乐清市小区漏损治理评估数据

通过整合小区远传供水数据及营收抄表数据，调整供水及售水统计周期，并结合校准漏损率、月均KL值、抄表率，设置治理意见判断规则，生成小区月度异常分析报表，供水务公司管理决策。1、数据获取
获取小区名称、日期、供水量（m³），校准售水（m³）、校准漏损量（m³）、校准漏损率（%）、月均夜间最小流量（m³）、月均KL值、水表总数（个）、抄见表数（个）、未抄表数（个）、抄表率（%）。
2、规则设定
设定结论判断条件，根据校准漏损率（%）、月均KL值、抄表率（%）设置判断条件。
3、结果输出
根据数据挖掘算法及机器学习算法（随机森林、支持向量机、神经网络等），每月执行计算，用当月数据对比设定的判断条件，生成治理意见。

This dataset integrates remote water supply data and revenue meter reading data of residential communities, adjusts the statistical cycles of water supply and water sales, combines calibrated water loss rate, monthly average KL value and meter reading compliance rate, and establishes governance opinion judgment rules to generate monthly abnormal analysis reports for residential communities, so as to support the management decision-making of water supply enterprises.
1. Data Acquisition
Obtain the following data indicators: residential community name, date, water supply volume (m³), calibrated water sales volume (m³), calibrated water loss volume (m³), calibrated water loss rate (%), monthly average nighttime minimum flow rate (m³), monthly average KL value, total number of water meters, number of read meters, number of unread meters, and meter reading compliance rate (%).
2. Rule Setting
Establish conclusion judgment conditions based on the calibrated water loss rate (%), monthly average KL value and meter reading compliance rate (%).
3. Result Output
Execute monthly calculations using data mining algorithms and machine learning algorithms including Random Forest, Support Vector Machine, Neural Network, etc., compare the current month's data with the pre-set judgment conditions, and generate targeted governance opinions.