Mine thermal disaster early warning method, system, device and medium

Abstract

The application provides a mine thermal dynamic disaster early warning method and system, equipment and medium, and belongs to the technical field of mine safety prediction. The method comprises the following steps: for each target monitoring area, obtaining geological radar detection data and target parameter data of the target monitoring area; determining a damage index of a coal seam molecular structure according to the geological radar detection data and coal seam molecular structure data; determining a damage characteristic value of the target monitoring area according to an analytic hierarchy process, electromagnetic radiation data, electron spin resonance signal data and the damage index of the coal seam molecular structure; determining a damage grade of the coal seam molecular structure of the target monitoring area according to the damage characteristic value and a preset damage threshold table, and determining an early warning grade according to the damage grade. The mine thermal dynamic disaster early warning method and system, equipment and medium provided by the application can adapt to complex and changeable disaster conditions and improve the accuracy of early warning.

Description

Technical Field

[0001] This application belongs to the field of mine safety prediction technology, and more specifically, it relates to a mine thermal and dynamic disaster early warning method, system, equipment, and medium. Background Technology

[0002] Thermal disasters in mines (such as coal and gas outbursts and rock bursts) can release enormous energy instantly, causing casualties among underground personnel, damage to equipment, disruption of the mine's production system, and even secondary disasters such as gas explosions, posing a serious threat to mine safety and the safety of people's lives.

[0003] To prevent such disasters, current technologies mainly rely on empirical models built from years of accumulated experience in mines and limited experimental data for early warning. For example, in laboratories, different coal seam structures and gas contents are used to simulate mine disaster conditions. However, early warning based on these empirical models lacks in-depth analysis at the microscopic level, such as changes in the molecular structure of coal seams. Furthermore, the experimental data and limited laboratory conditions make it difficult to accurately replicate the actual underground environment, resulting in low accuracy in predicting mine disasters. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, equipment, and medium for early warning of thermal and dynamic disasters in mines, which determines the damage index of the coal seam molecular structure based on coal seam molecular structure data and ground-penetrating radar detection data, and accurately reflects the damage status of the coal seam molecular structure by combining damage characteristic values, adapting to complex and ever-changing disaster situations and improving the accuracy of early warning.

[0005] A first aspect of this application provides a method for early warning of thermal and dynamic disasters in mines, including:

[0006] Based on the mine grid map, the area to be monitored is determined and divided into multiple target monitoring areas;

[0007] For each target monitoring area, the ground-penetrating radar detection data and target parameter data of the target monitoring area are acquired. The target parameter data is standardized, including electromagnetic radiation data, coal seam molecular structure data and electron spin resonance signal data.

[0008] The destruction index of the coal seam molecular structure was determined based on ground-penetrating radar detection data and coal seam molecular structure data.

[0009] The damage characteristic value of the target monitoring area was determined based on the analytic hierarchy process and electromagnetic radiation data, electron spin resonance signal data, and the destruction index of the coal seam molecular structure.

[0010] The damage level of the coal seam molecules in the target monitoring area is determined based on the damage characteristic values ​​of the target monitoring area and the preset damage threshold table. The preset damage threshold table is used to characterize the correlation between the range of damage characteristic values ​​and the damage level.

[0011] The warning level is determined based on the damage level of the coal seam molecules in the target monitoring area, and the warning level is directly proportional to the damage level of the coal seam molecules.

[0012] A second aspect of this application provides a mine thermal disaster early warning system, comprising:

[0013] The target monitoring area determination module is used to determine the area to be monitored based on the mine grid map and divide the area to be monitored into multiple target monitoring areas.

[0014] The data processing module is used to acquire the ground-penetrating radar detection data and target parameter data for each target monitoring area, and to standardize the target parameter data, which includes electromagnetic radiation data, coal seam molecular structure data and electron spin resonance signal data.

[0015] The damage index determination module is used to determine the damage index of the coal seam molecular structure based on ground-penetrating radar detection data and coal seam molecular structure data.

[0016] The damage characteristic value determination module is used to determine the damage characteristic value of the target monitoring area based on the analytic hierarchy process and electromagnetic radiation data, electron spin resonance signal data and the destruction index of coal seam molecular structure.

[0017] The damage level determination module is used to determine the damage level of coal seam molecules in the target monitoring area based on the damage characteristic values ​​of the target monitoring area and the preset damage threshold table. The preset damage threshold table is used to characterize the correlation between the range of damage characteristic values ​​and the damage level.

[0018] The early warning level determination module is used to determine the early warning level based on the damage level of coal seam molecules in the target monitoring area. The early warning level is directly proportional to the damage level of coal seam molecules.

[0019] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described mine thermal disaster early warning method.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described mine thermal disaster early warning method.

[0021] A fifth aspect of this application provides a computer program product, including a computer program or computer-executable instructions, wherein when the computer program or computer-executable instructions are executed by a processor, the steps of the above-described mine thermal disaster early warning method are implemented.

[0022] The beneficial effects of the mine thermal and dynamic disaster early warning method, system, equipment, and medium provided in this application are as follows: This application determines the damage index of the coal seam molecular structure based on ground-penetrating radar detection data and coal seam molecular structure data, combining macroscopic ground-penetrating radar data with microscopic coal seam molecular structure data, realizing the judgment from molecular damage to energy release, and reducing the prediction false alarm rate; then, through the analytic hierarchy process, it comprehensively determines the damage characteristic value of the target monitoring area by integrating various indicators (electromagnetic radiation data, electron spin resonance signal data, and coal seam molecular structure damage index), which can more comprehensively and accurately reflect the actual damage situation of the coal seam in the target monitoring area, and determine the mine disaster early warning level based on the actual damage situation of the coal seam, which can adapt to the complex and ever-changing disaster situation inside the mine and improve the accuracy of mine disaster early warning. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A schematic flowchart of a mine thermal and dynamic disaster early warning method provided in an embodiment of this application;

[0025] Figure 2 A flowchart illustrating a method for determining the degree of damage to the target molecular structure of each key parameter according to an embodiment of this application;

[0026] Figure 3 A structural block diagram of a mine thermal disaster early warning system provided in an embodiment of this application;

[0027] Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0030] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a mine thermal disaster early warning method according to an embodiment of this application. The method may include:

[0031] S101: Determine the area to be monitored based on the mine grid map, and divide the area to be monitored into multiple target monitoring areas.

[0032] In this embodiment, the mine area is divided into several square grids of equal size. The monitoring area is divided by selecting continuous grid cells. In this embodiment, all grid cells containing each target monitoring area can be selected on the grid map by the staff, and all selected grid cells are combined to form the monitoring area. Alternatively, the corresponding grid cells can be selected according to the roadways and numbers inside the mine, and the selected grid cells are combined to form the monitoring area.

[0033] The area to be monitored is further divided into multiple target monitoring areas. These divisions can be based on various methods: areas with similar geological conditions can be grouped into one target monitoring area; areas with interconnected mining activities can be grouped into one target monitoring area; or areas with relatively uniform stress distribution can be grouped into one target monitoring area. The range, number, geological conditions, mining activities, and stress distribution of each of the divided target monitoring areas are then compiled and stored in a database. The relevant information for each target monitoring area is also marked on the mine grid map to facilitate subsequent monitoring data management and analysis.

[0034] S102: For each target monitoring area, acquire the ground-penetrating radar detection data and target parameter data for that target monitoring area.

[0035] In this embodiment, a high-frequency ground-penetrating radar is used to detect the target monitoring area and obtain ground-penetrating radar detection data within the target monitoring area. This ground-penetrating radar detection data includes ground-penetrating radar echo data and other related detection data.

[0036] Target parameter data within the target monitoring area is acquired. In this embodiment, the target parameter data includes electromagnetic radiation data, coal seam molecular structure data, and electron spin resonance signal data. Electromagnetic radiation intensity is monitored within the target monitoring area using electromagnetic radiation monitoring equipment. Coal seam samples are collected within the target monitoring area, and the molecular structure of the coal seam samples is analyzed using infrared spectroscopy, X-ray diffraction, etc., to obtain coal seam molecular structure data, including the type, length, and angle of molecular bonds. Electron spin resonance signals are detected in the coal seam data (e.g., free radicals in the coal seam) within the target monitoring area using an electron spin resonance analyzer to obtain electron spin resonance signal data. Further, the acquired target parameter data is standardized. The target parameter data is checked for missing values. If missing values ​​are found, samples containing missing values ​​are deleted or interpolation is used to fill in the missing values, depending on the data characteristics. In this embodiment, the Z-score standardization method can be used to standardize the target parameter data.

[0037] S103: Determine the destruction index of the coal seam molecular structure based on ground-penetrating radar detection data and coal seam molecular structure data.

[0038] In step S103, the destruction index of the coal seam molecular structure is determined based on the ground-penetrating radar detection data and the coal seam molecular structure data, including:

[0039] Based on ground-penetrating radar data and coal seam molecular structure data, and through a pre-trained coupling model, the structural changes of coal seam molecular bonds were determined.

[0040] The destruction index of the molecular structure of coal seams is determined based on the structural changes in the molecular bonds of the coal seams.

[0041] In this embodiment, the coal seam molecular structure data belongs to the microscopic level, representing information such as the structure of the coal seam at the molecular scale. This embodiment uses the spatial coordinates of the mine as a reference, spatially aligning the ground-penetrating radar (GPR) data and the coal seam molecular structure data. All spatial alignment and other correlation operations between the GPR data and the coal seam molecular structure data are based on the mine's spatial coordinates. If any data is missing, interpolation can be used to supplement it, ensuring spatial consistency between the GPR data and the coal seam molecular structure data.

[0042] The pre-trained coupling model can be a pre-trained deep learning-based coupling model. In this embodiment, a model combining convolutional neural networks and long short-term memory networks can be used as the coupling model. The convolutional neural network extracts the spatial features of the ground-penetrating radar data, and the long short-term memory network processes the correlation or temporal features of the coal seam molecular structure data. Correlation features refer to the characteristics exhibited by the interrelationships or connections between different data features. For example, the relationship between the spatial distance between different atomic groups in the coal seam molecule, the type of chemical bond, and the overall stability of the molecule. Temporal features emphasize the characteristics that the data exhibits as it changes over time. For example, coal seam molecular structure data is collected at different time points. That is, the changing trend of the coal seam molecular structure at different time points, and the fluctuation of coal seam molecular parameters (e.g., the vibrational frequency of molecular bonds) over time, all belong to temporal features.

[0043] A predetermined number of coal seam samples showing changes in their molecular bond structure are selected from the coal seam sample data. These samples, including ground-penetrating radar data and coal seam molecular structure data, are used as validation samples. This coal seam sample data is then input into a pre-trained coupled model to validate the model's output of coal seam molecular bond structure changes. Multiple output results are compared with actual results. If the errors between the output and actual results are all within a predetermined range (e.g., 5%), the pre-trained coupled model is deemed capable of detecting coal seam sample data. If at least one error is outside the predetermined range, the model is deemed insufficient in predictive ability under certain circumstances, and the coupled model needs to be retrained.

[0044] Spatially aligned ground-penetrating radar (GPR) data and coal seam molecular structure data are input into a pre-trained coupled model. The spatially aligned GPR data is then fed into the convolutional neural network (CNN) portion of the coupled model for convolution operations to extract GPR spatial features. These features characterize the spatial features of geological structures, such as coal seam thickness variations and rock strata interface features. The coal seam molecular structure data is then input into the fully connected layer of the long short-term memory (LSTM) network portion of the coupled model for feature extraction, yielding the coal seam molecular structure features. These features include the type, length, and angle of molecular bonds.

[0045] Based on the feature fusion part of the coupling model, the spatial features of the above-mentioned geological radar and the molecular structure features of the coal seam are fused across modes to obtain a fused feature vector that includes two-dimensional information (spatial dimension and molecular dimension).

[0046] The fused feature vector is input into a pre-trained coupled model to predict the changes in the molecular bond structure features of the coal seam.

[0047] Changes in the molecular bond structure of coal seams include changes in bond type breaking, bond length, and bond angle. Using the analytic hierarchy process (AHP), the weights of different changes in the molecular bond structure of coal seams on the degree of damage to the coal seam's molecular structure are determined. In this embodiment, bond type breaking is determined to have the greatest impact on the degree of damage to the coal seam's molecular structure; therefore, the weight for bond type is set to 0.6, the weight for bond length to 0.3, and the weight for bond angle to 0.1. Based on the changes in the characteristics of the coal seam's molecular bond structure and their corresponding weights, a damage index for the coal seam's molecular structure is determined.

[0048] In this embodiment, the larger the destruction index of the coal seam molecular structure, the more severe the destruction of the coal seam molecular structure.

[0049] S104: The damage characteristic value of the target monitoring area is determined based on the analytic hierarchy process and electromagnetic radiation data, electron spin resonance signal data, and the destruction index of the coal seam molecular structure.

[0050] In step S104, the damage characteristic values ​​of the target monitoring area are determined based on the analytic hierarchy process and electromagnetic radiation data, electron spin resonance signal data, and the destruction index of the coal seam molecular structure. These values ​​include:

[0051] The initial weights of electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure were determined using the analytic hierarchy process.

[0052] Acquire historical disaster monitoring data and construct a Bayesian network based on the historical disaster monitoring data and the corresponding disasters;

[0053] The conditional probability relationship between electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure and coal seam damage was determined using Bayesian networks.

[0054] The initial weights corresponding to electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure are corrected according to the conditional probability relationship, so as to obtain the target weights corresponding to electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure after correction.

[0055] The damage characteristic value of the target monitoring area is determined based on electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure and the corresponding target weight.

[0056] This embodiment uses the Analytic Hierarchy Process (AHP) to determine a hierarchical model, which includes a target layer, a criterion layer, and a scheme layer. The target layer clarifies that the objective of this embodiment is the damage characteristic value of the target monitoring area. The criterion layer selects key factors that influence the damage characteristic value, including electromagnetic radiation data, electron spin resonance signal data, and the damage index of the coal seam molecular structure. The scheme layer treats each target monitoring area to be evaluated as an independent scheme (the object to be evaluated), determining the damage characteristic value of the target monitoring area by comprehensively considering the influencing factors selected in the criterion layer. In the weight allocation stage, the AHP is used to clarify the initial weights corresponding to each key factor (electromagnetic radiation data, electron spin resonance signal data, and the damage index of the coal seam molecular structure), providing support for the accurate calculation of subsequent damage characteristic values.

[0057] Based on three factors influencing damage characteristic values ​​in the criterion layer (electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure), the importance of each influencing factor is compared between two of them. Based on this comparison result, a judgment matrix is ​​determined using the 1-9 scaling method. A , ,in, Indicates the first i The influencing factor is relative to the first j The importance of each influencing factor is determined. This embodiment uses a 1-9 scale, where 1 indicates that the two influencing factors are equally important, 3 indicates that the first influencing factor is slightly more important than the second, 5 indicates that the first influencing factor is significantly more important than the second, 7 indicates that the first influencing factor is strongly more important than the second, and 9 indicates that the first influencing factor is extremely more important than the second. 2, 4, 6, and 8 represent the median values ​​of the adjacent importance levels, with 3, 5, 7, and 9 corresponding to an increasing degree of importance between the first and second influencing factors.

[0058] For the judgment matrix A, multiply the elements of each row to obtain the row product. , ,in, i =1,2,3. For each row product, determine its geometric mean. , Normalize each geometric mean to obtain the initial weight vector W. ,in, ,in, k For the summation index, representing the first... k One influencing factor, These are the initial weights corresponding to electromagnetic radiation data, electromagnetic spin resonance signal data, and the destruction index of coal seam molecular structure, respectively. .

[0059] According to the judgment matrix A and initial weight vector W Determine the judgment matrix A The largest eigenvalue, based on the judgment matrix A Determine the judgment matrix for the largest eigenvalue A Consistency index, based on the judgment matrix A The order of randomness is used to obtain the average random consistency index, based on the judgment matrix. A The consistency index and the average random consistency index determine the judgment matrix. A If the consistency ratio of the judgment matrix is ​​less than a preset consistency threshold (the preset consistency threshold can be 0.1), then the judgment matrix is ​​determined to be invalid. A If the consistency is acceptable, the above initial weights are valid; otherwise, the judgment matrix needs to be adjusted and the initial weights redefined.

[0060] From historical coal seam monitoring data, we obtain electromagnetic radiation data, electron spin resonance signal data, coal seam molecular structure destruction index data, and corresponding coal seam damage data for different target monitoring areas before and after the occurrence of mine thermal and dynamic disasters. The obtained monitoring data are then standardized or normalized.

[0061] Based on electromagnetic radiation data, electron spin resonance signal data, coal seam molecular structure destruction index data, and corresponding coal seam damage data, the correlation between these data and coal seam damage is determined. This correlation is either causal or dependent. Based on this correlation, the nodes of the Bayesian network (including the aforementioned data nodes and coal seam damage nodes) and the directed edges between nodes are determined, thus defining the structure of the Bayesian network. Using standardized or normalized data, the conditional probability table of each node in the Bayesian network is determined through maximum likelihood estimation or Bayesian estimation methods to construct the Bayesian network.

[0062] In this embodiment, the nodes of the conditional probability table of each node represent random variables (e.g., in the scenario of early warning of thermal and dynamic disasters in mines, electromagnetic radiation data, electron spin resonance signal data, coal seam molecular structure destruction index data, and coal seam damage status, etc.), and the directed edges represent the causal or dependency relationships between variables.

[0063] This Bayesian network is used to determine the conditional probability relationships between electromagnetic radiation data, electron spin resonance signal data, coal seam molecular structure destruction index, and coal seam damage. Specifically, the query target is first defined, which can be P(coal seam damage|electromagnetic radiation data, electron spin resonance signal data, coal seam molecular structure destruction index), P(coal seam damage|electromagnetic radiation data), P(coal seam damage|electromagnetic resonance signal data), and P(coal seam damage|coal seam molecular structure destruction index). Based on the constructed Bayesian network, historical data is input, and the conditional probability relationships between electromagnetic radiation data, electron spin resonance signal data, coal seam molecular structure destruction index, and coal seam damage are determined using the joint tree algorithm (or the particle filter algorithm in approximate inference algorithms) within the Bayesian network.

[0064] Based on the conditional probability relationships, the contribution of the three data points (electromagnetic radiation data, electron spin resonance signal data, and coal seam molecular structure destruction index) to the occurrence of coal seam damage is determined, thereby identifying the influence of conditional probabilities on the weights. If the conditional probability of a certain data point occurring during coal seam damage is higher, it indicates a stronger indicative role of that data in coal seam damage, and the initial weight corresponding to that data is increased; conversely, if the conditional probability is lower, the initial weight corresponding to that data is decreased.

[0065] For each data point (electromagnetic radiation data, electron spin resonance signal data, and coal seam molecular structure damage index), a corresponding correction coefficient is determined based on the conditional probability relationship between each data point and coal seam damage. In this embodiment, the correction coefficient can be determined through a linear function relationship between the conditional probability and the correction coefficient.

[0066] The corrected weights are determined by multiplying the initial weights of each data point by their corresponding correction coefficients, and these corrected weights are then used as the target weights. The formula for calculating the corrected weights is as follows: ,in, For the first i The adjusted weights of the key factors For the first i Correction coefficients for each data point For the first i The initial weights corresponding to each key factor.

[0067] This embodiment modifies the weights of each data point using the analytic hierarchy process (AHP) and Bayesian networks, making the final weights more accurate and better reflecting the actual impact of each data point on coal seam damage. The modified weights can more reasonably reflect the importance of each monitoring data point, thereby enabling the disaster warnings determined based on these weights to more accurately identify disaster risks, improve the reliability of mine thermal disaster warnings, and help to take preventive measures in advance to ensure safe production in the mine.

[0068] This embodiment standardizes electromagnetic radiation data, electromagnetic spin resonance signal data, and the destruction index of coal seam molecular structure, and sets... These are the standardized electromagnetic radiation data values, electromagnetic spin resonance signal data values, and the damage index value of the coal seam molecular structure, respectively. The formula for calculating the damage characteristic value of the target monitoring area is: ,in, Let be the damage characteristic value of the m-th target monitoring area.

[0069] S105: Determine the damage level of the coal seam molecules in the target monitoring area based on the damage characteristic values ​​of the target monitoring area and the preset damage threshold table. The preset damage threshold table is used to characterize the correlation between the range of damage characteristic values ​​and the damage level.

[0070] In step S105, the damage level of the coal seam molecules in the target monitoring area is determined based on the damage characteristic values ​​of the target monitoring area and a preset damage threshold table, including:

[0071] The damage characteristic values ​​of the target monitoring area are matched with a preset damage threshold table;

[0072] If the damage characteristic value of the target monitoring area is within the first preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the first damage level;

[0073] If the damage characteristic value of the target monitoring area is within the second preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the second damage level.

[0074] If the damage characteristic value of the target monitoring area is within the third preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the third damage level, wherein the damage degree of the first damage level, the second damage level and the third damage level increases sequentially.

[0075] In this embodiment, the preset damage threshold table is determined based on the geological conditions of the coal seam, historical monitoring data, and industry standards. The preset damage threshold table is used to characterize the relationship between the range of damage characteristic values, the corresponding damage level, and the degree of damage. This embodiment sets three damage levels for coal seam elements, including a first damage level, a second damage level, and a third damage level, wherein the degree of damage increases sequentially from the first damage level to the second damage level and the third damage level. In this embodiment, the first preset range of the damage characteristic value corresponding to the first damage level can be set to [0, 0.3). At this time, very few coal seam molecular bonds are deformed, no coal seam molecular bonds are broken, the overall stability of the coal seam is good, and there is no risk of disaster. The second preset range of the damage characteristic value corresponding to the second damage level can be set to [0.3, 0.7]. At this time, a preset first proportion of coal seam molecular bonds are broken (the preset first proportion can be less than 30%), the local stability of the coal seam decreases, and the monitoring efficiency needs to be improved. The third preset range of the damage characteristic value corresponding to the third damage level can be [0.7, 1]. At this time, a preset second proportion of coal seam molecular bonds are broken (the preset second proportion can be greater than 30%), the overall stability of the coal seam is poor, and there is a risk of thermodynamic disaster.

[0076] S106: Determine the warning level based on the damage level of coal seam molecules in the target monitoring area.

[0077] In this embodiment, the warning level is directly proportional to the damage level of the coal seam molecules. If the damage level in the target monitoring area is the first damage level, the corresponding warning level is the first warning level, which is represented by blue. If the damage level in the target monitoring area is the second damage level, the corresponding warning level is the second warning level, which is represented by yellow. If the damage level in the target monitoring area is the third damage level, the corresponding warning level is the third warning level, which is represented by red. The warning levels increase sequentially from first to third.

[0078] If the molecular damage level of the coal seam in the target monitoring area is at the critical state between the first damage level and the second damage level, in order to avoid missing risks, the corresponding warning level can be upgraded to the second warning level. The damage level in the target monitoring area is reviewed within a preset time. If the damage level is at the first damage level after review, the corresponding second warning level is downgraded to the first warning level to ensure the safety of underground workers.

[0079] For example, if the detected value of the coal seam molecular damage characteristic in the target monitoring area is 0.3, which is at the critical state between the first damage level ([0,0.3)) and the second damage level ([0.3,0.7)), the warning level of the coal seam molecular damage in the target monitoring area is upgraded to the second warning level, and staff are prompted to strengthen the monitoring efficiency in the target monitoring area. A review is then conducted within a preset 12-hour period. If the detected value of the coal seam molecular damage characteristic in the target monitoring area decreases to 0.25, which is within the range corresponding to the first damage level, the second warning level is downgraded to the first warning level, indicating that the coal seam molecular damage in the target monitoring area is of good stability and there is no risk of disaster.

[0080] As can be seen from the above, the embodiments of this application determine the damage index of the coal seam molecular structure based on ground-penetrating radar detection data and coal seam molecular structure data. By combining macroscopic ground-penetrating radar data with microscopic coal seam molecular structure data, the judgment from molecular damage to energy release is realized, which can capture microscopic changes in the occurrence of disasters in advance and reduce the false alarm rate. Furthermore, by using the analytic hierarchy process (AHP) to comprehensively consider the weights of various indicators (electromagnetic radiation, electron spin resonance signal, and coal seam molecular structure damage index), the damage characteristic value is determined, which can more comprehensively and accurately reflect the actual damage of the coal seam in the target monitoring area and improve the accuracy of underground prediction. The disaster warning level of the mine is determined based on the actual damage of the coal seam, which can adapt to the complex and ever-changing disaster situation inside the mine and improve the accuracy of mine disaster warning.

[0081] In one embodiment of this application, before determining the destruction index of the coal seam molecular structure based on the structural changes of the coal seam molecular bonds, the method further includes:

[0082] Historical coal seam monitoring data is acquired, and a database of correspondences between structural changes in coal seam molecular bonds and the degree of destruction of the coal seam molecular structure is determined based on the historical coal seam monitoring data. The database of correspondences is the correspondence between key parameters of structural changes in coal seam molecular bonds and the corresponding scores of the degree of destruction of the molecular structure. Key parameters include the number of molecular bonds broken per unit volume, information on molecular bond recombination, and the disorder coefficient of the spatial distribution of molecular bonds. Information on molecular bond recombination includes the type and proportion of molecular bond recombination.

[0083] Among them, the destruction index of coal seam molecular structure is determined based on structural changes in coal seam molecular bonds, including:

[0084] Determine the key parameters of the structural changes in the molecular bonds of coal seams based on the structural changes in the molecular bonds of coal seams;

[0085] The score for the degree of destruction of the target molecular structure for each key parameter is determined based on the key parameters and the corresponding relationship database.

[0086] The target weights corresponding to each key parameter are determined using the entropy weight method.

[0087] The destruction index of the coal seam molecular structure is determined based on the molecular structure destruction scores of each key parameter and their corresponding target weights.

[0088] In this embodiment, historical coal seam monitoring data from different periods and monitoring areas of the mine are acquired. This historical coal seam monitoring data may include coal seam data under normal mine conditions and coal seam data before and after the occurrence of thermal and dynamic disasters in the mine. The acquired coal seam data includes ground-penetrating radar detection data, electromagnetic radiation data, coal seam molecular structure data, and electron spin resonance signal data obtained through ground-penetrating radar, electromagnetic radiation monitoring, electron spin resonance, and other means.

[0089] From historical coal seam monitoring data, key parameters of structural changes in the molecular bonds of coal seams per unit volume can be obtained. These key parameters may include the number of broken molecular bonds, information on molecular bond recombination (type and proportion of molecular bond recombination), and the disorder coefficient of the spatial distribution of molecular bonds.

[0090] By combining historical coal seam monitoring data with actual coal seam damage (e.g., the intensity of the disaster, the degree of damage to the coal seam's molecular structure), a molecular structure damage score is determined for each key parameter based on the actual damage. In this embodiment, if the number of broken molecular bonds per unit volume exceeds a preset number, the proportion of molecular bond recombination types exceeds a preset proportion, and the spatial distribution disorder coefficient of molecular bonds exceeds a preset coefficient, the coal seam's molecular structure damage score is determined to be relatively high.

[0091] A database of corresponding relationships was established based on the correlation between each key parameter and the score of the degree of damage to the molecular structure of the coal seam.

[0092] Based on the acquired ground-penetrating radar data and coal seam molecular structure data for the current target monitoring area, the structural changes of the current coal seam molecular bonds are determined. Based on the structural changes of the current coal seam molecular bonds, the specific values ​​of key parameters such as the number of broken molecular bonds per unit volume, the information on molecular bond recombination (type and proportion of molecular bond recombination), and the disorder coefficient of the spatial distribution of molecular bonds are determined.

[0093] The specific values ​​corresponding to each key parameter are compared with the corresponding relational database to determine the molecular structure damage score corresponding to each key parameter, thus obtaining the target molecular structure damage score for each key parameter.

[0094] The acquired data corresponding to each key parameter are standardized to eliminate the influence of dimensions. The information entropy of each key parameter is determined according to the information entropy formula. The smaller the information entropy of a key parameter, the greater its variability, the more information it provides, and the greater its weight. Based on weight calculation, the weight of each key parameter is determined according to its corresponding information entropy. The information entropy of all key parameters is then calculated. Summing is performed to obtain the total information entropy. H t For the first The key parameters and their corresponding target weights are: ,in, For the first The target weights corresponding to the key parameters For the first The information entropy of a key parameter.

[0095] The product of the target molecular structure damage score of each key parameter and the target weight corresponding to the key parameter is obtained, and all products are added together to obtain the damage index of the coal seam molecular structure.

[0096] This embodiment constructs a database containing the correspondence between key parameters of coal seam molecular bond structure changes and damage degree scores. It analyzes the breakage, recombination, and spatial distribution of coal seam molecular bonds at the microscopic level, more accurately reflecting the damage to the coal seam molecular structure and providing a more reliable microscopic basis for subsequent early warning. This embodiment also combines the entropy weight method to determine the weights of key parameters, which greatly improves the accuracy of assessing the degree of damage to the coal seam molecular structure, thereby improving the accuracy of predicting mine thermal and dynamic disasters.

[0097] For example, taking a target monitoring area as an example, the destruction index of the molecular structure of the coal seam within the target monitoring area is determined.

[0098] Historical coal seam monitoring data from the coal mine were obtained, and multiple sets of data were determined using the analytic hierarchy process (AHP).

[0099] As the number of broken molecular bonds per unit volume increases, the corresponding score for the degree of damage to the molecular structure of the coal seam increases. When the number of broken molecular bonds per unit volume is 8, the corresponding damage score is 2; when the number of broken molecular bonds per unit volume is 10, the corresponding damage score is 3; when the number of broken molecular bonds per unit volume is 12, the corresponding damage score is 4; and when the number of broken molecular bonds per unit volume is 15, the corresponding damage score is 6.

[0100] As the proportion of unstable types in molecular bond recombination increases, the corresponding score for the degree of damage to the coal seam's molecular structure also increases. When the proportion of unstable types in molecular bond recombination is 40%, the corresponding damage score is 2; when the proportion is 50%, the corresponding damage score is 3; when the proportion is 60%, the corresponding damage score is 4; and when the proportion is 70%, the corresponding damage score is 5.

[0101] As the disorder coefficient of molecular bond spatial distribution increases, the corresponding score for the degree of damage to the molecular structure of the coal seam also increases. A disorder coefficient of 0.5 corresponds to a damage score of 3, 0.6 to 4, 0.7 to 5, and 0.8 to 6. The entropy weight method was used to calculate the weights for the number of molecular bond breaks (0.3), molecular bond recombination information (0.4), and the disorder coefficient of molecular bond spatial distribution (0.3), establishing a corresponding relationship database. This database characterizes the relationship between key parameters (the number of molecular bond breaks, the proportion of unstable types in molecular bond recombination information, and the disorder coefficient of molecular bond spatial distribution) and the corresponding scores for the degree of damage to the molecular structure of the coal seam, including the weights for each key parameter.

[0102] In one embodiment of this application, the determination of the target molecular structure destruction degree score for each key parameter based on a key parameter and a corresponding relational database includes:

[0103] The initial molecular structure destruction score for each key parameter is determined based on the key parameters and their corresponding relationship database.

[0104] The macroscopic fracture quantification index of the coal seam is determined based on the ground-penetrating radar detection data and image segmentation technology, and the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam is determined based on the macroscopic fracture quantification index of the coal seam.

[0105] The initial molecular structure damage score of each key parameter is compared with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam. If the comparison result shows that the initial molecular structure damage score is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, then the initial molecular structure damage score is taken as the target molecular structure damage score of each key parameter.

[0106] If the comparison result shows that the initial molecular structure damage score is outside the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, then the steps of re-acquiring each key parameter, determining the initial molecular structure damage score of each key parameter, and comparing the initial molecular structure damage score of each key parameter with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam are repeated until the initial molecular structure damage score of each key parameter is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam.

[0107] In this embodiment, see Figure 2 , Figure 2 This is a flowchart illustrating the method for determining the target molecular structure damage degree scores for each key parameter in this embodiment. For the current target monitoring area, the initial molecular structure damage degree scores for each key parameter are first determined based on the key parameters and their corresponding relational database. The preprocessed ground-penetrating radar (GPR) data is then segmented using image segmentation techniques to separate the coal seam area from the GPR image and identify and extract macroscopic fracture features within the coal seam. Based on these macroscopic fracture features, a macroscopic fracture quantification index for the coal seam is determined. Through analysis of historical coal seam sample data, combined with the coal seam damage degree and the macroscopic fracture quantification index, the damage trend range corresponding to different macroscopic fracture quantification indices is determined.

[0108] The initial molecular structure damage score of each key parameter is compared one by one with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam. The molecular structure damage score is determined based on the comparison results. If the initial molecular structure damage score is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, this score is retained as the target molecular structure damage score for that key parameter. If the initial molecular structure damage score is not within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, the parameter is marked and the process enters a loop. For key parameters not within the damage trend range, their relevant data is reacquired, and the process enters a loop again. This loop involves repeatedly reacquiring each key parameter, determining the initial molecular structure damage score for each key parameter, and comparing the initial molecular structure damage score of each key parameter with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, until the initial molecular structure damage score of each key parameter is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam.

[0109] This embodiment verifies the deviation of the initial molecular structure damage degree score by comparing the initial molecular structure damage degree score of the key parameters at the molecular structure level with the macroscopic coal seam fracture damage trend range. This makes the molecular structure damage degree score of the key parameters more accurately reflect the actual coal seam damage situation, providing more accurate basic data for subsequent mine thermal and dynamic disaster early warning and other work, and improving the accuracy of early warning.

[0110] Corresponding to the mine thermal and dynamic disaster early warning method in the above embodiment, Figure 3 This is a structural block diagram of a mine thermal disaster early warning system provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 3 The mine thermal disaster early warning system 30 includes: a target monitoring area determination module 31, a data processing module 32, a damage index determination module 33, a damage characteristic value determination module 34, a damage level determination module 35, and an early warning level determination module 36.

[0111] Among them, the target monitoring area determination module 31 is used to determine the area to be monitored based on the mine grid map and divide the area to be monitored into multiple target monitoring areas;

[0112] Data processing module 32 is used to acquire ground radar detection data and target parameter data for each target monitoring area, and to standardize the target parameter data, which includes electromagnetic radiation data, coal seam molecular structure data and electron spin resonance signal data.

[0113] The destruction index determination module 33 is used to determine the destruction index of the coal seam molecular structure based on the ground radar detection data and the coal seam molecular structure data.

[0114] The damage characteristic value determination module 34 is used to determine the damage characteristic value of the target monitoring area based on the analytic hierarchy process, electromagnetic radiation data, electron spin resonance signal data and the destruction index of the coal seam molecular structure.

[0115] The damage level determination module 35 is used to determine the damage level of the coal seam molecules in the target monitoring area based on the damage characteristic values ​​of the target monitoring area and the preset damage threshold table. The preset damage threshold table is used to characterize the correlation between the range of damage characteristic values ​​and the damage level.

[0116] The early warning level determination module 36 is used to determine the early warning level based on the damage level of the coal seam molecules in the target monitoring area. The early warning level is proportional to the damage level of the coal seam molecules.

[0117] In one embodiment of this application, when determining the damage index of the coal seam molecular structure based on ground-penetrating radar detection data and coal seam molecular structure data, the damage index determination module 33 is specifically used for:

[0118] Based on ground-penetrating radar data and coal seam molecular structure data, and through a pre-trained coupling model, the structural changes of coal seam molecular bonds were determined.

[0119] The destruction index of the molecular structure of coal seams is determined based on the structural changes in the molecular bonds of the coal seams.

[0120] In one embodiment of this application, when determining the destruction index of the coal seam molecular structure based on the structural changes of the coal seam molecular bonds, the destruction index determination module 33 is further specifically used for:

[0121] Historical coal seam monitoring data is acquired, and a database of correspondences between structural changes in coal seam molecular bonds and the degree of destruction of the coal seam molecular structure is determined based on the historical coal seam monitoring data. The database of correspondences is the correspondence between key parameters of structural changes in coal seam molecular bonds and the corresponding scores of the degree of destruction of the molecular structure. Key parameters include the number of molecular bonds broken per unit volume, information on molecular bond recombination, and the disorder coefficient of the spatial distribution of molecular bonds. Information on molecular bond recombination includes the type and proportion of molecular bond recombination.

[0122] Determine the key parameters of the structural changes in the molecular bonds of coal seams based on the structural changes in the molecular bonds of coal seams;

[0123] The score for the degree of destruction of the target molecular structure for each key parameter is determined based on the key parameters and the corresponding relationship database.

[0124] The target weights corresponding to each key parameter are determined using the entropy weight method.

[0125] The destruction index of the coal seam molecular structure is determined based on the molecular structure destruction scores of each key parameter and their corresponding target weights.

[0126] In one embodiment of this application, when determining the target molecular structure damage score of each key parameter based on the key parameters and the corresponding relational database, the damage index determination module 33 is specifically used for:

[0127] The initial molecular structure destruction score for each key parameter is determined based on the key parameters and their corresponding relationship database.

[0128] The macroscopic fracture quantification index of the coal seam is determined based on the ground-penetrating radar detection data and image segmentation technology, and the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam is determined based on the macroscopic fracture quantification index of the coal seam.

[0129] The initial molecular structure damage score of each key parameter is compared with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam. If the comparison result shows that the initial molecular structure damage score is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, then the initial molecular structure damage score is taken as the target molecular structure damage score of each key parameter.

[0130] If the comparison result shows that the initial molecular structure damage score is outside the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, then the steps of re-acquiring each key parameter, determining the initial molecular structure damage score of each key parameter, and comparing the initial molecular structure damage score of each key parameter with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam are repeated until the initial molecular structure damage score of each key parameter is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam.

[0131] In one embodiment of this application, when determining the damage characteristic value of the target monitoring area based on the analytic hierarchy process, electromagnetic radiation data, electron spin resonance signal data, and the destruction index of the coal seam molecular structure, the damage characteristic value determination module 34 is specifically used for:

[0132] The target weights corresponding to electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure are determined based on the analytic hierarchy process.

[0133] The damage characteristic value of the target monitoring area is determined based on electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure and the corresponding target weight.

[0134] In one embodiment of this application, when determining the target weights corresponding to the analytic hierarchy process (AHP), electromagnetic radiation data, electron spin resonance signal data, and the damage index of the coal seam molecular structure, the damage feature value determination module 34 is further specifically used for:

[0135] The initial weights of electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure were determined using the analytic hierarchy process.

[0136] Acquire historical disaster monitoring data and construct a Bayesian network based on the historical disaster monitoring data and the corresponding disasters;

[0137] The conditional probability relationship between electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure and coal seam damage was determined using Bayesian networks.

[0138] The initial weights corresponding to electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure are corrected according to the conditional probability relationship, so as to obtain the target weights corresponding to electromagnetic radiation data, electron spin resonance signal data, and the destruction index of coal seam molecular structure after correction.

[0139] In one embodiment of this application, when determining the damage level of coal seam molecules in the target monitoring area based on the damage characteristic values ​​of the target monitoring area and a preset damage threshold table, the damage level determination module 35 is specifically used for:

[0140] The damage characteristic values ​​of the target monitoring area are matched with a preset damage threshold table;

[0141] If the damage characteristic value of the target monitoring area is within the first preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the first damage level;

[0142] If the damage characteristic value of the target monitoring area is within the second preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the second damage level.

[0143] If the damage characteristic value of the target monitoring area is within the third preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the third damage level, wherein the damage degree of the first damage level, the second damage level and the third damage level increases sequentially.

[0144] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 400 in this embodiment may include one or more processors 401, one or more input devices 402, one or more output devices 403, and one or more memories 404. The processors 401, input devices 402, output devices 403, and memories 404 communicate with each other via a communication bus 405. The memories 404 store computer programs, including program instructions. The processors 401 execute the program instructions stored in the memories 404. Specifically, the processors 401 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned system embodiments, for example... Figure 3 The functions of the target monitoring area determination module 31, data processing module 32, damage index determination module 33, damage characteristic value determination module 34, damage level determination module 35, and early warning level determination module 36 are shown.

[0145] It should be understood that, in the embodiments of this application, the processor 401 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0146] Input device 402 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 403 may include a display (LCD, etc.), a speaker, etc.

[0147] The memory 404 may include read-only memory and random access memory, and provides instructions and data to the processor 401. A portion of the memory 404 may also include non-volatile random access memory. For example, the memory 404 may also store information such as ground-penetrating radar detection data, target parameter data, damage index of coal seam molecular structure, and damage characteristic values ​​of the target monitoring area within the device's target monitoring area.

[0148] In specific implementations, the processor 401, input device 402, and output device 403 described in the embodiments of this application can execute the implementation method described in the mine thermal disaster early warning method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0149] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to implement these processes. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or system capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0150] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0151] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0154] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0155] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0156] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered 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. A method for early warning of thermal and dynamic disasters in mines, characterized in that, include: Based on the mine grid map, the area to be monitored is determined, and the area to be monitored is divided into multiple target monitoring areas; For each target monitoring area, the ground-penetrating radar detection data and target parameter data of that target monitoring area are acquired. The target parameter data includes electromagnetic radiation data, coal seam molecular structure data, and electron spin resonance signal data. Historical coal seam monitoring data is acquired, and a correspondence database between structural changes in coal seam molecular bonds and the degree of damage to the coal seam molecular structure is determined based on the historical coal seam monitoring data. The correspondence database is the correspondence between key parameters of structural changes in coal seam molecular bonds and the corresponding molecular structure damage scores. The key parameters include the number of broken molecular bonds per unit volume, molecular bond recombination information, and the disorder coefficient of molecular bond spatial distribution. The molecular bond recombination information includes the type and proportion of molecular bond recombination. Based on the ground-penetrating radar detection data and the coal seam molecular structure data, and through a pre-trained coupling model, the structural changes of the coal seam molecular bonds are determined; Determine the key parameters of the structural changes in the molecular bonds of the coal seam based on the structural changes in the molecular bonds of the coal seam. The target molecular structure destruction degree score of each key parameter is determined based on the key parameters and the corresponding relationship library. The target weights corresponding to each key parameter are determined using the entropy weight method. The destruction index of the coal seam molecular structure is determined based on the molecular structure destruction degree scores of each key parameter and the corresponding target weights. The damage characteristic value of the target monitoring area is determined by the analytic hierarchy process and based on the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure. The damage level of the coal seam molecules in the target monitoring area is determined based on the damage characteristic values ​​of the target monitoring area and the preset damage threshold table, wherein the preset damage threshold table is used to characterize the correlation between the range of damage characteristic values ​​and the damage level. The warning level is determined based on the damage level of the coal seam molecules in the target monitoring area, and the warning level is proportional to the damage level of the coal seam molecules.

2. The mine thermal and dynamic disaster early warning method as described in claim 1, characterized in that, The step of determining the target molecular structure damage score for each key parameter based on the key parameters and the corresponding relationship database includes: The initial molecular structure destruction score of each key parameter is determined based on the key parameters and the corresponding relationship library. The macroscopic fracture quantification index of the coal seam is determined based on the ground-penetrating radar detection data and image segmentation technology, and the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam is determined based on the macroscopic fracture quantification index of the coal seam. The initial molecular structure damage score of each key parameter is compared with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam. If the comparison result is that the initial molecular structure damage score is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, then the initial molecular structure damage score is taken as the target molecular structure damage score of each key parameter. If the comparison result shows that the initial molecular structure damage score is outside the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam, then the steps of re-acquiring each key parameter, determining the initial molecular structure damage score of each key parameter, and comparing the initial molecular structure damage score of each key parameter with the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam are repeated until the initial molecular structure damage score of each key parameter is within the damage trend range corresponding to the macroscopic fracture quantification index of the coal seam.

3. The mine thermal and dynamic disaster early warning method as described in claim 1, characterized in that, The determination of damage characteristic values ​​for the target monitoring area based on the analytic hierarchy process (AHP), the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure includes: The target weights corresponding to the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure are determined according to the analytic hierarchy process. The damage characteristic value of the target monitoring area is determined based on the electromagnetic radiation data, the electron spin resonance signal data, the destruction index of the coal seam molecular structure, and the corresponding target weight.

4. The mine thermal and dynamic disaster early warning method as described in claim 3, characterized in that, The target weights corresponding to the analytic hierarchy process, the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure, respectively, include: The initial weights corresponding to the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure are determined according to the analytic hierarchy process. Acquire historical disaster monitoring data, and construct a Bayesian network based on the historical disaster monitoring data and the corresponding disasters; The conditional probability relationship between the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure and coal seam damage is determined based on the Bayesian network. The initial weights corresponding to the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure are corrected according to the conditional probability relationship to obtain the target weights corresponding to the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure after correction.

5. The mine thermal and dynamic disaster early warning method as described in claim 1, characterized in that, The step of determining the damage level of coal seam molecules in the target monitoring area based on the damage characteristic values ​​of the target monitoring area and a preset damage threshold table includes: The damage characteristic values ​​of the target monitoring area are matched with the preset damage threshold table; If the damage characteristic value of the target monitoring area is within the first preset range, then the damage level of the coal seam molecular structure in the target monitoring area is determined to be the first damage level; If the damage characteristic value of the target monitoring area is within the second preset range, then the damage level of the coal seam molecular structure in the target monitoring area is determined to be the second damage level; If the damage characteristic value of the target monitoring area is within a third preset range, the damage level of the coal seam molecular structure in the target monitoring area is determined to be the third damage level, wherein the damage degree of the first damage level, the second damage level and the third damage level increases sequentially.

6. A mine thermal disaster early warning system, characterized in that, include: The target monitoring area determination module is used to determine the area to be monitored based on the mine grid map and divide the area to be monitored into multiple target monitoring areas. The data processing module is used to acquire ground-penetrating radar detection data and target parameter data for each target monitoring area, and to standardize the target parameter data, which includes electromagnetic radiation data, coal seam molecular structure data, and electron spin resonance signal data. The destruction index determination module is used to acquire historical coal seam monitoring data and determine a correspondence database between structural changes in coal seam molecular bonds and the degree of destruction of the coal seam molecular structure based on the historical coal seam monitoring data. The correspondence database is the correspondence between key parameters of structural changes in coal seam molecular bonds and the corresponding molecular structure destruction degree scores. The key parameters include the number of broken molecular bonds per unit volume, molecular bond recombination information, and the disorder coefficient of molecular bond spatial distribution. The molecular bond recombination information includes the type and proportion of molecular bond recombination. Based on the ground-penetrating radar detection data and the coal seam molecular structure data, and through a pre-trained coupling model, the structural changes of the coal seam molecular bonds are determined; Determine the key parameters of the structural changes in the molecular bonds of the coal seam based on the structural changes in the molecular bonds of the coal seam. The target molecular structure destruction degree score of each key parameter is determined based on the key parameters and the corresponding relationship library. The target weights corresponding to each key parameter are determined using the entropy weight method. The destruction index of the coal seam molecular structure is determined based on the molecular structure destruction degree scores of each key parameter and the corresponding target weights. The damage characteristic value determination module is used to determine the damage characteristic value of the target monitoring area based on the analytic hierarchy process, the electromagnetic radiation data, the electron spin resonance signal data, and the destruction index of the coal seam molecular structure. The damage level determination module is used to determine the damage level of coal seam molecules in the target monitoring area based on the damage characteristic values ​​of the target monitoring area and a preset damage threshold table. The preset damage threshold table is used to characterize the correlation between the range of damage characteristic values ​​and the damage level. The early warning level determination module is used to determine the early warning level based on the damage level of the coal seam molecules in the target monitoring area, wherein the early warning level is proportional to the damage level of the coal seam molecules.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

Images