京津冀地区基于压力监测的用气安全异常预警数据

通过大数据分析燃气用户的表具压力值数据，结合历史压力数据和用气设备信息，分享压力异常规律，计算压力异常风险区间值，对实际使用中的风险及时预警，预防燃气事故。1、获取地区、户号、预警时间、预警时压力、预警时小时用气量、同时期地区最大用气量、同时期地区最小用气量、同时期地区平均用气量、是否安装壁挂炉、是否安装热水器、表具型号、口径等数据项
2、分析用气压力异常的规律、识别用户用气高峰和低峰期关联规律，根据用气量数据获取平均值、峰值、波动率，结合用户用气环境和季节变化给出压力异常发生与用气行为特征的关联规律
3、使用机器学习算法（随机森林、支持向量机、神经网络等）进行分类或回归分析识别用户异常用气行为、计算出用气安全系数、划分风险区间进行精准预警。
4、根据计算的压力异常风险区间值，及时向用户和燃气公司是否发出预警，防止潜在的燃气事故。

This dataset leverages big data analytics on meter pressure data of gas users, integrates historical pressure data and gas appliance information, elucidates the patterns of abnormal pressure events, calculates the risk interval thresholds for pressure anomalies, and delivers timely warnings for on-site risks to prevent gas accidents.
1. Collected data attributes include region, household ID, early warning timestamp, pressure at early warning, hourly gas consumption at early warning, regional maximum gas consumption in the same period, regional minimum gas consumption in the same period, regional average gas consumption in the same period, whether a wall-hung boiler is installed, whether a water heater is installed, meter model, pipe caliber, and other relevant items.
2. Analyze the patterns of abnormal gas consumption pressure, identify the correlation rules between user gas consumption peak and off-peak periods, derive the average value, peak value and volatility from gas consumption data, and establish the correlation rules between pressure anomaly occurrences and gas consumption behavior characteristics by combining user gas consumption environments and seasonal variations.
3. Adopt machine learning algorithms (random forest, support vector machine, neural network, etc.) for classification or regression analysis to identify users' abnormal gas consumption behaviors, calculate gas safety coefficients, and divide risk intervals for precise early warning.
4. Issue timely warnings to users and gas companies based on the calculated risk interval thresholds for pressure anomalies, so as to prevent potential gas accidents.

搜集汇总

数据集介绍

特点

该数据集为京津冀地区燃气用户的压力监测数据，用于用气安全异常预警，包含65533条记录，每日更新，通过机器学习算法实现精准预警。