煤矿安全风险预警是煤炭开采过程中实现安全保障的关键一环，在记录矿井下安全隐患数据时，采集相关的数据容易受到外界环境因素（如光线、温度、湿度等）和人为因素的影响，导致划分安全风险等级出现误差，从而使得安全隐患得不到有效处理。为解决煤矿等相关企业存在的安全风险等级划分不精确的问题，研究了一种基于改进基于粒子群算法（PSO）和卷积神经网络（CNN）的煤矿安全风险智能分级管控与信息预警系统。该系统采用基于改进PSO的智能数据筛选模型，利用PSO算法查找全局最优解的特性，筛选掉不合理的数据，减少了人工计算数据不准确或采集矿区信息过程中出现纰漏的问题；采用基于改进的CNN的智能风险分级模型，通过对数据特征的采集、融合处理，实现了高精确划分安全风险等级。实验室训练与应用结果表明:该系统对安全风险数值的查全率为85.6%，查准率为91.7%，较其他系统查全率提升了4.2%，查准率提升了2.8%，大幅提升了安全风险等级划分的精确度，对出现的安全隐患及时预警效果显著。此方法有效减少了安全隐患发生的频率。

Coal mine safety risk pre-warning is a key part of the safety guarantee in coal mining process. When recording hidden safety hazard data in underground mines,collecting relevant data is susceptible to external environmental factors(such as light,temperature,humidity,etc.) and human factors.This leads to errors in the classification of safety risk levels,which makes hidden safety hazards unable to be effectively dealt with. In order to solve problem of inaccurate classification of safety risk in coal mines and other related companies,a coal mine safety risk intelligent hierarchical control and information pre-warning system based on improved particle swarm optimization(PSO) and convolutional Neural Networks(CNN) was designed. The system adopts an intelligent data screening model based on improved PSO,and uses the feature of PSO algorithm to find the global optimal solution to filter out unreasonable data,and reduces problems of inaccurate manual calculation of data or inaccuracies in the process of collecting mining area information. The improved CNN’s intelligent risk classification model realizes high-precision classification of security risk levels through collection and fusion of data features.The experiment and application results show that system has a recall rate of 85.6% anda precision rate of 91.7%. Compared with other systems,the recall rate has increased by 4.2%,and the precision rate has increased by 2.8%,which greatly improved accuracy of security risk classification,and the timely warning effect on occurrence of security risks is significant. This method effectively reduces frequency of potential safety hazards.

0 引言

1 智能分级管控与信息预警系统设计

   1.1 数据采集层

   1.2 智能模型层

   1.3 数据处理层

   1.4 风险预警层

2 智能数据筛选模型构建

   2.1 模型结构

   2.2 PSO算法计算步骤

   2.3 算法仿真与分析

3 基于改进CNN的智能风险分级模型构建

   3.1 模型结构

   3.2 改进的CNN算法步骤

   3.3 算法仿真与分析

4 实验室分析与应用

5 结论