Intelligent coal mine centralized control center monitoring system

Abstract

The application discloses an intelligent coal mine centralized control center monitoring system, and relates to the technical field of coal mine monitoring, comprising a centralized control management center, which is communicatively connected with a data acquisition module, a data processing and analysis module, a model calculation module, a monitoring and early warning evaluation module, an alarm module and a remote operation module, wherein, the modules are connected through electric signals, the data acquisition module is used for collecting environmental data, equipment state data and geological data of a coal mine underground, and the collected data is integrated into a coal mine comprehensive data set, and the data processing and analysis module is used for receiving the underground comprehensive data set from the data transmission module for preprocessing, the application can timely discover potential safety hazards and issue early warnings by monitoring the environmental parameters and equipment states of the underground in real time, automatically start an emergency response mechanism, effectively prevent accidents, and record and analyze historical data of safety accidents, thereby providing data support for formulating more scientific safety management strategies.

Description

Technical Field

[0001] This invention relates to the field of coal mine monitoring technology, specifically to an intelligent coal mine centralized control center monitoring system. Background Technology

[0002] With the continuous development of the coal industry, the scale of coal mines is expanding. As a high-risk industry, the coal mining industry has complex production processes and many uncertainties in terrain and working environment, making it extremely prone to accidents. In order to improve coal output, quality, and safety, while reducing production costs, coal mining enterprises need to carry out refined management of many aspects such as mining, transportation, ventilation, and drainage. With the continuous advancement of technology, video surveillance technology, Internet of Things technology, and artificial intelligence technology have been widely used. These technologies have made intelligent management of the coal mining industry possible. By introducing these advanced technologies, real-time monitoring and management of the coal mine production process can be achieved, production processes can be optimized, energy waste and pollutant emissions can be reduced, and coal mining enterprises can be helped to meet the requirements of environmental protection policies.

[0003] In existing technologies, due to the complex underground environment of coal mines, the data collection and monitoring process may be affected by the complexity of the coal mine environment, such as dust and humidity, resulting in incomplete and inaccurate data. This leads to deviations in the prediction of the overall production status and potential risks. Therefore, how to reduce the impact of the complex coal mine environment and accurately predict the production status and potential risks is the problem we need to solve. Here, we propose an intelligent coal mine centralized control center monitoring system. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent coal mine centralized control center monitoring system to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: An intelligent coal mine centralized control center monitoring system includes a centralized control management center, which is communicatively connected to a data acquisition module, a data processing and analysis module, a model calculation module, a monitoring and early warning assessment module, an alarm module, and a remote operation module, wherein the modules are electrically connected to each other. The data acquisition module is used to collect environmental data, equipment status data, and geological data from underground coal mines. The collected data is integrated into a comprehensive coal mine dataset, providing a comprehensive and original data foundation for the entire monitoring system. This ensures that the system can understand various conditions underground in real time and is the primary link in achieving accurate monitoring, prediction, and decision-making. Environmental data includes gas concentration, dust concentration, humidity, temperature, etc. Equipment status data includes equipment temperature, vibration frequency, rotation speed, oil pressure, etc. Geological data includes stratigraphic structure, fault distribution, coal quality, etc. The data processing and analysis module is used to receive the underground comprehensive dataset from the data transmission module for preprocessing, and to extract feature information from environmental data, equipment status data and geological data respectively. It analyzes the correlation and potential patterns between environmental data, equipment status data and geological data, such as the relationship between equipment failure and changes in operating parameters, the relationship between changes in gas concentration and geological structure and mining activities, and predicts the production status and potential risks of coal mines. The model calculation module analyzes and establishes a coal mine production prediction model based on the data transmitted from the data processing and analysis module, and uses the coal mine production prediction model to perform calculations to predict the geological conditions, environmental change trends, and equipment operating status of the coal mine. The monitoring and early warning assessment module determines the degree of danger of the current coal mine production status based on the results of the model calculation module and real-time data, classifies different early warning levels, and monitors the coal mine's production process and environmental conditions in real time to determine the early warning level of the current coal mine production status and generate corresponding early warning information. At the same time, it triggers the alarm module. The early warning information includes the early warning level, the cause of the early warning, the location of the early warning, and suggested countermeasures, so that monitoring personnel can respond in a timely manner. The alarm module is used to receive alarm trigger signals from the monitoring and early warning assessment module and issue alarms, and synchronize early warning information to monitoring personnel. Alarm methods include sound and light alarms, voice alarms, and SMS notifications to relevant personnel to ensure that on-site coal mine workers and relevant management personnel can be aware of the occurrence of abnormal situations in a timely manner so that they can take rapid countermeasures, such as evacuating personnel, stopping relevant operations, and activating emergency equipment. The remote operation module is used to receive operation instructions from the centralized control management center to remotely control and operate equipment underground in the coal mine, including turning equipment on or off, adjusting equipment parameters, etc. Operators at the centralized control management center send remote control instructions to equipment underground in the coal mine, such as remotely starting or stopping ventilation fans to adjust the underground ventilation volume, and remotely adjusting the working parameters of coal mining machines to adapt to different coal seam conditions. After receiving abnormal situation information from the monitoring and early warning assessment module, operators can take corresponding emergency operations through the remote operation module according to the actual situation to ensure the safety and stability of coal mine production. At the same time, it receives feedback after the equipment executes the remote operation instructions so that operators can confirm whether the operation was successfully executed. If it is unsuccessful, the cause can be further investigated and remedial measures can be taken.

[0006] A further improvement to the technical solution of this invention lies in the following: the process of acquiring the comprehensive coal mine dataset in the data acquisition module is as follows: Before coal mining, geological exploration is conducted to assess the geological information of the mine. During the mining process, measuring instruments and tools are used to monitor and measure the underground geological conditions in real time, such as roadway deformation and roof stability. The data from geological exploration and underground measurement are integrated into geological data to provide a scientific basis for coal mining. Sensors such as temperature sensors, humidity sensors, and gas concentration sensors are deployed underground in coal mines to monitor underground environmental data in real time. Monitoring devices are also installed on coal mine equipment to monitor the equipment's operating status in real time, such as motor current, voltage, vibration, and temperature. The collected environmental data, equipment status data, and geological data are integrated to form a comprehensive coal mine dataset. This dataset is then divided into real-time data and historical data. The integrated dataset is wirelessly transmitted to the centralized control and management center. Each data point is also marked with a timestamp and location information to facilitate subsequent data traceability and analysis.

[0007] A further improvement to the technical solution of this invention lies in the following: the specific process for extracting feature information from environmental data, equipment status data, and geological data in the data processing and analysis module is as follows: The collected coal mine comprehensive dataset is cleaned, denoised, and outlier processed. The processed data is then normalized to remove redundant information. Feature extraction is performed based on the preprocessed coal mine comprehensive dataset. Based on environmental data, features are extracted for gas concentration, temperature, humidity and dust concentration to obtain environmental disturbance features. Based on equipment status data, features are extracted for equipment vibration, equipment temperature and equipment operating parameters to obtain equipment status disturbance features. Based on geological data, features are extracted for roof pressure, coal seam thickness and geological structure to obtain geological disturbance features. Based on the extracted environmental disturbance features, equipment status disturbance features, and geological disturbance features, the correlation between environmental data, equipment status data, and geological data is analyzed. By combining historical data with environmental disturbance characteristics, equipment status disturbance characteristics, and geological disturbance characteristics, machine learning algorithms or data mining techniques are used to predict the production status and potential risks of coal mines.

[0008] A further improvement to the technical solution of this invention lies in the following: the prediction process for the geological conditions, environmental change trends, and equipment operating status of the coal mine in the model calculation module is as follows: By combining environmental disturbance characteristics, equipment status disturbance characteristics, geological disturbance characteristics, and preprocessed historical data, a unified dataset is formed. Based on the extracted environmental disturbance features, equipment status disturbance features, and geological disturbance features, a coal mine production prediction model is constructed using a variety of machine learning algorithms. The integrated dataset is divided into a training set, a validation set, and a test set. The training set data is used to learn and train the coal mine production prediction model, and the validation set and test set are used to validate the trained coal mine production prediction model and evaluate its generalization ability. Using a trained model and preprocessed historical data, environmental disturbance index, equipment condition disturbance index, and geological disturbance index are obtained. The environmental disturbance index is used to predict environmental change trends, such as changes in gas concentration and temperature and humidity fluctuations. The equipment condition disturbance index is used to monitor the operating status of equipment in real time, such as equipment failure type and failure time. The geological disturbance index is used to predict the geological conditions of the coal mine, including the stability of geological structures, the trend of coal seam thickness changes, and the safe range of roof pressure. The prediction results are output and presented in graphical, tabular, or report form so that coal mine managers and decision-makers can intuitively understand the production status and future trends of the coal mine.

[0009] A further improvement to the technical solution of this invention is that the calculation formula for the environmental interference index is: ; in, The environmental disturbance index. For gas concentration, This serves as the baseline value for gas concentration. For temperature, This serves as the reference value for temperature. For humidity, The weighting coefficients for gas concentration and temperature are: This is the weighting factor for humidity; The formula for calculating the device state interference index is as follows: ; in, This refers to the equipment status interference index. For equipment vibration, This serves as the reference value for equipment vibration. For equipment temperature, This serves as the reference value for the equipment temperature. For equipment operating parameters, These are the weighting factors for equipment vibration and temperature. These are the weighting coefficients for the equipment's operating parameters; The formula for calculating the geological disturbance index is as follows: ; in, Geological disturbance index For the pressure of the top plate, This serves as the baseline value for the top plate pressure. For coal seam thickness, This serves as a benchmark value for coal seam thickness. Due to the complexity of the geological structure, The weighting coefficients for roof pressure and coal seam thickness are: This is a weighting coefficient for the complexity of geological structures.

[0010] A further improvement to the technical solution of this invention lies in the following: the process for obtaining the early warning level of coal mine production status in the monitoring and early warning assessment module is as follows: By combining the environmental disturbance index, equipment condition disturbance index, and geological disturbance index with historical data, the early warning coefficient for coal mine production is calculated, and the correlation between coal mine production and the environmental disturbance index, equipment condition disturbance index, and geological disturbance index is analyzed. Based on historical data on coal mine production environment and coal mine safety production standards, the current warning levels for coal mine production are determined as Level 1, Level 2, Level 3, and Level 4. Among them, the road hazard level increases progressively from Level 1 to Level 4. The calculation results of the coal mine production early warning coefficient are matched with different early warning levels, and corresponding early warning thresholds are set. When the warning level reaches the preset warning threshold, the alarm module is triggered to issue an alarm signal, including multiple methods such as audible and visual alarms, SMS alarms, and email alarms, to ensure that monitoring personnel can receive the warning information in a timely manner. At the same time, corresponding warning information is generated and synchronized to the monitoring personnel.

[0011] A further improvement to the technical solution of this invention is that the calculation formula for the coal mine production early warning coefficient is: ; in, Coal mine production early warning coefficient The environmental disturbance index. This serves as the benchmark value for the environmental disturbance index. This refers to the equipment status interference index. This serves as the baseline value for the equipment status interference index. Geological disturbance index These are the weighting coefficients for the environmental disturbance index, equipment condition disturbance index, and geological disturbance index. This is an adjustment coefficient for the geological disturbance index, used to control its impact on the early warning coefficient.

[0012] A further improvement of the technical solution of the present invention is that: the multiple warning levels correspond to multiple warning thresholds, wherein the warning evaluation thresholds include an upper limit threshold and a lower limit threshold; The multiple warning levels and the multiple warning assessment thresholds satisfy the following relationship: Level 1 warning ; Level II warning ; Level III Warning ; Level IV Warning ; in, Coal mine production early warning coefficient These are the upper threshold for Level 1 warning and the lower threshold for Level 2 warning. These are the upper threshold values ​​corresponding to Level II warning and the lower threshold values ​​corresponding to Level III warning. These are the upper limit thresholds corresponding to Level 3 warning and the lower limit thresholds corresponding to Level 4 warning.

[0013] A further improvement to the technical solution of this invention is that the process of synchronizing the early warning information to the monitoring personnel is as follows: It receives alarm signals from the monitoring and early warning assessment module in real time and verifies them after receiving the alarm signals. Based on the received alarm and warning information, activate the audible and visual alarm, issue voice broadcasts, and send SMS notifications to remind monitoring personnel; The early warning information is displayed synchronously in the central control center and converted into text and charts for display. At the same time, the associated information of each alarm event is recorded, including alarm time, alarm type, early warning information, and the response of monitoring personnel, so as to facilitate subsequent analysis and auditing. Based on the recorded related information, alarm event reports are generated, and the safety risks in coal mine production are summarized and analyzed to provide a basis for improving the monitoring and early warning system.

[0014] A further improvement to the technical solution of this invention lies in the following: the process of remotely controlling and operating equipment underground in a coal mine is as follows: Receive operation instructions from the centralized control management center, verify the operation instructions, and parse the verified operation instructions into specific control commands; The system uses a communication network to send control commands to downhole equipment, monitors the operating status of downhole equipment in real time, and feeds back to the centralized control management center. Record the sending, receiving, execution, and result information of each operation instruction, and generate an operation report to summarize and analyze the equipment operation status and safety risks in coal mine production.

[0015] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: 1. This invention provides an intelligent coal mine centralized control center monitoring system. By collecting, transmitting, and analyzing various data in real time from underground coal mines, it enhances the ability to warn and prevent safety hazards. The data acquisition module can accurately acquire various data such as gas concentration, roof pressure, and equipment temperature. Through the collaborative work of the model calculation module and the monitoring and early warning assessment module, a risk prediction model is constructed based on historical data and real-time monitoring data to predict possible accident risks in advance and promptly trigger the alarm module, enabling coal mining enterprises to take effective preventive measures before accidents occur.

[0016] 2. This invention provides an intelligent coal mine centralized control center monitoring system. By monitoring the operating status and various safety parameters of underground equipment in real time, it can promptly detect and warn of potential safety risks. The system can automatically analyze equipment operating data, identify abnormal states, and immediately notify relevant personnel for handling, greatly reducing the risk of safety accidents caused by equipment failure or operational errors. At the same time, the system can also record and analyze historical data to help managers summarize safety patterns and formulate more effective safety management measures.

[0017] 3. This invention provides an intelligent coal mine centralized control center monitoring system. By monitoring the operating status and various safety parameters of underground equipment in real time, it can promptly detect and warn of potential safety risks. The system can automatically analyze equipment operating data, identify abnormal states, and immediately notify relevant personnel for handling, greatly reducing the risk of safety accidents caused by equipment failure or operational errors. At the same time, the system can also record and analyze historical data to help managers summarize safety patterns and formulate more effective safety management measures.

[0018] 4. This invention provides an intelligent coal mine centralized control center monitoring system. By monitoring underground environmental parameters and equipment status in real time, it can promptly detect potential safety hazards and issue early warnings, automatically activate emergency response mechanisms, and effectively prevent accidents from occurring. At the same time, the system can also record and analyze historical data of safety accidents, providing data support for the formulation of more scientific safety management strategies. This not only improves the safety management level of coal mining enterprises, but also provides employees with a safer working environment and effectively protects their lives. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a diagram showing the module configuration of the present invention; Figure 2 This is a flowchart illustrating the calculation process of the coal mine production prediction model of this invention. Figure 3 This is a flowchart illustrating the process of obtaining early warning levels for coal mine production status according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1, as Figures 1 to 3 As shown, the present invention provides an intelligent coal mine centralized control center monitoring system, including a centralized control management center, which is communicatively connected to a data acquisition module, a data processing and analysis module, a model calculation module, a monitoring and early warning assessment module, an alarm module, and a remote operation module, wherein the modules are electrically connected to each other. The data acquisition module is used to collect environmental data, equipment status data, and geological data from underground coal mines. The collected data is integrated into a comprehensive coal mine dataset, providing a comprehensive and original data foundation for the entire monitoring system. This ensures that the system can understand various conditions underground in real time and is the primary link in achieving accurate monitoring, prediction, and decision-making. Environmental data includes gas concentration, dust concentration, humidity, temperature, etc. Equipment status data includes equipment temperature, vibration frequency, rotation speed, oil pressure, etc. Geological data includes stratigraphic structure, fault distribution, coal quality, etc. The process of acquiring a comprehensive coal mine dataset is as follows: Before coal mining, geological exploration is conducted to gather geological information about the mine. During mining, measuring instruments and tools are used to monitor and measure underground geological conditions in real time, such as roadway deformation and roof stability. The data from geological exploration and underground measurements are integrated into geological data to provide a scientific basis for coal mining. Sensors, such as temperature sensors, humidity sensors, and gas concentration sensors, are deployed underground to monitor underground environmental data in real time. Monitoring devices are also installed on coal mine equipment to monitor the equipment's operating status in real time, such as motor current, voltage, vibration, and temperature. Specifically, high-precision gas sensors are installed at key locations such as various mining faces, return airways, and electromechanical chambers underground. These sensors are based on... Different working principles, such as catalytic combustion and infrared absorption, can detect the methane content in the air in real time and convert the methane concentration data into electrical signals. Temperature and humidity sensors are distributed underground, using thermistors, humidity capacitors and other components to sense changes in ambient temperature and humidity, converting the physical quantities of temperature and humidity into corresponding electrical signals for output. Laser dust sensors are used to collect dust concentration data in the air. When the laser beam passes through dusty air, the dust particles scatter the light. The sensor calculates the dust concentration based on the intensity of the scattered light and generates a corresponding electrical signal. Multiple sensors are installed on the coal mining machine. For example, current sensors monitor the operating current of the motor. By observing changes in the current value, the load condition of the motor and whether there is an overload or short circuit risk can be determined. Vibration sensors detect the vibration of the coal mining machine body. Vibrations of different frequencies and amplitudes may indicate wear, loosening, or other malfunctions in the equipment's components. Temperature sensors monitor the temperature of critical components such as motor windings and bearings to prevent damage from overheating. For conveying equipment such as scraper conveyors and belt conveyors, speed sensors and torque sensors are installed on their drive motors. Speed ​​sensors monitor the motor's rotational speed to determine if the conveyor's operating speed is normal and if slippage is present. Torque sensors detect the torque output by the motor to determine if the transport load is too heavy or if there is a mechanical fault in the equipment. In addition, belt misalignment sensors are installed to detect if the conveyor belt deviates from its normal operating track. Once misalignment occurs, the sensor will immediately issue an electrical signal warning. Ventilation fans are equipped with air pressure sensors, air volume sensors, and motor status sensors. Air pressure sensors measure the air pressure of the ventilation system in real time to ensure sufficient ventilation pressure in all areas underground. Air volume sensors measure the air volume of the ventilation fan to ensure effective air circulation underground. Pressure sensors are installed below the roof. These sensors can be hydraulic or electronic. Hydraulic pressure sensors reflect the pressure of the roof on the supports through changes in hydraulic oil pressure. Electronic pressure sensors convert roof pressure into electrical signals using components such as strain gauges. Ground-penetrating radar (GPR) or ultrasonic sensors are used to detect coal seam thickness. GPR emits electromagnetic waves, which are reflected at the coal and rock interface. The coal seam thickness is calculated based on parameters such as the time difference and wave velocity of the reflected waves. Ultrasonic sensors utilize the propagation characteristics of ultrasound in different media to measure coal seam thickness. The collected coal seam thickness data is transmitted as electrical signals, providing a basis for adjusting the cutting height of the coal mining machine. It also helps to understand the occurrence of coal seams and geological structural changes. The collected environmental data, equipment status data, and geological data are integrated to form a comprehensive coal mine dataset, which is divided into real-time data and historical data. Simultaneously, the integrated coal mine dataset is transmitted wirelessly to the centralized control management center. Each data point is marked with a timestamp and location information to facilitate subsequent data traceability and analysis. For example, gas concentration data will be tagged with the collection time and the location of the roadway. This dataset can be in the form of a structured data table, where each row represents information about a data collection point, including fields such as data type, specific data value, collection time, and collection location. This allows the data processing and analysis module to easily read and process the data, providing a comprehensive and accurate data foundation for intelligent management and decision-making in the coal mine. The centralized control management center can receive, store, and analyze this data in real time, enabling remote monitoring and management of mine production and operations. The data processing and analysis module receives and preprocesses the comprehensive underground dataset from the data transmission module. It extracts feature information from environmental data, equipment status data, and geological data, analyzing the correlations and potential patterns among these data, such as the relationship between equipment failures and changes in operating parameters, and the relationship between gas concentration changes and geological structures and mining activities. This helps predict the coal mine's production status and potential risks. The specific process for extracting feature information from environmental, equipment status, and geological data involves: cleaning, denoising, and handling outliers in the collected comprehensive coal mine dataset; and processing... The data is then normalized to remove redundant information. Feature extraction is performed on the preprocessed coal mine comprehensive dataset. Based on environmental data, features are extracted for gas concentration, temperature, humidity, and dust concentration to obtain environmental interference features. Based on equipment status data, features are extracted for equipment vibration, equipment temperature, and equipment operating parameters to obtain equipment status interference features. Based on geological data, features are extracted for roof pressure, coal seam thickness, and geological structure to obtain geological interference features. Based on the extracted environmental interference features, equipment status interference features, and geological interference features, the correlation between environmental data, equipment status data, and geological data is analyzed. For example, analyzing the correlation between gas concentration and roof pressure reveals that when roof pressure increases, gas may be squeezed out of coal and rock pores, leading to an increase in gas concentration. There is also a correlation between equipment operating parameters and environmental data. For instance, when the cutting speed of the coal mining machine increases, the dust concentration will increase accordingly. At the same time, equipment vibration and temperature may also increase. There is also a connection between geological structure and equipment failure. Near faults, due to complex geological conditions, the impact and vibration experienced by the equipment during operation may increase, leading to an increase in equipment failure rate. By combining historical data with environmental disturbance characteristics, equipment status disturbance characteristics, and geological disturbance characteristics, machine learning algorithms or data mining techniques can be used to predict the production status and potential risks of coal mines. The model calculation module, based on data from the data processing and analysis module, analyzes and establishes a coal mine production prediction model, and uses this model to predict the geological conditions, environmental change trends, and equipment operating status of the coal mine. The prediction process involves combining environmental disturbance features, equipment status disturbance features, geological disturbance features, and preprocessed historical data to form a unified dataset. Based on the extracted environmental disturbance features, equipment status disturbance features, and geological disturbance features, various machine learning algorithms are used to construct the coal mine production prediction model. The integrated dataset is divided into training, validation, and test sets. The training set data is used to learn and train the coal mine production prediction model, and the validation and test sets are used to validate the trained model and evaluate its generalization ability. Finally, the trained model, combined with preprocessed historical data, is used to obtain the environmental disturbance index, equipment status disturbance index, and geological disturbance index. Simultaneously, the environmental disturbance index is used to predict environmental change trends, such as changes in gas concentration and fluctuations in temperature and humidity. The equipment condition disturbance index is used to monitor the real-time operating status of equipment, such as equipment failure type and failure time. The geological disturbance index is used to predict the geological conditions of the coal mine, including the stability of geological structures, the changing trend of coal seam thickness, and the safe range of roof pressure. The prediction results are output and presented in graphical, tabular, or report formats to allow coal mine managers and decision-makers to intuitively understand the mine's production status and future trends. The calculation process for the environmental disturbance index, equipment condition disturbance index, and geological disturbance index is as follows: For the environmental disturbance index, changes in gas concentration can be used as a predictor of environmental conditions. Environmental disturbance features such as the rate of temperature and humidity fluctuations and the number of times dust concentration exceeds the standard are input into the trained LSTM model. The model learns patterns and relationships from historical data and outputs an index that comprehensively reflects the degree of environmental disturbance. If the environmental disturbance index is high, it indicates that the environmental changes are more drastic and may have a greater impact on coal mine production. For example, a rapid increase in gas concentration may lead to an increased risk of gas accidents, and abnormal changes in temperature and humidity may affect the normal operation of equipment and the work efficiency of personnel. For the equipment condition disturbance index, equipment condition disturbance features such as equipment vibration characteristic values, temperature change rate of key parts, and number of abnormal equipment operating parameters are input into the SVM model, and the model outputs the equipment condition disturbance index. When the equipment status interference index exceeds a certain threshold, it indicates that the equipment may have potential faults or poor operating conditions, requiring timely inspection and maintenance. For example, when the equipment status interference index of a coal mining machine is high, it may be due to excessive vibration of the cutting section or excessive motor temperature, requiring the machine to be stopped to check the wear of the cutting teeth or the motor cooling system. The geological interference index is used to predict the geological conditions of the coal mine. Geological interference characteristics such as changes in stratum resistivity, roof pressure, and coal seam thickness are input into the random forest model. The model outputs the geological interference index and predicts the geological conditions. For example, a high geological interference index may indicate complex geological structures, such as the presence of faults or folds, which may lead to increased difficulty in coal mining and increased risk of roof collapse. The coal mine can adjust the coal mining process and roof support scheme in advance based on the prediction results. The monitoring and early warning assessment module, based on the results of the model calculation module and real-time data, judges the degree of danger of the current coal mine production status, classifies different early warning levels, and simultaneously monitors the coal mine's production process and environmental conditions in real time. It determines the early warning level of the current coal mine production status, generates corresponding early warning information, and triggers the alarm module. The early warning information includes the early warning level, cause of the early warning, location of the early warning, and suggested countermeasures, enabling monitoring personnel to respond promptly. The process of obtaining the early warning level of the coal mine production status is as follows: The environmental interference index, equipment status interference index, and geological interference index are combined with historical data to calculate the coal mine production early warning coefficient. The correlation between the coal mine production situation and these indices is analyzed. Based on historical data on the coal mine production environment and coal mine safety production standards, the early warning level of the current coal mine production status is determined, which includes Level 1, Level 2, Level 3, and Level 4 early warning levels. The warning levels for road hazards increase progressively from Level 1 to Level 4. The calculation results of the coal mine production warning coefficient are matched with different warning levels, and corresponding warning thresholds are set. When the warning level reaches the preset warning threshold, the alarm module is triggered to issue an alarm signal, including multiple methods such as audible and visual alarms, SMS alarms, and email alarms, to ensure that monitoring personnel can receive the warning information in a timely manner. At the same time, corresponding warning information is generated and synchronized to the monitoring personnel, including the warning type, such as gas over-limit, equipment failure, roof collapse risk, etc., the warning level, the warning location, such as the specific coal mining face, roadway location or equipment number, the warning time, the possible scope of impact, such as the number of workers involved, the size of the affected production area, and response suggestions, such as ventilation adjustment measures for gas over-limit and shutdown and maintenance procedures for equipment failure. The alarm module is used to receive alarm trigger signals from the monitoring and early warning assessment module and issue alarms, and synchronize early warning information to monitoring personnel. Alarm methods include audible and visual alarms, voice alarms, and SMS notifications to relevant personnel to ensure that on-site coal mine workers and relevant management personnel can be aware of the occurrence of abnormal situations in a timely manner so that they can take rapid countermeasures, such as evacuating personnel, stopping relevant operations, and activating emergency equipment. The process of synchronizing early warning information to monitoring personnel is as follows: The system receives alarm signals from the monitoring and early warning assessment module in real time, verifies the received signals, and, based on the received alarm and early warning information, activates the audible and visual alarm, voice broadcast, and SMS notification to alert monitoring personnel. The audible and visual alarm emits strong audible and visual signals, such as flashing red lights and a high-decibel alarm, to attract the attention of monitoring personnel and ensure they are aware of the early warning situation immediately. The voice broadcast system plays the early warning information in audio format, using clear and loud speech synthesis technology to deliver the key content of the early warning information, such as... The alarm type, location, and level are broadcast word by word, allowing monitoring personnel to gain a more comprehensive understanding of the warning situation through hearing while simultaneously seeing the audio and visual signals. The text message contains detailed warning information so that personnel not at the central control center can also promptly understand the coal mine's production status and respond accordingly. The warning information is simultaneously displayed at the central control center and converted into text and charts for presentation, such as on the large monitoring screen at the central control center. On the monitoring interface, the warning information is highlighted with eye-catching colors and icons, such as marking the warning location on the coal mine map with a flashing red icon, and displaying the warning type, level, and detailed information next to it. Monitoring personnel can click on the icon to view more detailed information, including historical data curves such as gas concentration change curves and relevant equipment status information, for further analysis and decision-making. Simultaneously, it records the associated information for each alarm event, including alarm time, alarm type, warning information, and the monitoring personnel's response, for subsequent analysis and auditing. Based on the recorded associated information, an alarm event report is generated to summarize and analyze the safety risks in coal mine production, providing a basis for improving the monitoring and early warning system. The alarm event report includes the following: an overview of the alarm event, including time, location, and type; specific content of the warning information, including the warning level and recommended response measures; the monitoring personnel's response, including actions taken and response time; and a summary and analysis of safety risks, identifying potential risks and trends in production. The remote operation module is used to receive operation instructions from the centralized control management center to remotely control and operate equipment in the coal mine, including turning equipment on or off and adjusting equipment parameters. Operators in the centralized control management center send remote control instructions to the equipment in the coal mine, such as remotely starting or stopping the ventilation fan to adjust the underground ventilation volume, and remotely adjusting the working parameters of the coal mining machine to adapt to different coal seam conditions. After receiving abnormal situation information from the monitoring and early warning assessment module, operators can take corresponding emergency operations through the remote operation module according to the actual situation to ensure the safety and stability of coal mine production. At the same time, it receives feedback after the equipment executes the remote operation instructions so that operators can confirm whether the operation has been successfully executed. If unsuccessful, further investigation can be conducted to identify the cause and take remedial measures. The process of remotely controlling and operating equipment underground in a coal mine is as follows: receiving operation instructions from the centralized control management center and verifying the operation instructions. Specifically, this involves: designing a secure communication protocol to ensure the security and integrity of the instructions during transmission; using digital signatures or encryption technology to verify the source and authenticity of the instructions; verifying the format and content of the instructions to ensure they meet the expected format requirements. Operation instructions typically include specific operational requirements for the underground equipment, such as starting, stopping, and adjusting parameters. The verified operation instructions are then parsed into specific control commands. Specifically, this involves: designing an instruction parser to convert the operation instructions into control commands that the underground equipment can understand. Control commands may include operations such as starting, stopping, and adjusting parameters. Different control command formats are designed according to the type and function of the equipment. The control commands are sent to the underground equipment using a communication network, and the operating status of the underground equipment is monitored in real time, such as the equipment's operating speed, temperature, vibration, and current. This information is then fed back to the centralized control management center. The sending, receiving, execution, and result information of each operation instruction is recorded, and an operation report is generated to summarize and analyze the equipment operation status and safety risks in coal mine production.

[0023] Example 2, as Figures 1 to 3 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the formula for calculating the environmental disturbance index is: ; in, The environmental disturbance index. For gas concentration, This serves as the baseline value for gas concentration. For temperature, This serves as the reference value for temperature. For humidity, The weighting coefficients for gas concentration and temperature are: This is the weighting factor for humidity; The formula for calculating the equipment status interference index is: ; in, This refers to the equipment status interference index. For equipment vibration, This serves as the reference value for equipment vibration. For equipment temperature, This serves as the reference value for the equipment temperature. For equipment operating parameters, These are the weighting factors for equipment vibration and temperature. These are the weighting coefficients for the equipment's operating parameters; The formula for calculating the geological disturbance index is: ; in, Geological disturbance index For the pressure of the top plate, This serves as the baseline value for the top plate pressure. For coal seam thickness, This serves as a benchmark value for coal seam thickness. Due to the complexity of the geological structure, The weighting coefficients for roof pressure and coal seam thickness are: This is a weighting coefficient for the complexity of geological structures; The formula for calculating the early warning coefficient for coal mine production is: ; in, Coal mine production early warning coefficient The environmental disturbance index. This serves as the benchmark value for the environmental disturbance index. This refers to the equipment status interference index. This serves as the baseline value for the equipment status interference index. Geological disturbance index These are the weighting coefficients for the environmental disturbance index, equipment condition disturbance index, and geological disturbance index. This is an adjustment coefficient for the geological disturbance index, used to control its impact on the early warning coefficient; Multiple warning levels correspond to multiple warning thresholds, among which the warning assessment thresholds include an upper limit threshold and a lower limit threshold; Multiple warning levels and multiple warning assessment thresholds satisfy the following relationship: Level 1 warning Recommended measures include: Environmental aspects: Increase the frequency of routine monitoring of environmental parameters, doubling the frequency of temperature and humidity monitoring; assign dedicated personnel to inspect the ventilation system, including checking for air leaks in ventilation ducts, ensuring the normal operation of the ventilation fans, and checking for dust accumulation on the fan blades, to ensure the continuous and stable operation of the ventilation system and maintain good underground air circulation; maintain and adjust the dust suppression sprinkler system, checking for nozzle blockages and normal water pressure to ensure it effectively suppresses dust generation and diffusion; Equipment aspects: Increase the frequency of equipment inspections from once per shift to twice per shift, focusing on checking the appearance of the equipment, and also checking and replenishing the lubrication system. Apply grease to ensure good lubrication of all moving parts of the equipment, reduce wear and frictional heat generation, conduct a detailed analysis of recent equipment operating data, compare with normal operating parameter ranges, identify potential problems, and record them for subsequent tracking and observation. Geological aspects: Geological technicians re-examine the geological conditions around the current mining area, check for signs of small geological structural changes based on geological maps and actual on-site observations, evaluate the roof support, check whether the support density meets the requirements, and whether the pre-tightening force of anchor bolts and cables meets the standards. If necessary, the support strength in some areas can be appropriately increased, such as by increasing the density of anchor bolts or increasing the tensile strength grade of anchor cables. Level II warning Recommended measures are as follows: Environmental aspects: In addition to further increasing the frequency of environmental parameter monitoring, activate backup ventilation equipment for combined ventilation to increase the circulation of air underground, quickly dilute potentially excessive methane or harmful gas concentrations, and organize professional personnel to investigate potential methane accumulation points underground, such as the edges of goaf areas and ventilation dead zones. Use portable methane detectors for detailed testing and develop targeted methane control plans based on the test results, such as setting up local ventilation facilities or conducting methane extraction. For areas with high dust concentrations, in addition to strengthening water spraying for dust suppression, consider using chemical dust suppressants for spraying to improve dust suppression effectiveness. Equipment aspects: Immediately shut down and inspect equipment, especially key equipment such as coal mining machines and main transportation equipment. Conduct in-depth inspections of key components of the equipment, using non-destructive testing techniques to check for internal cracks or damage. Replace any faulty parts promptly based on the test results, and conduct a comprehensive debugging of the equipment to ensure that the equipment is back to normal operation before gradually resuming production. At the same time, check and upgrade the equipment's control system to see if there are any abnormalities in the control program and whether the sensor signal transmission is accurate, in order to prevent equipment misoperation or unstable operation due to control system failure. In terms of geology: Geological engineers use advanced detection equipment such as ground-penetrating radar to conduct detailed surveys of the mining area ahead, draw more accurate geological structure maps, identify potential geological hazards such as faults and collapse columns, adjust the mining plan based on the survey results, comprehensively reinforce the support of the roof and sides, increase the strength and quantity of support materials, and set up roof delamination monitoring instruments to monitor the stability of the roof in real time, and promptly detect and deal with roof subsidence or delamination problems; Level III Warning Recommended measures are as follows: Environmental aspects: Stop all non-essential underground operations, and only keep key systems such as ventilation and drainage running. Conduct a comprehensive inspection of all aspects of the ventilation system, including ventilation fans, ventilation ducts, and ventilation structures, to ensure that the ventilation system operates at maximum capacity and reduces the gas concentration to below the safe threshold as soon as possible. Carry out comprehensive gas extraction operations underground, using a combination of extraction methods. At the same time, a comprehensive inspection and maintenance of environmental safety facilities such as fire prevention, dust prevention, and waterproofing in the mine will be carried out to ensure that they can function properly in response to possible disasters. In terms of equipment, a comprehensive shutdown inspection and maintenance will be carried out on all equipment. Not only will damaged or potentially hazardous parts be replaced, but the overall performance of the equipment will also be tested and evaluated. Technical personnel from the equipment manufacturer or professional maintenance teams will be invited to the site to provide guidance and maintenance. Professional testing equipment and tools will be used to conduct comprehensive testing on the mechanical, electrical, and hydraulic performance of the equipment. Based on the test results, develop detailed equipment maintenance and upgrade plans, and, if necessary, technically modify the equipment to improve its reliability and adaptability to complex environments. During equipment maintenance, review and revise the equipment's operating procedures and maintenance systems, strengthen training for operators and maintenance personnel, and enhance their skills and safety awareness. In terms of geology, organize a multidisciplinary expert team to conduct a comprehensive diagnosis of the coal mine's geological conditions. Combining geological exploration data, on-site monitoring data, and advanced geological modeling technology, conduct an in-depth analysis of the current geological hazard risks, develop detailed geological hazard prevention and control plans, and use advanced support technology to pre-reinforce the roof. For possible water inrush accidents, conduct detailed hydrogeological investigations to determine the location and volume of water sources, and take measures such as drainage and pressure reduction, grouting and water blocking for prevention and control. Before the geological hazard risks are effectively controlled, mining operations must not be resumed to ensure the safety of personnel and equipment. Level IV Warning Recommended measures are as follows: Environmental aspects: Quickly organize the evacuation of all personnel underground to a safe area, and conduct personnel count and resettlement in accordance with the emergency plan. Set up a warning area at the wellhead and strictly prohibit unauthorized personnel from entering. Continuously monitor underground environmental parameters, especially gas concentration and water inflow, to provide data support for subsequent rescue and disaster management. At the same time, contact was made with the local government's emergency rescue department to request support and coordinate resource sharing with surrounding coal mines, such as ventilation equipment, drainage equipment, and rescue personnel. Regarding equipment: on the premise of ensuring the safe evacuation of personnel, emergency protection measures were taken for the equipment, such as cutting off the power supply to the equipment and shutting off the water and gas supply pipelines to prevent the equipment damage from further expanding or causing secondary disasters due to the disaster. Key data of important equipment were backed up and saved so that the operation and production of the equipment could be quickly restored after the disaster.

[0024] The organization mobilizes equipment technicians to predict and analyze potential equipment damage, develop post-disaster equipment recovery and reconstruction plans, including equipment repair, replacement, reinstallation, and commissioning, and prepare necessary equipment parts and repair tools in advance. Geologically, geological experts and rescue personnel work closely together, utilizing various geological detection technologies to monitor and assess underground geological hazards in real time, determining the scope, scale, and development trend of the disaster, and developing detailed geological disaster emergency rescue plans. For example, in the case of large roof collapse accidents, large lifting equipment and support materials are used to clean and re-support the roof; for severe water inrush accidents, professional drainage teams and equipment are organized for rapid drainage, and effective sealing measures are taken to prevent further expansion of the water inrush. During the rescue process, the complexity and variability of geological conditions must be fully considered to ensure the safety of rescue personnel and avoid secondary disasters caused by improper rescue. in, Coal mine production early warning coefficient These are the upper threshold for Level 1 warning and the lower threshold for Level 2 warning. These are the upper threshold values ​​corresponding to Level II warning and the lower threshold values ​​corresponding to Level III warning. These are the upper limit thresholds corresponding to Level 3 warning and the lower limit thresholds corresponding to Level 4 warning.

[0025] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent coal mine centralized control center monitoring system, comprising a centralized control management center, characterized in that: The centralized control management center has communication connections to a data acquisition module, a data processing and analysis module, a model calculation module, a monitoring and early warning assessment module, an alarm module, and a remote operation module, wherein the modules are connected by electrical signals. The data acquisition module is used to collect environmental data, equipment status data, and geological data in the coal mine, and integrate the collected data into a comprehensive coal mine dataset. The data processing and analysis module is used to receive the underground comprehensive dataset from the data transmission module for preprocessing, and to extract feature information from environmental data, equipment status data and geological data respectively, analyze the correlation and potential patterns between environmental data, equipment status data and geological data, and predict the production status and potential risks of the coal mine. The model calculation module analyzes and establishes a coal mine production prediction model based on the data transmitted from the data processing and analysis module, and uses the coal mine production prediction model to perform calculations to predict the geological conditions, environmental change trends, and equipment operating status of the coal mine. The monitoring and early warning assessment module determines the degree of danger of the current coal mine production status based on the results of the model calculation module and real-time data, classifies different early warning levels, monitors the coal mine production process and environmental conditions in real time, determines the early warning level of the current coal mine production status, generates corresponding early warning information, and triggers the alarm module. The alarm module is used to receive alarm trigger signals from the monitoring and early warning assessment module and issue alarms, and synchronize early warning information to monitoring personnel; The remote operation module is used to receive operation instructions from the centralized control management center and to remotely control and operate the equipment in the coal mine.

2. The intelligent coal mine centralized control center monitoring system according to claim 1, characterized in that: The process of acquiring the comprehensive coal mine dataset in the data acquisition module is as follows: Before coal mining, geological exploration is conducted to assess the geological information of the mine. During the mining process, measuring instruments and tools are used to monitor and measure the underground geological conditions in real time, and the data from geological exploration and underground measurement are integrated into geological data. Sensors are deployed underground in coal mines to monitor underground environmental data in real time, and monitoring devices are installed on coal mine equipment to monitor the operating status of the equipment in real time; The collected environmental data, equipment status data, and geological data are integrated to form a comprehensive coal mine dataset, which is then divided into real-time data and historical data. The integrated coal mine dataset is then transmitted to the centralized control and management center wirelessly.

3. The intelligent coal mine centralized control center monitoring system according to claim 2, characterized in that: The specific process for extracting feature information from environmental data, equipment status data, and geological data in the data processing and analysis module is as follows: The collected coal mine comprehensive dataset is cleaned, denoised, and outlier processed. The processed data is then normalized to remove redundant information. Feature extraction is performed based on the preprocessed coal mine comprehensive dataset. Based on environmental data, features are extracted for gas concentration, temperature, humidity and dust concentration to obtain environmental disturbance features. Based on equipment status data, features are extracted for equipment vibration, equipment temperature and equipment operating parameters to obtain equipment status disturbance features. Based on geological data, features are extracted for roof pressure, coal seam thickness and geological structure to obtain geological disturbance features. Based on the extracted environmental disturbance features, equipment status disturbance features, and geological disturbance features, the correlation between environmental data, equipment status data, and geological data is analyzed. By combining historical data with environmental disturbance characteristics, equipment status disturbance characteristics, and geological disturbance characteristics, machine learning algorithms are used to predict the production status and potential risks of coal mines.

4. The intelligent coal mine centralized control center monitoring system according to claim 3, characterized in that: In the model calculation module, the prediction process for the geological conditions, environmental change trends, and equipment operating status of the coal mine is as follows: By combining environmental disturbance characteristics, equipment status disturbance characteristics, geological disturbance characteristics, and preprocessed historical data, a unified dataset is formed. Based on the extracted environmental disturbance features, equipment status disturbance features, and geological disturbance features, a coal mine production prediction model is constructed using a variety of machine learning algorithms. The integrated dataset is divided into a training set, a validation set, and a test set. The training set data is used to learn and train the coal mine production prediction model, and the validation set and test set are used to validate the trained coal mine production prediction model and evaluate its generalization ability. Using a trained model and preprocessed historical data, we obtain environmental disturbance index, equipment status disturbance index, and geological disturbance index. We use the environmental disturbance index to predict environmental change trends, the equipment status disturbance index to monitor the operating status of equipment in real time, and the geological disturbance index to predict the geological conditions of the coal mine. We output the prediction results.

5. The intelligent coal mine centralized control center monitoring system according to claim 4, characterized in that: The formula for calculating the environmental disturbance index is as follows: ； in, The environmental disturbance index. For gas concentration, This serves as the baseline value for gas concentration. For temperature, This serves as the reference value for temperature. For humidity, The weighting coefficients for gas concentration and temperature are: This is the weighting factor for humidity; The formula for calculating the device state interference index is as follows: ； in, This refers to the equipment status interference index. For equipment vibration, This serves as the reference value for equipment vibration. For equipment temperature, This serves as the reference value for the equipment temperature. For equipment operating parameters, These are the weighting factors for equipment vibration and temperature. These are the weighting coefficients for the equipment's operating parameters; The formula for calculating the geological disturbance index is as follows: ； in, Geological disturbance index For the pressure of the top plate, This serves as the baseline value for the top plate pressure. For coal seam thickness, This serves as a benchmark value for coal seam thickness. Due to the complexity of the geological structure, This represents the weighting coefficients for roof pressure and coal seam thickness. This is a weighting coefficient for the complexity of geological structures.

6. The intelligent coal mine centralized control center monitoring system according to claim 5, characterized in that: In the monitoring and early warning assessment module, the process for obtaining the early warning level of coal mine production status is as follows: By combining the environmental disturbance index, equipment condition disturbance index, and geological disturbance index with historical data, the early warning coefficient for coal mine production is calculated, and the correlation between coal mine production and the environmental disturbance index, equipment condition disturbance index, and geological disturbance index is analyzed. Based on historical data on coal mine production environment and coal mine safety production standards, the current warning levels for coal mine production are determined as Level 1, Level 2, Level 3, and Level 4. Among them, the road hazard level increases progressively from Level 1 to Level 4. The calculation results of the coal mine production early warning coefficient are matched with different early warning levels, and corresponding early warning thresholds are set. When the warning level reaches the preset warning threshold, the alarm module is triggered to issue an alarm signal and generate corresponding warning information, which is then synchronized to the monitoring personnel.

7. The intelligent coal mine centralized control center monitoring system according to claim 6, characterized in that: The formula for calculating the coal mine production early warning coefficient is as follows: ； in, Coal mine production early warning coefficient The environmental disturbance index. This serves as the benchmark value for the environmental disturbance index. This refers to the equipment status interference index. This serves as the baseline value for the equipment status interference index. Geological disturbance index These are the weighting coefficients for the environmental disturbance index, equipment condition disturbance index, and geological disturbance index. This is the adjustment coefficient for the geological disturbance index.

8. The intelligent coal mine centralized control center monitoring system according to claim 7, characterized in that: Multiple warning levels correspond to multiple warning thresholds, wherein the warning assessment thresholds include an upper limit threshold and a lower limit threshold; The multiple warning levels and the multiple warning assessment thresholds satisfy the following relationship: Level 1 warning ; Level II warning ; Level III Warning ; Level IV Warning ; in, Coal mine production early warning coefficient These are the upper threshold for Level 1 warning and the lower threshold for Level 2 warning. These are the upper threshold values ​​corresponding to Level II warning and the lower threshold values ​​corresponding to Level III warning. These are the upper limit thresholds corresponding to Level 3 warning and the lower limit thresholds corresponding to Level 4 warning.

9. The intelligent coal mine centralized control center monitoring system according to claim 8, characterized in that: The process of synchronizing the early warning information to monitoring personnel is as follows: It receives alarm signals from the monitoring and early warning assessment module in real time and verifies them after receiving the alarm signals. Based on the received alarm and warning information, activate the audible and visual alarm, issue voice broadcasts, and send SMS notifications to remind monitoring personnel; The early warning information is displayed synchronously in the central control center and converted into text and charts for display. At the same time, the associated information of each alarm event is recorded, including alarm time, alarm type, early warning information, and the response of monitoring personnel. Based on the recorded related information, alarm event reports are generated to summarize and analyze the safety risks in coal mine production.

10. The intelligent coal mine centralized control center monitoring system according to claim 9, characterized in that: The process of remotely controlling and operating equipment underground in a coal mine is as follows: Receive operation instructions from the centralized control management center, verify the operation instructions, and parse the verified operation instructions into specific control commands; The system uses a communication network to send control commands to downhole equipment, monitors the operating status of downhole equipment in real time, and feeds back to the centralized control management center. Record the sending, receiving, execution, and result information of each operation instruction, and generate an operation report to summarize and analyze the equipment operation status and safety risks in coal mine production.

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