Working face water inflow prediction method and device, electronic equipment and storage medium

Abstract

The application provides a working face water inflow prediction method and device, electronic equipment and a storage medium. The working face water inflow prediction method comprises the following steps: obtaining the vertical distance data of each aquifer in a target area where a working face to be monitored is located, historical water inflow data of the working face to be monitored and microseismic overburden damage height data; if the microseismic overburden damage height and the target aquifer satisfy a preset spatial relationship, determining the correlation degree of the microseismic overburden damage height data and the historical water inflow data; and predicting the working face water inflow according to the correlation degree. The application predicts the periodic change of the working face water inflow under multiple aquifers by constructing the correlation between the periodic change of the working face water inflow and the periodic change of the microseismic overburden damage height, thereby effectively improving the accuracy of the working face water inflow prediction.

Description

Technical Field

[0001] This invention relates to the field of water hazard prevention and control technology, and in particular to a method, device, electronic equipment and storage medium for predicting the water inflow at a working face. Background Technology

[0002] Coal mining disrupts the stability and integrity of the original strata, causing discontinuous or continuous deformation of the overlying strata or the floor strata of the coal seam, accompanied by the generation of cracks of varying degrees. When cracks connect to aquifers, there is a possibility of them entering the mining area, increasing the risk to mine safety. Therefore, analyzing the periodic changes in the height of microseismic overlying rock damage and the amount of water inflow is crucial to improving the accuracy of water inflow prediction at the working face and providing a reference for the construction and improvement of mine drainage systems.

[0003] At present, the prediction of water inflow at the working face mainly relies on single water inflow change data or combined with mine pressure monitoring data for correlation analysis, which fails to fully reflect the influence of the upper aquifer on the water inflow at the working face. Specifically, it has the following shortcomings: (1) the existing working face water inflow monitoring data fails to truly reflect the law of overburden movement and deformation; (2) the changes in working face water inflow under multiple aquifers exhibit the characteristics of large and small cycles, which are difficult to reflect by existing methods.

[0004] In summary, the problems existing in the current technology urgently need to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for predicting water inflow at a working face, thereby addressing the deficiency of relying on a single basis for predicting water inflow at a working face in the prior art.

[0006] This invention provides a method for predicting water inflow at a working face, comprising:

[0007] Acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden failure height data;

[0008] Based on the historical water inflow data and the microseismic overburden failure height data, it is determined whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined. The target aquifer is the aquifer with the largest vertical distance in the target area.

[0009] Based on the correlation, the water inflow at the working face is predicted.

[0010] According to the present invention, a method for predicting the water inflow at a working face is provided, which, based on the historical water inflow data and the microseismic overburden failure height data, determines whether the vertical distance between the microseismic overburden failure height and the target aquifer satisfies a preset spatial relationship, specifically including:

[0011] The preset vertical distance range is determined based on the microseismic overburden damage height data;

[0012] The vertical distance of the target aquifer is determined based on the vertical distance data of each aquifer.

[0013] If the vertical distance of the target aquifer is within the preset vertical distance range, then the microseismic overburden failure height and the vertical distance of the target aquifer satisfy the preset spatial relationship.

[0014] According to the method for predicting water inflow at a working face provided by the present invention, determining the correlation between the microseismic overburden failure height data obtained from monitoring and the historical water inflow data specifically includes:

[0015] The absolute value matrix is ​​obtained by calculating the difference between the microseismic overburden failure height data and the historical water inflow data;

[0016] Construct a correlation coefficient matrix based on the absolute value matrix;

[0017] The correlation coefficient matrix is ​​averaged to obtain the correlation degree between the microseismic overburden damage height data and the historical water inflow data.

[0018] According to the method for predicting working face water inflow provided by the present invention, before calculating the absolute value matrix by performing difference calculation based on the microseismic overburden failure height data and the historical water inflow data, the method further includes:

[0019] The data on the height of the microseismic overburden damage and the historical water inflow data are averaged.

[0020] According to the method for predicting working face water inflow provided by the present invention, the specific implementation of constructing the correlation coefficient matrix based on the absolute value matrix is ​​as follows:

[0021]

[0022] L = [l ij ] N ;

[0023] Δ min =minδ ij , (i=1, 2,...,N; j=1);

[0024] Δ max =maxδ ij, (i=1, 2,...,N; j=1);

[0025] Where, δ ij Let L be the absolute value matrix, k be the resolution coefficient, k∈[0,1], and L be the correlation coefficient matrix. ij These are the elements of the correlation coefficient matrix.

[0026] According to the method for predicting the water inflow at a working face provided by the present invention, the specific implementation of predicting the water inflow at the working face based on the correlation degree is as follows:

[0027]

[0028]

[0029] Among them, B i T is the average water inflow recorded at the working face over a fixed time period. i Let T1 be the time point of the maximum water inflow, T2 be the time point of the minimum water inflow adjacent to the maximum water inflow, and T_t be the time when the maximum microseismic overburden failure height precedes the maximum water inflow. B J T represents the historical maximum water inflow. 00 B represents the time when the overlying rock failure height reaches its maximum value during microseismic events. 00 For T 00 The water inflow at the working face at that time.

[0030] The present invention also provides a working face water inflow prediction device, comprising:

[0031] The data acquisition unit is used to acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden damage height data;

[0032] The correlation determination unit is used to determine, based on the historical water inflow data and the microseismic overburden failure height data, whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, then the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined; the target aquifer is the aquifer with the largest vertical distance in the target area.

[0033] The prediction unit is used to predict the water inflow at the working face based on the correlation degree.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the working face water inflow prediction method as described above.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the working face water inflow prediction method as described above.

[0036] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the working face water inflow prediction method as described above.

[0037] This invention provides a method, apparatus, electronic device, and storage medium for predicting working face water inflow. It acquires vertical distance data of each aquifer in the target area where the working face is to be monitored, historical water inflow data of the working face, and microseismic overburden failure height data. Based on the historical water inflow data and the microseismic overburden failure height data, it determines whether the vertical distance between the microseismic overburden failure height and the target aquifer satisfies a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer satisfies the preset spatial relationship, it determines the correlation between the acquired microseismic overburden failure height data and the historical water inflow data. The target aquifer is the aquifer with the largest vertical distance in the target area. Based on the correlation, the working face water inflow is predicted. This invention, by constructing a correlation between the periodic changes in working face water inflow and the periodic changes in microseismic overburden failure height, predicts the periodic changes in water inflow under multiple aquifers, effectively improving the accuracy of working face water inflow prediction. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the method for predicting the water inflow at the working face provided by the present invention.

[0040] Figure 2 This is a schematic diagram of the monitoring substation layout provided by the present invention;

[0041] Figure 3 This is a schematic diagram showing the spatial relationship between the development height of the water-conducting fracture zone and each aquifer provided by the present invention;

[0042] Figure 4 This is a schematic diagram of the sliding correlation cycle analysis provided by the present invention;

[0043] Figure 5 This is a waveform diagram of the microseismic overburden damage height data of the working face provided by the present invention;

[0044] Figure 6 This is a waveform diagram of historical water inflow data at the working face provided by the present invention;

[0045] Figure 7 This is a schematic diagram of the working face water inflow prediction device provided by the present invention;

[0046] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] Coal mining disrupts the stability and integrity of the original strata, causing discontinuous or continuous deformation of the overlying strata or the floor strata of the coal seam, accompanied by the generation of cracks of varying degrees. When cracks connect to aquifers, there is a possibility of them entering the mining area, increasing the risk to mine safety. Therefore, analyzing the periodic changes in the height of microseismic overlying rock damage and the amount of water inflow is crucial to improving the accuracy of water inflow prediction at the working face and providing a reference for the construction and improvement of mine drainage systems.

[0049] Currently, the prediction of water inflow at the working face mainly relies on single data on water inflow changes or on correlation analysis combined with data from mine pressure monitoring. This approach fails to fully reflect the impact of the upper aquifer on water inflow at the working face, and has the following specific drawbacks:

[0050] (1) Existing water inflow monitoring data at the working face fails to accurately reflect the patterns of overlying rock movement and deformation.

[0051] Currently, some scholars analyze the characteristics of periodic water inflow changes at the working face from the perspective of mine pressure monitoring data. However, mine pressure monitoring equipment mainly supports the rock strata within the caving zone. Outside the caving zone, especially near the apex of the water-conducting fracture zone, the periodic inflow of aquifers into the working face is difficult to capture by the mine pressure monitoring system. Therefore, the data obtained from mine pressure monitoring can only reflect information on periodic water inflow at part of the working face.

[0052] (2) The changes in water inflow at the working face under multiple aquifers exhibit the characteristics of large and small cycles, which are difficult to reflect by existing methods.

[0053] The water inflow at working faces under multiple aquifers often exhibits periodic variations, and these variations are often irregular. Relying solely on this single parameter makes it difficult to demonstrate the scientific validity and rationality of predictions. Microseismic monitoring, however, allows for better management of overlying rock movement and deformation, thus improving the prediction of water inflow at the working face.

[0054] To address the shortcomings of existing technologies that rely on a single method for predicting working face water inflow, this invention proposes a method for predicting working face water inflow, which includes, but is not limited to:

[0055] Step 110: Obtain the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden damage height data.

[0056] In step 110, the microseismic overburden failure height data includes the microseismic overburden failure height, which refers to the height at which rock masses crack, fracture, or collapse due to microseismic effects generated by underground mining activities. This height is typically used to assess the stability and safety of mining or underground engineering projects and is crucial for predicting and preventing rock mass failure in underground engineering. Microseismic activity refers to minor seismic activity caused by changes in mine pressure and other factors during underground mining. This microseismic activity induces stress and displacement changes in the rock, leading to rock fracturing and deformation. The magnitude of the microseismic overburden failure height depends on multiple factors, including the physical and mechanical properties of the rock, geological conditions, mining methods, and the location of goaf areas. Therefore, in mining or underground engineering, on-site investigation and numerical simulation analysis are necessary to determine the microseismic overburden failure height and take corresponding measures to ensure the safety and stability of the project.

[0057] Aquifer thickness data includes the vertical distance from each aquifer to the coal seam. Aquifer thickness refers to the vertical distance of groundwater depth, that is, the distance from the surface to the top surface of the aquifer. In this embodiment, aquifer thickness refers to the distance from the coal seam to the aquifer (top or bottom surface). An aquifer is a stratum in underground rock or soil that stores and transports groundwater, and its thickness is one of the important indicators of hydrological characteristics.

[0058] The historical water inflow data in this embodiment mainly refers to the water inflow at the working face. This data is collected over a period of time due to the movement, deformation, and damage of the overlying strata caused by coal mining, which diverts water from aquifers in the overlying strata into the goaf through mining-induced fractures. Water inflow data can be collected through various methods, such as groundwater level observation, hydrogeological surveys, and hydrogeophysical exploration. Processing and analyzing this data can help determine groundwater flow characteristics and hydrogeological conditions, assess the potential for groundwater resource development, predict and control groundwater inflow and pressure, and ensure the safety and stability of underground engineering projects.

[0059] Specifically, such as Figure 2 As shown, this embodiment can collect data on microseismic overburden damage height, aquifer vertical distance, and historical water inflow through ground and underground substations of the monitoring network. The ground and underground substations are equipped with target acquisition devices, which can be sensors. For example, a pipeline flow monitoring sensor can be used to obtain the historical water inflow at the working face drainage point, and a vibration sensor or seismic pickup can be used to monitor and acquire microseismic events, obtaining the corresponding microseismic overburden damage height data, such as locating the microseismic event coordinates x (working face advance direction), y (working face tilt direction), and z (vertical upward direction of coal seam overburden), as well as acquiring the microseismic event energy.

[0060] Step 120: Based on the historical water inflow data and the microseismic overburden damage height data, determine whether the vertical distance between the microseismic overburden damage height and the target aquifer meets the preset spatial relationship. If the vertical distance between the microseismic overburden damage height and the target aquifer meets the preset spatial relationship, then determine the correlation between the monitored microseismic overburden damage height data and the historical water inflow data; the target aquifer is the aquifer with the largest vertical distance in the target area.

[0061] In step 120, the microseismic overburden failure height and the target aquifer must satisfy a preset spatial relationship for the next step of water inflow prediction. In other words, satisfying this preset spatial relationship is the prerequisite and foundation for predicting water inflow. Specifically, the expected boundary of the water-conducting fracture zone needs to be determined based on the collected microseismic overburden failure height, serving as the preset vertical distance range for the target aquifer. When the target aquifer is within the preset vertical distance range, the preset spatial relationship is satisfied, thus determining the correlation between the microseismic overburden failure height data and historical water inflow data, which serves as the data basis for subsequent water inflow prediction. Specifically, the periodic changes in monitoring data can be analyzed using a time sliding window approach. The maximum and minimum values ​​of the microseismic overburden failure height monitoring period are recorded as t1, t2, etc., respectively, according to the time data. Similarly, the maximum and minimum values ​​of the water inflow monitoring period are recorded as T1, T2, etc., respectively, according to the time data. Grey relational analysis is then used to analyze the correlation between the microseismic overburden failure height data and historical water inflow data.

[0062] Step 130: Based on the correlation, predict the water inflow at the working face.

[0063] In step 130, the water inflow at the working face is predicted based on the correlation between the microseismic overburden failure height data obtained in step 120 and the historical water inflow data. Specifically, the trend of increasing water inflow at the working face is analyzed by combining the microseismic monitoring data. It is assumed that the time between the maximum microseismic monitoring height and the maximum water inflow is T_t, and the maximum water inflow at the working face is B. J Based on the periodic variation of water inflow, the time point after the overlying strata damage height reaches its maximum value in microseismic monitoring is defined as T. 00 The corresponding water inflow at the working face is B. 00 The water inflow at the working face is:

[0064]

[0065]

[0066] When the working face enters a growth cycle again, T is redefined. 00 The periodic changes in the expected water inflow at the working face are predicted to repeat themselves.

[0067] After coal seam mining, rock strata at different distances from the roof will deform to varying degrees. Areas more significantly affected will experience movement and deformation, and may even fracture and rotate. During the deformation and failure of the rock strata, numerous mining-induced fractures are generated. Microseismic events always occur when coal and rock masses fail.

[0068] This embodiment acquires the vertical distance data of each aquifer in the target area where the working face is to be monitored, the historical water inflow data of the working face, and the microseismic overburden failure height data. If the microseismic overburden failure height and the target aquifer satisfy a preset spatial relationship, the correlation between the acquired microseismic overburden failure height data and the historical water inflow data is determined. Based on the correlation, the water inflow of the working face is predicted. This embodiment improves the accuracy of water inflow prediction by constructing a correlation between the periodic changes in the working face water inflow and the periodic changes in the microseismic overburden failure height, thereby predicting the periodic changes in water inflow under multiple aquifers.

[0069] As a further optional embodiment, the aquifer vertical distance data includes the vertical distance of each aquifer, and the aquifer with the largest vertical distance is taken as the target aquifer. If the microseismic overburden failure height and the target aquifer satisfy a preset spatial relationship, then the correlation between the monitored and acquired microseismic overburden failure height data and the historical water inflow data is determined, specifically including:

[0070] The preset vertical distance range is determined based on the microseismic overburden damage height.

[0071] If the target aquifer is within the preset vertical distance range, then the correlation between the monitored microseismic overburden damage height data and the historical water inflow data is determined.

[0072] In this embodiment, it is necessary to determine whether the microseismic overburden failure height and the target aquifer satisfy a preset spatial relationship. Specifically, it is necessary to determine the expected water-conducting fracture zone boundary H based on the collected microseismic overburden failure height. li And based on the expected boundary H of the water-conducting fracture zone li The preset vertical distance range for the target aquifer is set, preferably 0.8H. li ~H li If the target aquifer is within a preset vertical distance range, it is considered to satisfy the preset spatial relationship. The aquifer furthest from the coal seam is considered the target aquifer. Figure 3 As shown, assuming the vertical distance between aquifer 1 and the coal seam roof is z1~z2, the vertical distance between aquifer 2 and the coal seam roof is z3~z4, and the vertical distance between aquifer 3 and the coal seam roof is z5~z6, then aquifer 3 is taken as the target aquifer. If z5~z6 is within the preset vertical distance range of 0.8H... li ~H li If the preset spatial relationship is satisfied, then the correlation between the microseismic overburden damage height data and the historical water inflow data is determined.

[0073] As a further optional embodiment, determining the correlation between the monitored and acquired microseismic overburden failure height data and the historical water inflow data specifically includes:

[0074] The absolute value matrix is ​​obtained by calculating the difference between the microseismic overburden failure height data and the historical water inflow data;

[0075] Construct a correlation coefficient matrix based on the absolute value matrix;

[0076] The correlation coefficient matrix is ​​averaged to obtain the correlation between the microseismic overburden damage height data and the historical water inflow data.

[0077] In this embodiment, grey relational analysis is used to analyze the correlation between the microseismic overburden failure height data and the historical water inflow data. Specifically, the difference between the averaged working face water inflow data and the microseismic monitoring height data is calculated, and the absolute value is taken. The absolute values ​​are then used to construct an absolute value matrix Δ, as shown in the following formula:

[0078] [Δ]=[δ i1 ] N ;

[0079] Where, δ ij =|x ij '-x i0 '|, (i=1, 2,...,N; j=1).

[0080] Subsequently, the correlation coefficient between the water inflow at the working face and the height of the overlying strata failure was calculated, and the correlation coefficient matrix L was constructed.

[0081] The correlation coefficient can only reflect x j The relationship between the proximity of x0 and x0 at time i is relatively dispersed. In order to compare the correlation between the water inflow and the overburden failure height of the working face as a whole, it is necessary to take the average value of the correlation coefficient between the water inflow and the overburden failure height of the entire working face, which is called the correlation degree γ(j), as shown in the following formula:

[0082]

[0083] As a further optional embodiment, the lag period can also be analyzed. Specifically, a sliding window is used to obtain the lag period through grey relational analysis, and combined with periodic data, it provides a reference and basis for the periodic changes in the subsequent working face water inflow. The grey relational relationship between the periodic data is analyzed sequentially using microseismic and hydrological data, and the maximum values ​​of each grey relational degree are obtained. The data between two maximum values ​​is the periodic data, which is used for subsequent analysis of the microseismic overburden failure height data (A1, A2, A3...A...). n-1 A n ) and historical water inflow data (B1, B2, B3...B n-1 B nThe correlation between the two factors and the prediction of periodic increases in water inflow at the working face can be better understood and referenced. See details below. Figure 4 .

[0084] As a further optional embodiment, before calculating the difference between the microseismic overburden failure height data and the historical water inflow data to obtain the absolute value matrix, the method further includes:

[0085] The data on the height of the microseismic overburden damage and the historical water inflow data are averaged.

[0086] Specifically, based on the measured data, an initial data matrix is ​​constructed:

[0087]

[0088] Where: x 1,0 ...x N,0 The objective function represents the overburden failure height data from microseismic monitoring, where N represents the number of data sets; x 1,1 ...x N,1 This represents the monitoring data of water inflow at the working face.

[0089] To avoid the differences in individual factors among the main control factors, the data is averaged, as shown in the following formula.

[0090]

[0091] As a further optional embodiment, the specific implementation of constructing the correlation coefficient matrix based on the absolute value matrix is ​​as follows:

[0092]

[0093] L = [l ij ] N ;

[0094] Δ min =minδ ij , (i=1, 2,...,N; j=1);

[0095] Δ max =maxδ ij , (i=1, 2,...,N; j=1);

[0096] Where, δ ij Let L be the absolute value matrix, k be the resolution coefficient, k∈[0,1], and L be the correlation coefficient matrix. ij These are the elements of the correlation coefficient matrix.

[0097] In this embodiment, the correlation coefficient between the water inflow at the working face and the overburden failure height can be calculated using this formula, and a correlation coefficient matrix L can be constructed. Here, k is the resolution coefficient, used to adjust for the maximum absolute difference Δ. max An excessively large value can cause data distortion in the correlation function calculation, thereby increasing the significance of data differences.

[0098] As a further optional embodiment, the specific implementation of predicting the water inflow at the working face based on the correlation is as follows:

[0099]

[0100]

[0101] Among them, B i T is the average water inflow recorded at the working face over a fixed time period. i Let T1 be the time point of the maximum water inflow, T2 be the time point of the minimum water inflow adjacent to the maximum water inflow, and T_t be the time when the maximum microseismic overburden failure height precedes the maximum water inflow. B J T represents the historical maximum water inflow. 00 B represents the time when the height of the overlying rock failure reaches its maximum value. 00 For T 00 The working face water inflow at that time. Among them, the maximum and minimum water inflow values ​​are determined based on the collected historical water inflow data. Similarly, the maximum water inflow value preceding the maximum value of the microseismic overburden failure height is also determined based on the microseismic overburden failure height data.

[0102] In this embodiment, it is necessary to predict the water inflow at the working face based on the collected data. Specifically, refer to... Figure 5 , Figure 6 Using a time-sliding window approach, the periodic changes in monitoring data are analyzed. The maximum and minimum values ​​of the microseismic monitoring height period are recorded as t1, t2, etc., respectively, based on the time data. Similarly, the maximum and minimum values ​​of the water inflow monitoring period are recorded as T1, T2, etc., based on the time data. Subsequently, combining the microseismic monitoring data, the trend of increasing water inflow at the working face is analyzed. It is assumed that the time between the maximum microseismic monitoring height value and the maximum water inflow value is T_t, and the maximum water inflow value at the working face is B. J Based on the periodic variation of water inflow, the time point after the overlying strata damage height reaches its maximum value in microseismic monitoring is defined as T. 00 The corresponding water inflow at the working face is B. 00 The water inflow at the working face is

[0103]

[0104]

[0105] When the working face enters a growth cycle again, T is redefined. 00 The periodic changes in the expected water inflow at the working face are predicted to repeat themselves.

[0106] The following describes the working face water inflow prediction device provided by the present invention. The working face water inflow prediction device described below and the working face water inflow prediction method described above can be referred to in correspondence.

[0107] Data acquisition unit 710 is used to acquire vertical distance data of each aquifer in the target area where the working face to be monitored is located, historical water inflow data of the working face to be monitored, and microseismic overburden damage height data;

[0108] The correlation determination unit 720 is used to determine, based on the historical water inflow data and the microseismic overburden failure height data, whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, then the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined; the target aquifer is the aquifer with the largest vertical distance in the target area.

[0109] The prediction unit 730 is used to predict the water inflow at the working face based on the correlation degree.

[0110] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a working face water inflow prediction method, which includes:

[0111] Acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden failure height data;

[0112] Based on the historical water inflow data and the microseismic overburden failure height data, it is determined whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined. The target aquifer is the aquifer with the largest vertical distance in the target area.

[0113] Based on the correlation, the water inflow at the working face is predicted.

[0114] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the working face water inflow prediction method provided by the above methods, the method comprising:

[0116] Acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden failure height data;

[0117] Based on the historical water inflow data and the microseismic overburden failure height data, it is determined whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined. The target aquifer is the aquifer with the largest vertical distance in the target area.

[0118] Based on the correlation, the water inflow at the working face is predicted.

[0119] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the working face water inflow prediction method provided by the above methods, the method comprising:

[0120] Acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden failure height data;

[0121] Based on the historical water inflow data and the microseismic overburden failure height data, it is determined whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined. The target aquifer is the aquifer with the largest vertical distance in the target area.

[0122] Based on the correlation, the water inflow at the working face is predicted.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting water inflow at a working face, characterized in that, include: Acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden failure height data; Based on the historical water inflow data and the microseismic overburden failure height data, it is determined whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined. The target aquifer is the aquifer with the largest vertical distance in the target area. Based on the correlation, the water inflow at the working face is predicted; The determination of the correlation between the microseismic overburden damage height data and the historical water inflow data specifically includes: averaging the microseismic overburden damage height data and the historical water inflow data; calculating the difference between the microseismic overburden damage height data and the historical water inflow data to obtain an absolute value matrix; constructing a correlation coefficient matrix based on the absolute value matrix; and averaging the correlation coefficient matrix to obtain the correlation between the microseismic overburden damage height data and the historical water inflow data. The specific implementation method for constructing the correlation coefficient matrix based on the absolute value matrix is ​​as follows: ； ； ,(i=1,2,…,N;j=1); ,(i=1,2,…,N;j=1); in, It is an absolute value matrix. k The resolution coefficient, , L The correlation coefficient matrix, These are the elements of the correlation coefficient matrix; Based on the aforementioned correlation, the specific implementation method for predicting the water inflow at the working face is as follows: ，（ T i < T_t + T 00 ）； ，（ T_t + T 00 +0.5 T 2-0.5 T 1> T i > T_t + T 00 ）； in, B i This refers to the average water inflow recorded at the working face over a fixed time period. T i At time i, T 1 represents the time point of maximum water inflow. T 2 represents the time point of the minimum inflow rate adjacent to the maximum inflow rate. T_t The time between the maximum value of the overburden failure height due to microseismic events and the maximum value of the water inflow. B J The monitoring of the working face includes the maximum and minimum water inflow values ​​adjacent to each other within a period. T 00 The time when the height of the overlying rock damage reaches its maximum value due to microseismic events. B 00 for T 00 The water inflow at the working face at that time.

2. The method for predicting water inflow at the working face according to claim 1, characterized in that, Based on the historical water inflow data and the microseismic overburden failure height data, determine whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship, specifically including: The preset vertical distance range is determined based on the microseismic overburden damage height data; The vertical distance of the target aquifer is determined based on the vertical distance data of each aquifer. If the vertical distance of the target aquifer is within the preset vertical distance range, then the microseismic overburden failure height and the vertical distance of the target aquifer satisfy the preset spatial relationship.

3. A device for predicting water inflow at a working face, characterized in that, include: The data acquisition unit is used to acquire the vertical distance data of each aquifer in the target area where the working face to be monitored is located, the historical water inflow data of the working face to be monitored, and the microseismic overburden damage height data acquired by microseismic monitoring. The correlation determination unit is used to determine, based on the historical water inflow data and the microseismic overburden failure height data, whether the vertical distance between the microseismic overburden failure height and the target aquifer meets a preset spatial relationship. If the vertical distance between the microseismic overburden failure height and the target aquifer meets the preset spatial relationship, then the correlation between the monitored microseismic overburden failure height data and the historical water inflow data is determined; the target aquifer is the aquifer with the largest vertical distance in the target area. The prediction unit is used to predict the water inflow at the working face based on the correlation degree. The determination of the correlation between the microseismic overburden damage height data and the historical water inflow data specifically includes: averaging the microseismic overburden damage height data and the historical water inflow data; calculating the difference between the microseismic overburden damage height data and the historical water inflow data to obtain an absolute value matrix; constructing a correlation coefficient matrix based on the absolute value matrix; and averaging the correlation coefficient matrix to obtain the correlation between the microseismic overburden damage height data and the historical water inflow data. The specific implementation method for constructing the correlation coefficient matrix based on the absolute value matrix is ​​as follows: ； ； ,(i=1,2,…,N;j=1); ,(i=1,2,…,N;j=1); in, It is an absolute value matrix. k The resolution coefficient, , L The correlation coefficient matrix, These are the elements of the correlation coefficient matrix; Based on the aforementioned correlation, the specific implementation method for predicting the water inflow at the working face is as follows: ，（ T i < T_t + T 00 ）； ，（ T_t + T 00 +0.5 T 2-0.5 T 1> T i > T_t + T 00 ）； in, B i This refers to the average water inflow recorded at the working face over a fixed time period. T i At time i, T 1 represents the time point of maximum water inflow. T 2 represents the time point of the minimum inflow rate adjacent to the maximum inflow rate. T_t The time between the maximum value of the overburden failure height due to microseismic events and the maximum value of the water inflow. B J The monitoring of the working face includes the maximum and minimum water inflow values ​​adjacent to each other within a period. T 00 The time when the height of the overlying rock damage reaches its maximum value due to microseismic events. B 00 for T 00 The water inflow at the working face at that time.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the working face water inflow prediction method as described in any one of claims 1 to 2.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the working face water inflow prediction method as described in any one of claims 1 to 2.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the working face water inflow prediction method as described in any one of claims 1 to 2.

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