煤矿安全2026，Vol.57Issue(6)：25-35,11.DOI:10.13347/j.cnki.mkaq.20260303
基于深度神经网络的煤矿数据中台元数据特征提取与清洗策略研究
Research on metadata feature extraction and cleaning strategy of coal mine data middle platform based on deep neural network
摘要
Abstract
To address the issues of significant quality differences in multi-source heterogeneous data,coarse metadata management,manual dependency and inability to dynamically evolve cleaning rules,and mismatch between cold and hot data storage and busi-ness characteristics in coal mine data middle platforms,a self-evolving metadata feature extraction and cleaning strategy based on deep neural networks is proposed:①embed an intelligent cleaning and quality feedback layer in the traditional data platform to build a six-in-one full-link governance architecture of"collection-cleaning-integration-storage-analysis-service";②design a deep learning model integrating convolutional neural networks,bidirectional long short-term memory networks,and attention mechan-isms(CNN-BiLSTM-Attention)to extract deep semantic features from field names and statistical features,and automatically gener-ate candidate sets of field-level cleaning rules;③establish a four-level quality label system and use Q-learning to dynamically adjust the weights of cleaning rules and achieve self-evolution of strategies;④build a cold and hot data stratified storage model based on data temperature perception,storing hot,warm,and cold data in Redis,SQL Server,and HBase respectively;⑤propose a five-di-mensional data quality index(DQI)and an automatic quality report generation framework to form a"cleaning-evaluation-feed-back-optimization"closed loop.Application in a coal mine with an annual output of over 10 million tons shows that the F1 value of the deep learning model reaches 91.7％,a 24.3 percentage points increase compared to regular matching;the rule iteration cycle is re-duced from 7 days to 4 hours,and the average repair time for rule failure is decreased from 4.2 hours to 0.5 hours;the data quality pass rate increases from 72.6％to 94.3％,and the DQI rises from 64.7 to 91.2;the average query delay is reduced from 3.8 seconds to 1.2 seconds,and storage costs are cut by 47％.This method realizes a fundamental transformation in coal mine data governance from manual experience-driven to deep neural network-driven and from static rules to self-evolving intelligent cleaning.关键词
煤矿智能化/煤矿数据中台/元数据特征提取/CNN-BiLSTM-Attention/清洗策略自演化/冷热分层存储/数据质量指数Key words
coal mine intellectualization/coal mine data middle platform/metadata feature extraction/CNN-BiLSTM-Attention/self-evolution of cleaning strategies/cold and hot stratified storage/data quality index(DQI)分类
矿业与冶金引用本文复制引用
李翔,芦胜金,阳升,张正清..基于深度神经网络的煤矿数据中台元数据特征提取与清洗策略研究[J].煤矿安全,2026,57(6):25-35,11.基金项目
国家科技重大专项资助项目(2025ZD1700805) （2025ZD1700805）
天地科技股份有限公司科技创新创业资金专项资助项目(2024-TDZD013-05) （2024-TDZD013-05）
