Coal machine equipment data analysis method and system based on big data platform

By using a multi-sensor network based on a big data platform and a dynamic update mechanism for a structured feature warehouse, the problems of monitoring lag and misjudgment in coal mining equipment data analysis have been solved, enabling real-time and accurate monitoring of equipment status and timely identification of abnormal events, thereby improving equipment maintenance efficiency.

CN122133040APending Publication Date: 2026-06-02BEIJING TIANMA INTELLIGENT CONTROL TECHNOLOGY CO LTD +3
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING TIANMA INTELLIGENT CONTROL TECHNOLOGY CO LTD
Filing Date
2026-05-06
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies lack systematicness and comprehensiveness in coal mining equipment data analysis, resulting in lagging equipment status monitoring, inability to capture dynamic changes in a timely manner, frequent misjudgments or omissions in anomaly detection, and difficulty in accurately identifying equipment faults.

Method used

Based on a big data platform, multi-dimensional operational data is collected in real time through a multi-sensor network to build a structured feature warehouse. Feature parameters are dynamically updated using a sliding time window and exponentially weighted moving average mechanism. A context-aware mechanism is built to automatically identify abnormal thresholds, and secondary verification and correlation analysis are performed in conjunction with static features.

Benefits of technology

It enables real-time and accurate monitoring of the status of coal mining equipment, timely identification of abnormal events, generation of structured reports, and improves equipment maintenance efficiency and stability.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention provides a data analysis method and system for coal mining equipment based on a big data platform, belonging to the field of coal mining equipment data analysis technology. In this method, multi-sensor networks are used to collect multi-dimensional operational data in real time, which is then standardized and processed by an industrial cloud platform to form a raw data pool. A structured feature warehouse is constructed to comprehensively extract and structure and store static, dynamic, and contextual features, providing multi-dimensional data support for in-depth analysis of equipment status. Mechanisms such as sliding time windows are used to dynamically update feature statistical distribution parameters, reflecting dynamic changes in the equipment in a timely manner. A context-aware mechanism is constructed to automatically identify dynamic feature anomaly judgment thresholds, considering differences in different operating conditions to accurately judge anomalies. Combined with secondary verification of static features and correlation analysis of abnormal features, the system comprehensively and reliably identifies equipment operating status and abnormal events, facilitating rapid development of maintenance strategies and improving maintenance efficiency.
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