1. influxDB连接

首先在浏览器中输入influxDB的IP和端口，然后输入账号密码进入到influxDB数据库来进行数据的相关操作：

里面的bucket相当于sql中的数据库，_measurement相当于sql中的表

 2. 获取influxDB数据库的token方法

3. 写查询语句来查询数据

然后和平时写sql查询语句一样，先创建连接client，然后调用其query函数来查询获取数据

from (bucket: "bt")

              |> range(start: 2024-04-01T15:16:21Z, stop: 2024-04-02T15:16:21Z)
              |> filter(fn: (r) => r["_measurement"] == "monitor")
              |> filter(fn: (r) => r["d"] == "102519")
              |> filter(fn: (r) => r["c"] == "10")
              |> filter(fn: (r) => r["i"] == "1")

4. 完整代码（code）

import yaml
from params import pool_num
import os
import time
from pathlib import Path
import influxdb_client
from params import influx_host, influx_port
import pandas as pd

from utils.logger import Log

os_file_name = Path(__file__).name


class InfluxInfo(object):
    def __init__(self):
        pass

    def influx_conn(self):  # 读取的influxdb库地址
        url = "http://" + influx_host + ":" + str(influx_port)  # 安装时的配置参数中查找
        org = "test_org"  # 安装时的配置参数中查找
        token = "my_token"  # 获取方式见图片
        self.client = influxdb_client.InfluxDBClient(url=url, token=token,  org=org)

    def query_data(self, table_name, expression, begin_time, end_time):
        self.influx_conn()
        query_api = self.client.query_api()
        bucket = "bt"
        dev, cg, id = expression.split("_")
        query_sql = f"""from (bucket: "{bucket}") \
                        |> range(start: {begin_time}, stop: {end_time}) \
                        |> filter(fn: (r) => r["_measurement"] == "{table_name}") \
                        |> filter(fn: (r) => r["d"] == "{dev}") \
                        |> filter(fn: (r) => r["c"] == "{cg}") \
                        |> filter(fn: (r) => r["i"] == "{id}")"""
        tables = query_api.query(query=query_sql)
        # data_csv = query_api.query_csv(query=query_sql)
        self.client.close()
        returndata = pd.DataFrame()
        if len(tables) > 0:
            table = tables[0]
            records = table.records
            if len(records) > 0:
                timelist = []  #
                expression_list = []
                dev_list = []
                cg_list = []
                id_list = []
                valuelist = []
                for record in records:
                    r = record.values
                    timestr = r['_time'].strftime("%Y-%m-%d %H:%M:%S")
                    timelist.append(timestr)
                    dev_list.append(r['d'])
                    cg_list.append(r['c'])
                    id_list.append(r['i'])
                    expression_list.append("_".join([r['d'], r['c'], r['i']]))
                    valuelist.append(r['_value'])
                if len(timelist) > 0:
                    returndata = pd.DataFrame({'time': timelist, 'expression': expression_list, 'dev': dev_list,
                                               'cg': cg_list, 'id': id_list, 'value': valuelist})
        return returndata


if __name__ == '__main__':
    begin_time = '2024-04-01T15:16:21Z'
    end_time = '2024-04-02T15:16:21Z'
    expression = "102519_10_1"
    query_rslt = InfluxInfo().query_data("monitor", expression, begin_time, end_time)