Coal mine water disaster prediction system based on data analysis and machine learning technology

Abstract

The invention relates to the technical field of coal mine safety, in particular to a coal mine water disaster prediction system based on a data analysis and machine learning technology, which comprises a multi-source data acquisition module, a dynamic data preprocessing module, a multi-modal feature engineering module, an integrated prediction model construction module and a prediction optimization control module, the multi-source data acquisition module fuses geological and hydrological data, micro-seismic data and equipment working condition data, the dynamic data preprocessing module constructs a noise feature library and realizes noise elimination and data standardization, and the multi-modal feature engineering module extracts dynamic causal feature vectors of a water diversion coefficient change rate and a micro-seismic energy release rate based on convergence cross mapping; the integrated prediction model construction module fuses and outputs a water disaster risk probability value through a meta-learner; and the prediction optimization control module triggers a sampling rate adjustment and disaster response linkage mechanism according to the risk probability value. The method has the advantages of high reliability, high adaptability and timely response, and is suitable for real-time prediction of water disasters in a complex coal mine environment.

Description

Technical Field

[0001] The present invention relates to the field of coal mine safety technology, and in particular to a coal mine water hazard prediction system based on data analysis and machine learning technologies. Background Art

[0002] Coal mine groundwater hazards, a major threat to mine safety and production, are characterized by complex triggering mechanisms, rapid propagation, and challenges in human intervention. Traditional groundwater early warning methods rely primarily on static methods such as manual inspections and analysis of hydrological data. These methods suffer from poor real-time performance, low prediction accuracy, and delayed response. In recent years, with the advancement of IoT sensors and machine learning technologies, coal mine groundwater prediction has gradually evolved towards multi-source sensing, intelligent modeling, and automated response.

[0003] Most existing coal mine water hazard prediction methods use single feature modeling (such as water level or microseismic characteristics), which makes it difficult to fully reflect the multi-factor coupling mechanism of water hazard evolution; although some systems have introduced neural network models, they lack the means to extract key causal features, and the model's interpretability and migration capabilities are insufficient; at the same time, the prediction results usually fail to directly link the control execution link, resulting in a delayed risk response. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a coal mine water hazard prediction system based on data analysis and machine learning technology, an intelligent coal mine water hazard prediction system that integrates multimodal data processing, causal intelligent modeling and dynamic response control.

[0005] The coal mine water hazard prediction system based on data analysis and machine learning technology includes a multi-source data acquisition module, a dynamic data preprocessing module, a multimodal feature engineering module, an integrated prediction model construction module and a prediction optimization control module, among which; The multi-source data acquisition module collects monitoring data through geological and hydrological sensors, microseismic monitoring arrays, and mining equipment working condition sensors, wherein the microseismic monitoring array includes distributedly arranged broadband accelerometers; The dynamic data preprocessing module performs noise feature separation processing on the monitoring data based on transfer learning, matches the real-time noise spectrum pattern by building a noise feature library, and outputs standardized spatiotemporal series data with quality assessment labels; The multimodal feature engineering module extracts dynamic causal feature vectors of hydrological parameters and microseismic energy based on a convergent cross-mapping algorithm. The dynamic causal feature vectors include a nonlinear coupling index of the hydraulic conductivity change rate and the microseismic energy release rate. The integrated prediction model construction module adopts a dual-channel stacking architecture. The first channel is a TCN time series prediction model integrated with an attention mechanism, and the second channel is a mutation pattern recognition model based on a causal forest. The meta-learner dynamically assigns dual-channel weights and outputs a flood risk probability value. The prediction optimization control module triggers a gradient response mechanism based on the risk probability value. If the flood risk probability value is in the range of 0.4-0.7, the sampling rate of the microseismic monitoring array is increased to 1kHz. If the flood risk probability value is greater than 0.7, the drainage system and the disaster avoidance path push function of the underground personnel positioning terminal are synchronously activated.

[0006] Optionally, the multi-source data acquisition module includes: Deployment of geohydrological sensor arrays: Preset monitoring points at the coal mine working face and its surrounding areas, and deploy geohydrological sensor arrays including pore water pressure gauges, groundwater conductivity sensors, and osmometers to collect real-time data on pore water pressure, conductivity, and seepage changes, generating a preliminary set of geohydrological parameters. Deployment of microseismic monitoring arrays: Distributed installation of broadband accelerometer arrays along vertical and horizontal profiles in key overburden layers and goaf areas. The broadband accelerometers have a frequency response range of 0.1 Hz to 2 kHz and are arranged at 10m intervals to form a monitoring grid. This array is used to collect microseismic event waveform data in real time and generate a microseismic waveform data set. Integrated mining equipment operating condition sensors: Equipment operating condition sensors are deployed on mining equipment (including coal mining machines, scraper conveyors, and hydraulic supports). These sensors include vibration sensors, current sensors, and workload sensors. These sensors collect equipment operating vibration characteristics, power consumption data, and load conditions to generate a collection of equipment operating condition data. Establish a unified time synchronization mechanism: perform unified time stamp processing on the geological and hydrological parameter sets, microseismic waveform data sets, and equipment operating condition data sets, and use a joint calibration mechanism of GPS timing and high-precision internal clocks to generate synchronously annotated multi-source original monitoring data packets; Perform unified spatial coordinate mapping: Based on the 3D geological model of the coal mine, the spatial positions of sensors in the multi-source original monitoring data packets are mapped and converted to coordinates, ensuring that the sampling points corresponding to different types of monitoring data can be visualized and correlated within a unified spatial framework, and outputting a monitoring data structure with spatiotemporal identification. Monitoring data structure encapsulation output: The monitoring data structure with time and space identification is classified and encapsulated according to data type and sampling source, and output to the dynamic data preprocessing module as the input data source.

[0007] Optionally, the dynamic data preprocessing module includes: Raw data quality assessment: Receives the monitoring data structure output by the multi-source data acquisition module, marks the sampling discontinuities, drifts, and noise mutations according to the sensor calibration parameters and sampling anomaly rules, and generates a preliminary quality assessment label set; Noise feature library construction: collect typical noise patterns in historical coal mine monitoring data and construct a noise feature library based on spectrum analysis and waveform feature classification; Noise spectrum pattern matching: Using the transfer learning method based on the convolutional neural network pre-training model, the microseismic waveform data set in the current monitoring data structure is matched with the noise feature library for spectrum similarity, and the noise segments with high matching degree are identified and marked as removable areas.

[0008] Optionally, the dynamic data preprocessing module further includes: Standardized spatiotemporal series data generation: After removing the noise segments, the remaining data segments are subjected to unit unification, frequency resampling, and spatiotemporal interpolation to generate standardized spatiotemporal series data with quality assessment labels; Standardized spatiotemporal sequence data output: The standardized spatiotemporal sequence data is output to the multimodal feature engineering module.

[0009] Optionally, the multimodal feature engineering module includes: Hydrological parameter extraction: Analyze the time series of geohydrological parameters from standardized spatiotemporal sequence data, extract the hydraulic conductivity, conductivity change trends and pore water pressure fluctuation indicators, and generate a hydrological parameter vector set; Calculation of microseismic energy index: Energy integration analysis is performed on the microseismic waveform data set in the standardized space-time series data, the microseismic energy release rate within the unit time window is calculated, and the microseismic energy time series is generated.

[0010] Optionally, the multimodal feature engineering module further includes: Constructing a state space reconstruction model: Based on Takens embedding theory, the state space of the hydrological parameter vector set and the microseismic energy time series is reconstructed to form a state trajectory mapping space that can be used for causal identification; Perform convergent cross-mapping calculations: In the state-trajectory mapping space, a convergent cross-mapping algorithm is performed to calculate causal strength scores and identify a subset of hydrological-microseismic coupling features with significant nonlinear dependencies.

[0011] Dynamic causal feature vector output: Feature pairs with significant causal relationships are used to construct dynamic causal feature vectors and output them to the integrated prediction model construction module.

[0012] Optionally, the integrated prediction model building module includes: Constructing the first channel model: Receive the dynamic causal feature vector, build a TCN time series prediction model integrated with the attention mechanism, use it to model the long-term and short-term trends of the hydraulic conductivity change rate and microseismic energy release rate series, and output the first channel prediction value; Constructing the second channel model: Based on the dynamic causal feature vector, a causal forest mutation pattern recognition model is constructed to classify and identify the sudden patterns in feature changes and output the second channel prediction value; Training the meta-learner: Using the first channel prediction value and the second channel prediction value as input features, a meta-learner model is constructed and trained to dynamically adjust the channel weights under different feature distributions to generate the optimal fusion weight parameter set; Output flood risk probability value: Perform weighted calculation on the first channel prediction value and the second channel prediction value based on the optimal fusion weight parameter set, and output the final flood risk probability value.

[0013] Optionally, the prediction optimization control module includes: Risk interval judgment: Receive the flood risk probability value and classify the risk probability value into three risk states: low risk, medium risk and high risk according to the set risk interval threshold; Microseismic monitoring sampling rate adjustment: If the current risk status is medium, the sampling rate of the microseismic monitoring array is increased from the default value to 1kHz, and a sampling rate increase instruction is generated and fed back to the multi-source data acquisition module; Disaster emergency function activation: If the current risk status is high, a drainage system activation instruction and a disaster avoidance route push instruction for the underground personnel positioning terminal will be generated simultaneously to execute the emergency linkage control mechanism; Linkage control command feedback: The sampling rate increase command and the emergency linkage control command are uniformly encapsulated into a dynamic control execution command set, which is fed back to the multi-source data acquisition module and the mine emergency control system to achieve rapid response; Execution status monitoring: monitor the response status of each execution module to the dynamic control execution instruction set, and record the sampling rate change log and the drainage system startup time point.

[0014] Beneficial effects of the present invention: The present invention integrates geological and hydrological sensors, microseismic monitoring arrays, and mining equipment operating condition sensors through a multi-source data acquisition module, and combines the noise feature elimination and standardization processing mechanism based on transfer learning in the dynamic data preprocessing module. This effectively improves the quality consistency and spatiotemporal continuity of monitoring data, provides high-quality, low-noise input data for subsequent modeling, and significantly enhances the system's adaptability to data fluctuations in complex environments.

[0015] The multimodal feature engineering module proposed in this invention introduces a convergent cross-mapping algorithm to extract the nonlinear causal relationship between the rate of change of hydraulic conductivity and the microseismic energy release rate from the standardized spatiotemporal series. Then, through the dual-channel architecture (TCN time series prediction channel + causal forest identification channel) of the integrated prediction model construction module and the meta-learner fusion optimization mechanism, the accurate output of the coal mine water hazard risk probability is achieved. It has the advantages of strong interpretability of the modeling mechanism and high reliability of the prediction results.

[0016] The present invention introduces a gradient response control strategy based on the risk probability interval through a predictive optimization control module, supports real-time dynamic adjustment of the microseismic monitoring sampling rate, and triggers the drainage system and personnel evacuation path push in a linked manner under high-risk conditions, realizing the "prediction and response" closed-loop linkage control logic, greatly improving the timeliness and practicality of early warning response in coal mine sudden water disaster scenarios, and has good industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a system flow diagram of an embodiment of the present invention; Figure 2 Schematic diagram of the multimodal feature engineering module process of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1-Figure 2 As shown, the coal mine water hazard prediction system based on data analysis and machine learning technology includes a multi-source data acquisition module, a dynamic data preprocessing module, a multimodal feature engineering module, an integrated prediction model construction module and a prediction optimization control module, wherein; The multi-source data acquisition module collects monitoring data through geological and hydrological sensors, microseismic monitoring arrays, and mining equipment working condition sensors. The microseismic monitoring array includes distributed broadband accelerometers. The dynamic data preprocessing module performs noise feature separation processing on the monitoring data based on transfer learning. By building a noise feature library to match the real-time noise spectrum pattern, it outputs standardized spatiotemporal series data with quality assessment labels. The multimodal feature engineering module extracts dynamic causal feature vectors of hydrological parameters and microseismic energy based on the convergent cross-mapping algorithm. The dynamic causal feature vectors include the nonlinear coupling index of the hydraulic conductivity change rate and the microseismic energy release rate. The integrated prediction model building module adopts a dual-channel stacking architecture. The first channel is a TCN time series prediction model integrated with an attention mechanism, and the second channel is a mutation pattern recognition model based on a causal forest. The meta-learner dynamically assigns weights to the two channels and outputs the flood risk probability value. The prediction and optimization control module triggers a gradient response mechanism based on the risk probability value. If the flood risk probability value is in the range of 0.4-0.7, the sampling rate of the microseismic monitoring array is increased to 1kHz. If the flood risk probability value is greater than 0.7, the drainage system and the disaster avoidance path push function of the underground personnel positioning terminal are synchronously activated.

[0023] The multi-source data acquisition module includes: Deployment of geohydrological sensor arrays: Preset monitoring points at the coal mine working face and its surrounding areas, and deploy geohydrological sensor arrays including pore water pressure gauges, groundwater conductivity sensors, and osmometers to collect real-time data on pore water pressure, conductivity, and seepage changes, generating a preliminary set of geohydrological parameters. Deployment of microseismic monitoring arrays: Distributed broadband accelerometer arrays are installed along vertical and horizontal profiles in key overburden layers and goaf areas. The broadband accelerometers have a frequency response range of 0.1 Hz to 2 kHz and are arranged at 10-meter intervals to form a monitoring grid. This array is used to collect real-time waveform data of microseismic events and generate a microseismic waveform data set. Integrated mining equipment operating condition sensors: Equipment operating condition sensors are deployed on mining equipment (including coal mining machines, scraper conveyors, and hydraulic supports). These sensors include vibration sensors, current sensors, and workload sensors. These sensors collect equipment operating vibration characteristics, power consumption data, and load conditions to generate a collection of equipment operating condition data. Establish a unified time synchronization mechanism: perform unified time stamp processing on the geological and hydrological parameter sets, microseismic waveform data sets, and equipment operating condition data sets, and use a joint calibration mechanism of GPS timing and high-precision internal clocks to generate synchronously annotated multi-source original monitoring data packets; Perform unified spatial coordinate mapping: Based on the 3D geological model of the coal mine, the spatial positions of sensors in the multi-source original monitoring data packets are mapped and converted to coordinates, ensuring that the sampling points corresponding to different types of monitoring data can be visualized and correlated within a unified spatial framework, and outputting a monitoring data structure with spatiotemporal identification. Monitoring data structure encapsulation output: The monitoring data structure with time and space identification is classified and encapsulated according to data type and sampling source, and output to the dynamic data preprocessing module as the input data source to support subsequent noise feature separation and standardization processing operations; The multi-source data acquisition module forms a multi-type, multi-scale perception network covering the coal mine area by laying out geological and hydrological sensors, deploying microseismic monitoring arrays and integrating mining equipment working condition sensors; combined with a unified time synchronization mechanism and spatial coordinate mapping process, it ensures that the generated monitoring data has high temporal and spatial consistency and source traceability, providing a high-quality data foundation for subsequent standardized processing and dynamic causal feature extraction.

[0024] The dynamic data preprocessing module includes: Raw data quality assessment: Receive the monitoring data structure output by the multi-source data acquisition module, mark the sampling discontinuities, drifts, and noise mutations according to the sensor calibration parameters and sampling anomaly rules, and generate a preliminary quality assessment label set, as follows: Sampling integrity analysis: Count the time series data for each sensor type to determine whether there are consecutive missing time periods. If the missing time period exceeds a set threshold Δt (e.g., 10 seconds), it is marked as an "intermittent anomaly."

[0025] Drift trend identification: Perform linear fitting in each sliding window to calculate the data drift trend and define the drift intensity as: ; in, For the The signal value at a moment; is the signal mean in the sliding window, is the sampling time interval, is the drift intensity index; Noise mutation detection: The short-term variance ratio method is used to detect abnormal mutations, which is expressed as: ; in, is the data of two adjacent sliding windows. is the variance function, is the mutation factor. If it is greater than the set threshold, it is judged as "mutation abnormality" Quality label generation: Combining the results of sampling integrity analysis, drift trend identification, and noise mutation detection, each piece of data is labeled "normal," "intermittent," "drift," and "mutation" to form a preliminary quality assessment label set; Noise feature library construction: The typical noise patterns in historical coal mine monitoring data are collected and a noise feature library is constructed based on spectrum analysis and waveform feature classification. The details are as follows: Spectral feature extraction: Use fast Fourier transform (FFT) to extract spectral features from historical noise data: ; Features include: frequency ; Frequency Energy Center: ; Waveform morphology classification: Extract the morphological features of each waveform, including kurtosis ( ), skewness ( ) and wavelet packet energy distribution ( ); Feature vector construction and storage: Constructing noise feature vectors , expressed as: , and classify them into noise feature library according to noise type; Noise spectrum pattern matching: Using a transfer learning method based on a convolutional neural network pre-trained model, the microseismic waveform data set in the current monitoring data structure is matched with the noise feature library for spectral similarity, and noise segments with high matching scores are identified and marked as removable areas. Spectrum generation: Short-time Fourier transform (STFT) is performed on the microseismic waveform data to generate a two-dimensional spectrum; Transfer learning model feature extraction: Use a CNN model (such as ResNet) pre-trained in earthquake or industrial vibration tasks to extract feature vectors from the two-dimensional spectrogram: ; The CNN pre-training model is a form of transfer learning, in which the parameters of the first few layers of the network are fixed, and only the latter layers or the output layer are fine-tuned to adapt to the coal mine noise recognition task; Similarity calculation and matching: Use the cosine similarity formula to calculate the similarity between the current waveform feature and the noise feature, expressed as: ; If the similarity is greater than 0.9, mark it as a removable area.

[0026] The dynamic data preprocessing module also includes: Standardized spatiotemporal series data generation: After removing the noise segments, the remaining data segments are subjected to unit unification, frequency resampling, and spatiotemporal interpolation to generate standardized spatiotemporal series data with quality assessment labels; Standardized spatiotemporal sequence data output: The standardized spatiotemporal sequence data is output to the multimodal feature engineering module for subsequent extraction of dynamic causal feature vectors; The dynamic data preprocessing module effectively improves the purification quality of the original coal mine monitoring data by constructing a noise feature library and combining it with transfer learning for noise identification. Through quality assessment and standardization processing, it ensures the stability, consistency and computability of the data in subsequent analysis, providing a reliable data input basis for causal modeling.

[0027] The multimodal feature engineering module includes: Hydrological parameter extraction: Analyze the time series of geohydrological parameters from the standardized spatiotemporal sequence data, extract the hydraulic conductivity, conductivity change trend and pore water pressure fluctuation index, and generate the hydrological parameter vector set, as follows: Parameter channel separation: Extract records with the "geological and hydrological parameters" label from the standardized spatiotemporal series data, and extract the time series based on the field information: Pore ​​water pressure time series ; Conductivity time series ; Estimation of hydraulic conductivity: Based on Darcy's law Make an estimate: ; in, Seepage , is the fluid viscosity, is the cross-sectional area, is the hydraulic head gradient, is the hydraulic conductivity time series; Trend indicator calculation: perform first-order differences on the hydraulic conductivity, electrical conductivity, and pore water pressure series to obtain the rate of change indicator : ; Merge into a set of hydrological parameter vectors: ; Microseismic energy index calculation: Energy integration analysis is performed on the microseismic waveform data set in the standardized time-space series data, and the microseismic energy release rate within the unit time window is calculated to generate a microseismic energy time series. The details are as follows: Window division: Divide the microseismic waveform data into time windows of fixed length (such as 10 seconds) ; Waveform energy calculation: The total energy of the microseismic signal in each window is defined as: ,in, is the microseismic waveform signal, is the window length, For the Microseismic energy in a window Release rate sequence generation: Construct microseismic energy release rate sequence: .

[0028] The Multimodal Feature Engineering module also includes: Constructing a state space reconstruction model: Based on Takens embedding theory, the state space of the hydrological parameter vector set and the microseismic energy time series is reconstructed to form a state trajectory mapping space that can be used for causal identification. The details are as follows: Reconstruction parameter determination: Setting the embedding dimension and delay time , determined by the autocorrelation function and the false nearest neighbor method; State vector reconstruction: Time series Construct the state vector, expressed as: ; Hydrological parameter series and microseismic energy sequence Perform state space reconstruction and obtain the trajectory data matrix, which is expressed as: ; Perform convergent cross-mapping calculations: In the state trajectory mapping space, a convergent cross-mapping algorithm is performed to calculate the causal strength scores and identify a subset of hydrological-microseismic coupling features with significant nonlinear dependencies, as follows: Cross-mapping weight estimation: Using the simplex projection principle, Neighbor weights in reconstruction space to estimate The point in ; in, is the true microseismic value, is the microseismic value predicted by hydrological state reconstruction, is the weight coefficient calculated based on similarity, is the number of neighboring points; Calculate causal strength: Estimate causal strength by calculating the correlation coefficient between the predicted value and the true value: ; Causal Strength The tendency to approach 1 indicates that hydrological parameters have a strong causal influence on microseismic energy.

[0029] Dynamic causal feature vector output: Feature pairs with significant causal relationships (such as the rate of change of hydraulic conductivity and the rate of microseismic energy release) are used to construct dynamic causal feature vectors and output them to the integrated prediction model construction module, as follows: Screening for significant coupling feature pairs: Select hydrological-microseismic feature combinations with causal strength C > 0.8 to form coupling pairs; Dynamic causal feature vector construction: Construct the final dynamic causal feature vector: ; Will Output to the integrated prediction model building module to establish a dual-channel prediction structure; The multimodal feature engineering module uses a convergent cross-mapping algorithm to extract dynamic causal relationships from hydrological parameters and microseismic energy data, effectively improving the feature interpretability and data-driven modeling capabilities in coal mine water hazard prediction, and providing high-quality coupled causal input for multi-channel modeling.

[0030] The integrated prediction model building blocks include: Construct the first channel model: Receive the dynamic causal feature vector and build a TCN time series prediction model that integrates the attention mechanism to model the long-term and short-term trends of the hydraulic conductivity change rate and the microseismic energy release rate series. Output the first channel prediction value, as follows: Input format conversion: Dynamic causal feature vector The input tensor is organized as a sliding window, with the following structure: ; Temporal Convolutional Network Encoding: The input tensor Input to the TCN time series prediction model with fusion attention mechanism and perform one-dimensional causal convolution operation: ; Among them, * is a one-dimensional convolution operation, 、 For the Layer convolution kernel parameters, For the Layer output; Attention mechanism embedding: The attention mechanism is introduced in the output layer of the TCN time series prediction model to apply dynamic weighting to the output of different time steps, which is expressed as: ; The final output weighted result is used as the prediction input: ; The first channel prediction value output: Mapped to the initial value of flood risk probability, denoted as , as the first channel prediction value; Constructing the second channel model: Based on the dynamic causal feature vector, a causal forest mutation pattern recognition model is constructed to classify and identify the sudden patterns in feature changes and output the second channel prediction value, as follows: Calculation of feature mutation rate: From Extract the change amplitude (increase rate) of each component: ; in, :No. Features in time The value of : Smoothing term, preventing division by zero, :feature relative rate of increase.

[0031] Mutation pattern label sampling: Labeled historical flood disaster sample data is introduced as a training set, and labeled according to the explosive increase in microseismic energy and the step-like change in hydrological parameters; Causal forest model training and inference: Use the causal forest model to train the input feature change rate vector For training and classification prediction, the model estimates the prediction probability by grouping and weighting samples: ; in, is the number of decision trees, For the The predicted output of the tree, is the predicted value of the second channel; Training the meta-learner: Take the first channel prediction value and the second channel prediction value as input features, build a meta-learner model, and train it to dynamically adjust the channel weights under different feature distributions to generate the optimal fusion weight parameter set, as follows: Input fusion feature construction: Construct fusion input vector: ; Constructing a meta-learning model structure: Using a lightweight neural network (such as MLP) as the meta-learner structure to perform dynamic weight calculations: ; in, is the weight assigned to the first channel and the second channel, , normalization is guaranteed by Softmax; Output flood risk probability value: perform weighted calculation on the first channel prediction value and the second channel prediction value based on the optimal fusion weight parameter set, and output the final flood risk probability value , passed to the prediction optimization control module; ; in, is the water disaster risk probability value output after the final fusion, ranging from [0,1]. ,triggering high-risk response mechanisms; The integrated prediction model construction module uses the complementary fusion of the TCN model and the causal forest model, and adopts a meta-learner to achieve dynamic weight adaptive optimization, which effectively improves the model's generalization ability and prediction accuracy for different types of risk evolution patterns and enhances the system's intelligent recognition level.

[0032] The predictive optimization control module includes: Risk interval judgment: Receive the flood risk probability value and classify the risk probability value into three risk states: low risk, medium risk, and high risk according to the set risk interval threshold, as follows: Receive flood risk probability value: Receive the output flood risk probability value from the integrated prediction model building module ; Set risk interval threshold: Set segment risk determination threshold 、 , define the following risk states: like , it is a low-risk state; like , it is a medium risk state; like , it is a high-risk state; Generate risk status identification: Encode the current risk status into a three-valued logic label, namely "L" (Low), "M" (Medium), and "H" (High), which is used for control strategy branch judgment; Microseismic monitoring sampling rate adjustment: If the current risk status is medium, the sampling rate of the microseismic monitoring array is increased from the default value to 1kHz. A sampling rate increase instruction is generated and fed back to the multi-source data acquisition module. The details are as follows: Sampling rate judgment trigger: Determine whether the current risk status label is "M". If so, perform the sampling rate increase operation; Generate sampling rate increase instructions: Construct a sampling control parameter set, expressed as: Sampling rate control instructions Sensor_Type=Microseismic Accelerometer, New_Rate ; Feedback to the multi-source data acquisition module: Through the module communication interface, the sampling rate increase instruction is fed back to the control end of the microseismic monitoring array, dynamically updating its operating sampling frequency and improving monitoring sensitivity and data resolution under medium-risk conditions; Disaster emergency function activation: If the current risk status is high, a drainage system activation instruction and a disaster avoidance path push instruction for the underground personnel positioning terminal are generated simultaneously, and the emergency linkage control mechanism is executed, as follows: High-risk status trigger: If the current risk status label is "H", the emergency response process will be entered.

[0033] Build drainage system startup instructions: Build an automatic drainage control command set, expressed as: Drainage control command = {Pump_ID, Start_Flag = 1, Mode = automatic drainage}; Generate disaster avoidance path push instructions: Based on the current underground personnel positioning information and tunnel topology map, select the shortest safe path and push it to the positioning terminal: Disaster avoidance route instruction = {Person_ID, Route = [P1 → P3 → E1], Instruction = voice + graphics}; Synchronous execution of emergency response: Synchronously execute drainage system control and disaster avoidance route push tasks, and record event triggering timestamps and response device identification; Linkage control command feedback: The sampling rate increase command and the emergency linkage control command are uniformly encapsulated into a dynamic control execution command set, which is fed back to the multi-source data acquisition module and the mine emergency control system to achieve rapid response, as follows: Unified instruction encapsulation: Integrate the generated control commands into a standard instruction structure, expressed as: Control execution instruction set Instruction type, target module, execution parameters, execution timestamp ; Instruction distribution and channel confirmation: Through the system communication gateway, the control execution instruction set is routed to the multi-source data acquisition module (for sampling rate control) and the mine emergency control system (for drainage and evacuation linkage); The receipt mechanism confirms the execution status: Receive the execution receipt returned by the target module to confirm whether the control instruction has been issued and executed. If it fails, it will be recorded and reissued; Execution status monitoring: Monitor the response status of each execution module to the dynamic control execution instruction set, record the sampling rate change log and the drainage system startup time point, and use it for subsequent operation evaluation and model retraining feedback, as follows: Sampling rate log record: When the microseismic monitoring array sampling rate is changed, the system records the following information: Operation time, target sampling frequency, response latency, and execution confirmation status (success / failure); Emergency response record archiving: record the drainage system response delay, pump station operation status and disaster avoidance route push status respectively; Feedback to the model optimization interface: Upload the above logs and execution status data to the prediction model retraining module to serve as reference data for model accuracy evaluation and adaptive adjustment of control strategies; The predictive optimization control module triggers a dynamic control strategy based on the probability value of water disaster risk, realizing the intelligent improvement of monitoring accuracy and the rapid linkage of disaster response mechanism. It has significant practical value and response time advantages in the early warning and decision-making intervention of sudden water disasters in coal mines.

[0034] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0035] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A coal mine water disaster prediction system based on data analysis and machine learning technology, characterized by: It includes multi-source data acquisition module, dynamic data preprocessing module, multimodal feature engineering module, integrated prediction model construction module and prediction optimization control module, among which; The multi-source data acquisition module collects monitoring data through geological and hydrological sensors, microseismic monitoring arrays, and mining equipment working condition sensors, wherein the microseismic monitoring array includes distributedly arranged broadband accelerometers; The dynamic data preprocessing module performs noise feature separation processing on the monitoring data based on transfer learning, matches the real-time noise spectrum pattern by building a noise feature library, and outputs standardized spatiotemporal series data with quality assessment labels; The multimodal feature engineering module extracts dynamic causal feature vectors of hydrological parameters and microseismic energy based on a convergent cross-mapping algorithm. The dynamic causal feature vectors include a nonlinear coupling index of the hydraulic conductivity change rate and the microseismic energy release rate. The integrated prediction model construction module adopts a dual-channel stacking architecture. The first channel is a TCN time series prediction model integrated with an attention mechanism, and the second channel is a mutation pattern recognition model based on a causal forest. The meta-learner dynamically assigns dual-channel weights and outputs a flood risk probability value. The prediction optimization control module triggers a gradient response mechanism based on the risk probability value. If the flood risk probability value is in the range of 0.4-0.7, the sampling rate of the microseismic monitoring array is increased to 1kHz. If the flood risk probability value is greater than 0.7, the drainage system and the disaster avoidance path push function of the underground personnel positioning terminal are synchronously activated.

2. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 1 is characterized in that: The multi-source data acquisition module includes: Deployment of geohydrological sensor arrays: Preset monitoring points at the coal mine working face and its surrounding areas, and deploy geohydrological sensor arrays including pore water pressure gauges, groundwater conductivity sensors, and osmometers to collect real-time data on pore water pressure, conductivity, and seepage changes, generating a preliminary set of geohydrological parameters. Deployment of microseismic monitoring arrays: Distributed installation of broadband accelerometer arrays along vertical and horizontal profiles in key overburden layers and goaf areas. The broadband accelerometers have a frequency response range of 0.1 Hz to 2 kHz and are used to collect microseismic event waveform data in real time to generate microseismic waveform data sets. Integrated mining equipment operating condition sensors: Equipment operating condition sensors are deployed on mining equipment. These sensors include vibration sensors, current sensors, and workload sensors. These sensors collect equipment operating vibration characteristics, power consumption data, and load conditions to generate a collection of equipment operating condition data. Establish a unified time synchronization mechanism: perform unified time stamp processing on the geological and hydrological parameter sets, microseismic waveform data sets, and equipment operating condition data sets, and use a joint calibration mechanism of GPS timing and internal clocks to generate synchronously annotated multi-source original monitoring data packets; Perform unified spatial coordinate mapping: Based on the three-dimensional geological model of the coal mine, the spatial positions of sensors in the multi-source original monitoring data packets are converted into coordinates and output as a monitoring data structure with spatiotemporal identification; Monitoring data structure encapsulation output: The monitoring data structure with time and space identification is classified and encapsulated according to data type and sampling source, and output to the dynamic data preprocessing module as the input data source.

3. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 2 is characterized in that: The dynamic data preprocessing module includes: Raw data quality assessment: Receives the monitoring data structure output by the multi-source data acquisition module, marks the sampling discontinuities, drifts, and noise mutations according to the sensor calibration parameters and sampling anomaly rules, and generates a preliminary quality assessment label set; Noise feature library construction: collect typical noise patterns in historical coal mine monitoring data and construct a noise feature library based on spectrum analysis and waveform feature classification; Noise spectrum pattern matching: Using the transfer learning method based on the convolutional neural network pre-training model, the microseismic waveform data set in the current monitoring data structure is matched with the noise feature library for spectrum similarity, and the noise segments with high matching degree are identified and marked as removable areas.

4. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 3 is characterized in that: The dynamic data preprocessing module also includes: Standardized spatiotemporal sequence data generation: After removing the noise segments, the remaining data segments are subjected to unit unification, frequency resampling, and spatiotemporal interpolation to generate standardized spatiotemporal sequence data with quality assessment labels. The standardized spatiotemporal sequence data is then output to the multimodal feature engineering module.

5. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 4 is characterized in that: The multimodal feature engineering module includes: Hydrological parameter extraction: Analyze the time series of geohydrological parameters from standardized spatiotemporal sequence data, extract the hydraulic conductivity, conductivity change trends and pore water pressure fluctuation indicators, and generate a hydrological parameter vector set; Calculation of microseismic energy index: Energy integration analysis is performed on the microseismic waveform data set in the standardized space-time series data, the microseismic energy release rate within the unit time window is calculated, and the microseismic energy time series is generated.

6. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 5 is characterized in that: The multimodal feature engineering module further includes: Constructing a state space reconstruction model: Based on Takens embedding theory, the state space of the hydrological parameter vector set and the microseismic energy time series is reconstructed to form a state trajectory mapping space that can be used for causal identification; Perform convergent cross-mapping calculations: In the state trajectory mapping space, a convergent cross-mapping algorithm is performed to calculate causal strength scores and identify a subset of hydrological-microseismic coupling features with significant nonlinear dependencies. Dynamic causal feature vector output: Feature pairs with significant causal relationships are used to construct dynamic causal feature vectors and output them to the integrated prediction model construction module.

7. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 6, characterized in that: The integrated prediction model building module includes: Constructing the first channel model: Receive the dynamic causal feature vector, build a TCN time series prediction model integrated with the attention mechanism, use it to model the long-term and short-term trends of the hydraulic conductivity change rate and microseismic energy release rate series, and output the first channel prediction value; Constructing the second channel model: Based on the dynamic causal feature vector, a causal forest mutation pattern recognition model is constructed to classify and identify the sudden patterns in feature changes and output the second channel prediction value; Training the meta-learner: Using the first channel prediction value and the second channel prediction value as input features, a meta-learner model is constructed and trained to dynamically adjust the channel weights under different feature distributions to generate the optimal fusion weight parameter set; Output flood risk probability value: Perform weighted calculation on the first channel prediction value and the second channel prediction value based on the optimal fusion weight parameter set, and output the final flood risk probability value.

8. The coal mine water hazard prediction system based on data analysis and machine learning technology according to claim 7, characterized in that: The prediction optimization control module includes: Risk interval judgment: Receive the flood risk probability value and classify the risk probability value into three risk states: low risk, medium risk and high risk according to the set risk interval threshold; Microseismic monitoring sampling rate adjustment: If the current risk status is medium, the sampling rate of the microseismic monitoring array is increased from the default value to 1kHz, and a sampling rate increase instruction is generated and fed back to the multi-source data acquisition module; Disaster emergency function activation: If the current risk status is high, a drainage system activation instruction and a disaster avoidance route push instruction for the underground personnel positioning terminal will be generated simultaneously to execute the emergency linkage control mechanism; Linkage control command feedback: The sampling rate increase command and the emergency linkage control command are uniformly encapsulated into a dynamic control execution command set, which is fed back to the multi-source data acquisition module and the mine emergency control system to achieve rapid response; Execution status monitoring: monitor the response status of each execution module to the dynamic control execution instruction set, and record the sampling rate change log and the drainage system startup time point.

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