Coal mine major disaster risk prevention and control system and method based on artificial intelligence

Abstract

The invention relates to the technical field of coal mine safety, in particular to a coal mine major disaster risk prevention and control system and method based on artificial intelligence, and the system comprises a data collection module, a data transmission module, a data processing module, a disaster prediction module, a decision support module and a user interaction module. The data transmission module transmits collected environment data to a ground control center, the data processing module ensures the accuracy and consistency of data through data preprocessing, data fusion and anomaly detection, the disaster prediction module predicts potential disasters and generates a risk assessment report, and the decision support module determines the risk assessment report according to the prediction result. According to the system, multiple intelligent technologies are integrated, comprehensive prevention and control of major disasters of the coal mine are achieved, and the safety management level of the coal mine is improved.

Description

Technical Field

[0001] The present invention relates to the field of coal mine safety technology, and in particular to an artificial intelligence-based coal mine major disaster risk prevention and control system and method. Background Art

[0002] With the widespread use of coal as an important energy resource around the world, the coal mining industry occupies a vital position in the economic development of various countries. However, safety issues in the coal mining process, especially the risk prevention and control of major disasters, have always been the core issues that need to be urgently addressed in the industry. The complex geological conditions inside the coal mine, flammable and explosive gas and coal dust, as well as unstable roofs and water hazards, make the coal mine environment extremely risky.

[0003] Existing coal mine disaster prevention and control technologies still have many shortcomings. Traditional monitoring methods mostly rely on a single type of sensor and manual monitoring, which makes it difficult to fully cover the complex environment of coal mines, and the accuracy and real-time nature of data collection are also difficult to guarantee. In addition, most existing systems lack intelligent data processing and disaster prediction capabilities, and cannot effectively use a large amount of environmental data for in-depth analysis and anomaly detection, resulting in low accuracy of disaster warnings. At the same time, existing technologies also lag in emergency response, making it difficult to provide timely and reasonable emergency decision-making support, increasing the risk of ineffective response after a disaster occurs. Summary of the invention

[0004] Based on the above objectives, the present invention provides a major disaster risk prevention and control system and method for coal mines based on artificial intelligence.

[0005] A major disaster risk prevention and control system for coal mines based on artificial intelligence, comprising a data acquisition module, a data transmission module, a data processing module, a disaster prediction module, a decision support module and a user interaction module, wherein;

[0006] The data acquisition module uses a variety of sensor technologies to collect coal mine environmental data in real time, including gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level;

[0007] The data transmission module transmits the real-time environmental data collected by the data collection module to the ground control center by combining wired and wireless methods;

[0008] The data processing module includes a data preprocessing unit, a data fusion unit and an anomaly detection unit. The data preprocessing unit is used to clean, denoise and format the data transmitted by the data acquisition module. The data fusion unit fuses the data of different sensors through a multi-source data fusion algorithm to improve the accuracy and consistency of the data. The anomaly detection unit uses an anomaly detection algorithm based on deep learning to identify disaster signs.

[0009] The disaster prediction module analyzes the processed environmental data based on the machine learning model to predict the disasters that may occur;

[0010] The decision support module provides intelligent decision support based on the output of the disaster prediction module, including disaster warning, emergency plan suggestions and personnel evacuation route planning;

[0011] The user interaction module provides a visual interface for mine managers to view real-time data, historical data, disaster prediction results and emergency suggestions, and supports manual adjustment of system parameters and operation of emergency plans.

[0012] Optionally, the data acquisition module specifically includes:

[0013] Gas concentration sensor: used to monitor the gas concentration in the coal mine environment in real time. The sensor type is an infrared sensor;

[0014] Temperature sensor: used to monitor the temperature inside the coal mine. The sensor type is thermistor or thermocouple;

[0015] Humidity sensor: used to monitor the humidity in the air, the sensor type is a capacitive humidity sensor;

[0016] Coal dust concentration sensor: used to detect the coal dust concentration in the air, the sensor type is a light scattering sensor;

[0017] Geological pressure sensor: used to monitor geological pressure changes in coal seams or surrounding rocks. The sensor type is a fiber optic pressure sensor.

[0018] Roof displacement sensor: used to monitor the displacement of coal mine roof, the sensor type is laser displacement sensor;

[0019] Mine water level sensor: used to monitor water level changes in mines, the sensor type is ultrasonic water level sensor.

[0020] Optionally, the data transmission module specifically includes:

[0021] Wired transmission part: using industrial Ethernet or optical fiber communication technology to transmit the collected environmental data to the ground control center to ensure high-speed and stable data transmission;

[0022] Wireless transmission part: adopt wireless communication protocols, including Wi-Fi, LoRa, Zigbee and 5G, and select the optimal communication method according to environmental conditions;

[0023] Data cache unit: provides temporary storage function during data transmission to prevent data loss;

[0024] Redundant design: Enhance the system's anti-interference ability and reliability through redundant transmission channels and backup devices.

[0025] Optionally, the data processing module specifically includes:

[0026] Data preprocessing unit: used to clean the collected environmental data, remove noise and outliers, and ensure data quality;

[0027] Data fusion unit: The data from different sensors are fused through a multi-source data fusion algorithm. The fusion method adopts a weighted average method to improve the accuracy and consistency of the data.

[0028] Anomaly Detection Unit: Uses deep learning-based anomaly detection algorithms to identify anomalies in coal mine environments in real time.

[0029] Optionally, the disaster prediction module specifically includes:

[0030] Model selection: Machine learning model selection support vector machine;

[0031] Model training: Use historical disaster data and expert-labeled data to train the support vector machine model;

[0032] Model prediction: In actual operation, the trained model is used to analyze real-time environmental data to predict potential disasters. The loss function of the prediction algorithm is:

[0033]

[0034] Among them, L(θ) is the loss function, N is the number of samples, and y i is the actual label, is the predicted value;

[0035] Model update: Regularly update and optimize the model based on new data from actual applications to ensure that the prediction model can adapt to changes in the environment.

[0036] Optionally, the decision support module specifically includes:

[0037] Disaster warning: According to the evaluation results of the disaster prediction module, early warning information is automatically generated and notified to mine managers through the visual interface and alarm system;

[0038] Emergency plan suggestions: Generate emergency response plan suggestions based on the rule engine, including personnel evacuation, equipment shutdown and emergency rescue route planning;

[0039] Self-learning decision engine: During system operation, as data accumulates and models are optimized, decision support functions are continuously improved to enhance the effectiveness of emergency response.

[0040] Optionally, the user interaction module specifically includes:

[0041] Data display: used to visualize real-time monitoring data, historical data, disaster prediction results and emergency suggestions, supporting a variety of charts and data analysis tools, such as line charts, heat maps and statistical reports;

[0042] Control unit: allows users to manually adjust system parameters and operate emergency plans through a visual interface, and monitor the system's operating status in real time. It also provides remote access and ensures the security of data transmission through the HTTPS protocol.

[0043] Optionally, the disaster prediction module further includes a disaster emergency response unit, which automatically triggers an emergency response procedure when it detects that the disaster risk reaches a warning threshold, including activating an alarm, cutting off power to the dangerous area, turning on exhaust equipment, and guiding personnel to evacuate along the planned route.

[0044] A method for preventing and controlling major disaster risks in coal mines based on artificial intelligence is implemented by the above-mentioned system for preventing and controlling major disaster risks in coal mines based on artificial intelligence, and comprises the following steps:

[0045] S1, data collection: deploy multiple sensors at multiple key locations inside the coal mine to collect coal mine environmental data in real time, including gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level data;

[0046] S2, data transmission: using a combination of wired and wireless methods to transmit the collected environmental data to the ground control center to ensure the timeliness and integrity of the data;

[0047] S3, data processing: pre-processing of environmental data transmitted to the ground control center, including data cleaning, denoising and formatting, followed by fusion of data from different sensors through a multi-source data fusion algorithm to improve data accuracy and consistency, and identification of disaster signs using anomaly detection algorithms based on deep learning;

[0048] S4, Disaster Prediction: Using processed environmental data, based on pre-trained machine learning models, we can predict possible major coal mine disasters and generate corresponding risk assessment reports;

[0049] S5, decision support: Generate intelligent decision suggestions based on disaster prediction results, including disaster warning, emergency plan suggestions and personnel evacuation route planning, and push relevant information to mine managers;

[0050] S6, User Interaction: Through the provided visual interface, mine managers can view data, disaster prediction results and emergency suggestions in real time, support manual adjustment of system parameters and operation of emergency plans, and realize remote monitoring and control through the mobile terminal interface.

[0051] Beneficial effects of the present invention:

[0052] The present invention realizes all-round monitoring of the internal environment of coal mines through the comprehensive application of multiple sensors, including real-time collection of key parameters such as gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level. Through advanced data fusion algorithms, the processed data is more accurate and consistent, and can fully and timely reflect the safety status inside the coal mine. Combined with the prediction capabilities of anomaly detection based on deep learning and machine learning models, the system can accurately identify and predict potential major disasters, providing strong technical support for coal mine safety management and reducing the possibility of disasters.

[0053] The present invention can not only predict possible disasters, but also provide intelligent decision support. The system automatically generates emergency plan suggestions and personnel evacuation route planning by comprehensively analyzing the disaster prediction results, and provides real-time warning information in emergency situations. The intelligent decision support module greatly improves the efficiency and accuracy of emergency response, ensuring that mine managers can make response decisions quickly and accurately before disasters occur, reducing casualties and property losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention;

[0056] Figure 2 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0058] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1 As shown, a major disaster risk prevention and control system for coal mines based on artificial intelligence includes a data acquisition module, a data transmission module, a data processing module, a disaster prediction module, a decision support module and a user interaction module, wherein;

[0061] The data acquisition module is deployed at multiple key locations inside the coal mine, using a variety of sensor technologies to collect real-time coal mine environmental data, including gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level;

[0062] The data transmission module uses a combination of wired and wireless methods to transmit the real-time environmental data collected by the data acquisition module to the ground control center;

[0063] The data processing module includes a data preprocessing unit, a data fusion unit and an anomaly detection unit. The data preprocessing unit is used to clean, denoise and format the data transmitted by the data acquisition module. The data fusion unit uses a multi-source data fusion algorithm to fuse data from different sensors to improve the accuracy and consistency of the data. The anomaly detection unit uses an anomaly detection algorithm based on deep learning to identify disaster signs.

[0064] The disaster prediction module analyzes the processed environmental data based on the machine learning model to predict the occurrence of disasters;

[0065] The decision support module provides intelligent decision support based on the output of the disaster prediction module, including disaster warning, emergency plan suggestions and personnel evacuation route planning;

[0066] The user interaction module provides a visual interface for mine managers to view real-time data, historical data, disaster prediction results and emergency suggestions, and supports manual adjustment of system parameters and operation of emergency plans.

[0067] The data acquisition module specifically includes:

[0068] Gas concentration sensor: used to monitor the gas concentration in the coal mine environment in real time. The sensor type is an infrared sensor;

[0069] Temperature sensor: used to monitor the temperature inside the coal mine. The sensor type is thermistor or thermocouple;

[0070] Humidity sensor: used to monitor the humidity in the air, the sensor type is a capacitive humidity sensor;

[0071] Coal dust concentration sensor: used to detect the coal dust concentration in the air, the sensor type is a light scattering sensor;

[0072] Geological pressure sensor: used to monitor geological pressure changes in coal seams or surrounding rocks. The sensor type is a fiber optic pressure sensor.

[0073] Roof displacement sensor: used to monitor the displacement of coal mine roof, the sensor type is laser displacement sensor;

[0074] Mine water level sensor: used to monitor water level changes in mines, the sensor type is ultrasonic water level sensor.

[0075] The data transmission module specifically includes:

[0076] Wired transmission part: using industrial Ethernet or optical fiber communication technology to transmit the collected environmental data to the ground control center to ensure high-speed and stable data transmission;

[0077] Wireless transmission part: adopt wireless communication protocols, including Wi-Fi, LoRa, Zigbee and 5G, select the optimal communication method according to environmental conditions, and ensure the stability of data transmission under harsh conditions;

[0078] Data cache unit: provides temporary storage function during data transmission to prevent data loss;

[0079] Redundant design: Enhance the system's anti-interference ability and reliability through redundant transmission channels and backup devices.

[0080] The data processing module specifically includes:

[0081] Data preprocessing unit: used to clean the collected environmental data, remove noise and outliers, and ensure data quality;

[0082] Data fusion unit: The data from different sensors are fused through a multi-source data fusion algorithm. The fusion method adopts a weighted average method to improve the accuracy and consistency of the data.

[0083] Anomaly detection unit: Use an anomaly detection algorithm based on deep learning to identify abnormal situations in the coal mine environment in real time. The algorithm adopts an autoencoder structure, and the anomaly degree calculation formula is:

[0084]

[0085] Among them, x i For input data, is the data reconstructed by the autoencoder, and n is the data dimension.

[0086] The disaster prediction module specifically includes:

[0087] Model selection: Machine learning model selection support vector machine;

[0088] Model training: Use historical disaster data and expert-labeled data to train the support vector machine model;

[0089] Model prediction: In actual operation, the trained model is used to analyze real-time environmental data to predict potential disasters. The loss function of the prediction algorithm is:

[0090]

[0091] Among them, L(θ) is the loss function, N is the number of samples, and y i is the actual label, is the predicted value;

[0092] Model update: Regularly update and optimize the model based on new data from actual applications to ensure that the prediction model can adapt to changes in the environment.

[0093] The decision support module specifically includes:

[0094] Disaster warning: According to the evaluation results of the disaster prediction module, early warning information is automatically generated and notified to mine managers through the visual interface and alarm system;

[0095] Emergency plan suggestions: Generate emergency response plan suggestions based on the rule engine, including personnel evacuation, equipment shutdown and emergency rescue route planning;

[0096] Self-learning decision engine: During system operation, as data accumulates and models are optimized, decision support functions are continuously improved to enhance the effectiveness of emergency response.

[0097] The user interaction module specifically includes:

[0098] Data display: used to visualize real-time monitoring data, historical data, disaster prediction results and emergency suggestions, supporting a variety of charts and data analysis tools, such as line charts, heat maps and statistical reports;

[0099] Control unit: allows users to manually adjust system parameters and operate emergency plans through a visual interface, and monitor the system's operating status in real time. It also provides remote access and ensures the security of data transmission through the HTTPS protocol.

[0100] The disaster prediction module further includes a disaster emergency response unit, which automatically triggers the emergency response procedure when it detects that the disaster risk reaches the warning threshold, including activating the alarm, cutting off the power supply to the dangerous area, turning on the exhaust equipment, and guiding personnel to evacuate according to the planned route.

[0101] like Figure 2 As shown, a method for preventing and controlling major disaster risks in coal mines based on artificial intelligence is implemented by the above-mentioned system for preventing and controlling major disaster risks in coal mines based on artificial intelligence, and includes the following steps:

[0102] S1, data collection: deploy multiple sensors at multiple key locations inside the coal mine to collect coal mine environmental data in real time, including gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level data;

[0103] S2, data transmission: using a combination of wired and wireless methods to transmit the collected environmental data to the ground control center to ensure the timeliness and integrity of the data;

[0104] S3, data processing: pre-processing of environmental data transmitted to the ground control center, including data cleaning, denoising and formatting, followed by fusion of data from different sensors through a multi-source data fusion algorithm to improve data accuracy and consistency, and identification of disaster signs using anomaly detection algorithms based on deep learning;

[0105] S4, Disaster Prediction: Using processed environmental data, based on pre-trained machine learning models, we can predict possible major coal mine disasters and generate corresponding risk assessment reports;

[0106] S5, decision support: Generate intelligent decision suggestions based on disaster prediction results, including disaster warning, emergency plan suggestions and personnel evacuation route planning, and push relevant information to mine managers;

[0107] S6, User Interaction: Through the provided visual interface, mine managers can view data, disaster prediction results and emergency suggestions in real time, support manual adjustment of system parameters and operation of emergency plans, and realize remote monitoring and control through the mobile terminal interface.

[0108] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0109] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A coal mine major disaster risk prevention and control system based on artificial intelligence, characterized in that: It includes data acquisition module, data transmission module, data processing module, disaster prediction module, decision support module and user interaction module, among which; The data acquisition module uses a variety of sensor technologies to collect coal mine environmental data in real time, including gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level; The data transmission module transmits the real-time environmental data collected by the data collection module to the ground control center by combining wired and wireless methods; The data processing module includes a data preprocessing unit, a data fusion unit and an anomaly detection unit. The data preprocessing unit is used to clean, denoise and format the data transmitted by the data acquisition module. The data fusion unit fuses the data of different sensors through a multi-source data fusion algorithm to improve the accuracy and consistency of the data. The anomaly detection unit uses an anomaly detection algorithm based on deep learning to identify disaster signs. The disaster prediction module analyzes the processed environmental data based on the machine learning model to predict the disasters that may occur; The decision support module provides intelligent decision support based on the output of the disaster prediction module, including disaster warning, emergency plan suggestions and personnel evacuation route planning; The user interaction module provides a visual interface for mine managers to view real-time data, historical data, disaster prediction results and emergency suggestions, and supports manual adjustment of system parameters and operation of emergency plans.

2. The artificial intelligence-based coal mine major disaster risk prevention and control system according to claim 1 is characterized in that: The data acquisition module specifically includes: Gas concentration sensor: used to monitor the gas concentration in the coal mine environment in real time. The sensor type is an infrared sensor; Temperature sensor: used to monitor the temperature inside the coal mine. The sensor type is thermistor or thermocouple; Humidity sensor: used to monitor the humidity in the air, the sensor type is a capacitive humidity sensor; Coal dust concentration sensor: used to detect the coal dust concentration in the air, the sensor type is a light scattering sensor; Geological pressure sensor: used to monitor geological pressure changes in coal seams or surrounding rocks. The sensor type is a fiber optic pressure sensor. Roof displacement sensor: used to monitor the displacement of coal mine roof, the sensor type is laser displacement sensor; Mine water level sensor: used to monitor water level changes in mines, the sensor type is ultrasonic water level sensor.

3. The artificial intelligence-based coal mine major disaster risk prevention and control system according to claim 2 is characterized in that: The data transmission module specifically includes: Wired transmission part: using industrial Ethernet or optical fiber communication technology to transmit the collected environmental data to the ground control center; Wireless transmission part: adopt wireless communication protocols, including Wi-Fi, LoRa, Zigbee and 5G, and select the optimal communication method according to environmental conditions; Data cache unit: provides temporary storage function during data transmission to prevent data loss; Redundant design: Enhance the system's anti-interference ability and reliability through redundant transmission channels and backup devices.

4. The artificial intelligence-based coal mine major disaster risk prevention and control system according to claim 3 is characterized in that: The data processing module specifically includes: Data preprocessing unit: used to clean the collected environmental data, remove noise and outliers, and ensure data quality; Data fusion unit: The data from different sensors are fused through a multi-source data fusion algorithm. The fusion method adopts a weighted average method to improve the accuracy and consistency of the data. Anomaly Detection Unit: Uses deep learning-based anomaly detection algorithms to identify anomalies in coal mine environments in real time.

5. The artificial intelligence-based coal mine major disaster risk prevention and control system according to claim 4 is characterized in that: The disaster prediction module specifically includes: Model selection: Machine learning model selection support vector machine; Model training: Use historical disaster data and expert-labeled data to train the support vector machine model; Model prediction: In actual operation, the trained model is used to analyze real-time environmental data to predict potential disasters. The loss function of the prediction algorithm is: Among them, L(θ) is the loss function, N is the number of samples, and y i is the actual label, is the predicted value; Model update: Regularly update and optimize the model based on new data from actual applications to ensure that the prediction model can adapt to changes in the environment.

6. The artificial intelligence-based coal mine major disaster risk prevention and control system and method according to claim 5 is characterized in that: The decision support module specifically includes: Disaster warning: According to the evaluation results of the disaster prediction module, early warning information is automatically generated and notified to mine managers through the visual interface and alarm system; Emergency plan suggestions: Generate emergency response plan suggestions based on the rule engine, including personnel evacuation, equipment shutdown and emergency rescue route planning; Self-learning decision engine: During system operation, as data accumulates and models are optimized, decision support functions are continuously improved to enhance the effectiveness of emergency response.

7. The artificial intelligence-based coal mine major disaster risk prevention and control system according to claim 6 is characterized in that: The user interaction module specifically includes: Data display: used to visualize real-time monitoring data, historical data, disaster prediction results and emergency suggestions, supporting a variety of charts and data analysis tools; Control unit: allows users to manually adjust system parameters and operate emergency plans through a visual interface, and monitor the system's operating status in real time. It also provides remote access and ensures the security of data transmission through the HTTPS protocol.

8. The artificial intelligence-based coal mine major disaster risk prevention and control system according to claim 7 is characterized in that: The disaster prediction module further includes a disaster emergency response unit, which automatically triggers the emergency response procedure when it detects that the disaster risk reaches the warning threshold, including activating the alarm, cutting off the power supply to the dangerous area, turning on the exhaust equipment, and guiding personnel to evacuate according to the planned route.

9. A method for preventing and controlling major disaster risks in coal mines based on artificial intelligence, implemented by a system for preventing and controlling major disaster risks in coal mines based on artificial intelligence as described in claims 1-9, characterized in that: The following steps are involved: S1, data collection: deploy multiple sensors at multiple key locations inside the coal mine to collect real-time coal mine environmental data, including gas concentration, temperature, humidity, coal dust concentration, geological pressure, roof displacement and mine water level; S2, data transmission: using a combination of wired and wireless methods to transmit the collected environmental data to the ground control center to ensure the timeliness and integrity of the data; S3, data processing: pre-processing of environmental data transmitted to the ground control center, including data cleaning, denoising and formatting, followed by fusion of data from different sensors through a multi-source data fusion algorithm to improve data accuracy and consistency, and identification of disaster signs using anomaly detection algorithms based on deep learning; S4, Disaster Prediction: Using processed environmental data, based on pre-trained machine learning models, we can predict possible major coal mine disasters and generate corresponding risk assessment reports; S5, decision support: Generate intelligent decision suggestions based on disaster prediction results, including disaster warning, emergency plan suggestions and personnel evacuation route planning, and push relevant information to mine managers; S6, User Interaction: Through the provided visual interface, mine managers can view data, disaster prediction results and emergency suggestions in real time, support manual adjustment of system parameters and operation of emergency plans, and realize remote monitoring and control through the mobile terminal interface.

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