Coal mine goaf multi-risk comprehensive early warning method and system based on machine learning

Abstract

The invention belongs to the technical field of coal mine risk early warning, and particularly relates to a coal mine goaf multi-risk comprehensive early warning method and system based on machine learning, and the method comprises the steps: collecting mine pressure, gas and hydrological real-time data in real time through a multi-temporal-spatial-scale sensor, and obtaining a dynamic coupling relation basic data set based on the real-time data; preprocessing noise and missing values according to the dynamic coupling relationship basic data set, and modeling node connection between a geological structure and mine pressure change by adopting a graph neural network to obtain space-time heterogeneous feature representation; non-linear features are analyzed through spatial-temporal heterogeneous feature representation, and a multi-scale dynamic mode is determined; acquiring a risk conduction path in the multi-scale dynamic mode, and acquiring an early recognition signal of a potential disaster chain; based on the early recognition signal, a long-short-term memory network is used for processing a sequential sequence, and the probability of the compound disaster is judged; a high-risk area is extracted from the composite disaster probability, and real-time early warning model parameters are obtained; and generating alarm output according to the real-time early warning model parameters.

Description

Technical Field

[0001] This invention belongs to the field of coal mine risk early warning technology, and in particular relates to a comprehensive early warning method and system for multiple risks in coal mine goaf based on machine learning. Background Technology

[0002] Coal mine goafs, as unique geological environments formed after coal mining, present significant risks due to their unstable internal structure, stress redistribution, and accumulation of harmful gases. These factors make them a major source of concentrated hidden dangers in mine safety production. Accurate and proactive early warning of multiple concurrent hazards in goafs is fundamental to ensuring miners' safety and the sustainable development of mines.

[0003] Current risk monitoring methods for goaf areas, while capable of acquiring multi-source information, generally suffer from a deep-seated flaw at the analytical level: they tend to treat various monitoring data as independent risk indicators for assessment. This approach fundamentally ignores the fact that goaf areas are complex geological systems with profound physical coupling relationships between various hazard factors. For example, periodic pressure changes in the roof are not only a direct manifestation of ground pressure disasters, but can also affect the opening and closing of rock fissures, thereby influencing gas escape channels and groundwater seepage paths, forming an interconnected chain of disasters. This fragmented analytical framework makes it difficult for early warning systems to capture the early signs of complex disasters that evolve gradually from the combined effects of multiple factors.

[0004] The coupling effect of this risk is not a simple linear superposition, but rather exhibits complex nonlinear and spatiotemporal heterogeneous characteristics. Specifically, a small fluctuation in mine pressure in one region may, hours later, be transmitted through fracture networks, triggering anomalies in gas concentration in another region. This cross-temporal and spatiotemporal risk transmission and evolution mechanism is difficult to characterize using traditional models. Furthermore, risk characteristics vary significantly across different scales, including instantaneous abrupt changes in indicators such as mine pressure and gas, as well as long-term, gradual trends in surface subsidence and groundwater seepage. This dynamic interweaving of multi-scale characteristics makes early risk identification extremely challenging, often leading to delayed warnings or false alarms.

[0005] Therefore, how to effectively integrate monitoring data at different temporal and spatial scales within the goaf area, deeply explore and quantify the dynamic coupling relationship between multiple disasters such as geological structure, mine pressure, gas, and hydrology, and then construct an intelligent early warning model that can accurately identify early signs of complex disaster chains, has become a key issue in breaking through the current bottleneck of safety management and control in coal mine goaf areas. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a machine learning-based comprehensive early warning method and system for multiple risks in coal mine goaf areas. This invention can effectively integrate monitoring data at different spatiotemporal scales within the goaf area, deeply mine and quantify the dynamic coupling relationships among multiple hazards such as geological structure, mine pressure, gas, and hydrology, and construct an intelligent early warning model that can accurately identify early signs of complex disaster chains.

[0007] To achieve the above objectives, this invention provides a machine learning-based comprehensive early warning method for multiple risks in coal mine goaf areas, comprising:

[0008] Real-time data on mine pressure, gas, and hydrology are collected by multi-temporal and spatial scale sensors, and a basic dataset of dynamic coupling relationships is obtained based on the real-time data on mine pressure, gas, and hydrology.

[0009] Based on the dynamic coupling relationship dataset, noise and missing values ​​are preprocessed, and a graph neural network is used to model the node connections between geological structure and mineral pressure changes to obtain a spatiotemporal heterogeneous feature representation.

[0010] The nonlinear characteristics are analyzed by representing the spatiotemporal heterogeneous features, and the interaction between gas emission and hydrological infiltration paths is integrated to determine the multi-scale dynamic model.

[0011] Obtain the risk transmission path in the multi-scale dynamic model, and based on the risk transmission path, obtain early identification signals of potential disaster chains;

[0012] Based on the aforementioned early identification signals, a long short-term memory network is applied to process time series to predict evolution trends and determine the probability of complex disasters from the disaster chain;

[0013] High-risk areas are extracted from the probability of complex disasters, and new monitoring data are integrated using a coupling relationship update mechanism to obtain real-time early warning model parameters;

[0014] The alarm output is generated based on the parameters of the real-time early warning model. If the probability continues to rise, the feedback loop is activated to obtain an optimized risk control response.

[0015] Optionally, the basic dataset for obtaining dynamic coupling relationships based on the real-time data of mine pressure, gas, and hydrology includes:

[0016] Based on the real-time data of mine pressure, gas, and hydrology, standardized monitoring data are obtained;

[0017] Principal component analysis algorithm is used to extract key features from standardized monitoring data and generate a feature vector set;

[0018] Based on the feature vector set, a dynamic coupling model between variables is constructed using a linear regression algorithm to obtain the coupling relationship parameters;

[0019] Based on the coupling relationship parameters, the trends of mine pressure, gas, and hydrological indicators over time are calculated to obtain dynamic change trends;

[0020] By analyzing dynamic trends, the K-means clustering algorithm is used to classify the data, generate spatiotemporal distribution patterns, and identify outlier data points.

[0021] If the number of abnormal data points exceeds a preset threshold, the abnormal points are weighted and averaged using data fusion technology to obtain an optimized dynamic coupling dataset.

[0022] Based on the optimized dynamic coupling dataset, update the variable association matrix and generate the basic dataset of dynamic coupling relationships.

[0023] Optionally, based on the dynamic coupling relationship dataset, noise and missing values ​​are preprocessed, and a graph neural network is used to model the node connections between geological structure and mineral pressure changes to obtain a spatiotemporal heterogeneous feature representation, including:

[0024] The noise in the dynamic coupling relationship base dataset is processed using the median filtering method to obtain the first dataset.

[0025] The missing values ​​in the first dataset are filled in using Lagrange interpolation to generate the second dataset;

[0026] Based on the second dataset, a graph neural network model is constructed, defining geological structures as nodes and mineral pressure changes as edges, to obtain the first node connection representation;

[0027] If the degree of a node in the first node connection representation is lower than a preset threshold, a graph convolutional network is used to aggregate the node features to obtain the first spatiotemporal features.

[0028] Based on the first spatiotemporal characteristics, a long short-term memory network is used to model the time series and obtain the first dynamic change trend;

[0029] Based on the first dynamic change trend, a heterogeneous representation is calculated, and an attention mechanism is used to weight the spatiotemporal features of different nodes to obtain a second spatiotemporal heterogeneous feature representation.

[0030] For the second spatiotemporal heterogeneous feature representation, feature standardization is performed to obtain the spatiotemporal heterogeneous feature representation.

[0031] Optionally, the analysis of nonlinear characteristics through the spatiotemporal heterogeneous features, and the determination of multi-scale dynamic models by fusing the interaction between gas emission and hydrological infiltration paths, includes:

[0032] Principal component analysis was used to reduce the dimensionality of the spatiotemporal features in the original real-time data and identify the main nonlinear modes.

[0033] If the main nonlinear mode matches the preset interaction threshold, the interaction between gas emission and hydrological infiltration is classified by the support vector machine algorithm to obtain the interaction category.

[0034] Based on the interaction category, a convolutional neural network is used to extract multi-scale dynamic features and determine the feature fusion result;

[0035] By analyzing the infiltration path, the spatiotemporal variation trends of gas distribution and hydrological infiltration are obtained from the feature fusion results, thus yielding path correlation features;

[0036] If the spatiotemporal change trend of the path association features exceeds the preset pattern recognition threshold, cluster analysis is used to group the dynamic features and determine the multi-scale dynamic pattern.

[0037] Optionally, obtaining the risk transmission path in the multi-scale dynamic model, and based on the risk transmission path, obtaining early identification signals of potential disaster chains includes:

[0038] Obtain raw risk data from multi-scale dynamic sequences;

[0039] Multi-scale patterns are decomposed from the original risk data using wavelet transform to obtain multi-layer dynamic components;

[0040] A graph neural network is used to construct risk transmission paths from the multi-layer dynamic components to obtain a transmission intensity matrix; if the elements in the transmission intensity matrix exceed a preset threshold, the corresponding path is marked as a potential disaster chain to obtain a set of marked paths;

[0041] The connectivity strength of the disaster chain is calculated based on the marked path set to determine the chain topology map. The chain topology map is then grouped using a hierarchical clustering algorithm to obtain early identification signals.

[0042] Optionally, based on the early identification signals, applying a long short-term memory network to process time series and predicting the evolution trend from the disaster chain to determine the probability of compound disasters includes:

[0043] A long short-term memory network is used to process the temporal sequence to obtain the hidden state sequence;

[0044] Early signals are extracted from the hidden state sequence to obtain signal feature vectors, and disaster chains are constructed based on the signal feature vectors to obtain chain correlation matrices;

[0045] The evolution trend is predicted based on the chain correlation matrix, and a trend prediction sequence is obtained;

[0046] The probability distribution value is obtained by calculating the probability of compound disasters from the trend prediction sequence. If the probability distribution value exceeds the preset threshold, a high-risk compound disaster is identified and a risk warning label is obtained.

[0047] Optionally, high-risk areas are extracted from the probability of complex disasters, and new monitoring data is integrated using a coupling relationship update mechanism to obtain real-time early warning model parameters, including:

[0048] A spatial clustering algorithm is used to identify the initial set of high-risk areas in the probability of the complex disaster;

[0049] For the initial set of high-risk areas, real-time monitoring stations within their coverage area are matched, and multi-source heterogeneous monitoring indicator sequences are extracted;

[0050] Based on historical monitoring index sequences and disaster event records, a nonlinear dependency structure among disaster-causing factors is constructed to obtain a multidimensional joint distribution function.

[0051] When a new round of monitoring indicator sequence is input, the conditional failure probability is calculated using the multidimensional joint distribution function to obtain a dynamic risk probability map;

[0052] If the probability value of a specific grid in the dynamic risk probability graph exceeds the preset risk threshold, the corresponding grid will be included in the dynamic early warning area.

[0053] Within the dynamic early warning area, the parameters of the real-time early warning model are determined by using a pre-established decision tree model based on the risk probability value and the rate of change.

[0054] Optionally, an alarm output is generated based on the real-time early warning model parameters. If the probability continues to rise, a feedback loop is activated to obtain an optimized risk control response, including:

[0055] Based on the parameters of the real-time early warning model, an initial risk probability value is obtained through logistic regression model calculation;

[0056] The initial risk probability value is combined with multiple probability records in the historical time series to form a probability change sequence, and the slope analysis method is used to determine the change trend of the corresponding sequence.

[0057] If the trend of the sequence exceeds a preset continuous rise threshold, a feedback control module is activated to obtain a response adjustment command.

[0058] Based on the response adjustment instruction, relevant control measures are retrieved and reorganized from the preset risk control strategy library to generate an optimized risk disposal plan.

[0059] This invention also discloses a multi-risk comprehensive early warning system for coal mine goaf based on machine learning, including: a data acquisition module, a data preprocessing module, a data fusion module, a signal recognition module, a disaster probability judgment module, and a risk early warning module;

[0060] The data acquisition module is used to collect real-time data on mine pressure, gas, and hydrology through multi-temporal scale sensors, and to obtain a basic dataset of dynamic coupling relationships based on the real-time data on mine pressure, gas, and hydrology.

[0061] The data preprocessing module is used to preprocess noise and missing values ​​based on the dynamic coupling relationship basic dataset, and to use graph neural networks to model the node connections between geological structure and mineral pressure changes to obtain spatiotemporal heterogeneous feature representations.

[0062] The data fusion module is used to analyze nonlinear characteristics through the spatiotemporal heterogeneous feature representation, and to determine multi-scale dynamic patterns by fusing the interaction between gas emission and hydrological infiltration paths.

[0063] The signal recognition module is used to obtain the risk transmission path in the multi-scale dynamic pattern, and based on the risk transmission path, obtain early identification signals of potential disaster chains;

[0064] The disaster probability judgment module is used to process time series using a long short-term memory network based on the early identification signal, and to predict the evolution trend and judge the probability of compound disasters from the disaster chain.

[0065] The risk warning module is used to extract high-risk areas from the probability of complex disasters, and to integrate new monitoring data using a coupling relationship update mechanism to obtain real-time warning model parameters; it generates alarm output based on the real-time warning model parameters, and if the probability continues to rise, it activates the feedback loop to obtain an optimized risk control response.

[0066] Compared with the prior art, the present invention has the following advantages and technical effects:

[0067] This invention addresses the operational problem of accurately predicting disaster chains caused by the dynamic coupling of multiple factors such as geological structure, mine pressure, gas, and hydrology. It employs graph neural networks to model the spatiotemporal heterogeneous connections between geological and monitoring data, thereby obtaining nonlinear risk transmission paths. Long Short-Term Memory (LSTM) networks are then used to process the temporal characteristics of these paths to predict the evolution trend of complex disasters. Finally, a coupling relationship update and feedback loop mechanism is implemented. This invention can identify high-risk areas in real time and generate dynamically optimized risk control responses, significantly improving the early identification accuracy and proactive defense capabilities for complex mine disasters. Attached Figure Description

[0068] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0069] Figure 1 This is a flowchart of a comprehensive early warning method for multiple risks in coal mine goaf based on machine learning, according to an embodiment of the present invention. Detailed Implementation

[0070] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0071] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0072] This embodiment proposes a machine learning-based comprehensive early warning method for multiple risks in coal mine goaf areas, such as... Figure 1 As shown, the specific steps include:

[0073] Real-time data on mine pressure, gas, and hydrology are collected by sensors at multiple spatiotemporal scales, and a basic dataset of dynamic coupling relationships is obtained based on the real-time data on mine pressure, gas, and hydrology.

[0074] Based on the dynamic coupling relationship dataset, noise and missing values ​​are preprocessed, and a graph neural network is used to model the node connections between geological structure and mineral pressure changes to obtain a spatiotemporal heterogeneous feature representation.

[0075] Nonlinear characteristics are analyzed by representing spatiotemporal heterogeneous features, and the interaction between gas emission and hydrological infiltration pathways is integrated to determine multi-scale dynamic models.

[0076] Obtain risk transmission paths in multi-scale dynamic patterns, and based on these risk transmission paths, obtain early identification signals of potential disaster chains;

[0077] Based on early identification signals, long short-term memory networks are used to process time series to predict evolution trends and determine the probability of complex disasters from disaster chains.

[0078] High-risk areas are extracted from the probability of complex disasters, and new monitoring data are integrated using a coupling relationship update mechanism to obtain real-time early warning model parameters;

[0079] The alarm output is generated based on the parameters of the real-time early warning model. If the probability continues to rise, the feedback loop is activated to obtain an optimized risk control response.

[0080] Specifically, this invention addresses the operational problem of accurately predicting disaster chains caused by the dynamic coupling of multiple factors such as geological structure, mine pressure, gas, and hydrology. It employs graph neural networks to model the spatiotemporal heterogeneous connections between geological and monitoring data, thereby obtaining nonlinear risk transmission paths. Long Short-Term Memory (LSTM) networks are then used to process the temporal characteristics of these paths to predict the evolution trend of complex disasters. Finally, a coupling relationship update and feedback loop mechanism is implemented. This invention can identify high-risk areas in real time and generate dynamically optimized risk control responses, significantly improving the early identification accuracy and proactive defense capabilities for complex mine disasters.

[0081] Furthermore, the basic dataset for obtaining dynamic coupling relationships based on real-time data of mine pressure, gas, and hydrology includes:

[0082] Standardized monitoring data are obtained based on real-time data of mine pressure, gas, and hydrology.

[0083] Principal component analysis algorithm is used to extract key features from standardized monitoring data and generate a feature vector set;

[0084] Based on the feature vector set, a dynamic coupling model between variables is constructed using a linear regression algorithm to obtain the coupling relationship parameters.

[0085] Based on the coupling relationship parameters, the trends of mine pressure, gas, and hydrological indicators over time are calculated to obtain dynamic change trends;

[0086] By analyzing dynamic trends, the K-means clustering algorithm is used to classify the data, generate spatiotemporal distribution patterns, and identify outlier data points.

[0087] If the number of abnormal data points exceeds the preset threshold, the abnormal points are weighted and averaged using data fusion technology to obtain an optimized dynamic coupling dataset.

[0088] Based on the optimized dynamic coupling dataset, update the variable association matrix and generate the basic dataset of dynamic coupling relationships.

[0089] Specifically, in one embodiment, the multi-temporal scale design of the data acquisition phase can be further refined. For example, sensors at the top of the tunnel collect mine pressure data at 10-meter intervals, and sensors at the bottom of the tunnel monitor and analyze the data every 20 meters. The technical process is currently reviewing the user-provided coal mine monitoring description, focusing on the complete chain from data acquisition to the dynamic coupling model. Hydrological and gas sensors are arranged every 15 meters along the airflow direction to form a three-dimensional time series. This layout captures local and global changes, and after standardization, data noise is reduced by 15%, providing high-quality input for feature extraction. For example, if the cumulative contribution rate needs to reach 90% in principal component analysis, three principal components are retained: the first principal component loads mine pressure (0.85) and gas (0.12), the second principal component loads gas (0.78) and hydrological loads (0.55), and the third principal component loads hydrological loads (0.82). This dimensionality reduction result compresses the data volume by 70% while retaining key variations, reducing computational resource consumption. Specifically, when the correlation coefficient threshold is set to 0.75, geological variables such as rock stratum dip angle are correlated with the second principal component by 0.79. The linear regression model shows that gas concentration = 0.42 × dip angle + 0.18 × mine pressure + baseline value. The parameters reveal that an increase in dip angle leads to an increased risk of gas accumulation. This coupled model supports accurate prediction of gas outbursts.

[0090] In one possible implementation, trend calculation uses a sliding window updated every 30 minutes, with a mine pressure trend slope of 0.012 MPa / min, gas pressure of 0.003% / min, and a hydrological response lag of 10 minutes. The dynamic trend chart visually displays the coordinated changes of these three factors, facilitating decision-making by dispatchers. For example, after randomly selecting initial centers for K-means clustering and iterating 10 times to converge, the outlier class includes 5% of data points with mine pressure > 6 MPa. The spatiotemporal model shows that the anomalies are concentrated in the working face advancement area. This classification guides targeted ventilation adjustments, reducing the probability of gas exceeding limits. It should be noted that sensor reliability factors can be incorporated into the weights during data fusion. For example, a higher accuracy sensor has a weight of 0.6, and an outlier of 150 MPa becomes 147.8 MPa after fusion, optimizing the dataset variance by 12% and improving the stability of subsequent models. Based on the optimized dataset and updated correlation matrix, the mine pressure-gas element increased from 0.72 to 0.81, and the hydrological-geological element increased from 0.68 to 0.74. The final dataset was integrated into the monitoring platform, enabling automatic alarms for anomalies and reducing response time to within 5 seconds, effectively preventing roof collapses and water inrush accidents. The entire closed-loop process is iterated weekly, cumulatively reducing the false alarm rate by 30%. Multiple technologies, such as maintenance information, regression analysis, clustering patterns, and noise reduction, work together to construct an intelligent coal mine monitoring system, improving safety and production efficiency.

[0091] Furthermore, based on the preprocessing of noise and missing values ​​in the dynamic coupling relationship dataset, a graph neural network is used to model the node connections between geological structure and mineral pressure changes to obtain a spatiotemporal heterogeneous feature representation, including:

[0092] The noise in the dynamic coupling relationship base dataset is processed using the median filtering method to obtain the first dataset.

[0093] The missing values ​​in the first dataset were filled in using Lagrange interpolation to generate the second dataset;

[0094] Based on the second dataset, a graph neural network model is constructed, defining geological structures as nodes and mineral pressure changes as edges, to obtain the first node connection representation;

[0095] If the degree of a node in the first node connection representation is lower than a preset threshold, a graph convolutional network is used to aggregate the node features to obtain the first spatiotemporal features.

[0096] Based on the first spatiotemporal characteristics, a long short-term memory network is used to model the time series and obtain the first dynamic change trend;

[0097] Based on the first dynamic change trend, the heterogeneous representation is calculated, and the spatiotemporal features of different nodes are weighted using an attention mechanism to obtain the second spatiotemporal heterogeneous feature representation.

[0098] For the second spatiotemporal heterogeneous feature representation, feature standardization is performed to obtain the spatiotemporal heterogeneous feature representation.

[0099] Specifically, when constructing a graph neural network model, geological structures are defined as nodes, and changes in mine pressure are defined as edges. This is to unify the expression of discrete monitoring points in physical space and their inherent mechanical relationships. For example, key geological units in a mine, such as the F1 fault, C2 coal seam, and G3 goaf, can be abstracted as nodes in the graph. When the roof of goaf G3 collapses, causing a sharp change in mine pressure, this change is transmitted through the rock strata to the adjacent C2 coal seam, thus affecting the stress state of the F1 fault. At this time, dynamic "edges" are formed between G3 and C2, and between C2 and F1. The weight of the edge can represent the intensity or rate of the mine pressure impact. This approach integrates the static spatial relationship of geological structures with the dynamic mechanical relationship of mine pressure changes, providing a foundation for analyzing stress transmission paths. It is understandable that when the degree of a node in the graph is lower than a preset threshold, it means that there is less direct mine pressure correlation data between that geological structure and other structures. For example, a rock strata node in a newly developed area may only have a monitoring correlation with a main roadway node, making the information relatively isolated. At this point, by using graph convolutional networks to aggregate node features, it is possible to weight and aggregate the information of the neighbors of this "isolated" node, namely the features of the main roadway node, as well as the neighbors of the neighbors, such as the features of other coal mining faces connected to the main roadway, thereby generating a richer and more global feature representation for this new rock stratum node, making up for the lack of direct monitoring data.

[0100] In one possible implementation, the Long Short-Term Memory (LSTM) network processes time series data containing spatial structural information generated by a graph model. For example, the input received by the model is no longer isolated readings from a single sensor, but rather a sequence of comprehensive feature vectors from the F1 fault nodes, aggregated by a graph convolutional network. This sequence not only includes the time-varying pressure values ​​of the F1 fault itself, but also incorporates the state information of surrounding nodes such as the C2 coal seam and the G3 goaf at the corresponding time steps. Through its unique gating mechanism, the LSM network can learn long-term dependencies in such complex spatiotemporal feature sequences, such as identifying evolutionary patterns spanning a long period, like "after a week of continuous small-scale roof collapses in the G3 goaf, stress in the F1 fault begins to concentrate significantly." For example, using an attention mechanism to weight the spatiotemporal features of different nodes is to highlight key influencing factors during comprehensive risk assessment. When predicting the impact risk of the C2 coal seam working face in the next hour, the model may automatically assign a high weight of 0.7 to the directly connected F1 fault node, a weight of 0.2 to the more distant G3 goaf node, and only a weight of 0.1 to a stable far-field stratum node through an attention mechanism. This means that the model judges that the recent dynamics of the F1 fault have the greatest impact on the risk of the C2 coal seam. This dynamic weighting makes the model's predictions more targeted and interpretable, and can accurately capture the heterogeneous influence of different geological structures under specific spatiotemporal backgrounds.

[0101] Furthermore, by analyzing nonlinear characteristics through spatiotemporal heterogeneous feature representation, and integrating the interaction between gas emission and hydrological infiltration pathways, multi-scale dynamic models are determined, including:

[0102] Principal component analysis was used to reduce the dimensionality of the spatiotemporal features in the original real-time data and identify the main nonlinear modes.

[0103] If the main nonlinear mode matches the preset interaction threshold, the interaction between gas emission and hydrological infiltration is classified by the support vector machine algorithm to obtain the interaction category.

[0104] Based on the interaction category, a convolutional neural network is used to extract multi-scale dynamic features and determine the feature fusion result;

[0105] By analyzing the infiltration path, the spatiotemporal variation trends of gas distribution and hydrological infiltration are obtained from the feature fusion results, and path correlation features are derived.

[0106] If the spatiotemporal variation trend of path association features exceeds the preset pattern recognition threshold, cluster analysis is used to group the dynamic features and determine the multi-scale dynamic pattern.

[0107] Specifically, in one possible implementation, acquiring monitoring data refers to collecting data from multiple sensor groups within a specific coal seam working face. For example, a working face might have 50 monitoring points, each containing both a gas concentration sensor and a rock mass hydrological permeability and pressure sensor. These sensors record data every minute, along with their three-dimensional spatial coordinates, forming a high-dimensional raw dataset containing temporal, spatial, and multidimensional physical quantities. Principal component analysis (PCA) is used to process this high-dimensional data. Suppose the raw data contains 10 features, including gas concentration, water pressure, temperature, and rock mass stress. Through PCA, it's possible to find that the first two principal components explain 95% of the data variation. The first principal component might represent the overall coordinated trend of gas and water pressure changes across the entire area, while the second principal component might reflect the inverse fluctuation relationship between the two in local areas, such as near fault zones. These principal components are the identified main nonlinear patterns, simplifying the complex multivariate problem into the analysis of a few key dynamic patterns.

[0108] In one possible implementation, a support vector machine (SVM) algorithm is used to classify these patterns. A pre-defined interaction threshold can be a safety baseline based on historical data. For example, when the value of the first principal component exceeds a certain positive threshold, and the absolute value of the second principal component also exceeds a specific threshold, the system determines that this pattern matches the risk condition of "strong interaction." Based on these inputs, the SVM model classifies it into a predefined "high-risk seepage-blocking" category. This category implies that changes in the hydrological seepage path are blocking the normal escape route of gas, potentially leading to a sharp increase in local pressure.

[0109] Specifically, convolutional neural networks are used here to deeply mine spatial features. Using the "high-risk permeability-blocking" category data identified in the previous step as input, sensor data at specific time points are mapped onto a two-dimensional or three-dimensional grid, forming a "mine pressure-hydrological map." The convolutional kernels of the convolutional neural network can automatically learn and extract multi-scale spatial structures from this map. For example, a small convolutional kernel might identify anomalous jumps in a single sensor reading, while a large kernel can identify a high-pressure zone running through the working face or a closed "water wall" structure. The feature fusion result is these key feature maps extracted from different scales, characterizing the spatial morphology of the risk. For example, permeability path analysis is based on the aforementioned feature maps. This analysis aims to simulate the flow trends of gas and water in rock fractures. By analyzing the relative positions of the "water wall" structures and high-concentration gas zones identified in the feature maps, the system can infer that potential gas migration paths have been blocked. Path association features can be quantified as a "connectivity index," which continuously decreases over time, indicating that gas escape channels are gradually being closed and risks are accumulating.

[0110] In one possible implementation, cluster analysis is triggered when the rate of decline of the "connectivity index" exceeds a preset pattern recognition threshold, such as a decrease of more than 5% per hour. Cluster analysis groups all recent time periods with similar "connectivity index" change curves into one category. This may result in two different dynamic pattern groupings: Pattern 1 is characterized by a smooth, linear decline in the index, corresponding to a large-scale, slow, homogeneous permeability change; Pattern 2 is characterized by a step-like, sharp decline in the index at specific locations, corresponding to sudden path congestion caused by local tectonic activity.

[0111] Furthermore, by acquiring risk transmission paths in multi-scale dynamic models, and based on these risk transmission paths, early identification signals of potential disaster chains can be obtained, including:

[0112] Obtain raw risk data from multi-scale dynamic sequences;

[0113] Multi-scale patterns are decomposed from the original risk data using wavelet transform to obtain multi-layer dynamic components;

[0114] A graph neural network is used to construct risk transmission paths from multi-layer dynamic components to obtain a transmission intensity matrix; if the elements in the transmission intensity matrix exceed a preset threshold, the corresponding path is marked as a potential disaster chain to obtain a set of marked paths;

[0115] The connectivity strength of the disaster chain is calculated based on the marked path set to determine the chain topology map. The chain topology map is then grouped using a hierarchical clustering algorithm to obtain early identification signals.

[0116] Specifically, in one possible implementation, the acquired multi-scale dynamic sequence consists of second-level monitoring data from specific mining faces, including gas concentration, microseismic signals, water pressure, and ground stress sensors. These data collectively constitute the original risk dataset. The time-series characteristics of this data make analyzing its inherent periodicity and abrupt changes crucial for risk identification. For example, wavelet transform technology is used to decompose this original risk data. For instance, for the gas concentration time series, wavelet transform can decompose it into multiple levels of dynamic components. Low-frequency components may reflect the long-term trend of slow growth in gas emissions due to mining progress, while high-frequency components can accurately capture instantaneous pulses of gas concentration caused by roof rupture or geological tectonic activity. This decomposition separates risk signals of different natures, providing high-quality data input for subsequent accurate identification of the triggering sources of disaster chains.

[0117] In one possible implementation, the graph neural network is constructed with each sensor monitoring point as a node and the potential influence relationships between nodes as edges. For example, a microseismic sensor node, a water pressure sensor node, and a gas sensor node constitute part of the graph. The graph neural network learns the temporal correlations between multiple dynamic components and outputs a conduction intensity matrix. An element value in this matrix, such as 0.85, may indicate that the occurrence of a microseismic event has an extremely high conduction intensity to a sudden increase in gas concentration on a specific high-frequency component. When this value exceeds a preset threshold, such as 0.7, this "microseismic to gas" path is marked as a potential disaster chain. This helps to automatically filter out the most threatening risk transmission paths from massive data correlations. For example, the system identifies two paths in the marked path set: path one is "high ground stress to frequent microseismic events," and path two is "frequent microseismic events to water-conducting fracture propagation." These two paths are connected by the intermediate node "frequent microseismic events," forming a chain topology graph that intuitively shows the complete disaster chain structure from ground stress accumulation to increased water hazard risk. Specifically, hierarchical clustering algorithms can group multiple such chain topologies. This may form two types of signal clusters: one is the "tectonic stress type" disaster chain cluster initiated by geostress anomalies, and the other is the "hydraulic disturbance type" disaster chain cluster initiated by sudden drops in water pressure. By performing intensity aggregation analysis on all chains within the "tectonic stress type" signal cluster, it may be found that over 80% of the chains have the highest conduction intensity at their initial nodes. Therefore, geostress anomaly signals are identified as the dominant identification signal for this type of disaster chain and given the highest priority in the early warning system. This allows monitoring personnel to focus on the most critical precursor indicators, achieving early and accurate risk identification.

[0118] Furthermore, based on early identification signals, long short-term memory networks are applied to process time series, predicting evolution trends from disaster chains and determining the probability of complex disasters, including:

[0119] A long short-term memory network is used to process the temporal sequence to obtain the hidden state sequence;

[0120] Early signals are extracted from the hidden state sequence to obtain signal feature vectors, and disaster chains are constructed based on the signal feature vectors to obtain the chain correlation matrix;

[0121] The evolution trend is predicted based on the chain correlation matrix, and a trend prediction sequence is obtained.

[0122] The probability distribution value is obtained by calculating the probability of compound disasters from the trend prediction sequence. If the probability distribution value exceeds the preset threshold, a high-risk compound disaster is identified and a risk warning label is obtained.

[0123] Specifically, in one possible implementation, acquiring time-series data can be concretized as collecting daily trading data from multiple financial markets within a specific region, forming a multivariate raw time-series sequence. For example, this could involve collecting daily exchange rate fluctuations, stock market index closing prices, and credit default swap spreads for high-yield corporate bonds in an emerging market over the past five years. These three sets of data collectively constitute the raw time-series sequence, laying the foundation for subsequent analysis, as they are considered key indicators of regional financial risk transmission. Exemplarily, a Long Short-Term Memory (LSTM) network is used to process the raw time-series sequence, aiming to capture the complex nonlinear time dependencies between these financial indicators. Through its internal gating mechanism, the LTM network can learn which historical information is important and should be remembered long-term, and which is secondary and can be forgotten. The resulting hidden state sequence can be viewed as a condensed representation of the overall market state at each point in time. For example, the hidden state vector on a particular day might encode information such as: the exchange rate has depreciated sharply for three consecutive days, while stock market trading volume has increased abnormally; even if the bond market has not yet shown significant fluctuations, this state implies potential risk accumulation. In one possible implementation, extracting early signals from the hidden state sequence means identifying weak but crucial patterns that foreshadow an impending crisis. This can be achieved by applying an attention mechanism to the hidden state sequence, which automatically learns and focuses on the most predictive time points and feature dimensions for future risk evolution. The extracted signal feature vector is a low-dimensional vector containing key risk information. For example, a feature vector might show abnormally high values ​​in the dimensions of "increased exchange rate volatility" and "capital outflow speed," which is a clear early signal. Exemplarily, constructing a disaster chain based on the signal feature vector is done by analyzing the order and correlation of different signal feature vectors in historical data. If, historically, a signal of "increased exchange rate volatility" has repeatedly been followed by a signal of "sharp stock market decline" within 5 to 10 trading days, then a strong correlation is established between these two signals. The resulting chain correlation matrix represents the probability or intensity of the transmission from one risk signal to another. This provides a quantitative basis for understanding how risk spreads from one area to another.

[0124] In one possible implementation, predicting evolutionary trends based on a chain correlation matrix means that when the latest signal feature vector is detected, this matrix is ​​used to deduce the most likely risk transmission path in the future. For example, once the system identifies a strong signal of "increased exchange rate volatility," it predicts based on the correlation matrix that the probability of stock market turmoil is 0.75, and the probability of a wave of bond defaults is 0.6. This sequence, composed of probabilities and time order, is the trend prediction sequence, which clearly depicts the evolutionary path of future risks. For example, calculating the probability of a compound disaster from the trend prediction sequence quantifies the total probability of the entire disaster chain occurring. This requires calculating the joint probability of all links in the prediction sequence occurring sequentially. If the conditional probabilities of predicting "currency depreciation" to "stock market decline" to "bond market default" are 0.9, 0.75, and 0.6 respectively, then the probability distribution of the entire compound disaster is the product of these probabilities or the result of a more complex function calculation. This single numerical value intuitively measures the overall risk level of a systemic crisis.

[0125] In one possible implementation, a high-risk compound disaster is identified if the probability distribution value exceeds a preset threshold; this is a decision-making step. For example, the risk management department can set the threshold to 0.5. If the calculated probability of the compound disaster is 0.51, the system will automatically trigger an alarm and generate a risk warning label, such as "Financial Transmission Risk: Red Alert," clearly indicating that the current situation is a systemic financial risk caused by exchange rates. This provides decision-makers with timely and specific early warning information so that intervention measures can be taken.

[0126] Furthermore, high-risk areas are extracted from the probability of complex disasters, and new monitoring data is integrated using a coupling relationship update mechanism to obtain real-time early warning model parameters, including:

[0127] A spatial clustering algorithm is used to identify the initial set of high-risk areas in the probability of complex disasters;

[0128] For the initial set of high-risk areas, real-time monitoring stations within their coverage area are matched, and multi-source heterogeneous monitoring indicator sequences are extracted;

[0129] Based on historical monitoring index sequences and disaster event records, a nonlinear dependency structure among disaster-causing factors is constructed to obtain a multidimensional joint distribution function.

[0130] When a new round of monitoring indicator sequences are input, the probability of conditional failure is calculated using a multidimensional joint distribution function to obtain a dynamic risk probability map;

[0131] If the probability value of a specific grid in the dynamic risk probability graph exceeds the preset risk threshold, the corresponding grid will be included in the dynamic early warning area.

[0132] Within the dynamic early warning area, the parameters of the real-time early warning model are determined by using a pre-established decision tree model based on the risk probability value and the rate of change.

[0133] Specifically, in one possible implementation, the decision tree model can be constructed based on historical disaster cases and expert experience, setting up multiple layers of judgment nodes. For example, a decision tree first judges whether the probability value exceeds 0.6. If so, it proceeds to the next layer to judge whether the rate of change is greater than 0.1 / hour, and then combines terrain factors such as whether the slope is greater than 30 degrees. Assuming a certain grid has a probability of 0.72, a rate of change of 0.15 / hour, and a slope of 35 degrees, the decision tree outputs the highest response level, recommending the deployment of emergency teams and the evacuation of surrounding residents.

[0134] It's important to note that decision trees need regular optimization, incorporating the latest monitoring data and disaster records to improve prediction accuracy. For example, continuous updates to dynamic early warning zones can combine real-time data streams and historical trends. For instance, if after three consecutive days of rainfall in a region, the probability values ​​of five grids rise from 0.55 to 0.75, a change rate of 0.15 per hour, all of these grids are included in the dynamic early warning zone. If these grids cover a village, an immediate early warning notice needs to be issued. Specifically, the generation of dynamic risk probability maps can be achieved through visualization tools, such as displaying risk distribution in the form of heatmaps. Assuming a heatmap shows red areas representing probabilities greater than 0.7, yellow areas between 0.6 and 0.7, and green areas below 0.6, managers can intuitively identify high-risk areas and prioritize resource allocation.

[0135] In one possible implementation, the response level of an early warning event can be further linked to resource allocation. For example, a red alert area automatically triggers drone patrols and the deployment of ground rescue teams, while a yellow alert area prioritizes increased monitoring frequency. This linkage mechanism can effectively improve response efficiency. It should be noted that the integration of multi-source heterogeneous data requires consideration of data standardization. For example, rainfall data from weather stations and displacement data from geological sensors have different formats, requiring preprocessing using a unified timestamp and coordinate system to ensure analytical consistency. For instance, in a certain early warning, a dynamic risk probability map showed that 8 out of 10 grid cells in a valley area had a probability exceeding 0.7, with a change rate of 0.13 / hour, leading the decision tree to classify it as a red alert. Combined with real-time monitoring data, it was confirmed that rainfall reached 160 mm and soil moisture content was 92%, ultimately triggering a full evacuation and resource allocation. This combination of multi-dimensional analysis and rapid response can significantly improve the accuracy and timeliness of disaster response.

[0136] Furthermore, an alarm output is generated based on the parameters of the real-time early warning model. If the probability continues to rise, a feedback loop is activated to obtain an optimized risk control response, including:

[0137] Based on the parameters of the real-time early warning model, an initial risk probability value is obtained through logistic regression model calculation;

[0138] The initial risk probability value is combined with multiple probability records in the historical time series to form a probability change sequence, and the slope analysis method is used to determine the change trend of the corresponding sequence.

[0139] If the trend of the sequence exceeds the preset continuous rise threshold, a feedback control module is activated to obtain a response adjustment command.

[0140] Based on the response adjustment instructions, relevant control measures are retrieved and reorganized from the pre-set risk control strategy library to generate an optimized risk disposal plan.

[0141] Specifically, in one possible implementation, the real-time operational data stream specifically refers to multi-source monitoring data for high-risk areas of complex geological hazards. For example, in an early warning scenario of a landslide induced by heavy rainfall, this data stream aggregates continuous observation data from rain gauges, soil moisture sensors, deep displacement gauges, and pore water pressure gauges. The system analyzes these data streams in real time, extracting multi-dimensional initial event indicators such as "cumulative rainfall in the past hour," "current soil saturation," "slope shear displacement rate," and "pore water pressure value." These indicators collectively constitute the raw input for assessing disaster risk.

[0142] Specifically, the pre-set model library may store multiple early warning models for different geological structures and vegetation cover types. When the system receives monitoring data from region A, it automatically matches and loads the "Granite Slope Instability Logistic Regression Model V3.0" based on the pre-entered geological label "highly weathered granite residual soil" for region A. This model is trained based on a large amount of historical instability case data for this type of slope, and its parameter set to be calculated consists of weight coefficients for specific input indicators such as "cumulative rainfall" and "soil saturation." This matching mechanism ensures the targeting and accuracy of the early warning analysis. For example, using the loaded logistic regression model, the real-time acquired indicator sequence, such as cumulative rainfall of 70 mm, soil saturation of 85%, and displacement rate of 0.2 mm / h, is input into the model for calculation. The model outputs an initial risk probability value, for example, 0.55. This value itself only represents the static risk at the current moment. To capture the dynamic evolution of the risk, the system appends this probability value of 0.55 to the probability record sequence of the past few hours for that monitoring point, forming a probability change sequence such as [0.35, 0.42, 0.48, 0.55]. By performing slope analysis on this sequence, the rate of risk growth can be quantified.

[0143] In one embodiment, if the slope analysis results show that the risk probability has been continuously increasing at a rate exceeding a preset threshold for sustained increases over the past half hour (e.g., increasing by 0.05 every 10 minutes), this indicates that the conditions for disaster are rapidly accumulating and the risk is deteriorating sharply. At this point, the system activates the feedback control module. This module does not directly trigger an alarm but generates a more refined response adjustment instruction, such as "Instruction A7: The landslide risk in the target area is rapidly increasing; it is recommended to initiate proactive intervention measures and upgrade the early warning preparedness level." This instruction provides clear guidance for subsequent decision-making. Based on the received "Instruction A7," the system searches a pre-set risk control strategy library. The strategy library contains standardized control measures such as "activating the regional drainage system," "notifying the emergency management department," and "preparing evacuation routes." The system reorganizes the relevant control measures according to the instruction content to generate an optimized risk management plan.

[0144] This embodiment also discloses a multi-risk comprehensive early warning system for coal mine goaf based on machine learning, including: a data acquisition module, a data preprocessing module, a data fusion module, a signal recognition module, a disaster probability judgment module, and a risk early warning module;

[0145] The data acquisition module is used to collect real-time data on mine pressure, gas, and hydrology through multi-temporal scale sensors, and to obtain a basic dataset of dynamic coupling relationships based on the real-time data on mine pressure, gas, and hydrology.

[0146] The data preprocessing module is used to preprocess noise and missing values ​​based on the dynamic coupling relationship of the basic dataset, and to obtain a spatiotemporal heterogeneous feature representation by modeling the node connections between geological structure and mineral pressure changes using graph neural networks.

[0147] The data fusion module is used to analyze nonlinear characteristics through spatiotemporal heterogeneous feature representation, and to determine multi-scale dynamic models by fusing the interaction between gas emission and hydrological infiltration paths.

[0148] The signal recognition module is used to obtain the risk transmission path in multi-scale dynamic patterns, and based on the risk transmission path, to obtain early identification signals of potential disaster chains;

[0149] The disaster probability judgment module is used to process time series based on early identification signals using a long short-term memory network, and to predict the evolution trend and judge the probability of compound disasters from the disaster chain.

[0150] The risk warning module is used to extract high-risk areas from the probability of complex disasters, and integrate new monitoring data to obtain real-time warning model parameters using a coupling relationship update mechanism. Based on the real-time warning model parameters, an alarm output is generated. If the probability continues to rise, a feedback loop is activated to obtain an optimized risk control response.

[0151] The above are merely preferred 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. A comprehensive early warning method for multiple risks in coal mine goaf areas based on machine learning, characterized in that, include: Real-time data on mine pressure, gas, and hydrology are collected by multi-temporal and spatial scale sensors, and a basic dataset of dynamic coupling relationships is obtained based on the real-time data on mine pressure, gas, and hydrology. Based on the dynamic coupling relationship dataset, noise and missing values ​​are preprocessed, and a graph neural network is used to model the node connections between geological structure and mineral pressure changes to obtain a spatiotemporal heterogeneous feature representation. The nonlinear characteristics are analyzed by representing the spatiotemporal heterogeneous features, and the interaction between gas emission and hydrological infiltration paths is integrated to determine the multi-scale dynamic model. Obtain the risk transmission path in the multi-scale dynamic model, and based on the risk transmission path, obtain early identification signals of potential disaster chains; Based on the aforementioned early identification signals, a long short-term memory network is applied to process time series to predict evolution trends and determine the probability of complex disasters from the disaster chain; High-risk areas are extracted from the probability of complex disasters, and new monitoring data are integrated using a coupling relationship update mechanism to obtain real-time early warning model parameters; The alarm output is generated based on the parameters of the real-time early warning model. If the probability continues to rise, the feedback loop is activated to obtain an optimized risk control response.

2. The method for comprehensive early warning of multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, The basic dataset for obtaining dynamic coupling relationships based on the aforementioned real-time data on mine pressure, gas, and hydrology includes: Based on the real-time data of mine pressure, gas, and hydrology, standardized monitoring data are obtained; Principal component analysis algorithm is used to extract key features from standardized monitoring data and generate a feature vector set; Based on the feature vector set, a dynamic coupling model between variables is constructed using a linear regression algorithm to obtain the coupling relationship parameters; Based on the coupling relationship parameters, the trends of mine pressure, gas, and hydrological indicators over time are calculated to obtain dynamic change trends; By analyzing dynamic trends, the K-means clustering algorithm is used to classify the data, generate spatiotemporal distribution patterns, and identify outlier data points. If the number of abnormal data points exceeds a preset threshold, the abnormal points are weighted and averaged using data fusion technology to obtain an optimized dynamic coupling dataset. Based on the optimized dynamic coupling dataset, update the variable association matrix and generate the basic dataset of dynamic coupling relationships.

3. The comprehensive early warning method for multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, Based on the preprocessing of noise and missing values ​​in the aforementioned dynamic coupling relationship dataset, a graph neural network is used to model the node connections between geological structures and mineral pressure changes to obtain a spatiotemporal heterogeneous feature representation, including: The noise in the dynamic coupling relationship base dataset is processed using the median filtering method to obtain the first dataset. The missing values ​​in the first dataset are filled in using Lagrange interpolation to generate the second dataset; Based on the second dataset, a graph neural network model is constructed, defining geological structures as nodes and mineral pressure changes as edges, to obtain the first node connection representation; If the degree of a node in the first node connection representation is lower than a preset threshold, a graph convolutional network is used to aggregate the node features to obtain the first spatiotemporal features. Based on the first spatiotemporal characteristics, a long short-term memory network is used to model the time series and obtain the first dynamic change trend; Based on the first dynamic change trend, a heterogeneous representation is calculated, and an attention mechanism is used to weight the spatiotemporal features of different nodes to obtain a second spatiotemporal heterogeneous feature representation. For the second spatiotemporal heterogeneous feature representation, feature standardization is performed to obtain the spatiotemporal heterogeneous feature representation.

4. The comprehensive early warning method for multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, The analysis of nonlinear characteristics based on the spatiotemporal heterogeneous features, and the determination of multi-scale dynamic models by integrating the interaction between gas emission and hydrological infiltration paths, includes: Principal component analysis was used to reduce the dimensionality of the spatiotemporal features in the original real-time data and identify the main nonlinear modes. If the main nonlinear mode matches the preset interaction threshold, the interaction between gas emission and hydrological infiltration is classified by the support vector machine algorithm to obtain the interaction category. Based on the interaction category, a convolutional neural network is used to extract multi-scale dynamic features and determine the feature fusion result; By analyzing the infiltration path, the spatiotemporal variation trends of gas distribution and hydrological infiltration are obtained from the feature fusion results, thus yielding path correlation features; If the spatiotemporal change trend of the path association features exceeds the preset pattern recognition threshold, cluster analysis is used to group the dynamic features and determine the multi-scale dynamic pattern.

5. The method for comprehensive early warning of multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, Obtaining the risk transmission path in the multi-scale dynamic model, and based on the risk transmission path, obtaining early identification signals of potential disaster chains includes: Obtain raw risk data from multi-scale dynamic sequences; Multi-scale patterns are decomposed from the original risk data using wavelet transform to obtain multi-layer dynamic components; A graph neural network is used to construct risk transmission paths from the multi-layer dynamic components to obtain a transmission intensity matrix; if the elements in the transmission intensity matrix exceed a preset threshold, the corresponding path is marked as a potential disaster chain to obtain a set of marked paths; The connectivity strength of the disaster chain is calculated based on the marked path set to determine the chain topology map. The chain topology map is then grouped using a hierarchical clustering algorithm to obtain early identification signals.

6. The comprehensive early warning method for multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, Based on the aforementioned early identification signals, a Long Short-Term Memory (LSTM) network is used to process time-series sequences. The prediction of evolution trends and the determination of the probability of complex disasters from the disaster chain include: A long short-term memory network is used to process the temporal sequence to obtain the hidden state sequence; Early signals are extracted from the hidden state sequence to obtain signal feature vectors, and disaster chains are constructed based on the signal feature vectors to obtain chain correlation matrices; The evolution trend is predicted based on the chain correlation matrix, and a trend prediction sequence is obtained; The probability distribution value is obtained by calculating the probability of compound disasters from the trend prediction sequence. If the probability distribution value exceeds the preset threshold, a high-risk compound disaster is identified and a risk warning label is obtained.

7. The method for comprehensive early warning of multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, High-risk areas are extracted from the probability of complex disasters, and new monitoring data is integrated using a coupling relationship update mechanism to obtain real-time early warning model parameters, including: A spatial clustering algorithm is used to identify the initial set of high-risk areas in the probability of the complex disaster; For the initial set of high-risk areas, real-time monitoring stations within their coverage area are matched, and multi-source heterogeneous monitoring indicator sequences are extracted; Based on historical monitoring index sequences and disaster event records, a nonlinear dependency structure among disaster-causing factors is constructed to obtain a multidimensional joint distribution function. When a new round of monitoring indicator sequence is input, the conditional failure probability is calculated using the multidimensional joint distribution function to obtain a dynamic risk probability map; If the probability value of a specific grid in the dynamic risk probability graph exceeds the preset risk threshold, the corresponding grid will be included in the dynamic early warning area. Within the dynamic early warning area, the parameters of the real-time early warning model are determined by using a pre-established decision tree model based on the risk probability value and the rate of change.

8. The comprehensive early warning method for multiple risks in coal mine goaf based on machine learning according to claim 1, characterized in that, Based on the parameters of the real-time early warning model, an alarm output is generated. If the probability continues to rise, a feedback loop is activated to obtain an optimized risk control response, including: Based on the parameters of the real-time early warning model, an initial risk probability value is obtained through logistic regression model calculation; The initial risk probability value is combined with multiple probability records in the historical time series to form a probability change sequence, and the slope analysis method is used to determine the change trend of the corresponding sequence. If the trend of the sequence exceeds a preset continuous rise threshold, a feedback control module is activated to obtain a response adjustment command. Based on the response adjustment instruction, relevant control measures are retrieved and reorganized from the preset risk control strategy library to generate an optimized risk disposal plan.

9. A machine learning-based comprehensive early warning system for multiple risks in coal mine goaf areas, implemented according to the method described in any one of claims 1-8, characterized in that, include: The system includes a data acquisition module, a data preprocessing module, a data fusion module, a signal recognition module, a disaster probability assessment module, and a risk early warning module. The data acquisition module is used to collect real-time data on mine pressure, gas, and hydrology through multi-temporal scale sensors, and to obtain a basic dataset of dynamic coupling relationships based on the real-time data on mine pressure, gas, and hydrology. The data preprocessing module is used to preprocess noise and missing values ​​based on the dynamic coupling relationship basic dataset, and to use graph neural networks to model the node connections between geological structure and mineral pressure changes to obtain spatiotemporal heterogeneous feature representations. The data fusion module is used to analyze nonlinear characteristics through the spatiotemporal heterogeneous feature representation, and to determine multi-scale dynamic patterns by fusing the interaction between gas emission and hydrological infiltration paths. The signal recognition module is used to obtain the risk transmission path in the multi-scale dynamic pattern, and based on the risk transmission path, obtain early identification signals of potential disaster chains; The disaster probability judgment module is used to process time series using a long short-term memory network based on the early identification signal, and to predict the evolution trend and judge the probability of compound disasters from the disaster chain. The risk warning module is used to extract high-risk areas from the probability of compound disasters and to integrate new monitoring data using a coupling relationship update mechanism to obtain real-time warning model parameters. The alarm output is generated based on the parameters of the real-time early warning model. If the probability continues to rise, the feedback loop is activated to obtain an optimized risk control response.

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