Mine Disaster Tracing With Knowledge Graph Root-Cause Analysis
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Solution Overview
Problem
Existing mine disaster prediction methods fail to comprehensively identify both direct and root causes of disasters, limiting effective prevention and control measures.
Innovation Solution
A mine disaster tracing method based on a knowledge graph that constructs a conceptual model using expert knowledge, collects entity objects from a relational database, and applies graph traversal algorithms to identify direct and root causes through characteristic indexes and transmission rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional single-cause prediction methods are used, then the analysis process is simple, but the completeness of disaster cause identification deteriorates
Solution Approach 1:
The patent segments the disaster analysis process into multiple independent modules: knowledge graph construction module, graph traversal module, and cause identification module. Each module handles specific aspects of the analysis, allowing the system to comprehensively identify both direct and root causes while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent transitions from traditional linear single-cause analysis to a multi-dimensional knowledge graph structure that captures complex relationships between different disaster causes. By representing causes, effects, and their relationships in a graphical knowledge base with multiple dimensions (direct causes, root causes, contributing factors), the system achieves comprehensive cause identification without proportionally increasing analytical complexity.
2Measurement precision
If comprehensive disaster cause analysis is performed, then the completeness of cause identification is improved, but the analysis time increases
Solution Approach 1:
The patent performs preliminary action by pre-construction of the knowledge graph containing all possible disaster causes and their relationships before actual disaster analysis. This pre-organized structure enables rapid graph traversal and cause identification during emergency situations, achieving both comprehensive analysis and fast response time.
Solution Approach 2:
The patent replaces traditional mechanical step-by-step analysis methods with automated graph traversal algorithms. The algorithmic approach systematically explores the knowledge graph to identify all relevant causes efficiently, reducing manual analysis time while maintaining comprehensive coverage of direct and root causes.
3Measurement precision
If detailed entity object characteristics are monitored, then the accuracy of disaster tracing is improved, but the data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary layer (the knowledge graph) that processes and organizes detailed entity object characteristics. Instead of directly processing raw monitoring data, the system maps data to structured knowledge graph entities and relationships, simplifying the processing complexity while maintaining high tracing accuracy through the organized knowledge representation.
Data Source
AI summary
The present invention discloses a mine disaster tracing method based on a knowledge graph, applied in coal mine safety. First, a mine disaster-related knowledge graph is built, involving three main steps: constructing a conceptual model, extracting entities from relational databases and geological maps, and establishing entity relationships based on location and process logic. Next, characteristic indexes and transmission rules for entity objects are set, categorizing entities into four types: discrete reporting, continuous monitoring, geological structure, and geological continuity. When an early warning occurs, disaster tracing is performed using the entity transmission rules and a graph traversal algorithm, identifying direct and root causes. This invention offers systematic and timely disaster tracing by utilizing a specialized knowledge graph and graph algorithms.