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Article(id=1322158344299724922, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322158299525530318, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1724688000000, receivedDateStr=2024-08-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793156713795, onlineDateStr=2026-10-28, pubDate=1753372800000, pubDateStr=2025-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793156713795, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793156713795, creator=13701087609, updateTime=1793156713795, updator=13701087609, issue=Issue{id=1322158299525530318, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='7', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1753372800000', pubDateStr='2025-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793156703120, creator='13701087609', updateTime=1793158832445, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322167230608028625, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322158299525530318, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322167230608028626, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322158299525530318, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=161, endPage=168, ext={EN=ArticleExt(id=1322158344576548987, articleId=1322158344299724922, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Early Warning Method for Dynamic Roof Hazards in Mines Based on Multi-Point Displacement Data and Dynamically Weighted ARIMA Prediction, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Monitoring and early warning of dynamic roof hazards in mines is crucial for mining production and personnel safety. Traditional multi-point displacement monitoring and early warning methods rely on a single absolute threshold based on monitoring data, resulting in poor sensitivity and reliability of the warning. In response to this issue, a new early warning method for dynamic roof hazards in mines was proposed based on multi-point displacement data and the Autoregressive Integrated Moving Average (ARIMA) model. In this method, superposition techniques were employed to enhance the signal-to-noise ratio of multi-point displacement time series data, and a graded early warning method combining enhanced windowed waveform characteristic function ratio and kurtosis was introduced, which addressed the inadequacies of traditional threshold warnings. Furthermore, a dynamic weighted parameter ARIMA model was constructed to predict displacement data, enabling the integration of monitoring and prediction data for early warning of dynamic roof hazards in mines. The theoretical testing of this method shows a false alarm rate of 13.3%, with an average error of 15.4% for secondary or higher warning characteristic values. The test results of engineering actual data of kurtosis over two periods show that the warning false alarm rates are 11.8% and 6.7%, respectively, and the average errors of the secondary or higher warning characteristic values are 15.1% and 14.4%, respectively. In summary, the proposed early warning method for dynamic roof hazards in mine has a high application value.

, authors=Kaiwen SI1, Hongjie LIU2, Dezhi ZHU1, authorsList=Kaiwen SI, Hongjie LIU, Dezhi ZHU, authorCompany=null, correspAuthors=Hongjie LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322158348011683992, articleId=1322158344299724922, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于多点位移数据和动态加权ARIMA预警矿山顶板动力灾害的研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

矿山顶板动力灾害监测预警事关矿山生产和人员安全。传统多点位移监测预警方法基于监测数据的单一绝对阈值预警，灵敏度与可靠性较差。因此，提出了一种基于多点位移数据和差分自回归移动平均(ARIMA)模型预测矿山顶板动力灾害预警方法。该方法使用叠加技术增强了多点位移时序数据的信噪比，提出了联合增强型加窗波形特征函数比值和峰度值的分级预警方法，改善了传统阈值预警适应性不足的难题；构建了基于动态加权参数的ARIMA模型以预测位移数据，联合监测数据与预测数据来实现矿山顶板动力灾害的预警。该方法的理论测试预警误报率为13.3%，二级及以上预警特征值平均误差为15.4%；两个时间段的工程实际数据测试结果表明，预警误报率分别为11.8%和6.7%，二级及以上预警特征值平均误差分别为15.1%和14.4%。提出的矿山顶板动力灾害预警方法具有较高的应用价值。

, authors=司凯文1, 刘宏杰2, 朱德智1, authorsList=司凯文, 刘宏杰, 朱德智, authorCompany=null, correspAuthors=刘宏杰, authorNote=

司凯文（1989—），男，山西晋城人，硕士研究生，主要从事矿山智能化及机电一体化方面的相关工作。E-mail:

, correspAuthorsNote=
刘宏杰（1980—），男，河北遵化人，博士，正高级工程师，主要从事矿山信息化及智能化方面的相关工作。E-mail:
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司凯文（1989—），男，山西晋城人，硕士研究生，主要从事矿山智能化及机电一体化方面的相关工作。E-mail:

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司凯文（1989—），男，山西晋城人，硕士研究生，主要从事矿山智能化及机电一体化方面的相关工作。E-mail:

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Early warning rules

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峰度值STA/LTA
R<R1R1≤R<R2R≥R2
Ku< Ku1绿灯黄灯红灯
Ku1≤Ku< Ku2黄灯红灯红灯
Ku≥Ku2红灯红灯红灯
), ArticleFig(id=1322158352075964617, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322158344299724922, language=CN, label=表1, caption=

预警规则

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峰度值STA/LTA
R<R1R1≤R<R2R≥R2
Ku< Ku1绿灯黄灯红灯
Ku1≤Ku< Ku2黄灯红灯红灯
Ku≥Ku2红灯红灯红灯
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基于多点位移数据和动态加权ARIMA预警矿山顶板动力灾害的研究
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司凯文 1 , 刘宏杰 2 , 朱德智 1
矿业研究与开发 | 矿山安全与环保 2025,45(7): 161-168
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矿业研究与开发 |矿山安全与环保 2025 , 45 (7) : 161 -168
基于多点位移数据和动态加权ARIMA预警矿山顶板动力灾害的研究
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司凯文（1989—），男，山西晋城人，硕士研究生，主要从事矿山智能化及机电一体化方面的相关工作。E-mail:

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司凯文（1989—），男，山西晋城人，硕士研究生，主要从事矿山智能化及机电一体化方面的相关工作。E-mail:

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司凯文1 , 刘宏杰2 , 朱德智1
作者信息
  • 1.晋能控股装备制造集团有限公司，山西　晋城市　048006
  • 2.重庆大学 资源与安全学院，重庆　400044
通讯作者:
刘宏杰（1980—），男，河北遵化人，博士，正高级工程师，主要从事矿山信息化及智能化方面的相关工作。E-mail:
作者简介:

司凯文（1989—），男，山西晋城人，硕士研究生，主要从事矿山智能化及机电一体化方面的相关工作。E-mail:

Early Warning Method for Dynamic Roof Hazards in Mines Based on Multi-Point Displacement Data and Dynamically Weighted ARIMA Prediction
Kaiwen SI1 , Hongjie LIU2 , Dezhi ZHU1
Affiliations
  • 1.Jinneng Holding Equipment Manufacturing Group Co., Ltd., Jincheng, Shanxi 048006, China
  • 2.School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China
出版时间: 2025-07-25
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摘要
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矿山顶板动力灾害监测预警事关矿山生产和人员安全。传统多点位移监测预警方法基于监测数据的单一绝对阈值预警，灵敏度与可靠性较差。因此，提出了一种基于多点位移数据和差分自回归移动平均(ARIMA)模型预测矿山顶板动力灾害预警方法。该方法使用叠加技术增强了多点位移时序数据的信噪比，提出了联合增强型加窗波形特征函数比值和峰度值的分级预警方法，改善了传统阈值预警适应性不足的难题；构建了基于动态加权参数的ARIMA模型以预测位移数据，联合监测数据与预测数据来实现矿山顶板动力灾害的预警。该方法的理论测试预警误报率为13.3%，二级及以上预警特征值平均误差为15.4%；两个时间段的工程实际数据测试结果表明，预警误报率分别为11.8%和6.7%，二级及以上预警特征值平均误差分别为15.1%和14.4%。提出的矿山顶板动力灾害预警方法具有较高的应用价值。

顶板动力灾害  /  灾害预警  /  多点位移  /  ARIMA预测  /  位移监测

Monitoring and early warning of dynamic roof hazards in mines is crucial for mining production and personnel safety. Traditional multi-point displacement monitoring and early warning methods rely on a single absolute threshold based on monitoring data, resulting in poor sensitivity and reliability of the warning. In response to this issue, a new early warning method for dynamic roof hazards in mines was proposed based on multi-point displacement data and the Autoregressive Integrated Moving Average (ARIMA) model. In this method, superposition techniques were employed to enhance the signal-to-noise ratio of multi-point displacement time series data, and a graded early warning method combining enhanced windowed waveform characteristic function ratio and kurtosis was introduced, which addressed the inadequacies of traditional threshold warnings. Furthermore, a dynamic weighted parameter ARIMA model was constructed to predict displacement data, enabling the integration of monitoring and prediction data for early warning of dynamic roof hazards in mines. The theoretical testing of this method shows a false alarm rate of 13.3%, with an average error of 15.4% for secondary or higher warning characteristic values. The test results of engineering actual data of kurtosis over two periods show that the warning false alarm rates are 11.8% and 6.7%, respectively, and the average errors of the secondary or higher warning characteristic values are 15.1% and 14.4%, respectively. In summary, the proposed early warning method for dynamic roof hazards in mine has a high application value.

Dynamic roof hazard  /  Early warning of disaster  /  Multi-point displacement  /  ARIMA prediction  /  Displacement monitoring
司凯文, 刘宏杰, 朱德智. 基于多点位移数据和动态加权ARIMA预警矿山顶板动力灾害的研究. 矿业研究与开发, 2025 , 45 (7) : 161 -168 .
Kaiwen SI, Hongjie LIU, Dezhi ZHU. Early Warning Method for Dynamic Roof Hazards in Mines Based on Multi-Point Displacement Data and Dynamically Weighted ARIMA Prediction[J]. Mining Research and Development, 2025 , 45 (7) : 161 -168 .
正文
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0　引言
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矿山开采时，岩石顶板会因应力作用而产生变形，岩体应力超过其极限荷载时会出现局部冒顶，甚至大面积塌陷，给矿山安全开采造成了极大安全威胁[1-3]。为此，许多矿山使用了锚网、锚索+金属网等进行支护，但受破碎岩体、不可见小断层等影响，片帮、顶板垮落等动力灾害仍时有发生。目前在矿山生产中对岩石动力灾害仍缺乏有效的监测及预测手段[4-5]。常用的位移监测因具有监测简便、解读容易等特点而被广泛应用[6-8]。
传统的方法是基于位移监测数据大于位移阈值或加速度阈值来预警[9-11]，然而不同工程预警阈值通常是不同的，导致该类方法的工程应用受到诸多限制。为降低上述限制，研究者们开展了大量研究。王东等[12]认为岩体稳定时期位移服从正态分布，而加速变形阶段则不服从正态分布，由此提出了基于正态假设检验理论的边坡临滑时刻动态识别方法，使用假设检验P值降低了传统阈值的影响。王东等[13]还提出了一种基于表面与深部位移联合的滑面辨识和滑坡预警方法，基于深部位移超前于表面位移的假设实现分级预警。但该方法对岩体稳定时期位移时间序列的正态性要求较高，容易出现误预警。
一些研究者尝试使用预测数据来开展预警。例如，曹兰柱等[14]、王东等[15]分别提出了基于加权和层次聚类的马尔可夫链滑坡预警模型，这类方法试图从概率角度描述边坡演变过程。然而马尔可夫链模型预测仅考虑了最后时刻前的几个数据影响，忽略了海量历史数据的作用。李小贝等[16]将混沌学理论与广义神经网络相结合进行顶板位移预测，预测结果较为准确，但尚未建立起合理的预警模型。位移与其他数据联合的预警方法也相继被提出，殷大发[17]、刘杰等[18]联合巷道应力与离层位移数据进行预警，但仍使用传统的预警阈值，适用性有限。杜科科等[19]建立了巷道顶板离层失稳综合预警体系，但未给出离层位移数据的预测方法。
针对传统位移数据绝对阈值预警适应性有限、单一特征预警灵敏度与可靠性较差的问题，本文提出了一种基于多点位移数据和动态加权差分自回归移动平均（ARIMA)模型预测矿山顶板动力灾害预警的方法。该方法能够实现矿山顶板动力灾害的早期预警和超前预警，可作为矿山顶板动力灾害预警的一种重要方法，潜在应用价值广泛。
1　预警方法的提出
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本文预警方法首先对采集的多点位移监测数据进行叠加处理，再建立基于历史监测数据匹配和动态加权的ARIMA模型，实现叠加位移数据的准确预测。随后，计算监测数据和预测数据的增强型加窗波形特征函数比值和峰度值，最后进行矿山顶板动力灾害分级预警。
1.1　动态加权ARIMA模型
ARIMA模型是一种时间序列分析模型，其实质是差分运算与自回归移动平均（ARMA）模型的组合[20]，本文的位移时序数据预测主要包含三个部分：自回归（AR）、差分(I)和移动平均（MA）。自回归部分用来捕捉顶板位移时间序列数据的自相关性，即序列中相邻监测值之间的相关性。差分操作用于消除顶板位移时序数据的非平稳性，使其更容易建立模型和进行预测。移动平均部分则用于捕捉位移时序数据的噪声和随机波动。
不同于传统方法中的固定参数ARIMA模型，本文提出了动态加权参数的ARIMA模型，其基本原理是：利用ACF指标和PAC指标确定初步的自回归项数p1和滑动平均项数q1，进而确定自回归项数p和滑动平均项数q的取值范围：p∈[p1－ 3,p1+ 3],q∈[q1－3,q1+ 3]，且p、q≥0；在自回归项数p和滑动平均项数q的取值范围内，对p和q依次取值构成K个ARIMA模型，再分别计算每个ARIMA模型对顶板位移数据的预测效果，预测效果实质上是ARIMA模型预测出的顶板位移与监测到的顶板位移的累计误差，计算公式为：
式中：L为顶板位移时序数据的长度；hk为预测出的顶板位移，mm;x(i)为监测到的顶板位移，mm；εk为第k个模型预测出顶板位移的累计误差，mm。
在计算出顶板位移数据累计误差之后，基于此误差确定每个ARIMA模型的权重wk，计算公式为：
式中，c为预设的系数。
最后，对K个ARIMA模型加权得到最终的顶板位移预测表达，计算公式为：
式中:h(x(i))为顶板位移预测值，mm;h1(x(i))、h2(x(i))、hk(x(i))为第1个、第2个和第k个ARIMA模型的顶板位移预测值，mm;w1、w2、wk为第1个、第2个和第k个ARIMA模型所占的权重。
1.2　矿山顶板动力灾害分级方法
传统方法采用位移绝对值来确定是否预警，主观性强、适应性较差。为此，本文提出增强型加窗波形特征函数比值和峰度值作为预警特征参数，进而构建矿山顶板动力灾害分级预警方法。
1.2.1　增强型加窗波形特征函数比值
加窗波形特征函数比值（STA/LTA）充分利用了动力灾害前后位移时间序列变化的特性，同时使用差值的方式增强该特征，STA/LTA的计算公式为：
式中：R(i)为增强型加窗波形特征函数比值；x(i)为第i个点的位移时序数据，mm;n、m为设定的加窗1、加窗2的时间序列长度，m>n。
1.2.2　基于统计特征的峰度值
峰度值能表征概率密度分布曲线在平均值处峰值的高低，峰度值的计算公式为：
式中：Ku为峰度值；M为滑动时窗长度;为滑动时窗内振幅的平均值；σx为滑动时窗内振幅的标准差。
1.2.3　分级预警方法
在计算出上述特征值后，设置增强型加窗波形特征函数比值的预警阈值R1、R2和峰度值的预警阈值Ku1、Ku2，依据表1中的规则实现矿山顶板动力灾害的分级预警。具体地：
若R<R1且Ku<Ku1，视为安全，显示绿灯；
若R1≤R<R2, 且Ku<Ku1, 或者，若R<R1, 且Ku1≤Ku<Ku2, 均视为二级警示，显示黄灯;
其他情况均视为一级警示，显示红灯。
预警模型发出二级警示表明岩体位移加速，需采取围岩支护、应力转移等预防措施；一级警示表明矿山顶板稳定性很差，人员须立即撤出，并通知相关领导。
1.3　矿山顶板动力灾害预警模型
综合上述，基于多点位移监测数据、动态加权ARIMA预测数据和分级预警方法，构建基于多点位移数据和动态加权ARIMA的矿山顶板动力灾害预警模型，其实现的主要步骤如下。
步骤1：在矿山待监测顶板区域布设多点位移监测系统进行数据采集，用以获取不同深度的位移时序数据。
步骤2：对多点位移时序数据进行移动平均降噪和叠加处理，得到叠加位移时序数据。
步骤3：建立基于历史监测数据匹配的ARIMA模型，进而基于监测的叠加位移时序数据和改进的ARIMA模型，预测未来时刻的叠加位移时序数据。
步骤4：计算监测的叠加位移时序数据和预测的叠加位移时序数据的增强型加窗波形特征函数比值和峰度值。
步骤5：设置合理的预警阈值，利用提出的分级预警方法实现矿山顶板动力灾害分级预警。
在本文提出的方法中，使用叠加技术增强了多点位移时序数据的信噪比，提出了联合增强型加窗波形特征函数比值和峰度值的分级预警方法，改善了传统绝对阈值预警适应性不足的难题；构建了基于动态加权参数的ARIMA模型以预测位移数据，将监测数据与预测数据联动实现矿山顶板动力灾害早期预警和超前预警。
2　理论测试
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为验证本文提出方法的有效性和可行性，本节使用理论生成的数据对本方法进行理论测试。理论测试共生成包含59个采样点的多点位移时序数据（见图1），每个采样点的数据由三个传感器的数据组成。当顶板处于稳定时期，各传感器位移数据近似服从高斯分布；当顶板处于不稳定时期，传感器位移数据开始增加。对理论数据进行叠加和移动平均滤波处理得到叠加位移时序数据。移动平均滤波所使用的窗口长度为4个采样点，用以提高位移数据的信噪比，增强特征值的区分度。使用基于多点位移数据和动态加权ARIMA预测的矿山顶板动力灾害预警方法，从第44个采样点开始预测，计算特征值并进行预警，将预警误报率和特征误差值作为评判的定量标准。预警误报率为不准确预警次数与预警总次数之比，特征误差值是做出二级以上警示时预测数值特征值与原始数据特征值相差的百分比。
在对原始数据进行预处理之后，建立改进的ARIMA模型对未来时刻的叠加位移时序数据进行预测。首先，通过ACF和PAC指标来确定模型参数的初始值，进而确定自回归项数p和滑动平均项数q的取值范围，均为0~5；使用网格搜索法在取值范围内遍历各取值构建各类ARIMA模型，基于各模型的误差计算出权重，得到最终的顶板位移预测模型用于预测。理论数据遍历的36个不同参数ARIMA模型累计误差见图2，权重占比见图3。由图2可知，累计误差最小的参数为p=4, q=3，累计误差为13.84 mm，即每个传感器平均误差为0.078 mm。
在确定了各参数的权重后，建立预测模型对未来数据进行预测。叠加原始数据与叠加预测数据的对比如图4所示。由图4可知，加权动态ARIMA模型的趋势预测结果较好，能将多点位移数据变化趋势较为准确地预测出来，数据波动特征较好，为矿山顶板动力灾害超前预警提供了良好的数据基础。此外，与图1中单个传感器数据相比，叠加数据的信噪比优于多点位移计各点的原始位移数据，表明数据叠加对数据特征有增强作用。
在预测出未来时刻的叠加位移数据后，联合当前叠加位移时序数据以及未来时刻叠加位移时序数据计算两类特征值，此处增强型加窗波形特征函数比值计算使用的短时窗为3个采样点，长时窗为20个采样点，峰度值时窗为30个采样点。两类特征值时间序列如图5所示，预测数据特征值的趋势与原始数据特征值较接近，可以用于预警。
联合当前数据以及预测数据计算出特征值之后，可基于此进行矿山顶板动力灾害分级预警。针对增强型加窗波形特征函数比值（STA/LTA）设置两个预警经验阈值R1(2.5)、R2(3.5)，针对峰度值设置两个预警阈值Ku1(6)和Ku2(10)，基于两个特征值按照预警规则进行预警。在理论测试中成功为采样点53, 54, 55, 57, 58, 59做出了准确的二级及以上警示，预警误报率为13.3%，预警准确性较高。做出二级以上警示时的各特征值平均误差为15.4%。
3　工程应用
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以某矿山的两组多点位移监测数据为例，分别命名为数据1、数据2，具体如图6所示。图6中监测数据的波动较大，有明显的峰值，三个传感器的数据波动趋势相似。对监测数据进行叠加和移动平均滤波处理以降低位移数据的振荡，增强位移时序数据的信噪比，移动平均滤波窗口长度为4个采样点。从图6中可见滤波数据在信噪比上明显优于原始数据。
对原始数据进行预处理之后，建立加权动态ARIMA模型对未来时刻的位移数据进行预测。使用本文提出的方法对数据1从第30个采样点开始往后预测到第46个采样点；对于数据2，从第30个采样点开始往后预测到第44个采样点。两组数据的原始叠加数据与预测叠加数据的对比见图7，由图7可知，预测数据与原始数据的各类特征趋势符合度较好，预测结果较为准确，可以为矿山顶板动力灾害超前预警提供数据基础。
通过预测模型得到预测数据之后，计算增强型加窗波形特征函数比值和峰度值两类特征值，具体见图8。在进行矿山顶板动力灾害分级预警时，使用与理论测试中相同的预警阈值。
对于数据1，按照预警模型预警规则，根据监测数据应当对采样点42, 43进行一级警示，对采样点44, 45, 46进行二级警示，其余为三级警示；而预测数据对采样点43, 44进行了一级警示，对采样点42, 45, 46进行了二级警示，其余为三级警示，预测结果与实际应有结果较符合，预警准确性较高，预警误报率为11.8%，进行二级及以上预警时各特征值平均误差为15.1%。
对于数据2，按照预警模型预警规则，根据监测数据应当对采样点40, 41, 42, 43, 44进行一级警示，其余为三级警示；而预测数据对采样点41, 42, 43, 44进行了一级警示，其余为三级警示，预警误报率为6.7%，进行二级及以上预警时各特征值平均误差为14.4%。
4　结论
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本文提出了一种基于多点位移数据和动态加权ARIMA的矿山顶板动力灾害预警方法，并得出如下主要结论。
（1）使用叠加技术增强了多点位移时序数据的信噪比，提出了联合增强型加窗波形特征函数比值和峰度值的分级预警方法，能更好地捕获数据的特征变化，改善了传统绝对阈值预警适应性不足的难题。
（2）构建了基于动态加权参数的ARIMA模型以预测矿山顶板岩体位移数据，提高了ARIMA模型参数与当前工况的吻合度，联合监测数据与预测数据来实现矿山顶板动力灾害的预警。
（3）使用本文预警方法对理论数据和工程数据进行了测试，并获得了较好的预警效果。理论测试中预警误报率为13.3%，进行二级及以上预警时各特征值的平均误差为15.4%；工程应用中，对于两组现场数据，预警误报率分别为11.8%、6.7%，预警准确性较高，进行二级及以上预警时各特征值的平均误差分别为15.1%、14.4%，预测结果与期望结果符合良好。

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  • 接收时间：2024-08-27
  • 首发时间：2026-10-28
  • 出版时间：2025-07-25
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  • 收稿日期：2024-08-27
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    1.晋能控股装备制造集团有限公司，山西　晋城市　048006
    2.重庆大学 资源与安全学院，重庆　400044

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刘宏杰（1980—），男，河北遵化人，博士，正高级工程师，主要从事矿山信息化及智能化方面的相关工作。E-mail:
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