Multi-disaster fusion natural fission early warning method and system in coal mine

A coal mine multi-disaster fusion natural fission early warning system, wherein the system comprises a processor and a memory, and the processor is configured to execute the following modules:

• a monitoring and early warning data acquisition and storage module used to collect sensor monitoring data and prediction and early warning index data of a coal mine safety monitoring system and various disaster prediction and early warning systems at a specified frequency and store the data in a fusion early warning database;
• a relationship network constructing and updating module used to collect basic information of each sensor monitoring object and each prediction and early warning index object in a coal mine to generate a relationship entity list, then calculate a relationship weight between sensor monitoring objects and between a sensor monitoring object and a prediction and early warning index object by periodically editing the first relationships based on formation mechanisms and production processes, the second relationships based on spatial topology, and the third relationships based on correlation coefficients, and establish or update a relationship network of coal mine multi-disaster fusion early warning objects;
• a fission model training and updating module used to regularly train LSTM parameters in each sensor monitoring data trend prediction model and select appropriate parameters by comparing prediction precisions of two prediction models;
• a natural fission analyzing and pushing module used to traverse the object relationship network using a depth-first traversal algorithm when an early anomaly feature is detected in the prediction and early warning index,
• response to the sensor monitoring objects identified during the traversal, the natural fission analyzing and pushing model invokes the LSTM model to calculate the trend prediction index S;
• based on natural fission rules, thereby determining whether to continue traversing to identify abnormally correlated prediction and early warning indexes located at network endpoints, and highlighting the result data on the user interface before pushing back to the respective prediction and early warning systems;
• wherein the monitoring and early warning data acquisition and storage module is used for external data access to form an object data set;
• the relationship network constructing and updating module generates relationship entity object list data and weight data of the relationship between objects based on the object data set, and finally generates an object relationship network;
• meanwhile, the fission model training and updating module trains the LSTM model based on the object data set;
• and after the monitoring and early warning data acquisition and storage module recognizes an early anomaly feature of a prediction and early warning index, the natural fission analyzing and pushing module uses the depth-first algorithm to traverse the object relationship network, finds a prediction and early warning index for anomaly association based on the natural fission rules, and pushes result data.