Prediction and early warning method and system for mine underground water inrush and sand inrush

Abstract

The invention relates to the technical field of mine safety monitoring, and discloses a prediction and early warning method and system for mine underground water inrush and sand inrush. Comprising a ground control center module, a multi-source data acquisition module, a three-dimensional space model establishment module, a three-dimensional space comparison and judgment module, a water inrush and sand inrush risk induction module and a prediction and early warning result output module, a three-dimensional space model of a target area is established according to current geological parameters, and historical water inrush points are marked; a comprehensive three-dimensional model is constructed based on geological parameters of historical water inrush points, the water inrush risk of a target area is dynamically evaluated based on current geological parameters and historical water inrush data of the target area, the target area is divided into a safe area and a risk area, and in the risk area, a convolution neural network is adopted to obtain a water inrush and sand inrush risk induction value; the prediction and early warning result output of the target area is completed, and the timeliness and accuracy of the prediction and early warning result are ensured.

Description

Technical Field

[0001] This invention relates to the field of mine safety monitoring technology, and more specifically to a method and system for predicting and warning of underground water inrush and sand collapse in mines. Background Technology

[0002] With the increasing depth and intensity of mining, water inrush and sand collapse disasters have become a significant threat to mine safety. These disasters not only cause severe casualties and property damage but can also trigger large-scale geological environmental destruction, impacting the sustainable development of mines. Existing underground water inrush and sand collapse prediction and early warning technologies have made some progress in disaster monitoring, analysis, and prevention, such as monitoring rock fracturing through microseismic signals or assessing the risk of coal and rock dynamic hazards using ultra-low frequency electromagnetic induction signals. However, these technologies still have significant limitations and cannot meet the high-precision early warning requirements under complex working conditions.

[0003] Existing mine underground water inrush and sand collapse prediction and early warning technologies still have certain shortcomings in multi-source data fusion analysis, sand body movement dynamic monitoring, and adaptability under complex geological conditions. In particular, in practical applications, the occurrence of water inrush and sand collapse disasters often involves the combined effects of multiple factors, including the degree of rock fracture development, aquifer distribution, aquitard thickness, groundwater pressure changes, and surrounding rock stress anomalies. A single monitoring method is difficult to fully reflect the disaster evolution process. In addition, existing technologies also have significant shortcomings in the delineation of high-risk areas and three-dimensional spatial dynamic modeling, making it difficult to achieve accurate location and real-time monitoring of potential water inrush points. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a prediction and early warning system for underground water inrush and sand collapse in mines, so as to solve the problems existing in the background art.

[0005] This invention provides the following technical solution: a prediction and early warning system for water inrush and sand collapse in underground mines, comprising: a ground control center module, a multi-source data acquisition module, a three-dimensional spatial model establishment module, a three-dimensional spatial comparison and judgment module, a water inrush and sand collapse risk sensing module, and a prediction and early warning result output module; The ground control center module includes a target area positioning and guidance unit and a target area early warning command sending unit, which completes the sending of early warning information commands to the target area through positioning analysis of the target area; The multi-source data acquisition module is used to acquire the current geological parameters and historical water inrush data of the target area, and transmit the data to the three-dimensional spatial model building module and the water inrush and sand collapse risk analysis module respectively. The three-dimensional spatial model building module receives the current geological parameters transmitted by the multi-source data acquisition module. The current geological parameters include the degree of development of rock fractures, the uniformity of aquifer distribution, and the change in the thickness of the aquitard. At the same time, it constructs a three-dimensional spatial model of the target area through displacement monitoring equipment and marks historical water inrush areas. The three-dimensional spatial comparison and judgment module dynamically assesses the water inrush risk of the target area based on the current geological parameters and historical water inrush data of the target area, and divides it into safe areas and risk areas. The water inrush and sand collapse risk sensing module monitors the changes in groundwater pressure, flow rate and turbidity in real time within the risk area, and uses an involutional neural network to obtain the water inrush and sand collapse risk sensing value. The prediction and early warning result output module receives the early warning information sending instruction transmitted by the ground control center module and completes the output of prediction and early warning results for the target area.

[0006] Preferably, the ground control center module includes a target area positioning and guidance unit and a target area early warning command sending unit, the specific contents of which are as follows: The target area positioning and guidance unit: divides the target monitoring area in the mine underground by vertical line scanning, and divides the target monitoring area into n monitoring sub-areas, i=1,2,3,...,n, where i represents the number of each monitoring sub-area and n represents the total number of monitoring sub-areas. It uses fusion positioning technology to position and guide each monitoring sub-area. By analyzing the spatial coordinates of each monitoring sub-area, and combining the real-time position of the mining face, it generates positioning analysis results. The target area early warning instruction sending unit is used to receive the prediction and early warning results of each monitoring sub-area, automatically generate early warning information sending instructions based on the location analysis results, and transmit the instructions to the prediction and early warning result output module.

[0007] Preferably, the multi-source data acquisition module is used to acquire the current geological parameters and historical water inrush data of the target area, specifically as follows: The current geological parameters include: rock fracture development degree, aquifer distribution uniformity, and aquitard thickness variation. The specific details for obtaining the current geological parameters of the target area are as follows: based on a sensor array deployed in the target area, the rock fracture development degree is obtained by capturing microseismic signals generated by rock mass fracturing; electromagnetic pulses are emitted into the rock strata using a transient electromagnetic instrument, and the location and water-bearing capacity of the aquifer are identified by inducing secondary field differences, thus obtaining the aquifer distribution uniformity; high-frequency electromagnetic wave reflection is used to detect the aquitard thickness variation. The historical water inrush data includes: the time, region, volume, type of water inrush, and changes in geological parameters before and after the water inrush. The specific content of obtaining historical water inrush data for the target area is as follows: by querying geological exploration reports, historical water inrush records, and mine hydrogeological maps, historical water inrush events in the target area are integrated and analyzed to establish a historical water inrush database.

[0008] Preferably, the three-dimensional spatial model building module receives the current geological parameters transmitted by the multi-source data acquisition module, and simultaneously constructs a three-dimensional spatial model of the target area through displacement monitoring equipment. The specific details are as follows: Construction of the current three-dimensional geological parameter model: A three-dimensional spatial model is constructed for each monitoring sub-region of the target area. The degree of rock fracture development in each monitoring sub-region of the target area is used as the X-axis of the current three-dimensional geological parameter model, the uniformity of aquifer distribution in each monitoring sub-region of the target area is used as the Y-axis of the current three-dimensional geological parameter model, and the change in aquitard thickness in each monitoring sub-region of the target area is used as the Z-axis of the current three-dimensional geological parameter model, thus forming the three-dimensional spatial coordinates of each monitoring sub-region of the target area. Construction of a comprehensive three-dimensional model of historical water inrush geological parameters: Based on historical water inrush data, the number of water inrushes occurring in each monitoring sub-region of the target area and the geological parameters at the time of each water inrush occurred were obtained, resulting in a sequence A of rock stratum fracture development, a sequence B of aquifer distribution uniformity, and a sequence C of aquitard thickness variation. The geological parameters include the rock stratum fracture development, aquifer distribution uniformity, and aquitard thickness variation at the time of each water inrush. The minimum value minA, the minimum value minB, and the minimum value minC in the rock stratum fracture development sequence, the aquifer distribution uniformity sequence, and the aquitard thickness variation sequence were extracted using a minimum value algorithm.

[0009] Preferably, the three-dimensional spatial comparison and judgment module dynamically assesses the water inrush risk of the target area based on the current geological parameters and historical water inrush data of the target area, and the specific content of the risk warning zone division is as follows: A safe zone space is constructed based on the minimum value minA in the sequence of layer fracture development, the minimum value minB in the sequence of aquifer distribution uniformity, and the minimum value minC in the sequence of changes in aquitard thickness. The safe zone space is represented by coordinate points of the minimum values ​​minA, minB, and minC in the sequence of layer fracture development, aquifer distribution uniformity, and changes in aquitard thickness, with perpendicular lines drawn from these coordinate points along the X-axis, Y-axis, and Z-axis to form a spatial region, which is the safe zone. The three-dimensional spatial coordinates of each monitoring sub-region of the target area are compared with the safe area. If the three-dimensional spatial coordinates of the monitoring sub-region of the target area are within the safe area, the area is determined to be a safe area. Conversely, if the three-dimensional spatial coordinates of the monitoring sub-region of the target area are outside the safe area, the area is determined to be a risk area.

[0010] Preferably, the water inrush and sand erosion risk sensing module monitors changes in groundwater pressure, flow rate, and turbidity in real time within the risk area, and uses a convolutional neural network to obtain the specific content of the water inrush and sand erosion risk sensing value as follows: Calculate the rate of change of groundwater pressure in the risk area within a preset time interval based on the current groundwater pressure value and the groundwater pressure value at the previous time. Calculate the rate of change of water flow in the risk area within a preset time interval based on the current groundwater flow and the groundwater flow at the previous time. Calculate the rate of change of water turbidity in the risk area within a preset time interval based on the current water turbidity and the water turbidity at the previous time. The risk-sensitive values ​​for water inrush and sand collapse risk are obtained by inputting the groundwater pressure change rate, water flow change rate, and water turbidity change rate of the risk area into a convolutional neural network as different labels. The risk sensing value of water inrush and sand inrush in the risk area is input into the ground control center module. The ground control center module compares the risk sensing value of water inrush and sand inrush in the risk area with the preset risk sensing threshold. If the risk sensing value of water inrush and sand inrush is greater than or equal to the preset risk sensing threshold, the monitoring sub-area of ​​the target area is located through the target area positioning and guidance unit, and the warning information sending command is automatically generated through the target area warning command sending unit. Conversely, if the risk sensing value of water inrush and sand inrush is less than the preset risk sensing threshold, the data is retained and stored.

[0011] Preferably, the prediction and early warning result output module receives the early warning information sending instruction transmitted by the ground control center module, marks the positioning area and sends the early warning information, and completes the prediction and early warning result output for the target area.

[0012] A method for predicting and warning of water inrush and sand collapse in underground mines includes the following steps: Step S01: Send a warning message to the target area by analyzing the location of the target area; Step S02: Obtain the current geological parameters and historical water inrush data of the target area; Step S03: Receive current geological parameters, and simultaneously construct a three-dimensional spatial model of the target area using displacement monitoring equipment, and mark historical water inrush areas; Step S04: Dynamically assess the water inrush risk of the target area based on the current geological parameters and historical water inrush data, and divide the target area into safe areas and risk areas; Step S05: Within the risk area, monitor changes in groundwater pressure, flow rate, and turbidity in real time, and use an inductive convolutional neural network to obtain the risk sensing value of water inrush and sand collapse. Step S06: Receive the warning information sending instruction and complete the output of the prediction and warning results for the target area.

[0013] The technical effects and advantages of this invention are as follows: This invention comprises a ground control center module, a multi-source data acquisition module, a three-dimensional spatial model establishment module, a three-dimensional spatial comparison and judgment module, a water inrush and sand collapse risk sensing module, and a prediction and early warning result output module. Through multi-source data fusion analysis and three-dimensional dynamic modeling, it achieves accurate prediction and real-time monitoring of water inrush and sand collapse disasters. A three-dimensional spatial model of the target area is constructed based on the current geological parameters, and historical water inrush points are marked. A comprehensive three-dimensional model is constructed based on the geological parameters of the historical water inrush points. The water inrush risk of the target area is dynamically assessed based on the current geological parameters and historical water inrush data, and the area is divided into safe and risk areas. The target area is divided into two regions using three-dimensional modeling and parameter boundary value analysis, which effectively narrows the scope of secondary monitoring of the target area and improves the accuracy and efficiency of disaster early warning. Within the risk area, a convolutional neural network is used to obtain the risk sensing value of water inrush and sand collapse. The target area is then monitored a second time, and prediction and early warning of the target area are completed. This further improves the sensitivity and reliability of the early warning system. When the ground control center module receives the early warning information sending instruction, it can quickly mark the location area and send the early warning information to ensure that relevant personnel can take timely countermeasures, thereby effectively avoiding or reducing the occurrence of water inrush and sand collapse disasters and ensuring the safe production of the mine. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a predictive and early warning system for water inrush and sand collapse in underground mines.

[0015] Figure 2 This is a flowchart illustrating a method for predicting and warning of water inrush and sand collapse in underground mines. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The prediction and early warning method and system for underground water inrush and sand collapse in mines involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figure 1As shown, the present invention provides a prediction and early warning system for water inrush and sand collapse in underground mines, including: a ground control center module, a multi-source data acquisition module, a three-dimensional spatial model establishment module, a three-dimensional spatial comparison and judgment module, a water inrush and sand collapse risk sensing module, and a prediction and early warning result output module. The ground control center module includes a target area positioning and guidance unit and a target area early warning command sending unit, which completes the sending of early warning information commands to the target area through positioning analysis of the target area; The multi-source data acquisition module is used to acquire the current geological parameters and historical water inrush data of the target area, and transmit the data to the three-dimensional spatial model building module and the water inrush and sand collapse risk analysis module respectively. The three-dimensional spatial model building module receives the current geological parameters transmitted by the multi-source data acquisition module. The current geological parameters include the degree of development of rock fractures, the uniformity of aquifer distribution, and the change in the thickness of the aquitard. At the same time, it constructs a three-dimensional spatial model of the target area through displacement monitoring equipment and marks historical water inrush areas. The three-dimensional spatial comparison and judgment module dynamically assesses the water inrush risk of the target area based on the current geological parameters and historical water inrush data of the target area, and divides it into safe areas and risk areas. The water inrush and sand collapse risk sensing module monitors the changes in groundwater pressure, flow rate and turbidity in real time within the risk area, and uses an involutional neural network to obtain the water inrush and sand collapse risk sensing value. The prediction and early warning result output module receives the early warning information sending instruction transmitted by the ground control center module and completes the output of prediction and early warning results for the target area.

[0018] In this embodiment, it should be specifically noted that the ground control center module includes the target area positioning and guidance unit and the target area early warning command sending unit, the specific contents of which are as follows: The target area positioning and guidance unit: divides the target monitoring area in the mine underground by vertical line scanning, and divides the target monitoring area into n monitoring sub-areas, i=1,2,3,...,n, where i represents the number of each monitoring sub-area and n represents the total number of monitoring sub-areas. It uses fusion positioning technology to position and guide each monitoring sub-area. By analyzing the spatial coordinates of each monitoring sub-area, and combining the real-time position of the mining face, it generates positioning analysis results. The target area early warning instruction sending unit is used to receive the prediction and early warning results of each monitoring sub-area, automatically generate early warning information sending instructions based on the location analysis results, and transmit the instructions to the prediction and early warning result output module.

[0019] In this embodiment, it should be specifically explained that the multi-source data acquisition module is used to acquire the current geological parameters and historical water inrush data of the target area as follows: The current geological parameters include: rock fracture development degree, aquifer distribution uniformity, and aquitard thickness variation. The specific details for obtaining the current geological parameters of the target area are as follows: based on a sensor array deployed in the target area, the rock fracture development degree is obtained by capturing microseismic signals generated by rock mass fracturing; electromagnetic pulses are emitted into the rock strata using a transient electromagnetic instrument, and the location and water-bearing capacity of the aquifer are identified by inducing secondary field differences, thus obtaining the aquifer distribution uniformity; high-frequency electromagnetic wave reflection is used to detect the aquitard thickness variation. The historical water inrush data includes: the time, region, volume, type of water inrush, and changes in geological parameters before and after the water inrush. The specific content of obtaining historical water inrush data for the target area is as follows: by querying geological exploration reports, historical water inrush records, and mine hydrogeological maps, historical water inrush events in the target area are integrated and analyzed to establish a historical water inrush database.

[0020] In this embodiment, it should be specifically explained that the three-dimensional spatial model building module receives the current geological parameters transmitted by the multi-source data acquisition module, and simultaneously constructs a three-dimensional spatial model of the target area through the displacement monitoring device. The specific content is as follows: Construction of the current three-dimensional geological parameter model: A three-dimensional spatial model is constructed for each monitoring sub-region of the target area. The degree of rock fracture development in each monitoring sub-region of the target area is used as the X-axis of the current three-dimensional geological parameter model, the uniformity of aquifer distribution in each monitoring sub-region of the target area is used as the Y-axis of the current three-dimensional geological parameter model, and the change in aquitard thickness in each monitoring sub-region of the target area is used as the Z-axis of the current three-dimensional geological parameter model, thus forming the three-dimensional spatial coordinates of each monitoring sub-region of the target area. Construction of a comprehensive three-dimensional model of historical water inrush geological parameters: Based on historical water inrush data, the number of water inrushes occurring in each monitoring sub-region of the target area and the geological parameters at the time of each water inrush occurred were obtained, resulting in a sequence A of rock stratum fracture development, a sequence B of aquifer distribution uniformity, and a sequence C of aquitard thickness variation. The geological parameters include the rock stratum fracture development, aquifer distribution uniformity, and aquitard thickness variation at the time of each water inrush. The minimum value minA, the minimum value minB, and the minimum value minC in the rock stratum fracture development sequence, the aquifer distribution uniformity sequence, and the aquitard thickness variation sequence were extracted using a minimum value algorithm.

[0021] In this embodiment, it should be specifically explained that the three-dimensional spatial comparison and judgment module dynamically assesses the water inrush risk of the target area based on the current geological parameters and historical water inrush data of the target area, and the specific content of dividing the risk warning zone is as follows: A safe zone space is constructed based on the minimum value minA in the layer fracture development sequence, the minimum value minB in the aquifer distribution uniformity sequence, and the minimum value minC in the aquitard thickness variation sequence. The safe zone space is defined as a spatial region formed by drawing perpendicular lines along the X-axis, Y-axis, and Z-axis from the coordinate points of the minimum values ​​minA, minB, and minC in the layer fracture development sequence, respectively. The coordinates of the points in the spatial region are all less than or equal to the minimum value minA in the layer fracture development sequence, the minimum value minB in the aquifer distribution uniformity sequence, and the minimum value minC in the aquitard thickness variation sequence. The higher the degree of development of rock strata fissures, the greater the probability of water inrush. Fissures are channels for groundwater flow. When fissures are well-developed, groundwater can more easily enter the mine through fissures, thus increasing the risk of water inrush. The higher the uniformity of aquifer distribution, the greater the probability of water inrush. Aquifers are reservoirs of groundwater. When aquifers are evenly distributed, groundwater is more likely to form a stable recharge source around the mine, thus increasing the risk of water inrush. The change in aquitard thickness represents the difference between the standard value of the aquitard thickness and the current aquitard thickness. The greater the change in aquitard thickness, the thinner the current aquitard thickness, the weaker its ability to resist water pressure, and the higher the risk of water inrush. The change in aquitard thickness only considers positive values, that is, the standard value of the aquitard thickness is greater than the current aquitard thickness.

[0022] The three-dimensional spatial coordinates of each monitoring sub-region of the target area are compared with the safe area. If the three-dimensional spatial coordinates of the monitoring sub-region of the target area are within the safe area, the area is determined to be a safe area. Conversely, if the three-dimensional spatial coordinates of the monitoring sub-region of the target area are outside the safe area, the area is determined to be a risk area.

[0023] In this embodiment, it should be specifically noted that the water inrush and sand erosion risk sensing module monitors changes in groundwater pressure, flow rate, and turbidity in real time within the risk area. The specific content of the water inrush and sand erosion risk sensing value obtained by using a convolutional neural network is as follows: The rate of change of groundwater pressure in the risk area within a preset time interval is calculated based on the current groundwater pressure value and the groundwater pressure value at the previous time. The calculation formula is as follows: ,in This indicates the rate of change of groundwater pressure in the risk area within a preset time interval. This indicates the current groundwater pressure value. This indicates the groundwater pressure value at the previous moment. Indicates a preset time interval; The rate of change of groundwater flow in the risk area within a preset time interval is calculated based on the current groundwater flow and the groundwater flow at the previous time. The calculation formula is as follows: ,in This indicates the rate of change in water flow in the risk area within a preset time interval. This indicates the current groundwater flow rate. This indicates the groundwater flow rate at the previous moment. Indicates a preset time interval; The rate of change of water turbidity in the risk area within a preset time interval is calculated based on the current water turbidity and the water turbidity at the previous time. The calculation formula is as follows: ,in This indicates the rate of change in water turbidity in the risk area within a preset time interval. Indicates the turbidity of the water at the current moment. This indicates the turbidity of the water at the previous moment. Indicates a preset time interval; The rate of change in groundwater pressure, the rate of change in water flow, and the rate of change in water turbidity in the risk area are represented by different labels. The input convolutional neural network obtains the risk sensing value of sudden water inrush and sandstorm, where j represents the label number, j=1, 2, 3, and the calculation formula of the convolutional neural network is: ,in This indicates the risk sensitivity value for sudden water inrush and sand collapse in the risk area. These represent the numerical values ​​of different labels for risk areas: Indicates the rate of change of groundwater pressure in the risk area, Indicates the rate of change in water flow in the risk area, This indicates the rate of change in water turbidity in the risk area. The standard values ​​for different labels representing risk areas are as follows: Standard value representing the rate of change of groundwater pressure in a risk area Standard value representing the rate of change of water flow in the risk area Standard values ​​representing the rate of change in water turbidity in risk areas; The risk sensing value of water inrush and sand inrush in the risk area is input into the ground control center module. The ground control center module compares the risk sensing value of water inrush and sand inrush in the risk area with the preset risk sensing threshold. If the risk sensing value of water inrush and sand inrush is greater than or equal to the preset risk sensing threshold, the monitoring sub-area of ​​the target area is located through the target area positioning and guidance unit, and the warning information sending command is automatically generated through the target area warning command sending unit. Conversely, if the risk sensing value of water inrush and sand inrush is less than the preset risk sensing threshold, the data is retained and stored.

[0024] In this embodiment, it should be specifically explained that the prediction and early warning result output module receives the early warning information sending instruction transmitted by the ground control center module, marks the positioning area and sends the early warning information, and completes the prediction and early warning result output for the target area.

[0025] like Figure 2 As shown in this embodiment, it should be specifically explained that a method for predicting and warning of underground water inrush and sand collapse in mines includes the following steps: Step S01: Send a warning message to the target area by analyzing the location of the target area; Step S02: Obtain the current geological parameters and historical water inrush data of the target area; Step S03: Receive current geological parameters, and simultaneously construct a three-dimensional spatial model of the target area using displacement monitoring equipment, and mark historical water inrush areas; Step S04: Dynamically assess the water inrush risk of the target area based on the current geological parameters and historical water inrush data, and divide the target area into safe areas and risk areas; Step S05: Within the risk area, monitor changes in groundwater pressure, flow rate, and turbidity in real time, and use an inductive convolutional neural network to obtain the risk sensing value of water inrush and sand collapse. Step S06: Receive the warning information sending instruction and complete the output of the prediction and warning results for the target area.

[0026] In this embodiment, it should be specifically noted that the main difference between this embodiment and the prior art is that this embodiment is equipped with a ground control center module, a multi-source data acquisition module, a three-dimensional spatial model establishment module, a three-dimensional spatial comparison and judgment module, a water inrush and sand collapse risk sensing module, and a prediction and early warning result output module. Through multi-source data fusion analysis and three-dimensional dynamic modeling, it can achieve accurate prediction and real-time monitoring of water inrush and sand collapse disasters. A three-dimensional spatial model of the target area is constructed based on the current geological parameters, and historical water inrush points are marked. A comprehensive three-dimensional model is constructed based on the geological parameters of the historical water inrush points. The water inrush risk of the target area is dynamically assessed based on the current geological parameters and historical water inrush data, and the area is divided into safe and risk areas. The target area is divided into two regions using three-dimensional modeling and parameter boundary value analysis, which effectively narrows the scope of secondary monitoring of the target area and improves the accuracy and efficiency of disaster early warning. Within the risk area, a convolutional neural network is used to obtain the risk sensing value of water inrush and sand collapse. The target area is then monitored a second time, and prediction and early warning of the target area are completed. This further improves the sensitivity and reliability of the early warning system. When the ground control center module receives the early warning information sending instruction, it can quickly mark the location area and send the early warning information to ensure that relevant personnel can take timely countermeasures, thereby effectively avoiding or reducing the occurrence of water inrush and sand collapse disasters and ensuring the safe production of the mine.

[0027] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0028] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A prediction and early warning system for water inrush and sand collapse in underground mines, characterized in that: include: The module includes a ground control center module, a multi-source data acquisition module, a three-dimensional spatial model building module, a three-dimensional spatial comparison and judgment module, a water inrush and sand collapse risk sensing module, and a prediction and early warning result output module. The ground control center module includes a target area positioning and guidance unit and a target area early warning command sending unit, which completes the sending of early warning information commands to the target area through positioning analysis of the target area; The multi-source data acquisition module is used to acquire the current geological parameters and historical water inrush data of the target area, and transmit the data to the three-dimensional spatial model building module and the water inrush and sand collapse risk analysis module respectively. The three-dimensional spatial model building module receives the current geological parameters transmitted by the multi-source data acquisition module. The current geological parameters include the degree of development of rock fractures, the uniformity of aquifer distribution, and the change in the thickness of the aquitard. At the same time, it constructs a three-dimensional spatial model of the target area through displacement monitoring equipment and marks historical water inrush areas. The three-dimensional spatial comparison and judgment module dynamically assesses the water inrush risk of the target area based on the current geological parameters and historical water inrush data of the target area, and divides it into safe areas and risk areas. The water inrush and sand collapse risk sensing module monitors the changes in groundwater pressure, flow rate and turbidity in real time within the risk area, and uses an involutional neural network to obtain the water inrush and sand collapse risk sensing value. The prediction and early warning result output module receives the early warning information sending instruction transmitted by the ground control center module and completes the output of prediction and early warning results for the target area.

2. The prediction and early warning system for underground water inrush and sand collapse in mines according to claim 1, characterized in that: The specific contents of the ground control center module, including the target area positioning and guidance unit and the target area early warning command sending unit, are as follows: The target area positioning and guidance unit: divides the target monitoring area in the mine underground by vertical line scanning, and divides the target monitoring area into n monitoring sub-areas, i=1,2,3,...,n, where i represents the number of each monitoring sub-area and n represents the total number of monitoring sub-areas. It uses fusion positioning technology to position and guide each monitoring sub-area. By analyzing the spatial coordinates of each monitoring sub-area, and combining the real-time position of the mining face, it generates positioning analysis results. The target area early warning instruction sending unit is used to receive the prediction and early warning results of each monitoring sub-area, automatically generate early warning information sending instructions based on the location analysis results, and transmit the instructions to the prediction and early warning result output module.

3. The prediction and early warning system for underground water inrush and sand collapse in mines according to claim 1, characterized in that: The multi-source data acquisition module is used to obtain the current geological parameters and historical water inrush data of the target area. The specific details are as follows: The current geological parameters include: rock fracture development degree, aquifer distribution uniformity, and aquitard thickness variation. The specific details for obtaining the current geological parameters of the target area are as follows: based on a sensor array deployed in the target area, the rock fracture development degree is obtained by capturing microseismic signals generated by rock mass fracturing; electromagnetic pulses are emitted into the rock strata using a transient electromagnetic instrument, and the location and water-bearing capacity of the aquifer are identified by inducing secondary field differences, thus obtaining the aquifer distribution uniformity; high-frequency electromagnetic wave reflection is used to detect the aquitard thickness variation. The historical water inrush data includes: the time, region, volume, type of water inrush, and changes in geological parameters before and after the water inrush. The specific content of obtaining historical water inrush data for the target area is as follows: by querying geological exploration reports, historical water inrush records, and mine hydrogeological maps, historical water inrush events in the target area are integrated and analyzed to establish a historical water inrush database.

4. The prediction and early warning system for underground water inrush and sand collapse in mines according to claim 1, characterized in that: The three-dimensional spatial model building module receives the current geological parameters transmitted by the multi-source data acquisition module, and simultaneously constructs a three-dimensional spatial model of the target area through displacement monitoring equipment. The specific details are as follows: Construction of the current three-dimensional geological parameter model: A three-dimensional spatial model is constructed for each monitoring sub-region of the target area. The degree of rock fracture development in each monitoring sub-region of the target area is used as the X-axis of the current three-dimensional geological parameter model, the uniformity of aquifer distribution in each monitoring sub-region of the target area is used as the Y-axis of the current three-dimensional geological parameter model, and the change in aquitard thickness in each monitoring sub-region of the target area is used as the Z-axis of the current three-dimensional geological parameter model, thus forming the three-dimensional spatial coordinates of each monitoring sub-region of the target area. Construction of a comprehensive three-dimensional model of historical water inrush geological parameters: Based on historical water inrush data, the number of water inrushes occurring in each monitoring sub-region of the target area and the geological parameters at the time of each water inrush occurred were obtained, resulting in a sequence A of rock stratum fracture development, a sequence B of aquifer distribution uniformity, and a sequence C of aquitard thickness variation. The geological parameters include the rock stratum fracture development, aquifer distribution uniformity, and aquitard thickness variation at the time of each water inrush. The minimum value minA, the minimum value minB, and the minimum value minC in the rock stratum fracture development sequence, the aquifer distribution uniformity sequence, and the aquitard thickness variation sequence were extracted using a minimum value algorithm.

5. The prediction and early warning system for underground water inrush and sand collapse in mines according to claim 1, characterized in that: The three-dimensional spatial comparison and judgment module dynamically assesses the water inrush risk of the target area based on the current geological parameters and historical water inrush data of the target area, and the specific content of the risk warning zone division is as follows: A safe zone space is constructed based on the minimum value minA in the sequence of layer fracture development, the minimum value minB in the sequence of aquifer distribution uniformity, and the minimum value minC in the sequence of changes in aquitard thickness. The safe zone space is represented by coordinate points of the minimum values ​​minA, minB, and minC in the sequence of layer fracture development, aquifer distribution uniformity, and changes in aquitard thickness, with perpendicular lines drawn from these coordinate points along the X-axis, Y-axis, and Z-axis to form a spatial region, which is the safe zone. The three-dimensional spatial coordinates of each monitoring sub-region of the target area are compared with the safe area. If the three-dimensional spatial coordinates of the monitoring sub-region of the target area are within the safe area, the area is determined to be a safe area. Conversely, if the three-dimensional spatial coordinates of the monitoring sub-region of the target area are outside the safe area, the area is determined to be a risk area.

6. The prediction and early warning system for underground water inrush and sand collapse in mines according to claim 1, characterized in that: The water inrush and sand collapse risk sensing module monitors changes in groundwater pressure, flow rate, and turbidity in real time within the risk area. The specific content of the water inrush and sand collapse risk sensing value obtained using a convolutional neural network is as follows: Calculate the rate of change of groundwater pressure in the risk area within a preset time interval based on the current groundwater pressure value and the groundwater pressure value at the previous time. Calculate the rate of change of water flow in the risk area within a preset time interval based on the current groundwater flow and the groundwater flow at the previous time. Calculate the rate of change of water turbidity in the risk area within a preset time interval based on the current water turbidity and the water turbidity at the previous time. The risk-sensitive values ​​for water inrush and sand collapse risk are obtained by inputting the groundwater pressure change rate, water flow change rate, and water turbidity change rate of the risk area into a convolutional neural network as different labels. The risk sensing value of water inrush and sand inrush in the risk area is input into the ground control center module. The ground control center module compares the risk sensing value of water inrush and sand inrush in the risk area with the preset risk sensing threshold. If the risk sensing value of water inrush and sand inrush is greater than or equal to the preset risk sensing threshold, the monitoring sub-area of ​​the target area is located through the target area positioning and guidance unit, and the warning information sending command is automatically generated through the target area warning command sending unit. Conversely, if the risk sensing value of water inrush and sand inrush is less than the preset risk sensing threshold, the data is retained and stored.

7. The prediction and early warning system for underground water inrush and sand collapse in mines according to claim 1, characterized in that: The prediction and early warning result output module receives the early warning information sending instruction transmitted by the ground control center module, marks the positioning area and sends the early warning information, and completes the prediction and early warning result output for the target area.

8. A method for predicting and warning of underground water inrush and sand collapse in mines, used in conjunction with the prediction and warning system for underground water inrush and sand collapse in mines as described in any one of claims 1-7, characterized in that: Includes the following steps: Step S01: Send a warning message to the target area by analyzing the location of the target area; Step S02: Obtain the current geological parameters and historical water inrush data of the target area; Step S03: Receive current geological parameters, and simultaneously construct a three-dimensional spatial model of the target area using displacement monitoring equipment, and mark historical water inrush areas; Step S04: Dynamically assess the water inrush risk of the target area based on the current geological parameters and historical water inrush data, and divide the target area into safe areas and risk areas; Step S05: Within the risk area, monitor changes in groundwater pressure, flow rate, and turbidity in real time, and use an inductive convolutional neural network to obtain the risk sensing value of water inrush and sand collapse. Step S06: Receive the warning information sending instruction and complete the output of the prediction and warning results for the target area.

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