知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
108 lines
5.3 KiB
Python
108 lines
5.3 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/4/25 17:20
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Desc: 河北省空气质量预报信息发布系统
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https://110.249.223.67/publish
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每日 17 时发布
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等级划分
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1. 空气污染指数为0－50，空气质量级别为一级，空气质量状况属于优。此时，空气质量令人满意，基本无空气污染，各类人群可正常活动。
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2. 空气污染指数为51－100，空气质量级别为二级，空气质量状况属于良。此时空气质量可接受，但某些污染物可能对极少数异常敏感人群健康有较弱影响，建议极少数异常敏感人群应减少户外活动。
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3. 空气污染指数为101－150，空气质量级别为三级，空气质量状况属于轻度污染。此时，易感人群症状有轻度加剧，健康人群出现刺激症状。建议儿童、老年人及心脏病、呼吸系统疾病患者应减少长时间、高强度的户外锻炼。
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4. 空气污染指数为151－200，空气质量级别为四级，空气质量状况属于中度污染。此时，进一步加剧易感人群症状，可能对健康人群心脏、呼吸系统有影响，建议疾病患者避免长时间、高强度的户外锻练，一般人群适量减少户外运动。
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5. 空气污染指数为201－300，空气质量级别为五级，空气质量状况属于重度污染。此时，心脏病和肺病患者症状显著加剧，运动耐受力降低，健康人群普遍出现症状，建议儿童、老年人和心脏病、肺病患者应停留在室内，停止户外运动，一般人群减少户外运动。
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6. 空气污染指数大于300，空气质量级别为六级，空气质量状况属于严重污染。此时，健康人群运动耐受力降低，有明显强烈症状，提前出现某些疾病，建议儿童、老年人和病人应当留在室内，避免体力消耗，一般人群应避免户外活动。
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发布单位：河北省环境应急与重污染天气预警中心 技术支持：中国科学院大气物理研究所 中科三清科技有限公司
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"""
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import pandas as pd
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import requests
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from bs4 import BeautifulSoup
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def air_quality_hebei() -> pd.DataFrame:
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"""
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河北省空气质量预报信息发布系统-空气质量预报, 未来 6 天
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http://218.11.10.130:8080/#/application/home
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:return: city = "", 返回所有地区的数据; city="唐山市", 返回唐山市的数据
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:rtype: pandas.DataFrame
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"""
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url = "http://218.11.10.130:8080/api/hour/130000.xml"
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r = requests.get(url)
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soup = BeautifulSoup(r.content, features="xml")
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data = []
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cities = soup.find_all("City")
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for city in cities:
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pointers = city.find_all("Pointer")
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for pointer in pointers:
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row = {
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"City": city.Name.text if city.Name else None,
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"Region": pointer.Region.text if pointer.Region else None,
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"Station": pointer.Name.text if pointer.Name else None,
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"DateTime": pointer.DataTime.text if pointer.DataTime else None,
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"AQI": pointer.AQI.text if pointer.AQI else None,
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"Level": pointer.Level.text if pointer.Level else None,
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"MaxPoll": pointer.MaxPoll.text if pointer.MaxPoll else None,
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"Longitude": pointer.CLng.text if pointer.CLng else None,
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"Latitude": pointer.CLat.text if pointer.CLat else None,
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}
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polls = pointer.find_all("Poll")
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for poll in polls:
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poll_name = poll.Name.text if poll.Name else None
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poll_value = poll.Value.text if poll.Value else None
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row[f"{poll_name}_Value"] = poll_value
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row[f"{poll_name}_IAQI"] = poll.IAQI.text if poll.IAQI else None
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data.append(row)
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df = pd.DataFrame(data)
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numeric_columns = ["AQI", "Longitude", "Latitude"] + [
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col for col in df.columns if col.endswith("_Value") or col.endswith("_IAQI")
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]
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for col in numeric_columns:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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column_names = {
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"City": "城市",
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"Region": "区域",
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"Station": "监测点",
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"DateTime": "时间",
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"Level": "空气质量等级",
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"MaxPoll": "首要污染物",
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"Longitude": "经度",
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"Latitude": "纬度",
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"SO2_Value": "二氧化硫_浓度",
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"SO2_IAQI": "二氧化硫_IAQI",
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"CO_Value": "一氧化碳_浓度",
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"CO_IAQI": "一氧化碳_IAQI",
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"NO2_Value": "二氧化氮_浓度",
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"NO2_IAQI": "二氧化氮_IAQI",
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"O3-1H_Value": "臭氧1小时_浓度",
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"O3-1H_IAQI": "臭氧1小时_IAQI",
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"O3-8H_Value": "臭氧8小时_浓度",
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"O3-8H_IAQI": "臭氧8小时_IAQI",
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"PM2.5_Value": "PM2.5_浓度",
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"PM2.5_IAQI": "PM2.5_IAQI",
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"PM10_Value": "PM10_浓度",
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"PM10_IAQI": "PM10_IAQI",
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}
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df = df.rename(columns=column_names)
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basic_columns = [
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"城市",
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"区域",
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"监测点",
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"时间",
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"AQI",
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"空气质量等级",
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"首要污染物",
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"经度",
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"纬度",
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]
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pollutant_columns = [col for col in df.columns if col not in basic_columns]
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df = df[basic_columns + sorted(pollutant_columns)]
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return df
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if __name__ == "__main__":
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air_quality_hebei_df = air_quality_hebei()
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print(air_quality_hebei_df)
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