燃气管网终端设备监测分析数据集合

核心采用“状态监测-趋势分析-智能决策”三阶联动算法架构，细节如下：①状态监测算法：采用滑动窗口中位数算法（1分钟窗口含60个数据点），过滤瞬时干扰数据，精准提取终端设备有效运行参数与燃气浓度值，避免单点异常导致误判；②趋势分析算法：以24小时为周期，构建燃气用量-时间、设备故障率-使用时长双维度线性回归模型，通过斜率变化识别异常趋势（如非高峰时段用量突增、设备运行18个月后故障率陡升），提前触发预警；③智能决策算法：结合GB50028《城镇燃气设计规范》及200+终端用户实测数据，设置场景化动态阈值（如居民厨房：燃气浓度≥15%LEL触发一级告警，≥30%LEL触发二级告警并关闭阀门；商业厨房：浓度≥10%LEL触发一级告警，≥25%LEL触发二级告警）。算法经80万条终端监测数据训练，经第三方检测机构验证：故障识别准确率≥99.1%、漏报率≤0.9%、告警响应时间≤0.8秒，每月基于8万+条新增数据增量训练，适配终端场景动态变化。

The core algorithm adopts a three-stage linked architecture consisting of status monitoring, trend analysis, and intelligent decision-making. The details are as follows:
1. Status monitoring algorithm: The sliding window median algorithm (1-minute window containing 60 data points) is adopted to filter out transient interference data, accurately extract the effective operating parameters of terminal equipment and gas concentration values, and avoid misjudgments caused by single-point anomalies.
2. Trend analysis algorithm: A two-dimensional linear regression model of gas consumption vs. time and equipment failure rate vs. service duration is established on a 24-hour cycle. Abnormal trends are identified via slope changes (such as sudden surge in gas consumption during non-peak hours, sharp increase in equipment failure rate after 18 months of operation), triggering early warnings in advance.
3. Intelligent decision-making algorithm: Combining GB 50028 Code for Design of Urban Gas Engineering and measured data from over 200 terminal users, scenario-based dynamic thresholds are configured. For example: For residential kitchens, a level 1 alarm is triggered when gas concentration reaches ≥15% LEL, and a level 2 alarm is triggered along with valve closure when concentration reaches ≥30% LEL; For commercial kitchens, a level 1 alarm is triggered when concentration reaches ≥10% LEL, and a level 2 alarm is triggered when concentration reaches ≥25% LEL.
The algorithm is trained with 800,000 pieces of terminal monitoring data and verified by third-party testing institutions, with fault recognition accuracy ≥99.1%, false negative rate ≤0.9%, and alarm response time ≤0.8 seconds. Monthly incremental training is conducted based on over 80,000 newly added data to adapt to the dynamic changes of terminal scenarios.

搜集汇总

背景与挑战

背景概述

该数据集是燃气管网终端设备的监测分析数据集合，由贵州芯时代智能科技有限公司自行产生，数据规模1G，每日更新。它主要用于安全预警、运维优化和用量分析等场景，采用三阶联动算法实现高精度监测和快速响应，适用于城镇燃气管网运营企业。