Coal mine water inrush risk early warning method and system under multi-source information

Abstract

The invention provides a coal mine water inrush risk early warning method and system under multi-source information, and relates to the technical field of risk early warning, and the method comprises the steps: employing a multi-source information collection module to regularly collect geological structure data, hydrological data, microseismic signals and environment monitoring data in a coal mine operation area, and constructing a multi-source information database; performing data preprocessing to obtain a standard multi-source information database; carrying out multi-source data fusion, and constructing a water inrush risk comprehensive evaluation model in combination with a geological model, a hydrological model and a micro-seismic analysis model; analyzing the water inrush risk index in real time, and generating water inrush risk early warning information when the model output value is higher than a preset risk threshold value; and performing visual display of the water inrush risk early warning information through a visual interface. The technical problem that the prior art depends on a single data source and cannot comprehensively reflect complex condition changes in a coal mine operation area, so that accuracy of water inrush risk assessment is insufficient, and then early warning of the water inrush risk is missed is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk early warning, and particularly to a coal mine water inrush risk early warning method and system under multi-source information. Background Art

[0002] During the coal mine exploitation process, the water inrush risk has always been a major issue in the safe production of coal mines. Water inrush refers to the sudden entry of groundwater flow or accumulation into the coal mine, which may trigger coal mine disasters, even resulting in casualties, equipment damage, and interruption of coal mine production. Therefore, coal mine water inrush risk early warning and monitoring play a crucial role in mine safety management.

[0003] Currently, the research and application of coal mine water inrush risk early warning methods mainly focus on geological exploration, hydrogeological monitoring, and microseismic monitoring, etc. Traditional water inrush risk early warning methods often rely on a single data source for risk prediction. For example, only hydrogeological data, geological data, or microseismic signals are used to evaluate the risk. Although a single data source can provide certain risk prediction information, it cannot comprehensively reflect the complex geological, hydrogeological, and environmental changes in the coal mine operation area, resulting in insufficient accuracy of water inrush risk assessment, and thus may miss the early warning opportunity of water inrush events. Summary of the Invention

[0004] This application provides a coal mine water inrush risk early warning method and system under multi-source information, aiming to solve the technical problem that the existing technology relies on a single data source, cannot comprehensively reflect the complex situation changes in the coal mine operation area, resulting in insufficient accuracy of water inrush risk assessment, and further leading to the missed early warning of water inrush risk.

[0005] In the first aspect disclosed in this application, a coal mine water inrush risk early warning method under multi-source information is provided. The method includes: in the coal mine operation area, using a multi-source information acquisition module to regularly collect geological structure data, hydrogeological data, microseismic signals, and environmental monitoring data to construct a multi-source information database; performing data preprocessing on the multi-source information database to obtain a standard multi-source information database; performing multi-source data fusion on the standard multi-source information database, combining a geological model, a hydrogeological model, and a microseismic analysis model to construct a water inrush risk comprehensive assessment model; based on the water inrush risk comprehensive assessment model, analyzing water inrush risk indicators in real time, and when the model output value is higher than a preset risk threshold, generating a water inrush risk early warning information; and visualizing and displaying the water inrush risk early warning information through a visualization interface.

[0006] The second aspect disclosed in this application provides a coal mine water inrush risk early warning system under multi-source information. The system is used for the coal mine water inrush risk early warning method under the above multi-source information. The system includes: a multi-source information collection unit, which is used to regularly collect geological structure data, hydrological data, microseismic signals, and environmental monitoring data in the coal mine operation area by using a multi-source information collection module, and construct a multi-source information database; a data preprocessing unit, which is used to preprocess the data in the multi-source information database to obtain a standard multi-source information database; an evaluation model construction unit, which is used to perform multi-source data fusion on the standard multi-source information database, and combine a geological model, a hydrological model, and a microseismic analysis model to construct a comprehensive water inrush risk evaluation model; an early warning information generation unit, which is used to analyze the water inrush risk indicators in real time based on the comprehensive water inrush risk evaluation model, and generate a water inrush risk early warning information when the model output value is higher than a preset risk threshold; an early warning information display unit, which is used to visually display the water inrush risk early warning information through a visualization interface.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] By using a multi-source information collection module to regularly collect data from different sources and construct a multi-source information database, various environmental, geological, and hydrological factors in the coal mine area can be comprehensively monitored. The integration of this multi-source data enables potential risks in the coal mine operation area to be effectively identified in multiple dimensions; preprocessing the collected data to ensure that the data in the multi-source information database is standardized, which guarantees the quality of the data. This process ensures that the data used for subsequent model training and risk assessment is consistent, accurate, and complete; through multi-source data fusion, combining a geological model, a hydrological model, and a microseismic analysis model to construct a comprehensive water inrush risk evaluation model, the advantages of multiple models can be integrated to conduct a more refined and comprehensive assessment of the water inrush risk, avoiding the limitations that may be brought by a single model; based on the constructed comprehensive water inrush risk evaluation model, analyzing the water inrush risk indicators in real time and automatically generating a water inrush risk early warning information according to the set risk threshold. This real-time monitoring and automated risk early warning mechanism can help the mining area discover risks in a timely manner and improve the response speed; visually displaying the water inrush risk early warning information through a visualization interface enables the management personnel in the mining area to quickly understand and interpret the early warning results. The visualization display makes complex data and evaluation results easy to understand, which helps decision-makers grasp the safety status of the mining area in real time. Through clear graphical display, management personnel can quickly identify areas with higher risks and take corresponding countermeasures, thereby improving the emergency response efficiency.

[0009] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific implementation manners of this application are specifically given below. Description of the Drawings

[0010] Figure 1 It is a schematic flow chart of the coal mine water inrush risk early warning method under multi-source information provided by an embodiment of this application.

[0011] Figure 2 It is a schematic structural diagram of the coal mine water inrush risk early warning system under multi-source information provided by an embodiment of this application.

[0012] Description of the reference numerals: multi-source information acquisition unit 10, data preprocessing unit 20, evaluation model construction unit 30, early warning information generation unit 40, early warning information display unit 50. Specific Embodiment

[0013] By providing a coal mine water inrush risk early warning method and system under multi-source information in an embodiment of this application, the technical problem in the prior art that relies on a single data source and cannot comprehensively reflect the complex situation changes in the coal mine operation area, resulting in insufficient accuracy of the water inrush risk assessment and thus missing the early warning of the water inrush risk is solved.

[0014] After introducing the basic principle of this application, the various non-limiting implementation manners of this application will be specifically introduced below in conjunction with the drawings in the specification. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0015] Embodiment 1, as Figure 1 shown, an embodiment of this application provides a coal mine water inrush risk early warning method under multi-source information, and the method includes:

[0016] In the coal mine operation area, use a multi-source information acquisition module to regularly collect geological structure data, hydrological data, microseismic signals and environmental monitoring data to construct a multi-source information database.

[0017] According to the specific situation of the coal mine operation area, such as geological characteristics, mine area scale, environmental factors, etc., conduct an analysis of the water inrush risk monitoring requirements. According to the requirements, select appropriate acquisition modules. For example, for geological structure data, geological exploration equipment needs to be installed; for hydrological data, water level, water flow and other monitoring instruments are required; for microseismic signals, seismic monitoring instruments are needed; environmental monitoring data includes various data acquisition devices such as temperature, humidity, and air pressure. Set parameters such as the acquisition frequency, acquisition time period, and data accuracy of each acquisition module to ensure the accuracy and timeliness of the data.

[0018] The multi-source information acquisition module is used to automatically collect various required data at regular intervals, including geological structure data, such as rock layer thickness, fault information, and vein direction; hydrological data, such as groundwater level and groundwater flow rate; microseismic signals, i.e., small vibration data in or around the mine, which are related to factors such as groundwater seepage and ore body changes; environmental monitoring data, such as air quality, temperature, humidity, etc. These multi-source data collected at regular intervals are aggregated into the database to form a complete multi-source information database, ensuring that the data can be accessed on time and accurately.

[0019] Data preprocessing is performed on the multi-source information database to obtain a standard multi-source information database.

[0020] The first step of data preprocessing is to perform time alignment on the data in the multi-source information database. This is because the acquisition frequency and time of different data acquisition modules may be different, and the data needs to be uniformly processed according to the timestamp. After the time alignment, data anomaly identification is performed to detect and identify anomalies in the data, including missing data and duplicate data, and to fill in missing data, for example, using interpolation, mean filling or model-based supplementary methods; duplicate data is deduplicated, duplicate records are deleted or data is merged to ensure data consistency. After data cleaning and repair, a more accurate and neat standard multi-source information database is obtained, which provides a reliable data foundation for subsequent multi-source data fusion and risk assessment.

[0021] The standard multi-source information database is subjected to multi-source data fusion, and a comprehensive assessment model for water inrush risk is constructed by combining geological models, hydrological models and microseismic analysis models.

[0022] The purpose of fusing data from different sources in the standard multi-source information database is to extract more comprehensive feature information by integrating different types of data, so as to provide support for the assessment of water inrush risk. Specifically, the geological structure information in the area, including rock formations, faults, ore bodies, etc., is combined to understand the impact of geological conditions on water inrush. For example, if certain rock formations have strong water permeability, the risk of water inrush may be increased; hydrological data such as water level and water flow are integrated, especially the monitoring of groundwater dynamic changes. Changes in water level may be closely related to groundwater flow and water inrush risk; microseismic signals reflect tiny geological changes and possible structural damage during coal mine operations. By analyzing microseismic data, it is possible to identify in advance whether there are water hazards inside the mine.

[0023] By combining a geological model, a hydrogeological model, and a microseismic analysis model, a comprehensive water inrush risk assessment model is established. The output parameters of each model will reflect the data characteristics in different fields. Among them, the geological model is used to analyze the interaction between groundwater flow and geological structures, and identify potential water sources and groundwater seepage paths within the mining area; the hydrogeological model is used to simulate information such as groundwater levels, groundwater flow velocities, and flow directions, and predict the dynamic changes of groundwater in the coal mine operation area; the microseismic analysis model identifies possible geological structure changes by analyzing microseismic signals, and speculates whether there is geological activity related to water hazards. During the modeling process, measured data is used to train the model and optimize its parameters to enable it to more accurately reflect the changes in water inrush risks.

[0024] Based on the comprehensive water inrush risk assessment model, the water inrush risk indicators are analyzed in real time. When the model output value is higher than the preset risk threshold, a water inrush risk warning message is generated.

[0025] New data, including real-time hydrogeological data, microseismic signals, etc., is obtained in real time from the multi-source information collection module and input into the comprehensive water inrush risk assessment model. The model conducts risk assessment based on the real-time data and outputs a risk indicator, which is usually a numerical value representing the degree of water inrush risk. It involves multiple dimensions, such as anomalies in hydrogeological conditions, increases in microseismic activities, and changes in geological structures. When the output value of the model, that is, the risk indicator, exceeds the preset risk threshold, it indicates that the water inrush risk has reached a dangerous level. At this time, a water inrush risk warning message is automatically generated. The setting of the risk threshold is based on historical data analysis, expert experience, or simulation results. For example, if the groundwater level rises rapidly or microseismic activities are frequent, this indicates an increase in the water inrush risk.

[0026] The visualization display of the water inrush risk warning message is carried out through a visualization interface.

[0027] To enable operators to clearly understand the real-time situation of the water inrush risk, the warning message is displayed through a visualization interface, which is usually presented in ways such as charts, maps, and color changes. For example, different colors or icons are used to represent the risk levels. For example, green represents low risk, yellow represents medium risk, and red represents high risk, enabling operators to quickly identify the severity of the water inrush risk. The water inrush risk warning message needs to be updated in real time. Whenever new data is input, the risk assessment model will recalculate the risk indicator and display the result to the staff through the visualization interface to ensure that all personnel can always grasp the latest risk situation. The above steps, through comprehensive data fusion and real-time analysis, predict the water inrush risk in advance and help the staff make timely decisions through visualization to ensure the safety of coal mine operations.

[0028] Furthermore, the method includes:

[0029] Based on the regional structure information of the coal mine operation area, conduct an analysis of the water inrush risk monitoring requirements to determine multiple data acquisition modules and multiple module requirement parameters; based on the multiple module requirement parameters, integrate the multiple data acquisition modules to obtain the multi-source information acquisition module.

[0030] The regional structure information includes the geological structure, terrain, groundwater flow conditions, and possible water inrush risk factors in the coal mine operation area. This information helps to determine the geological characteristics and hydrogeological environment of the coal mine area, and further affects the water inrush risk assessment.

[0031] On the basis of the regional structure analysis, clarify the types of data required for water inrush risk monitoring, including geological data, such as rock layer distribution, geological structure (faults, fractures, etc.), vein information, etc.; hydrogeological data, such as groundwater level, flow velocity, flow direction, etc., to understand the dynamic changes of groundwater; microseismic signals, collect the signals of microseismic instruments to detect possible underground vibration changes; environmental data, such as temperature, humidity, air pressure, etc., which may indirectly affect the water inrush risk. According to different data types, determine the specific parameters of each data acquisition module, including acquisition frequency, measurement range, accuracy requirements, etc. Clarify the specific requirements and parameters of each data acquisition module so that it can effectively collect data related to water inrush risk. This requirement analysis provides clear guidance for the next module integration and configuration.

[0032] According to the requirement analysis, select appropriate hardware devices to configure the acquisition modules. The functions of each module will vary according to their requirement parameters. Specifically, it includes a geological data acquisition module for collecting geological structure information in the area, such as rock layers, faults, veins, etc., including geological exploration instruments, drilling equipment, or ground-penetrating radar, etc.; a hydrogeological data acquisition module for real-time monitoring of hydrogeological data such as groundwater level, flow velocity, and flow direction, including water level sensors, flow meters, water flow meters, etc.; a microseismic signal acquisition module for detecting microseismic activities to help evaluate the water inrush risk, including seismographs or microseismic sensors; an environmental monitoring module for collecting environmental data such as temperature, humidity, and air pressure to understand the possible impact of external conditions in the mine on groundwater infiltration and microseismic signals.

[0033] Configure the functions of each acquisition module according to the requirement parameters. For example, select suitable sensors, set appropriate sampling frequencies and accuracy requirements, and integrate different data acquisition modules through communication interfaces or data transmission channels to ensure that the data collected by each module can be uniformly transmitted and processed. Finally, construct a multi-source information acquisition module. This module can simultaneously collect multiple data types, forming a comprehensive monitoring system that can real-time monitor the water inrush risk in the coal mine operation area.

[0034] Furthermore, the method for using the multi-source information collection module to regularly collect geological structure data, hydrological data, microseismic signals, and environmental monitoring data to construct a multi-source information database includes:

[0035] Determine the first key monitoring point group based on the regional structure information; at the first key monitoring point group, deploy the multiple data collection modules respectively according to the multiple module requirement parameters to obtain the first multi-source information collection nodes; collect data based on the first multi-source information collection nodes, and add the data collection results to the multi-source information database.

[0036] Analyze the regional structure information. Specifically, according to the regional structure of the coal mine operation area, determine the possible water inrush risk points in the area, especially the key positions in the geological structure that may have faults, fissures, rock layer pores, etc. that affect the groundwater flow; determine the potential paths of groundwater infiltration and flow, especially the areas with relatively high groundwater levels or large water flow changes, which should be the key monitoring objects; determine the areas where the interaction between groundwater and rock layers may trigger microseismic events. These areas may have water inrush risks and thus need special attention. According to the analysis of the regional structure, select the areas that may have water inrush risks, locate the key monitoring point group, among which, more monitoring points are deployed in some high-risk areas, while the deployment of monitoring points can be reduced in relatively low-risk areas. Randomly extract a group from the key monitoring point group as the first key monitoring point group for subsequent analysis.

[0037] According to the determined requirement parameters, select appropriate data collection modules for each key monitoring point. Each monitoring point corresponds to a data type, such as geological structure, hydrological data, microseismic signals, and environmental data, etc. After deployment according to the selected data collection modules and parameters in the first key monitoring point group, form the first multi-source information collection nodes. This node has the ability to collect different types of data and can continuously monitor the water inrush risk in the designated area.

[0038] Through the already deployed first multi-source information collection nodes, obtain the data from each data collection module in real time. Each module will automatically collect data at the set sampling frequency, and the collected data is directly stored in the preset multi-source information database. This database will store all the data from different collection nodes and be marked by means of timestamps, etc. for subsequent analysis, processing, and fusion.

[0039] Furthermore, the method for preprocessing the data in the multi-source information database to obtain a standard multi-source information database includes:

[0040] Perform time series alignment and data anomaly identification on the multi-source information database to obtain multi-source information anomaly data, where the multi-source information anomaly data includes missing data and duplicate data; supplement the missing data and eliminate the duplicate data to obtain the standard multi-source information database.

[0041] Since the multi-source information collection module comes from different sensors or devices, these devices may have different sampling frequencies and time intervals. Therefore, it is necessary to perform time series alignment on the data from different collection modules. The goal of time series alignment is to unify the data from different sources in chronological order so that they can be compared and fused at the same timestamp. In the time series aligned data, identify and mark the outliers in the data, including missing data and duplicate data. Among them, in some cases, due to equipment failures, communication interruptions, or other reasons, some data points may not be recorded, resulting in missing data; due to system failures or data collection errors, duplicate recordings of data points may occur, resulting in data redundancy.

[0042] For the missing data, use an interpolation algorithm to fill it. For example, interpolate based on the adjacent valid data points before and after to fill in the missing values. The data supplement should ensure the consistency and rationality of the data and avoid data deviation caused by inappropriate interpolation methods; for duplicate data, determine which records are duplicates by comparing the timestamps and data values, and then delete the duplicate data points. After deduplication, check the integrity of the data to ensure that there is no loss or incompleteness of data due to the deletion operation. After supplementing the missing data and removing the duplicate data, the obtained data set will be cleaner and more accurate, and these data are used as the content of the standard multi-source information database.

[0043] Furthermore, the method for constructing the comprehensive water inrush risk assessment model includes:

[0044] Extract data feature information based on the standard multi-source information database to obtain multi-source data feature information; determine the multi-source data feature threshold based on the multi-source data feature information; identify outliers in the standard multi-source information database according to the multi-source data feature threshold to obtain a multi-source anomaly feature data set; perform model training on the geological model, hydrological model, and microseismic analysis model based on the multi-source anomaly feature data set to obtain the comprehensive water inrush risk assessment model.

[0045] The goal of data feature extraction is to extract key information from the standard multi-source information database that can effectively represent the water inrush risk. These feature information will contribute to subsequent outlier identification and model training. Specifically, geological data feature extraction is carried out, including rock stratum structure features, fault features, and fracture features, which are closely related to the fluidity of groundwater; hydrological data feature extraction is carried out, including groundwater level changes, flow velocity and discharge, water temperature and pH value, etc.; microseismic data feature extraction is carried out, including vibration frequency and amplitude, epicenter location and depth, etc. Integrate all the extracted features to form a data set containing multi-dimensional feature information such as geology, hydrology, and microseismicity.

[0046] Feature thresholds are used to distinguish normal situations from abnormal situations. By setting thresholds, outliers deviating from the norm in the data can be identified. These outliers may indicate potential water inrush risks. The setting of feature thresholds is determined based on historical data, expert experience, and the results of model training. For example, by calculating the mean and standard deviation of each feature, a certain range is set as the threshold. When the value of a certain feature is greater than the mean plus twice the standard deviation, it may be regarded as abnormal. Different thresholds are set for data from different sources, such as geological data, hydrological data, etc. For example, the threshold for groundwater level may be different from that for water discharge. These thresholds will be comprehensively evaluated based on multiple data features to ensure that they can fully reflect the water inrush risk.

[0047] For each data source, including geological data, hydrological data, microseismic signals, etc., check whether they exceed the preset thresholds. If a data point exceeds the threshold, it is regarded as an outlier. Mark the data exceeding the threshold as abnormal data and save it to a separate abnormal data set to obtain a multi-source abnormal feature data set. This set includes not only the points with abnormal data values but also the abnormalities caused by environmental changes or emergencies, etc.

[0048] Based on the geological data in the multi-source abnormal feature data set, train a geological model. By analyzing the abnormal data, the model can identify potential water inrush risk factors such as possible water source channels and fault ruptures; use the outliers of hydrological data to train a hydrological model, which helps the model understand the flow characteristics of groundwater and then evaluate the risk of groundwater seeping into the mine; by analyzing the abnormal features of microseismic signals, train a microseismic analysis model. These abnormal signals may imply changes in the underground structure, and the model can predict whether there is a potential risk of water inrush.

[0049] Integrate the geological model, hydrological model, and microseismic analysis model into a comprehensive evaluation model. The resulting comprehensive water inrush risk evaluation model can not only evaluate a certain type of risk alone but also comprehensively evaluate multiple risk factors. This model can provide timely risk warnings for coal mine operations and provide decision-making support for management personnel.

[0050] Furthermore, based on the multi-source abnormal feature data set, model training of a geological model, a hydrogeological model, and a microseismic analysis model is performed to obtain the comprehensive water inrush risk assessment model. The method includes:

[0051] Based on the multi-source abnormal feature data set, an association analysis between geological structure-water inrush risk factors is carried out to determine a set of abnormal geological structure evaluation indicators; the standard multi-source information database is labeled according to the set of abnormal geological structure evaluation indicators to obtain an abnormal geological structure risk assessment data set; based on the abnormal geological structure risk assessment data set, model training of the geological model is carried out; and so on, until the model training of the hydrogeological model and the microseismic analysis model is completed to obtain the comprehensive water inrush risk assessment model.

[0052] Based on the identified multi-source abnormal feature data set, an association analysis between geological structure and water inrush risk is carried out. During the analysis process, the main objective is to identify which geological structure features are directly related to water inrush risk. Among them, geological structure factors include rock layer type, fault distribution, ore body shape, etc., especially structural features that may affect groundwater flow, such as fissures, fault zones, etc.; water inrush risk factors include groundwater level, groundwater flow path, flow velocity, etc., especially those dynamic change factors related to water inrush.

[0053] Through correlation analysis, such as Pearson correlation coefficient, chi-square test, etc., the correlation between geological structure features and water inrush-related data such as hydrogeological data and microseismic signals is examined to analyze which geological features have a stronger relationship with water inrush risk. Based on the association analysis, a set of abnormal geological structure evaluation indicators is determined. These indicators will directly reflect the impact of geological structure features on water inrush risk. For example, fault density reflects the impact of faults on water flow penetration, rock layer porosity affects water mobility, and groundwater permeability is directly related to water inrush risk. Through these indicator sets, the impact of geological structure on water inrush risk can be quantified, thus providing important inputs for subsequent model training and risk assessment.

[0054] Based on the set of abnormal geological structure evaluation indicators, the data in the standard multi-source information database is labeled. The purpose of labeling is to clearly identify the relationship between different geological structure features and water inrush risk, providing a labeled data set for subsequent model training. For example, for the geological data of a specific mining area, the water inrush risk levels of different regions can be labeled, and whether there are high-risk areas can be marked. Through labeling, an abnormal geological structure risk assessment data set with risk assessment labels is obtained. This data set contains risk level information related to geological structures and will be used as the basis for training the geological model.

[0055] Use the labeled abnormal geological structure risk assessment data set to train the geological model. The purpose of the training is to enable the model to learn how to predict water inrush risks based on geological structure characteristics (such as fault density, rock layer porosity, etc.). Specifically, select machine learning algorithms for model training, such as decision trees, random forests, and support vector machines. Divide the labeled abnormal geological structure risk assessment data set into a training set and a validation set for model training and evaluation. Train the model according to the training data, and use methods such as cross-validation to optimize the model parameters to improve the prediction accuracy and robustness. After training, the geological model will be able to predict the water inrush risk in the mining area based on the input geological feature data and provide risk scores for different regions.

[0056] After completing the training of the geological model, similarly, according to the hydrological data and its related risk labels, use the hydrological data set to train the hydrological model; for microseismic data, through similar steps, use the microseismic data set for training, and the model will learn how to predict the water inrush risk through changes in microseismic signals. Through the independent training of the geological model, hydrological model, and microseismic analysis model, finally integrate these models to form a comprehensive evaluation model. The comprehensive evaluation model can give a comprehensive water inrush risk prediction based on the risk assessment results of various data sources.

[0057] Furthermore, the method for constructing the comprehensive water inrush risk assessment model further includes:

[0058] Extract the environmental monitoring data set based on the multi-source information database. Based on the environmental monitoring data set, simulate several normal environmental states and several abnormal environmental states; through the several normal environmental states and the several abnormal environmental states, perform model adaptive enhancement on the comprehensive water inrush risk assessment model under normal environmental states and abnormal environmental states respectively.

[0059] Extract the environmental monitoring data set from the multi-source information database. These environmental data include temperature, humidity, air pressure, air quality, wind speed, etc., especially those environmental factors that may indirectly affect groundwater flow and water inrush risk.

[0060] Under normal circumstances, the environmental data should be within a stable and conventional range. For example, temperature, humidity, air pressure, etc. should be within the expected normal fluctuation range. By analyzing historical data, it is possible to identify which environmental states are normal. Based on historical environmental data and statistical methods, simulate multiple environmental states that conform to the normal environmental fluctuation range. For example, simulate a typical climate pattern or seasonal change.

[0061] Mutations or abnormal changes in environmental data may increase the risk of water inrush in a mine. For example, abnormally high temperatures, excessive humidity, or rapid changes may cause changes such as evaporation and infiltration of groundwater, thereby affecting the hydrogeological environment of the mining area. By setting the mutation range of environmental data, such as extreme temperatures and rapid changes in humidity, possible abnormal environmental states can be simulated. These abnormal states may be caused by natural disasters (such as heavy rain and extreme climate) or human factors (such as equipment failures).

[0062] Adaptive enhancement means that in different environmental states, the model can automatically adjust its evaluation and prediction capabilities according to environmental changes. In the assessment of water inrush risk, environmental factors may affect the prediction results of hydrogeological and geological models. Therefore, the model needs to be able to maintain efficient prediction capabilities in different environmental states.

[0063] In the simulated normal environmental state, the model will learn how to process and evaluate standard environmental fluctuations, such as common seasonal changes. At this time, through the feedback of normal data, the model optimizes its response to geological, microseismic, and hydrogeological data, enabling the model to more accurately identify the water inrush risk under normal circumstances. For example, in the case of normal fluctuations in temperature and humidity, the model will learn more accurate risk thresholds and evaluation criteria based on historical data.

[0064] In the simulated abnormal environmental state, the model will learn how to handle environmental mutations, such as extreme weather and drastic changes in temperature and humidity. At this time, the model adjusts its prediction algorithm according to the changes in environmental data to identify the water inrush risk that may be caused by abnormal environments. For example, when the environment changes drastically, such as a sharp rise in temperature or a sudden increase in precipitation, the model will adjust the parameters of the hydrogeological model and microseismic analysis model to detect and warn of water inrush risks in a timely manner.

[0065] After training and enhancement in normal and abnormal environmental states, the final comprehensive water inrush risk assessment model will be able to automatically adjust its risk assessment output according to real-time environmental data, enabling accurate water inrush risk warnings in the mining area under various environmental conditions. Such an adaptive enhancement model can not only operate efficiently in a conventional environment but also respond promptly under extreme environmental conditions, maximizing the safety of the mining area.

[0066] Furthermore, based on the comprehensive water inrush risk assessment model, the water inrush risk indicators are analyzed in real time. When the model output value is higher than the preset risk threshold, a water inrush risk warning message is generated. The method includes:

[0067] Collect real-time multi-source information data through a multi-source information collection module; input the real-time multi-source information data into the water inrush risk comprehensive assessment model to obtain multiple real-time water inrush risk indicators as the model output values; obtain multiple water inrush risk levels based on the multiple real-time water inrush risk indicators; when any value among the multiple water inrush risk levels is higher than a preset risk threshold, generate a water inrush risk warning message.

[0068] The multi-source information collection module collects data from different data sources in real time to form real-time multi-source information data. Input the real-time data into the water inrush risk comprehensive assessment model. The assessment model analyzes and calculates using the input data to obtain multiple real-time water inrush risk indicators. Among them, the geological risk indicators include related indicators such as the rupture of underground rock formations and fault activities; the hydrogeological risk indicators such as the water level change rate and abnormal groundwater flow; the microseismic risk indicator is to evaluate the potential water inrush risk based on the intensity and frequency of microseismic activities. The model will calculate multiple real-time water inrush risk indicators as the model output value at this moment, and these indicators will help evaluate the current water inrush risk situation in the mining area.

[0069] Based on multiple real-time water inrush risk indicators, evaluate and calculate multiple water inrush risk levels. Each water inrush risk indicator has a corresponding risk level, which reflects the degree of influence of the indicator on water inrush. For example, a low risk level indicates that the indicator is within the normal range and does not pose a threat to the safety of the mine; a medium risk level indicates that the value of the indicator has become abnormal and needs attention, but there is no direct threat of water inrush at present; a high risk level indicates that the value of the indicator exceeds the normal range, and there may be a water inrush risk, and emergency measures need to be taken immediately. The level of each risk indicator is calculated based on different factors. For example, a sharp rise in the underground water level may lead to a high-risk hydrogeological level, and frequent microseismic activities may lead to a high-risk geological level.

[0070] Set a preset risk threshold. When a certain risk level calculated in real time exceeds this threshold, a warning will be triggered. The threshold can be set according to historical data, expert experience, or simulation analysis. For example, when the change in the underground water level exceeds a certain amplitude or the microseismic frequency reaches a certain value, it is judged that the water inrush risk has increased.

[0071] When the risk threshold is exceeded, generate a water inrush risk warning message. At this time, according to which indicators exceed the threshold, generate corresponding alarms. The warning message includes warning type, risk level, warning area, etc. The warning message will notify relevant staff through the system and trigger the corresponding emergency response mechanism. The staff will make further decisions and actions based on the warning message to ensure the safety of the mine.

[0072] Furthermore, the method further includes:

[0073] Activate the water inrush emergency response plan library based on the water inrush risk warning information; traverse the water inrush emergency response plan library for plan matching based on the multiple real-time water inrush risk indicators, and obtain the target water inrush emergency response plan; conduct emergency response to the water inrush risk based on the target water inrush emergency response plan.

[0074] The water inrush emergency response plan library is a database containing various emergency response plans. These plans provide specific emergency measures according to different types and levels of water inrush risks. When water inrush risk warning information is generated, the water inrush emergency response plan library is automatically activated. At this time, all emergency response plans in the library will be retrieved and prepared to select the most suitable response plan according to real-time data.

[0075] According to the calculated multiple real-time water inrush risk indicators, traverse the water inrush emergency response plan library, and match the most suitable emergency plan according to the current risk level, risk type and actual situation. For example, a high water level risk indicator triggers plans such as increasing the pump pressure and strengthening the water discharge channel in the mine; an increase in microseismic frequency triggers emergency responses such as strengthening monitoring and evacuating personnel; geological risks (such as fault activities) trigger response measures such as closing certain areas of the mine or suspending operations. After the matching is completed, the target water inrush emergency response plan is obtained. This plan is the most suitable for the current water inrush risk and conforms to the preset priority order.

[0076] When the target emergency response plan is determined, guide the staff to implement it according to the steps in the plan. During the implementation process, if the risk situation changes, such as the water level drops or the microseismicity decreases, the emergency response plan is dynamically adjusted according to the new real-time data. This means that the system can adaptively adjust the emergency measures to ensure the most effective response means are taken at different stages.

[0077] In summary, the coal mine water inrush risk warning method under multi-source information provided by the embodiments of the present application has the following technical effects:

[0078] By using a multi-source information collection module to regularly collect data from different sources and constructing a multi-source information database, various environmental, geological, and hydrogeological factors within the coal mine area can be comprehensively monitored. The integration of such multi-source data enables the effective identification of potential risks in the coal mine operation area from multiple dimensions; preprocessing the collected data to ensure that the data in the multi-source information database undergoes standardized processing guarantees the data quality. This process ensures that the data used for subsequent model training and risk assessment is consistent, accurate, and complete; through multi-source data fusion, combining geological models, hydrogeological models, and microseismic analysis models to construct a comprehensive water inrush risk assessment model can integrate the advantages of multiple models and conduct a more refined and comprehensive assessment of the water inrush risk, avoiding the limitations that a single model may bring; based on the constructed comprehensive water inrush risk assessment model, analyzing the water inrush risk indicators in real time and automatically generating water inrush risk warning information according to the set risk threshold. Such a real-time monitoring and automated risk warning mechanism can help the mining area promptly discover risks and improve the response speed; displaying the water inrush risk warning information through a visual interface enables the mining area management personnel to quickly understand and interpret the warning results. The visual display makes complex data and assessment results easy to understand, helps decision-makers grasp the safety status of the mining area in real time. Through clear graphical display, the management personnel can quickly identify areas with higher risks and take corresponding countermeasures, thus improving the emergency response efficiency.

[0079] Embodiment 2. Based on the same inventive concept as the method for warning coal mine water inrush risks under multi-source information in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a system for warning coal mine water inrush risks under multi-source information, and the system includes:

[0080] A multi-source information collection unit 10, configured to regularly collect geological structure data, hydrogeological data, microseismic signals, and environmental monitoring data in the coal mine operation area by using a multi-source information collection module, and construct a multi-source information database; a data preprocessing unit 20, configured to perform data preprocessing on the multi-source information database to obtain a standard multi-source information database; an evaluation model construction unit 30, configured to perform multi-source data fusion on the standard multi-source information database, combine geological models, hydrogeological models, and microseismic analysis models, and construct a comprehensive water inrush risk assessment model; a warning information generation unit 40, configured to, based on the comprehensive water inrush risk assessment model, analyze water inrush risk indicators in real time, and generate water inrush risk warning information when the model output value is higher than a preset risk threshold; a warning information display unit 50, configured to perform visual display of the water inrush risk warning information through a visual interface.

[0081] Furthermore, the multi-source information collection unit 10 includes the following operation steps:

[0082] According to the regional structural information of the coal mine operation area, a water inrush risk monitoring demand analysis is performed to determine multiple data acquisition modules and multiple module demand parameters; based on the multiple module demand parameters, the multiple data acquisition modules are integrated to obtain the multi-source information acquisition module.

[0083] Furthermore, the multi-source information acquisition unit 10 includes the following operation steps:

[0084] Based on the regional structure information, a first key monitoring point group is determined; in the first key monitoring point group, the multiple data acquisition modules are respectively deployed according to the multiple module requirement parameters to obtain a first multi-source information acquisition node; data acquisition is performed based on the first multi-source information acquisition node, and the data acquisition results are added to the multi-source information database.

[0085] Furthermore, the data preprocessing unit 20 includes the following operation steps:

[0086] Perform time alignment and data anomaly identification on the multi-source information database to obtain multi-source information anomaly data, wherein the multi-source information anomaly data includes missing data and duplicate data; perform data supplementation on the missing data and data removal on the duplicate data to obtain the standard multi-source information database.

[0087] Furthermore, the evaluation model building unit 30 includes the following steps:

[0088] Data feature extraction is performed based on the standard multi-source information database to obtain multi-source data feature information; based on the multi-source data feature information, a multi-source data feature threshold is determined; outlier identification is performed on the standard multi-source information database according to the multi-source data feature threshold to obtain a multi-source anomaly feature data set; based on the multi-source anomaly feature data set, model training of geological models, hydrological models and microseismic analysis models is performed to obtain the comprehensive assessment model for water inrush risk.

[0089] Furthermore, the evaluation model building unit 30 includes the following steps:

[0090] Based on the multi-source abnormal feature data set, a geological structure-water inrush risk factor correlation analysis is performed to determine an abnormal geological structure assessment index set; the standard multi-source information database is labeled according to the abnormal geological structure assessment index set to obtain an abnormal geological structure risk assessment data set; based on the abnormal geological structure risk assessment data set, model training of the geological model is performed; and so on, the model training of the hydrological model and the microseismic analysis model is completed to obtain the comprehensive water inrush risk assessment model.

[0091] Furthermore, the evaluation model construction unit 30 includes the following operating steps:

[0092] Extract an environmental monitoring data set based on the multi-source information database. Based on the environmental monitoring data set, simulate a number of normal environmental states and a number of abnormal environmental states; through the number of normal environmental states and the number of abnormal environmental states, perform model adaptive enhancement on the water inrush risk comprehensive evaluation model under normal environmental states and abnormal environmental states respectively.

[0093] Furthermore, the early warning information generation unit 40 includes the following operating steps:

[0094] Collect real-time multi-source information data through the multi-source information collection module; input the real-time multi-source information data into the water inrush risk comprehensive evaluation model to obtain multiple real-time water inrush risk indicators as the model output values; obtain multiple water inrush risk levels based on the multiple real-time water inrush risk indicators; when any value among the multiple water inrush risk levels is higher than the preset risk threshold, generate a water inrush risk early warning information.

[0095] Furthermore, the early warning information display unit 50 includes the following operating steps:

[0096] Activate the water inrush emergency response plan library based on the water inrush risk early warning information; based on the multiple real-time water inrush risk indicators, traverse the water inrush emergency response plan library for plan matching to obtain the target water inrush emergency response plan; perform emergency response to the water inrush risk based on the target water inrush emergency response plan.

[0097] Through the foregoing detailed description of the coal mine water inrush risk early warning method under multi-source information in this specification, those skilled in the art can clearly know the coal mine water inrush risk early warning system under multi-source information in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, refer to the description in the method part.

[0098] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for early warning of water inrush risk in coal mines under multi-source information, characterized in that, The method comprises: In the coal mine operation area, a multi-source information acquisition module is used to regularly collect geological structure data, hydrological data, microseismic signals and environmental monitoring data to build a multi-source information database; Performing data preprocessing on the multi-source information database to obtain a standard multi-source information database; Conduct multi-source data fusion on the standard multi-source information database, combine geological model, hydrological model and microseismic analysis model, and build a comprehensive assessment model for water inrush risk; Based on the comprehensive assessment model for water inrush risk, water inrush risk indicators are analyzed in real time, and when the model output value is higher than a preset risk threshold, water inrush risk warning information is generated; The water inrush risk warning information is visualized through a visualization interface.

2. The coal mine water inrush risk early warning method under multi-source information according to claim 1, characterized in that, The method comprises: According to the regional structure information of the coal mine operation area, the water inrush risk monitoring demand analysis is carried out to determine multiple data acquisition modules and multiple module demand parameters; Based on the multiple module requirement parameters, the multiple data acquisition modules are integrated to obtain the multi-source information acquisition module.

3. The coal mine water inrush risk early warning method under multi-source information according to claim 2, characterized in that, The method of using the multi-source information acquisition module to regularly acquire geological structure data, hydrological data, microseismic signals and environmental monitoring data to construct a multi-source information database includes: Determine a first key monitoring point group based on the regional structure information; In the first key monitoring point group, the plurality of data acquisition modules are respectively arranged according to the plurality of module requirement parameters to obtain a first multi-source information acquisition node; Data collection is performed based on the first multi-source information collection node, and the data collection result is added to the multi-source information database.

4. The coal mine water inrush risk early warning method under multi-source information according to claim 1, wherein, The method of performing data preprocessing on the multi-source information database to obtain a standard multi-source information database includes: Performing time series alignment and data anomaly identification on the multi-source information database to obtain multi-source information anomaly data, wherein the multi-source information anomaly data includes missing data and duplicate data; The missing data are supplemented, the duplicate data are eliminated, and the standard multi-source information database is obtained.

5. The coal mine water inrush risk early warning method under multi-source information according to claim 1, characterized in that The method for constructing a comprehensive water inrush risk assessment model includes: Extracting data features based on the standard multi-source information database to obtain multi-source data feature information; Determining a multi-source data feature threshold based on the multi-source data feature information; Performing outlier identification on the standard multi-source information database according to the multi-source data feature threshold to obtain a multi-source abnormal feature data set; Based on the multi-source abnormal feature data set, model training of geological model, hydrological model and microseismic analysis model is carried out to obtain the comprehensive assessment model of water inrush risk.

6. The coal mine water inrush risk early warning method under multi-source information according to claim 5, characterized in that The method of performing model training of a geological model, a hydrological model and a microseismic analysis model based on the multi-source abnormal feature data set to obtain the comprehensive assessment model of water inrush risk includes: Based on the multi-source abnormal characteristic data set, a geological structure-water inrush risk factor correlation analysis is performed to determine an abnormal geological structure assessment index set; Labeling the standard multi-source information database according to the abnormal geological structure assessment indicator set to obtain an abnormal geological structure risk assessment data set; Based on the abnormal geological structure risk assessment data set, model training of the geological model is carried out; And so on, complete the model training of the hydrogeological model and the microseismic analysis model, and obtain the comprehensive water inrush risk assessment model.

7. The coal mine water inrush risk early warning method under multi-source information according to claim 1, characterized in that The method for constructing the comprehensive water inrush risk assessment model further includes: Extract the environmental monitoring data set from the multi-source information database, and based on the environmental monitoring data set, simulate a number of normal environmental states and a number of abnormal environmental states; Through the number of normal environmental states and the number of abnormal environmental states, perform model adaptive enhancement on the comprehensive water inrush risk assessment model under normal environmental states and abnormal environmental states respectively.

8. The coal mine water inrush risk early warning method under multi-source information according to claim 1, characterized in that Based on the comprehensive water inrush risk assessment model, analyze the water inrush risk indicators in real time. When the model output value is higher than the preset risk threshold, generate a water inrush risk warning message. The method includes: Collect real-time multi-source information data through the multi-source information collection module; Input the real-time multi-source information data into the comprehensive water inrush risk assessment model to obtain multiple real-time water inrush risk indicators as the model output value; Obtain multiple water inrush risk levels based on the multiple real-time water inrush risk indicators; When any value in the multiple water inrush risk levels is higher than the preset risk threshold, generate a water inrush risk warning message.

9. The coal mine water inrush risk early warning method under multi-source information according to claim 8, characterized in that, The method further includes: Activate the water inrush emergency response plan library based on the water inrush risk warning message; Based on the multiple real-time water inrush risk indicators, traverse the water inrush emergency response plan library for plan matching to obtain the target water inrush emergency response plan; Carry out emergency response to the water inrush risk based on the target water inrush emergency response plan.

10. A coal mine water inrush risk early warning system under multi-source information, characterized in that, A system for implementing the coal mine water inrush risk warning method under multi-source information according to any one of claims 1-9, the system includes: A multi-source information collection unit for regularly collecting geological structure data, hydrogeological data, microseismic signals and environmental monitoring data in the coal mine operation area by using a multi-source information collection module to construct a multi-source information database; A data preprocessing unit for preprocessing the multi-source information database to obtain a standard multi-source information database; An evaluation model construction unit for performing multi-source data fusion on the standard multi-source information database, combining a geological model, a hydrogeological model and a microseismic analysis model to construct a comprehensive water inrush risk assessment model; A warning message generation unit for analyzing the water inrush risk indicators in real time based on the comprehensive water inrush risk assessment model, and generating a water inrush risk warning message when the model output value is higher than the preset risk threshold; A warning message display unit for visualizing the water inrush risk warning message through a visualization interface.

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