燃气终端报警自适应阈值数据

大规模提取和构建有助于预测用气趋势的用户画像数据特征，训练LSTM时序模型，对用气趋势进行预测；结合用气趋势和已知的安全标准，自主对当前的安全阈值是否适宜进行诊断，标记安全风险；基于自学习建立补偿机制，动态调整用气量预测，依据安全风险，自动调整安全报警阈值，提高对异常情况的敏感度或减少终端误报。

Conduct large-scale extraction and construction of user profile data features to support gas consumption trend prediction. Train LSTM time-series models to forecast gas consumption trends. Integrate the forecasted gas consumption trends with established safety standards to autonomously diagnose whether current safety thresholds are appropriate and mark safety risks. Build a self-learning-based compensation mechanism to dynamically adjust gas consumption predictions, and automatically adjust safety alarm thresholds according to identified safety risks, so as to improve sensitivity to abnormal situations or reduce terminal false alarms.

搜集汇总

背景与挑战

背景概述

该数据集专注于燃气终端安全监控，利用LSTM时序模型预测用气趋势并动态调整报警阈值，旨在提高异常检测的准确性和减少误报。数据集适用于政府/公共、AI和科创领域。