily_data[sersor] = data_depth
+    return daily_data
+
+
+def analyze_weather_data1(data):
+    # 1. 按日期分组数据
+    daily_data = defaultdict(lambda: defaultdict(list))
+    for metric, entries in data.items():
+        for entry in entries:
+            dt = datetime.strptime(entry["datetime"], "%Y-%m-%d %H:%M:%S")
+            date_key = dt.date()  # 按日期分组
+            if metric == "AIR_LUX":
+                value = float(entry["value_solar"])
+            else:
+                value = float(entry["value"])
+            daily_data[date_key][metric].append(value)
+    # 2. 计算每日统计值
+    result = {}
+    for date, metrics in daily_data.items():
+        date_stats = {}
+        for metric, values in metrics.items():
+            date_stats[metric] = {
+                "max": max(values),
+                "min": min(values),
+                "mean": statistics.mean(values),
+                'count': len(values),
+                'sum': sum(values)
+            }
+        result[str(date)] = date_stats  # 将日期转为字符串作为键
+
+    return result
+
+
+# 获取彭曼公式的入参
+def get_params(start_date, monitor_date, data):
+    delta = monitor_date - start_date
+    days = delta.days
+    dates = [start_date + timedelta(days=i) for i in range(days)]
+    date_strings = [date.strftime("%Y-%m-%d") for date in dates]
+    date_params = {}
+    for date in date_strings:
+        result_params = get_weather_params(data, date)
+        date_params[date] = result_params
+    return date_params
+
+
+def get_weather_params(data, date):
+    result = {}
+    elevation = 865  # 海拔(m)
+    lat = 41.34252110189814  # 纬度(度)
+    #计算当前日期位于当年的多少天
+    doy = day_of_year(date)
+
+    for item in data['AIR_LUX']:
+        item['value_solar'] = float(item['value']) * 0.0081 * 3600
+    data_tj = analyze_weather_data1(data)
+    data_tj_date = data_tj[date];
+    tmax = data_tj_date['AIR_TEMPERATURE']['max']
+    tmin = data_tj_date['AIR_TEMPERATURE']['min']
+    tmean = data_tj_date['AIR_TEMPERATURE']['mean']
+    rh_mean = data_tj_date['AIR_HUMIDITY']['mean']
+    wind_speed = data_tj_date['WIND_SPEED']['mean']
+    solar = data_tj_date['AIR_LUX']['sum'] / 1000000
+    result['tmax'] = round(tmax, 3)
+    result['tmin'] = round(tmin, 3)
+    result['tmean'] = round(tmean, 3)
+    result['rh_mean'] = round(rh_mean, 3)
+    result['wind_speed'] = round(wind_speed, 3)*0.94
+    result['solar_rad'] = round(solar, 3)
+    result['elevation'] = elevation
+    result['lat'] = round(lat, 3)
+    result['doy'] = doy
+    return result
+
+
+# 判断是否需要灌溉
+def isNeedirrigate(df, qx_coff, soil_coff,Di_1,zwlx_growth_stages):
+    df['RD'] = 0.0
+    df['Di'] = 0.0
+    df['Di-1'] = Di_1
+    df['mad'] = 0.0
+    df['irrigation_depth'] = 0.0
+    df['irrigation_acre'] = 0.0
+    df['isNeedirrigate'] = False
+    df['Reset_Flag'] = False
+    # df['irrigate_sd_predict'] = 0.0
+    df['f_t'] = 0.0
+    df['f_js'] = 0.0
+    df['f_soil'] = 0.0
+    for i in range(len(df)):
+        # 设置Di-1（前一天累计值）
+        if i > 0:
+            df.at[i, 'Di-1'] = df.at[i - 1, 'Di']
+        # 计算当前累计值（Di-1 + 当天ETc）
+        raw, rd, mad, k_rain = cal_raw(df.at[i, 'ETc'], df.at[i, 'Date'],zwlx_growth_stages)
+        if df.at[i, 'rain_effective']<5:
+            df.at[i, 'rain_effective'] = 0
+        else:
+            df.at[i, 'rain_effective'] = df.at[i, 'rain_effective'] * k_rain
+        current_cum = df.at[i, 'Di-1'] + df.at[i, 'ETc']- df.at[i, 'rain_effective']
+        df['RD'] = rd
+        df.at[i,'mad'] = mad
+        df.at[i, 'raw'] = round(raw, 3)
+        f_t = qx_coff[df.at[i, 'Date']]['qw']
+        f_js = qx_coff[df.at[i, 'Date']]['js']
+        f_soil = soil_coff[df.at[i, 'Date']]
+        raw_adj = cal_raw_adjust(raw, f_t, f_js, f_soil)
+        df.at[i, 'f_t'] = f_t
+        df.at[i, 'f_js'] = f_js
+        df.at[i, 'f_soil'] = f_soil
+        df.at[i, 'raw_adjust'] = round(raw_adj, 3)
+        # 判断是否需要重置
+        if current_cum > raw_adj:
+            df.at[i, 'Reset_Flag'] = True
+            df.at[i, 'irrigation_depth'] = round(current_cum, 3)
+            df.at[i, 'isNeedirrigate'] = True
+            df.at[i, 'Di'] = 0  # 保留当天ETc值
+            df.at[i, 'irrigation_acre'] = round((current_cum*667/1000),3)
+        else:
+            df.at[i, 'Di'] = round(current_cum, 3)  # 正常累加
+    return df
+
+
+def cal_raw(etc, monitor_date,zwlx_growth_stages):
+    theta_fc, theta_wp = cal_soil_fc_wp()
+    stage, details,hight,k_rain= cal_stagebyDate(monitor_date,zwlx_growth_stages)
+    rd = calculate_average_root_length(details)
+    mad = estimate_mad_from_et(etc)
+    raw = mad * (theta_fc - theta_wp) * rd * 10
+    return raw, rd,mad,k_rain
+
+
+def cal_raw_adjust(raw, f_t, f_rain, f_soil):
+    raw_adjust = raw * f_soil * f_t * f_rain
+    return raw_adjust
+
+
+def cal_qxcoefficient_adjust(start_date, monitor_date, qx_data):
+
+    qx_coefficient = {}
+    current_date = start_date
+    qxData_filter = {}
+    for key in qx_data:
+        if key == "AIR_TEMPERATURE" or key == "RAIN_FALL_REALTIME":
+            qxData_filter[key] = qx_data[key]
+    qxData_filter_tj = analyze_weather_data1(qxData_filter)
+    now_date = datetime.now().date()
+    while current_date < monitor_date:
+        qx_futureData = []
+        result = {}
+        if (current_date + timedelta(days=3)).date() < now_date:
+            start = datetime.strptime((current_date + timedelta(days=1)).strftime("%Y-%m-%d"), "%Y-%m-%d")
+            end = datetime.strptime((current_date + timedelta(days=3)).strftime("%Y-%m-%d"), "%Y-%m-%d")
+            # 筛选日期范围内的数据
+            filter = {
+                date: values
+                for date, values in qxData_filter_tj.items()
+                if start <= datetime.strptime(date, "%Y-%m-%d") <= end
+            }
+            qx_futureData = transform_data(filter)
+        else:
+            try:
+                # print(f"进入else分支: {current_date}")
+                qx_futureData = getFutureWeather(current_date)
+            except Exception as e:
+                # print(f"else分支异常: {e}")
+                raise  # 保留异常以便调试
+        qw_mean = (qx_futureData['qw_day1']['mean'] + qx_futureData['qw_day2']['mean'] + qx_futureData['qw_day3']['mean']) / 3
+        js_sum = qx_futureData['js_day1']['max'] + qx_futureData['js_day2']['max'] + qx_futureData['js_day3']['max']
+        qw_coff = 1.0
+        js_coff = 1.0
+        if qw_mean < 10:
+            qw_coff = 1.1
+        elif 10 < qw_mean <= 25:
+            qw_coff = 1
+        elif 25 < qw_mean <= 30:
+            qw_coff = 0.85
+        else:
+            qw_coff = 0.75
+        if js_sum <= 5:
+            js_coff = 1.0  # 少雨
+        elif 5 < js_sum <= 10:
+            js_coff = 1.2  # 中雨
+        elif 10 < js_sum <= 20:
+            js_coff = 1.4  # 大雨
+        else:
+            js_coff = 1.6  # 暴雨
+        result['qw'] = qw_coff
+        result['js'] = js_coff
+        qx_coefficient[current_date.strftime("%Y-%m-%d")] = result
+        current_date = current_date + timedelta(days=1)
+    return qx_coefficient
+
+
+def transform_data(original_data):
+    transformed = {}
+    # 处理温度数据 (qw_day)
+    for i, (date, metrics) in enumerate(original_data.items(), 1):
+        temp_data = metrics["AIR_TEMPERATURE"]
+        transformed[f"qw_day{i}"] = {
+            "max": round(temp_data["max"], 2),
+            "min": round(temp_data["min"], 2),
+            "mean": round(temp_data["mean"], 2),
+            "sum": round(temp_data["sum"], 2)
+        }
+
+    # 处理降雨数据 (js_day)
+    for i, (date, metrics) in enumerate(original_data.items(), 1):
+        rain_data = metrics["RAIN_FALL_REALTIME"]
+        transformed[f"js_day{i}"] = {
+            "max": round(rain_data["max"], 2),
+            "min": round(rain_data["min"], 2),
+            "mean": round(rain_data["mean"], 2),
+            "sum": round(rain_data["sum"], 2)
+        }
+
+    return transformed
+
+
+def cal_soil_coff(soil_data):
+    soil_data_coff = {}
+    for sensor in soil_data:
+        for date in soil_data[sensor]:
+            soil_cof = 1.0
+            sd_surf = float(soil_data['SOIL_MOISTURE_SURFACE'][date])
+            sd_mid = float(soil_data['SOIL_MOISTURE_MIDDLE'][date])
+            if sd_surf < 10:
+                soil_cof = 0.5
+            if 10 <sd_surf < 15:
+                soil_cof = 0.75
+            elif 15<sd_surf < 20:
+                soil_cof = 1.0
+            else:
+                soil_cof = 1.2
+            soil_data_coff[date] = soil_cof
+    return soil_data_coff
+
+
+#将两块地的传感器数据分隔开
+def get_soil_data_bydk(soil_data):
+    target_key = 7
+    dk1_soildata = {}
+    dk2_soildata = {}
+
+    for key, value in soil_data.items():
+        parts = key.split("号传感器")
+        if int(parts[0]) < target_key:
+            dk1_soildata[key] = value
+        else:
+            dk2_soildata[key] = value
+    return dk1_soildata, dk2_soildata
+# 整生育期监测
+def cal_dk_isNeegirrigate(zwlx_name,dk_name,start_date, monitor_date,Di_1):
+    auth_token = loginAuth()
+    date_start = start_date.strftime("%Y-%m-%d") + " 00:00:00.123"
+    date_end_qx=monitor_date
+    if (monitor_date + timedelta(days=3)).date()<= datetime.now().date():
+        date_end_qx=(monitor_date + timedelta(days=3)).strftime("%Y-%m-%d") + " 23:59:59.123"
+    else:
+        date_end_qx=datetime.now().date().strftime("%Y-%m-%d") + " 23:59:59.123"
+    date_end = monitor_date.strftime("%Y-%m-%d") + " 23:59:59.123"
+    startTs = dateToTimestamp(date_start)
+    endTs = dateToTimestamp(date_end)
+    zwlx_growth_stages = crop_growth_stages[zwlx_name]
+    kc_daily = get_daily_kc(zwlx_growth_stages)
+    # #获取气象数据
+    qxdata = getWeatherAndSoilData(auth_token, weather_params['device_id'], startTs, dateToTimestamp(date_end_qx), weather_params['agg'],
+                                   weather_params['interval'], weather_params['limit'], weather_params['orderBy'],
+                                   weather_params['keys'])
+    # # #彭曼公式气象入参
+    qx_params = get_params(start_date, monitor_date, qxdata)
+    # #获取土壤传感器数据
+    data = get_soil_data_dk1(auth_token, startTs, endTs)
+    soil_data = get_soil_data_night_eyveryDay(data)
+    dk1_soildata, dk2_soildata = get_soil_data_bydk(soil_data)
+    # 膜边探头优先原则，1号和7号传感器分别为1号地和5号地的膜边探头
+    dk1_soil_mean = dk1_soildata['1号传感器']
+    dk2_soil_mean = dk2_soildata['7号传感器']
+    # #计算参考蒸散量
+    et0_list = cal_et0_List(start_date, monitor_date, qx_params)
+    #计算土壤蒸发系数
+    ke_data = cal_ke_daily_list(start_date, monitor_date, et0_list, kc_daily, qxdata)
+    ke_daily = []
+    rain_daily=[]
+    irrigate_daily=[]
+    for date in sorted(ke_data.keys()):
+        ke_value = round(ke_data[date]['ke'],3)
+        rain_value= round(ke_data[date]['rain'],3)
+        irrigate_value = ke_data[date]['irrigation']
+        ke_daily.append(ke_value)
+        rain_daily.append(rain_value)
+        irrigate_daily.append(irrigate_value)
+
+    # #获取作物蒸散量
+    etc_list, date_strings, kc_list = calculate_etc_List_new(et0_list, start_date, monitor_date, kc_daily,ke_daily)
+    # # 获取未来3天气象数据
+    qx_coff = cal_qxcoefficient_adjust(start_date, monitor_date, qxdata)
+    if "1" in dk_name:
+        df = export_etc_et0_date(et0_list, etc_list, date_strings, kc_list,ke_daily, dk1_soil_mean,rain_daily,irrigate_daily,dk_name)
+        # 基于每天实测土壤相对湿度计算土壤系数
+        soil_coff = cal_soil_coff(dk1_soil_mean)
+    elif "5" in dk_name:
+        df = export_etc_et0_date(et0_list, etc_list, date_strings, kc_list,ke_daily, dk2_soil_mean,rain_daily,irrigate_daily,dk_name)
+        # 基于每天实测土壤相对湿度计算土壤系数
+        soil_coff = cal_soil_coff(dk2_soil_mean)
+    else:
+        return "该地块数据不存在"
+    # 判断是否需要灌溉
+    dataframe = isNeedirrigate(df, qx_coff, soil_coff,Di_1,zwlx_growth_stages)
+    random_uuid = uuid.uuid4()
+    uuid_str = str(random_uuid)
+    # df.to_csv(
+    #     'D:/悦柠/遥感/甘草/ceshi/' + dk_name + "_" + start_date.strftime("%Y-%m-%d") + "-" + monitor_date.strftime(
+    #         "%Y-%m-%d")+"_"+uuid_str + ".csv", index=False)  # CSV 文件
+    return dataframe
+def cal_dk_isNeegirrigate_Day(zwlx_name,dk_name,soil_key,start_date, monitor_date,Di_1,weather_key):
+    auth_token = loginAuth()
+    date_start = start_date.strftime("%Y-%m-%d") + " 00:00:00.123"
+    date_end_qx=monitor_date
+    if (monitor_date + timedelta(days=3)).date()<= datetime.now().date():
+        date_end_qx=(monitor_date + timedelta(days=3)).strftime("%Y-%m-%d") + " 23:59:59.123"
+    else:
+        date_end_qx=datetime.now().date().strftime("%Y-%m-%d") + " 23:59:59.123"
+    date_end = monitor_date.strftime("%Y-%m-%d") + " 23:59:59.123"
+    startTs = dateToTimestamp(date_start)
+    endTs = dateToTimestamp(date_end)
+    zwlx_growth_stages=crop_growth_stages[zwlx_name]
+    kc_daily = get_daily_kc(zwlx_growth_stages)
+    # #获取气象数据
+    qxdata = getWeatherAndSoilData(auth_token, weather_key, startTs, dateToTimestamp(date_end_qx), weather_params['agg'],
+                                   weather_params['interval'], weather_params['limit'], weather_params['orderBy'],
+                                   weather_params['keys'])
+    # 获取土壤传感器数据
+    data = get_soil_data_dk2(auth_token, soil_key, startTs, endTs)
+    soil_data = get_soil_data_night_eyveryDay(data)
+    # dk1_soildata, dk2_soildata = get_soil_data_bydk(soil_data)
+    # 膜边探头优先原则，1号和7号传感器分别为1号地和5号地的膜边探头
+    dk_soil_mean = soil_data[soil_key]
+    if len(qxdata) !=0 and len(dk_soil_mean) !=0:
+        # # #彭曼公式气象入参
+        qx_params = get_params(start_date, monitor_date, qxdata)
+        # #计算参考蒸散量
+        et0_list = cal_et0_List(start_date, monitor_date, qx_params)
+        # 计算土壤蒸发系数
+        ke_data = cal_ke_daily_list(start_date, monitor_date, et0_list, kc_daily, qxdata, zwlx_growth_stages)
+        ke_daily = []
+        rain_daily = []
+        irrigate_daily = []
+        for date in sorted(ke_data.keys()):
+            ke_value = round(ke_data[date]['ke'], 3)
+            rain_value = round(ke_data[date]['rain'], 3)
+            irrigate_value = ke_data[date]['irrigation']
+            ke_daily.append(ke_value)
+            rain_daily.append(rain_value)
+            irrigate_daily.append(irrigate_value)
+            # #获取作物蒸散量
+        etc_list, date_strings, kc_list = calculate_etc_List_new(et0_list, start_date, monitor_date, kc_daily,
+                                                                 ke_daily)
+        # # 获取未来3天气象数据
+        qx_coff = cal_qxcoefficient_adjust(start_date, monitor_date, qxdata)
+
+        df = export_etc_et0_date(et0_list, etc_list, date_strings, kc_list, ke_daily, dk_soil_mean, rain_daily,
+                                 irrigate_daily, dk_name)
+        # 基于每天实测土壤相对湿度计算土壤系数
+        soil_coff = cal_soil_coff(dk_soil_mean)
+        # 判断是否需要灌溉
+        dataframe = isNeedirrigate(df, qx_coff, soil_coff, Di_1, zwlx_growth_stages)
+        return dataframe
+    elif len(qxdata) ==0 and len(dk_soil_mean) !=0:
+        raise ValueError("未查询到气象数据")
+    elif len(qxdata) !=0 and len(dk_soil_mean) ==0:
+        raise ValueError("未查询到土壤数据")
+    else:
+        raise ValueError("未查询到气象和土壤数据")
+
+
+#计算土壤蒸发系数
+def cal_ke_daily_list(start_date,monitor_date,et0_list,kc_list,qx_data,zwlx_growth_stages):
+    stage, details, hight ,k_rain= cal_stagebyDate(monitor_date,zwlx_growth_stages)
+    REW=9
+    I=0.0
+    theta_fc=0.2884
+    theta_wp=0.1086
+    current_date=start_date
+    qxData_tj = analyze_weather_data1(qx_data)
+    ke_data= {}
+    i=0
+    while current_date< monitor_date:
+        date_key = str(current_date.date())
+        u2=qxData_tj[date_key]['WIND_SPEED']['mean']
+        rain=qxData_tj[date_key]['RAIN_FALL_REALTIME']['max']
+        rh_min=qxData_tj[date_key]['AIR_HUMIDITY']['min']
+        if current_date == start_date:
+            DE_i_1 = 0
+            fw=0.35
+        else:
+            # 从上一天的数据中获取DE_i（如果存在）
+            prev_date = current_date - timedelta(days=1)
+            prev_date_key = str(prev_date.date())
+            if prev_date_key in ke_data:
+                DE_i_1 = ke_data[prev_date_key]['DE_i']
+            else:
+                DE_i_1 = 0  # 处理特殊情况（如start_date等于monitor_date）
+                if I > 0 and rain < 4:
+                    fw = 0.35
+                elif I > 0 and rain < 4:
+                    fw = 1
+                elif rain > 4 and I == 0:
+                    fw = 1
+                else:
+                    fw = ke_data[prev_date_key]['fw']
+        et0 = 0.0
+        kc = 0.0
+        if i < len(et0_list):
+            et0 = et0_list[i]
+            kc = kc_list[i]
+        ke,few=calculate_ke(hight, u2, rh_min, kc, DE_i_1, REW, fw, theta_fc,theta_wp)
+        et_soil=ke*et0
+        DE_i = calculate_D_ei(DE_i_1, rain, I,et_soil, fw,few)
+        #创建当天的数据字典
+        daily_data = {
+            'ke': ke,
+            'DE_i': DE_i,
+            'et0': et0,
+            'et_soil': et_soil,
+            'fw':fw,
+            "rain":rain,
+            "irrigation":I
+        }
+        ke_data[date_key]=daily_data
+        current_date = current_date + timedelta(days=1)
+        i=i+1
+    return ke_data
+# 单天监测
+def cal_irrigationByDay(zwlx_name,dkname,start_date,end_date,soil_key,weather_key,irrigation_really):
+    yesterday_date=(start_date - timedelta(days=1)).strftime("%Y-%m-%d")
+    condition = {"Date": yesterday_date,"dkbm":dkname}
+    df=query_postgresql_to_dataframe(condition)
+    Di_1 = 0
+    if irrigation_really>0:
+        Di_1 = 0
+        update_irrigation_data(yesterday_date,dkname,"irrigation_really",irrigation_really)
+    else:
+        # 将Date列转换为日期时间类型
+        df['Date'] = pd.to_datetime(df['Date'])
+        if len(df[df.Date == (start_date - timedelta(days=1))])!=0:
+           isNeedjg=df[df.Date == (start_date- timedelta(days=1))]['isNeedirrigate'].values[0]
+           if isNeedjg:
+              Di_1 = df[df.Date == (start_date - timedelta(days=1))]['Di-1'].values[0]+df[df.Date == (start_date - timedelta(days=1))]['ETc'].values[0]
+           else:
+              Di_1 = df[df.Date == (start_date - timedelta(days=1))]['Di'].values[0]
+    dataframe=cal_dk_isNeegirrigate_Day(zwlx_name,dkname,soil_key,start_date,end_date,Di_1,weather_key)
+    condition_new = {"Date": start_date.strftime("%Y-%m-%d"), "dkbm": dkname}
+    df_query=query_postgresql_to_dataframe(condition_new)
+    if len(df_query)==0:
+        dataframe_to_postgresql_batch(dataframe)
+    else:
+        print("库表中已存在该数据")
+    return dataframe
+if __name__ == "__main__":
+    #调用气象数据接口--获取具体日期的气象数据(气象)
+    # auth_token = loginAuth()
+    start_date = datetime(2025, 7, 16)
+    monitor_date = datetime(2025, 7, 17)
+    dk_name = "5"
+    zwlx_name="甘草"
+    soil_key = "8637b970-561b-11f0-a556-4f10f26fc07f"
+    weather_key="18d121f0-561b-11f0-a556-4f10f26fc07f"
+    irrigation_really=5
+    cal_irrigationByDay(zwlx_name,dk_name,start_date,monitor_date,soil_key,weather_key,irrigation_really)
+    # cal_dk_isNeegirrigate(dk_name, planting_date, monitor_date,0)