青岛市燃气压力检测及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 conducts big data analysis on the meter pressure data of gas users, integrates historical pressure data and gas appliance information, uncovers the regularity of abnormal pressure, calculates the risk interval values of pressure anomalies, issues timely warnings for on-site risks, and prevents gas accidents. 1. Collected data items include: region, household ID, warning time, pressure at the time of warning, hourly gas consumption during warning, regional concurrent maximum gas consumption, regional concurrent minimum gas consumption, regional concurrent average gas consumption, whether a wall-mounted gas boiler is installed, whether a water heater is installed, meter model, meter caliber, etc. 2. Analyze the regularity of gas pressure anomalies, identify the correlation rules between users' gas consumption peak and off-peak periods; derive the average value, peak value and volatility from gas consumption data, and combine user gas consumption environment and seasonal variations to establish the correlation between pressure anomaly occurrences and gas consumption behavior characteristics. 3. Adopt machine learning algorithms including Random Forest, Support Vector Machine (SVM), Neural Network, etc., to conduct classification or regression analysis for identifying abnormal gas consumption behaviors of users, calculate gas safety coefficients, and divide risk intervals to implement accurate early warnings. 4. Issue timely warnings to users and gas companies based on the calculated risk interval values of pressure anomalies, so as to prevent potential gas accidents.