Coal seam water disaster early warning system based on hydrological monitoring

Abstract

The invention relates to the technical field of coal mine safety monitoring, in particular to a coal seam water disaster early warning system based on hydrological monitoring, which comprises the steps of collecting hydrogeological data of a shallow coal seam area, performing deep analysis, transmitting information to a data center through a data transmission network for centralized processing and analysis, and performing early warning on coal seam water disaster. The method comprises the following steps: analyzing the influence of mining on dynamic migration characteristics of mine water in a coal mining process, determining early warning thresholds of different danger levels through statistics based on monitoring data and mining influence analysis, carrying out fusion analysis on the monitoring data, establishing a water disaster early warning model by utilizing a machine learning technology, and automatically triggering early warning when the data exceed the thresholds. And obtaining the coal seam water disaster early warning system. According to the method, the problems that in shallow coal seam mining, hydrogeological characteristics are not comprehensively mastered, monitoring points are not reasonably arranged, mining influence analysis is not deep, a water inrush danger early warning threshold value is not scientific, and an efficient water disaster early warning model is lacked for timely and accurate early warning of coal seam water disasters are solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of coal mine safety monitoring, and particularly relates to a coal seam water disaster early warning system based on hydrological monitoring. BACKGROUND

[0002] In the process of coal mining, coal seam water disaster is one of the key disasters that seriously affect safety production. Shallow coal seams are more susceptible to the influence of groundwater systems due to their special occurrence conditions. Once a water disaster occurs, it will not only cause casualties, but also lead to equipment damage and production interruption, causing huge economic losses to coal mining enterprises. The traditional coal seam water disaster monitoring and early warning method has many limitations. On the one hand, the hydrogeological data collection for shallow coal seam areas is not comprehensive and in-depth, making it difficult to accurately obtain the characteristics of the groundwater system affecting the safety of the coal seam, resulting in insufficient understanding of the hydrogeological conditions of the mine, and failing to provide reliable basis for subsequent monitoring and early warning. On the other hand, the arrangement of monitoring points lacks scientificity and rationality, and does not fully consider key monitoring indicators such as water pressure, stress, and water temperature, making it difficult to form an effective monitoring network and unable to timely and accurately capture the hydrological changes in the coal seam and its surrounding areas. In addition, the existing technology has deficiencies in analyzing the influence of mining on the dynamic migration characteristics of mine water, making it difficult to accurately obtain the development of the water-conducting fractured zone and the thickness changes of the roof water-resisting layer, and making it difficult to make forward-looking judgments on the occurrence of water disasters. Moreover, the water inrush risk evaluation method is not perfect, and the determination of the early warning threshold lacks scientific basis, resulting in a big discount in the accuracy and timeliness of the early warning. With the continuous expansion of the scale of coal mining and the increase of mining depth, the problem of coal seam water disaster is becoming increasingly prominent, and the traditional monitoring and early warning method has been unable to meet the needs of modern coal mine safety production.

[0003] The existing technology has the deficiencies of not comprehensive and in-depth analysis of the hydrogeological characteristics of shallow coal seams, lack of precise planning of monitoring point arrangement, insufficient analysis of the influence of mining, unscientific determination of the early warning threshold, and insufficient use of machine learning to establish an efficient early warning model. SUMMARY

[0004] In view of the above status, the present application provides a coal seam water disaster early warning system based on hydrological monitoring, which can solve the problems of not comprehensive understanding of hydrogeological characteristics, unreasonable arrangement of monitoring points, not in-depth analysis of the influence of mining, unscientific water inrush risk early warning threshold, and lack of efficient water disaster early warning model to timely and accurately warn coal seam water disasters. To achieve the above purpose, the present application adopts the following technical solutions:

[0005] The coal seam water disaster early warning system based on hydrological monitoring comprises a geological feature acquisition module, a monitoring point arrangement module, a monitoring data analysis module, a migration feature analysis module, a water inrush danger early warning module and a coal seam water disaster early warning module.

[0006] Further, the geological feature acquisition module comprises a geological data submodule, an information analysis submodule, the geological data submodule is used for collecting hydrogeological data of a shallow coal seam area, analyzing the data in detail, and extracting information related to a groundwater system, the information related to the groundwater system includes a groundwater level, a water flow direction, and recharge and discharge conditions, the information analysis submodule is used for systematically arranging and deeply researching the extracted information, obtaining groundwater system feature information affecting the safety of a coal seam, and the groundwater system feature information includes water layer distribution, water abundance, and hydraulic connection information, and a comprehensive and accurate mine hydrogeological feature report is obtained according to the analysis result.

[0007] Further, the monitoring point arrangement module comprises a geological analysis submodule and a grid layout submodule, the geological analysis submodule is used for adopting a comprehensive and detailed hydrogeological condition analysis method for a target area, studying the influence of geological structures and aquifer characteristics on monitoring requirements, and extracting three key indicators, namely water pressure, stress and water temperature, which have a significant influence on the safety of a coal seam, the grid layout submodule is used for taking a coal seam mining area and a surrounding area as a key monitoring range, rationally arranging monitoring points according to terrain, geological conditions and mining planning, forming a dense grid layout, and obtaining a detailed index list including water pressure, stress and water temperature indexes.

[0008] Further, the monitoring data analysis module comprises: a monitoring data submodule for ensuring comprehensive and real-time acquisition of monitoring data by precisely installing sensors and data acquisition modules at monitoring points; a secure transmission submodule for transmitting information acquired by the sensors and data acquisition modules to the data center in a secure and rapid manner by constructing a stable and efficient data transmission network; and a centralized analysis submodule for centralized processing and analysis of the transmitted data by the data center using professional software and algorithms, extraction of valuable information therefrom, integration of the processed and analyzed data, and obtaining of real-time monitoring data streams and preliminary analysis results.

[0009] Further, the migration feature analysis module comprises: a dynamic migration submodule for continuously collecting data during mining by arranging multiple types of monitoring devices in the coal seam mining area, simulating and analyzing the dynamic migration process of mine water under mining by establishing a numerical simulation model and combining field measured data; a simulation data submodule for extracting key information from the simulation and measured data, the key information in the measured data including water flow direction, speed, and pressure; and a result comparison submodule for comparing the analysis results with geological data to obtain the dynamic changes of the development height and range of the water flowing fractured zone and the thickness of the roof water-resisting layer with mining, integrating all analysis results, and obtaining a comprehensive and accurate mining water dynamic migration feature analysis report.

[0010] Further, the water inrush danger early warning module comprises: a danger evaluation submodule for developing research based on monitoring data and mining influence analysis, extracting a water inrush danger evaluation method suitable for the scene by detailed analysis of the monitoring data features and the changes of the geological structure caused by mining; a warning threshold submodule for determining the warning threshold corresponding to different danger levels by processing and analyzing a large amount of related data using statistical methods; and a warning threshold integration submodule for systemically integrating the evaluation method and the determined warning threshold to obtain complete water inrush danger evaluation method and warning threshold information, and obtaining a water inrush danger evaluation method and warning threshold table.

[0011] Further, the coal seam water disaster early warning module comprises: a data cleaning submodule for collecting data related to coal seam water disasters by monitoring devices, and removing invalid and abnormal data by data cleaning and preprocessing to ensure data quality; a water disaster early warning submodule for deeply integrating different types of data, extracting key feature information, and establishing a precise water disaster early warning model based on the extracted features using machine learning algorithms; and a trigger early warning submodule for setting a reasonable threshold, automatically triggering the early warning mechanism when the real-time monitoring data exceeds the threshold, integrating the early warning model and the trigger mechanism, and obtaining a complete coal seam water disaster early warning system.

[0012] Further, the monitoring data submodule comprises: for adopting the geological conditions of the monitoring area, the water disaster risk distribution characteristics, accurately determining the position of the monitoring point, through the accurate installation of the adapted sensor at the monitoring point, ensuring that various key parameter changes can be sensitively captured, and the data acquisition module is equipped to ensure the stability and accuracy of data acquisition, and through the cooperative work of the sensor and the acquisition module, the physical quantity and chemical quantity data of the monitoring area are extracted in real time.

[0013] Further, the early warning threshold submodule comprises: for adopting a standardized statistical method, processing and analyzing a large amount of collected early-stage risk assessment data, removing abnormal values and missing values through data cleaning, extracting the intrinsic characteristics of the data by using cluster analysis, classifying similar data into a class, dividing data clusters of different risk levels, and determining the correlation between each factor and the risk level by regression analysis, determining the accurate numerical range corresponding to different risk levels through simulation and verification, setting the determined numerical range as the early warning threshold, and obtaining the early warning threshold system of different risk levels.

[0014] In the technical scheme provided by the present application, the geological feature acquisition module is used to collect hydrogeological data of the shallow coal seam area, analyze in depth, obtain feature information of the underground water system affecting the safety of the coal seam, and obtain a mine hydrogeological feature report; the monitoring point arrangement module is used to extract water pressure, stress and water temperature as key monitoring indexes according to the hydrogeological conditions, and arrange the monitoring points in the coal seam mining area and the surrounding area to form a dense grid, and obtain a monitoring point arrangement scheme and an index list; the monitoring data analysis module is used to utilize the data monitored by the sensor and the data acquisition module, transmit the information to the data center through a data transmission network for centralized processing and analysis, and obtain real-time monitoring data flow and preliminary analysis results; the migration feature analysis module is used to analyze the influence of mining on the water dynamic migration feature of the mine, obtain the development of the water flowing fractured zone and the thickness change of the roof water-resisting layer, and obtain a mining water dynamic migration feature analysis report; the water inrush danger early warning module is used to extract a water inrush danger evaluation method based on the monitoring data and the mining influence analysis, determine the early warning threshold of different danger levels through statistics, and obtain the water inrush danger evaluation method and the early warning threshold table; the coal seam water disaster early warning module is used to fuse and analyze the monitoring data, establish a water disaster early warning model by using machine learning technology, automatically trigger the early warning when the data exceeds the threshold, and obtain a coal seam water disaster early warning system. The present application solves the problems of incomplete understanding of the hydrogeological features, unreasonable arrangement of the monitoring points, not in-depth analysis of the mining influence, unscientific water inrush danger early warning threshold, and lack of an efficient water disaster early warning model to timely and accurately warn the coal seam water disaster in the shallow coal seam mining. BRIEF DESCRIPTION OF DRAWINGS

[0015] Various other advantages and benefits will become apparent to those of ordinary skill in the art, upon reading the following detailed description of the preferred embodiment. The accompanying drawings are included to provide a description of preferred embodiments, and are not intended to limit the scope of the application.

[0016] Figure 1 A first embodiment schematic diagram of a coal seam water disaster early warning system based on hydrological monitoring in an embodiment of the present application.

[0017] Figure 2 A second embodiment schematic diagram of a coal seam water disaster early warning system based on hydrological monitoring in an embodiment of the present application.

[0018] Figure 3 A third embodiment schematic diagram of a coal seam water disaster early warning system based on hydrological monitoring in an embodiment of the present application.

[0019] Figure 4 A fourth embodiment schematic diagram of a coal seam water disaster early warning system based on hydrological monitoring in an embodiment of the present application.

[0020] Figure 5 A fifth embodiment schematic diagram of a coal seam water disaster early warning system based on hydrological monitoring in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0022] Those skilled in the art can understand that the singular forms "a", "an" and "the" used herein include plural forms, unless specifically stated otherwise. It should be further understood that the use of the term "including" in the specification of the present application means that the features, integers, steps, operations, elements and / or components described exist, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0023] A coal seam water disaster early warning system based on hydrological monitoring, such as Figure 1As shown, it includes: a geological feature acquisition module, used to collect hydrogeological data of shallow coal seam areas, conduct in-depth analysis, obtain groundwater system feature information affecting coal seam safety, and obtain a mine hydrogeological feature report; a monitoring point layout module, used to extract water pressure, stress, and water temperature as key monitoring indicators based on hydrogeological conditions, and rationally arrange monitoring points in and around the coal seam mining area to form a dense grid, obtaining a monitoring point layout plan and indicator list; and a monitoring data analysis module, used to utilize the data monitored in real time by sensors and data acquisition modules, and transmit the information to the data center through a data transmission network for centralized processing and analysis, obtaining a real-time monitoring data stream. The system includes: a preliminary analysis result module; a migration characteristic analysis module, used to analyze the impact of mining activities on the dynamic migration characteristics of mine water during coal seam mining, obtain information on the development of water-conducting fracture zones and the thickness changes of the roof aquitard, and generate a report on the dynamic migration characteristics of mining water; a water inrush hazard early warning module, used to extract water inrush hazard assessment methods based on monitoring data and mining impact analysis, determine early warning thresholds for different hazard levels through statistics, and obtain a water inrush hazard assessment method and early warning threshold table; and a coal seam water hazard early warning module, used to fuse and analyze monitoring data, establish a water hazard early warning model using machine learning technology, and automatically trigger an early warning when the data exceeds the threshold, thus obtaining a coal seam water hazard early warning system.

[0024] like Figure 2 As shown in this embodiment, the geological data submodule is used to collect hydrogeological data of the shallow coal seam area, analyze these data in detail, and extract information related to the groundwater system, including groundwater level, water flow direction, and recharge and discharge conditions. The information analysis submodule is used to systematically organize and conduct in-depth research on the extracted information to obtain groundwater system characteristic information that affects coal seam safety. Groundwater system characteristic information includes water layer distribution, water abundance, and hydraulic connection information. Based on the above analysis results, a comprehensive and accurate mine hydrogeological characteristic report is obtained.

[0025] The geological data submodule precisely focuses on shallow coal seam areas, comprehensively collecting hydrogeological data and extracting key groundwater system information such as groundwater levels, laying a solid foundation for subsequent analysis. The information analysis submodule deeply processes and studies the extracted information, obtaining characteristic information affecting coal seam safety, such as water layer distribution. Through the collaborative work of these two submodules, a comprehensive and accurate mine hydrogeological characteristic report can be generated, helping staff to clearly understand the mine's hydrogeological conditions, identify potential water hazard risks in advance, provide a scientific basis for safe mining, and effectively ensure production safety and personnel safety.

[0026] like Figure 3As shown, in this embodiment, the geological analysis submodule is used to adopt a comprehensive and detailed hydrogeological condition analysis method for the target area, to study the influence of geological structure, aquifer characteristics and other factors on monitoring needs, and to extract three key indicators of water pressure, stress and water temperature which have significant influence on coal seam mining safety; the grid layout submodule is used to take the coal seam mining area and the surrounding area as the key monitoring range, to reasonably arrange the monitoring points according to the terrain, geological conditions and mining plan, to form a dense grid layout, and to obtain a detailed index list including water pressure, stress and water temperature indicators.

[0027] The geological analysis submodule analyzes the hydrogeological conditions of the target area in detail, deeply explores the influence of geological structure and other factors on monitoring needs, and accurately extracts three key indicators of water pressure, stress and water temperature, providing a clear direction and focus for subsequent monitoring. The grid layout submodule focuses on the coal seam mining area and the surrounding area, reasonably arranges the monitoring points in combination with the terrain, geology and mining plan, forms a dense grid layout, and can comprehensively and accurately obtain various index data. The two modules work together to obtain a detailed index list, which helps to master the changes of the coal seam mining environment in real time, prevent disasters in advance, and ensure safe and efficient mining.

[0028] As shown in Figure 4 , in this embodiment, the monitoring data submodule is used to accurately install sensors and data acquisition modules at monitoring points to ensure that monitoring data can be obtained comprehensively and in real time; the safe transmission submodule is used to build a stable and efficient data transmission network to transmit information obtained by sensors and data acquisition modules to the data center in a safe and fast manner; the centralized analysis submodule is used to process and analyze the transmitted data in the data center by using professional software and algorithms, to extract valuable information, to integrate the processed and analyzed data, and to obtain real-time monitoring data stream and preliminary analysis results.

[0029] The monitoring data acquisition submodule accurately installs sensors and data acquisition modules at monitoring points to ensure the comprehensiveness and real-time nature of monitoring data, providing a rich and timely data basis for subsequent analysis. The safe transmission submodule builds a stable and efficient data transmission network to ensure that information can be safely and quickly transmitted to the data center, avoiding data loss and delay. The centralized analysis submodule uses professional software and algorithms to centrally process and analyze data, accurately extracts valuable information and integrates it to obtain real-time monitoring data stream and preliminary analysis results, which helps staff to master the situation in a timely manner.

[0030] As shown in Figure 5As shown in this embodiment, the dynamic migration submodule is used to continuously collect data during the mining process by deploying multiple types of monitoring equipment in the coal seam mining area. By establishing a numerical simulation model and combining it with on-site measured data, the dynamic migration process of mine water under mining action is simulated and analyzed. The simulation data submodule is used to extract key information from the simulation and measured data. The key information in the measured data includes water flow direction, velocity, and pressure. The result comparison submodule is used to compare the analysis results with geological data to obtain the dynamic changes in the development height and range of the water-conducting fracture zone and the thickness of the roof aquitard layer with mining action. All analysis results are integrated to obtain a comprehensive and accurate analysis report on the dynamic migration characteristics of mining water.

[0031] The dynamic migration submodule deploys various types of monitoring equipment in the coal seam mining area to continuously collect data and establish numerical simulation models. This accurately simulates the dynamic migration process of mine water under mining conditions, providing a dynamic perspective for research. The simulation data submodule extracts key information such as water flow direction from simulated and measured data, making the analysis more targeted. The results comparison submodule compares the analysis results with geological data, clearly showing the dynamic changes of water-conducting fracture zones and other features during mining. The synergy of these three modules yields a comprehensive and accurate analysis report on the dynamic migration characteristics of mining water, helping to identify water hazard risks in advance.

[0032] In this embodiment, the risk assessment submodule is used to conduct research based on monitoring data and mining impact analysis. By analyzing the characteristics of monitoring data and the changes in geological structure caused by mining in detail, it extracts a water inrush risk assessment method applicable to the scenario. The early warning threshold submodule is used to process and analyze a large amount of relevant data using statistical methods to determine the early warning thresholds corresponding to different risk levels. The early warning threshold integration submodule is used to systematically integrate the assessment method with the determined early warning thresholds to obtain complete water inrush risk assessment method and early warning threshold information, resulting in a water inrush risk assessment method and early warning threshold table.

[0033] The hazard assessment submodule, based on monitoring data and mining impact analysis, deeply analyzes data characteristics and geological structure changes to extract a scenario-appropriate water inrush hazard assessment method, laying a solid foundation for accurate water inrush risk assessment. The early warning threshold submodule uses statistical methods to process large amounts of data, accurately determining early warning thresholds for different hazard levels, providing clear standards for risk warnings. The early warning threshold integration submodule integrates the assessment methods with the early warning threshold system, forming complete information and creating tables.

[0034] In this embodiment, the data cleaning submodule is used to collect data related to coal seam water disaster by using monitoring equipment, and through data cleaning and preprocessing, invalid and abnormal data are excluded to ensure data quality; the water disaster early warning submodule is used to integrate different types of data in depth by using fusion analysis technology, extract key feature information, and use machine learning algorithm to establish a precise water disaster early warning model based on the extracted features; the trigger early warning submodule is used to set a reasonable threshold, and when the real-time monitoring data exceeds the threshold, the system automatically triggers the early warning mechanism, integrates the early warning model and the trigger mechanism to obtain a complete coal seam water disaster early warning system.

[0035] The data cleaning submodule collects relevant data by means of monitoring equipment, and through cleaning and preprocessing, invalid and abnormal data are excluded to provide a high-quality data basis for subsequent analysis and ensure the reliability of the results. The water disaster early warning submodule integrates different types of data by using fusion analysis technology, extracts key features, and uses machine learning algorithm to establish a precise early warning model, which can accurately capture the precursor information of water disaster. The trigger early warning submodule sets a reasonable threshold, and when the real-time monitoring data exceeds the threshold, the system automatically triggers the early warning, integrates the early warning model and the trigger mechanism to form a complete system, which can timely warn of coal seam water disaster, effectively ensure mine production safety, and reduce accident loss.

[0036] In this embodiment, the geological conditions of the monitoring area and the water disaster risk distribution characteristics are used to accurately determine the location of the monitoring point, and by accurately installing the appropriate sensor at the monitoring point, it is ensured that various key parameter changes can be sensitively captured, and the data acquisition module is equipped to ensure the stability and accuracy of data acquisition, and through the cooperative work of the sensor and the acquisition module, the physical and chemical quantity data of the monitoring area are extracted in real time.

[0037] According to the geological conditions and water disaster risk distribution characteristics of the monitoring area, the location of the monitoring point is accurately determined, which can focus on key areas and improve the monitoring efficiency and pertinence. By accurately installing the appropriate sensor at the monitoring point, various key parameter changes can be sensitively captured, and no potential risk signal is missed. The data acquisition module is equipped to ensure the stability and accuracy of data acquisition, and avoid data distortion. The sensor and the acquisition module work cooperatively to extract physical and chemical quantity data in real time, which provides rich and timely information for comprehensively mastering the situation of the monitoring area, helps to discover water disaster hidden dangers in advance, and provides a solid basis for subsequent decision-making and response measures.

[0038] In this embodiment, a standardized statistical method is used to process and analyze a large amount of collected risk assessment data, abnormal values and missing values are removed through data cleaning, the inherent characteristics of the data are extracted using cluster analysis, similar data is classified into a category, different risk level data clusters are divided, the correlation between each factor and the risk level is determined through regression analysis, the corresponding precise numerical range of different risk levels is determined through simulation and verification, the determined numerical range is set as the early warning threshold, and the early warning threshold system of different risk levels is obtained.

[0039] The large amount of risk assessment data collected in the early stage is processed by using a standardized statistical method, thereby ensuring the scientificity and rigor of the analysis. The data cleaning removes abnormal values and missing values, thereby improving the data quality and laying a reliable foundation for subsequent analysis. The cluster analysis is used to extract the inherent characteristics of the data and divide the data clusters, thereby clearly presenting the data distribution of different risk levels. The regression analysis is used to determine the correlation between each factor and the risk level, thereby helping to accurately grasp the risk causes. The precise numerical range of different risk levels is determined through simulation and verification, and the early warning threshold is set, thereby constructing a perfect early warning threshold system, and the risk can be warned in advance.

[0040] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A coal seam water disaster early warning system based on hydrological monitoring, characterized in that, The coal seam water disaster early warning system based on hydrological monitoring comprises: The geological feature acquisition module is used for collecting the hydrogeological data of the shallow coal seam area, performing in-depth analysis, acquiring the underground water system feature information affecting the safety of the coal seam, and obtaining a mine hydrogeological feature report; The monitoring point arrangement module is used for extracting water pressure, stress, and water temperature as key monitoring indexes according to the hydrogeological conditions, reasonably arranging the monitoring points in the coal seam mining area and the periphery, forming a dense point grid, and obtaining a monitoring point arrangement scheme and an index list; The monitoring data analysis module is used for monitoring the data in real time by using the sensors and the data acquisition module, transmitting the information to the data center for centralized processing and analysis through a data transmission network, obtaining real-time monitoring data flow and preliminary analysis results; The migration feature analysis module is used for analyzing the influence of mining on the water dynamic migration feature of the mine, acquiring the development of the water flowing fractured zone and the thickness change of the roof water-resisting layer, and obtaining a mining water dynamic migration feature analysis report; The water inrush danger early warning module is used for extracting a water inrush danger evaluation method based on the monitoring data and the mining influence analysis, determining the early warning threshold values of different danger degrees by statistics, and obtaining the water inrush danger evaluation method and the early warning threshold value table; The coal seam water disaster early warning module is used for fusing and analyzing the monitoring data, establishing a water disaster early warning model by using a machine learning technology, automatically triggering early warning when the data exceeds the threshold value, and obtaining a coal seam water disaster early warning system.

2. The coal seam water disaster early warning system based on hydrological monitoring of claim 1, characterized in that, The geological feature acquisition module comprises: The geological data submodule is used for collecting the hydrogeological data of the shallow coal seam area, performing in-depth analysis of the data, and extracting the information related to the underground water system, wherein the information related to the underground water system includes underground water level, water flow direction, and recharge and discharge conditions; The information analysis submodule is used for systematically arranging and deeply researching the extracted information, acquiring the underground water system feature information affecting the safety of the coal seam, and obtaining a comprehensive and accurate mine hydrogeological feature report according to the analysis results.

3. The coal seam water disaster early warning system based on hydrological monitoring of claim 1, characterized in that, The monitoring point arrangement module comprises: The geological analysis submodule is used for adopting a comprehensive and detailed hydrogeological condition analysis method for the target area, researching the influence of geological structure and aquifer characteristic factors on monitoring demand, and extracting three key indexes, namely, water pressure, stress, and water temperature, which have a significant influence on the safety of the coal seam mining; The grid layout submodule is used for taking the coal seam mining area and the periphery as a key monitoring range, reasonably arranging the monitoring points according to the terrain, geological conditions, and mining plan, forming a dense point grid layout, and obtaining a detailed index list including the water pressure, stress, and water temperature indexes.

4. The coal seam water disaster early warning system based on hydrological monitoring of claim 1, characterized in that, The monitoring data analysis module comprises: The monitoring data submodule is used for adopting the way of accurately installing sensors and data acquisition modules at the monitoring points to ensure that the monitoring data can be comprehensively and in real time acquired; The safe transmission submodule is used for constructing a stable and efficient data transmission network, transmitting the information acquired by the sensors and the data acquisition modules to the data center in a safe and fast manner. The centralized analysis submodule is configured to use professional software and algorithms to centrally process and analyze the transmitted data, extract valuable information therefrom, integrate the processed and analyzed data, and obtain real-time monitoring data flow and preliminary analysis results.

5. The coal seam water disaster early warning system based on hydrological monitoring of claim 1, characterized in that, The migration feature analysis module comprises: The dynamic migration submodule is configured to use a method of arranging multiple types of monitoring devices in a coal seam mining area, continuously collect data during mining, and simulate and analyze the dynamic migration process of mine water under mining action by establishing a numerical simulation model and combining field measured data. The simulation data submodule is configured to extract key information from simulation and measured data, and the key information in the measured data includes water flow direction, speed, and pressure. The result comparison submodule is configured to compare analysis results with geological data, obtain the dynamic change of the development height and range of the water flowing fractured zone and the thickness of the roof water-resisting layer with mining, integrate all analysis results, and obtain a comprehensive and accurate mining water dynamic migration feature analysis report.

6. The coal seam water disaster early warning system based on hydrological monitoring of claim 1, characterized in that, The water inrush danger early warning module comprises: The danger evaluation submodule is configured to use a method based on monitoring data and mining influence analysis to carry out research, extract a water inrush danger evaluation method suitable for the scene by analyzing the characteristics of the monitoring data and the changes of the geological structure caused by mining, and determine the corresponding early warning threshold value of different danger levels. The early warning threshold integration submodule is configured to integrate the evaluation method and the determined early warning threshold value, obtain complete water inrush danger evaluation method and early warning threshold value information, and obtain a water inrush danger evaluation method and early warning threshold value table. The coal seam water disaster early warning module comprises:

7. The coal seam water disaster early warning system based on hydrological monitoring of claim 1, characterized in that, The data cleaning submodule is configured to use monitoring devices to collect data related to coal seam water disasters, and remove invalid and abnormal data through data cleaning and preprocessing to ensure data quality. The water disaster early warning submodule is configured to use fusion analysis technology to deeply integrate different types of data, extract key feature information, and use a machine learning algorithm to establish a precise water disaster early warning model based on the extracted features. The trigger early warning submodule is configured to set a reasonable threshold value, and when the real-time monitoring data exceeds the threshold value, the system automatically triggers the early warning mechanism, integrates the early warning model and the trigger mechanism, and obtains a complete coal seam water disaster early warning system. The monitoring data submodule comprises a monitoring area geological condition and water disaster risk distribution feature, which is used to accurately determine the position of the monitoring point, accurately install an adaptive sensor at the monitoring point, ensure that various key parameter changes can be sensitively captured, and ensure the stability and accuracy of data acquisition by equipping a data acquisition module. Through the cooperation of the sensor and the acquisition module, physical and chemical quantity data of the monitoring area are extracted in real time. 8.The coal seam water disaster early warning system based on hydrological monitoring of claim 4, wherein, ​ 9.The coal seam water disaster early warning system based on hydrological monitoring of claim 6, wherein, The early warning threshold submodule comprises: a statistical method is used to analyze a large amount of collected risk assessment data, abnormal values and missing values are removed through data cleaning, clustering analysis is used to extract the intrinsic characteristics of the data, similar data is classified into a category, different risk level data clusters are divided, regression analysis is used to determine the correlation between each factor and the risk level, the corresponding accurate numerical range of different risk levels is determined through simulation and verification, the determined numerical range is set as the early warning threshold, and an early warning threshold system for different risk levels is obtained.

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