Water area expansion dynamic prediction method and system for coal mining subsidence ponding area

Abstract

The invention discloses a water area expansion dynamic prediction method and system for a coal mining subsidence ponding area, and belongs to the field of coal mining surface subsidence amount dynamic prediction. The method comprises the following steps: monitoring elevation data of water area and land area positions of a research area based on an unmanned aerial vehicle and an unmanned ship, and obtaining all-terrain elevation data of a coal mining subsidence area through a data fusion mode; according to the geological mining conditions and the predicted parameters of the predicted working face, predicting the surface subsidence amount at each moment caused by mining of the working face; determining the water surface elevation based on the all-terrain elevation data and the orthoimage of the coal mining subsidence area, and calculating the subsidence water area expansion area and the water resource amount at each moment caused by working face mining according to the surface subsidence amount; the prediction method provided by the invention can accurately predict the area expansion range and water resource quantity of the subsidence water area, and makes up the limitation that the traditional method only aims at single or several working faces and is difficult to consider the actual terrain.

Description

Technical Field

[0001] The present invention belongs to the field of dynamic prediction of surface subsidence in coal mining, and in particular relates to a method and system for dynamic prediction of water area expansion in coal mining subsidence waterlogging areas. Background Art

[0002] During coal mining, the fracturing and deformation of overlying rock strata are transmitted upward to the surface, causing widespread surface subsidence. In mining areas with high groundwater levels, groundwater seepage and surface subsidence interact, easily leading to the formation of large areas of subsidence and waterlogging. This phenomenon not only floods farmland and destroys vegetation, but can also trigger secondary disasters such as soil erosion and soil salinization, severely harming the regional ecological environment, agricultural production, and residents' lives. Therefore, accurately estimating the extent and volume of subsidence water is crucial for ecological restoration, water resource management, and disaster prevention and control in subsidence areas.

[0003] Currently, the expansion of water areas in coal mining subsidence areas is typically predicted using models such as the probability integral method. Static surface subsidence predictions are then combined with the local water table (generally considered to be 1.5 to 2.0 meters) to calculate the extent of the subsidence and the amount of water resources. However, this approach has significant limitations: The predictions are based only on a single or a few working faces, making it difficult to comprehensively account for the cumulative impact of historical mining on the subsidence area. Traditional methods fail to fully consider the impact of actual topography on the expansion of subsidence, affecting the accuracy of the predictions. Furthermore, static prediction methods cannot reflect the dynamic changes in water expansion during the mining process, making it impossible to obtain real-time information on the expansion of subsidence areas based on the mining progress. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for dynamically predicting water area expansion in coal mining subsidence areas, thereby solving the problems in the prior art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for dynamically predicting water area expansion in coal mining subsidence waterlogging areas comprises the following steps:

[0007] Using drones and unmanned boats to monitor the elevation data of the water and land areas in the study area, and using data fusion to obtain the full terrain elevation data of the coal mining subsidence area;

[0008] Based on the geological mining conditions and expected parameters of the working face, the estimated surface subsidence at each moment caused by mining at the working face;

[0009] The water surface elevation is determined based on the full terrain elevation data and orthophoto images of the coal mining subsidence area, and the expansion area of ​​the subsidence water area and the amount of water resources at each moment caused by working face mining are calculated based on the amount of surface subsidence.

[0010] Furthermore, the steps of obtaining full terrain elevation data of the coal mining subsidence area include:

[0011] S11, using drones to obtain surface elevation data and orthophotos, and using orthophotos to obtain the extent of subsidence waters;

[0012] S12, using an unmanned vessel to measure the underwater topography of the sunken waters within the range to obtain underwater elevation data;

[0013] S13, using surface elevation data and underwater elevation data to generate land DEM and water DEM respectively, the land and water boundary is calculated by averaging the elevation data of the two data to generate the full terrain DEM, and the elevation value E of the surface sampling point in the study area is obtained. i (x,y).

[0014] Furthermore, the estimated process of surface subsidence at each moment is as follows:

[0015] S21, collecting geological mining condition information of the working face to be mined and the probability integral method estimated parameters of the adjacent mining area;

[0016] S22, statically predicting surface subsidence based on the collected geological mining condition information of the working face to be mined and the probability integral method prediction parameters of the adjacent mining area;

[0017] S23, based on the static prediction of surface subsidence, introduces the Boltzmann time function to perform dynamic prediction of surface subsidence;

[0018] S24, superimpose the estimated result of the subsidence of the working face to be mined at the estimated time on the current elevation data E of the surface sampling point i In (x, y), the elevation value E of the surface sampling point after the working face is mined is obtained f (x,y).

[0019] Furthermore, the formula for dynamic prediction of surface subsidence is:

[0020]

[0021] Among them, w(x, y, t) is the surface subsidence at the estimated time; w(x, y) is the parameter that affects the final subsidence, t is the time interval from the start of mining to the estimated time; t0 is the time when the maximum subsidence velocity occurs; H0 is the average mining depth, p is the lithology coefficient of the overburden; c is the average mining velocity of the working face.

[0022] Furthermore, the elevation value E of the surface sampling point after mining f The calculation formula for (x,y) is:

[0023] E f(x,y)=E i (x,y)-0.001w(x,y,t).

[0024] Furthermore, the calculation process of the expansion area of ​​subsided waters and the amount of water resources at each moment is as follows:

[0025] S31: The average value of the water boundary elevation in the terrain data is regarded as the water surface elevation value.

[0026] S32, based on the elevation value E of the surface sampling point after mining f (x,y), using drawing software, extract all elevation values ​​less than or equal to the water surface elevation value The area around the subsidence area with elevation lower than or equal to the water surface elevation is defined as the water area;

[0027] S33, calculating the expanded water area and the expanded water resources.

[0028] A water area expansion dynamic prediction system for coal mining subsidence waterlogging areas, comprising:

[0029] Elevation data acquisition module: Based on the monitoring of the elevation data of the water and land areas in the study area by drones and unmanned boats, the full terrain elevation data of the coal mining subsidence area is obtained through data fusion;

[0030] Surface subsidence prediction module: Based on the geological mining conditions and predicted parameters of the expected working face, the surface subsidence at each moment caused by working face mining is estimated;

[0031] In addition, the water area expansion prediction module: determines the water surface elevation based on the full terrain elevation data and orthophoto images of the coal mining subsidence area, and calculates the expansion area of ​​the subsidence water area and the amount of water resources at each moment caused by working face mining based on the amount of surface subsidence.

[0032] A computer storage medium stores a readable program, which, when run by a processor, can execute the above-mentioned method for dynamically predicting water area expansion in coal mining subsidence waterlogging areas.

[0033] An electronic device comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0034] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for dynamic prediction of water area expansion in coal mining subsidence and waterlogging areas.

[0035] A computer program product includes computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned method for dynamic prediction of water area expansion in coal mining subsidence areas.

[0036] Beneficial effects of the present invention:

[0037] 1. The present invention realizes the dynamic prediction of surface subsidence caused by coal mining by combining the probability integral method with the time function, and can reflect the impact of mining progress on subsidence water areas in real time; combined with the elevation data of the entire terrain of the coal mining subsidence area obtained by drones and unmanned boats, the subsidence prediction results based on the actual terrain are obtained. According to the water surface elevation threshold, the area expansion range and water resource amount of the subsidence water area can be accurately predicted, which makes up for the limitation of traditional methods that only target single or several working faces and are difficult to consider the actual terrain.

[0038] 2. The dynamic prediction method of water area expansion of the present invention can provide reliable technical support for ecological restoration, soil and water conservation and disaster prevention and control in subsidence areas, and help reduce the negative impact of subsidence water on the ecological environment and agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 This is a flow chart of a method for dynamically predicting water area expansion in coal mining subsidence and waterlogging areas;

[0041] Figure 2 It is the predicted result of the expansion of subsidence water area after simulated mining in the study area of ​​this invention (combining DEM and subsidence prediction method);

[0042] Figure 3 It is the predicted result of the expansion of subsidence water area after simulated mining in the study area of ​​this invention (only the subsidence prediction means);

[0043] Figure 4 It is the changes in the area of ​​subsided water and water resources after simulated mining in the study area of ​​this invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] Example 1

[0046] like Figure 1 As shown, a method for dynamically predicting water area expansion in coal mining subsidence waterlogging areas includes the following steps:

[0047] S1, based on the elevation data of the water and land locations in the study area monitored by drones and unmanned boats, and the full terrain elevation data of the coal mining subsidence area was obtained through data fusion;

[0048] The specific steps to obtain the full terrain elevation data of the coal mining subsidence area are as follows:

[0049] S11, UAV data acquisition;

[0050] Using drones equipped with high-precision cameras and LiDAR (Light Detection And Ranging, LiDAR), the surface of the study area is photographed or scanned to obtain surface elevation data and orthophotos; and the orthophotos are used to obtain the scope of the subsidence water area.

[0051] S12: Unmanned vessel data acquisition;

[0052] Use unmanned vessels equipped with sonar equipment to measure the underwater topography of sunken waters within the range and obtain underwater elevation data;

[0053] S13: Data fusion and processing;

[0054] In order to generate a full-terrain digital elevation model (DEM) of the coal mining subsidence waterlogging area, the study area was divided into two types: land and water. The land DEM was generated using surface elevation data obtained by drones, and the water DEM was generated using underwater elevation data obtained by unmanned boats. The elevation data at the junction of land and water were averaged to generate a water-land integrated full-terrain DEM of the coal mining subsidence waterlogging area, and the elevation values ​​E of the surface sampling points in the study area were obtained. i (x,y).

[0055] S2, based on the geological mining conditions and expected parameters of the working face, the estimated surface subsidence at each moment caused by the working face mining;

[0056] The estimated process of surface subsidence at each moment is as follows:

[0057] S21, collecting information of the working face to be mined;

[0058] Collect geological mining condition information such as the location, strike length, dip length, mining thickness, mining depth, coal seam inclination, average mining speed of the working face to be mined; collect the probability integral method prediction parameters of the working face to be mined near the mining area.

[0059] In this embodiment, in order to explore the feasibility of the method, based on historical mining information, 5 working faces were simulated and planned to predict mining subsidence. The probability integral method prediction parameters selected the values ​​of the adjacent working faces, see Table 1 and Table 2.

[0060] Table 1 Probability integral method estimated parameter values

[0061]

[0062] Table 2 Expected geological mining conditions at the working face

[0063]

[0064] S22, static estimation of surface subsidence;

[0065] Using the most widely used probability integral method to predict surface subsidence, the estimated value of mining subsidence for the entire working face can be calculated as follows:

[0066]

[0067] Where: w max is the maximum surface subsidence, mm; m is the coal seam mining thickness, mm; q is the subsidence coefficient; α is the coal seam inclination, (°); l is the calculated length of the working face strike, m; L is the calculated length of the working face dip, m; L0 is the final coal seam mining strike length, m; L1 is the dip length of the rectangular mining area, m; S1 and S2 are the offset distances of the turning points on the left and right sides of the strike, m; S3 and S4 are the offset distances of the turning points in the uphill and downhill directions, m; θ is the main influencing propagation angle, (°); tanβ is the tangent of the main influencing angle; H0 is the average mining depth, m; H1 is the mining depth in the downhill direction, m; H2 is the mining depth in the uphill direction, m; w 0 (x) is the subsidence of the point with the horizontal coordinate x on the main section of the strike (when the dip direction is fully mined), mm; w 0 (y) is the subsidence of the point with ordinate y on the main dip section (when the strike direction is fully mined), mm; w(x,y) is the final subsidence of any point on the surface caused by mining, mm.

[0068] Among them, the probability integral method predicts the parameters DATA (including q, tanβ, S1, S2, S3, S4, θ) by selecting the predicted parameters of the working face in the adjacent mining area with similar geological mining conditions to the working face to be mined.

[0069] S23, dynamic prediction of surface subsidence;

[0070] The Boltzmann time function is combined with the probability integral method to achieve dynamic prediction of surface subsidence. To facilitate parameter calculation, a simplified parameter version of the Boltzmann function is used, and the formula is as follows:

[0071]

[0072] Where t is the time interval from the start of mining to the estimated time, d; t0 is the time when the maximum sinking velocity occurs, d; B is the coefficient of sinking rapidity; t0 and B are both related to geological mining conditions; A is the parameter affecting the final sinking amount, mm, which is replaced by w(x, y) in the probability integral method; w(t) is the surface sinking amount at the estimated time, mm.

[0073] Parameter B is calculated using the average recovery velocity of the working face and the lithology coefficient of the overburden. The formula is:

[0074]

[0075] Where H0 is the average mining depth, p is the lithology coefficient of the overburden, and c is the average mining velocity of the working face, m / d.

[0076] After combining the probability integral method with the time function, the final formula for dynamic prediction of surface subsidence is:

[0077]

[0078] Where w(x, y) is a parameter that affects the final subsidence. The overburden lithology coefficient p can be obtained by inverting the subsidence data of the mined working face. The overburden lithology coefficient p of the working face to be mined can be selected from the values ​​of other working faces with similar conditions in the adjacent mining area. In actual cases, the overburden lithology coefficient p of the working face is estimated based on the value of the adjacent mining area, here p = 1.9.

[0079] S24, the predicted surface subsidence results are superimposed on the actual terrain;

[0080] The estimated result of the subsidence of the working face to be mined at the estimated time is superimposed on the current elevation data E of the surface sampling point. i In (x, y), the elevation value E of the surface sampling point after the working face is mined for a period of time t is obtained f (x,y), as follows:

[0081] E f (x,y)=E i (x,y)-0.001w(x,y,t)

[0082] S3, based on the full terrain elevation data and orthophoto of the coal mining subsidence area in S1, determines the water surface elevation, and according to the surface subsidence obtained in S2, calculates the expansion area of ​​the subsidence water area and the increase in water resources at each moment caused by working face mining.

[0083] The process of calculating the expansion area of ​​subsided waters and the amount of water resources at each moment is as follows:

[0084] S31, water surface elevation determination;

[0085] The average value of the water boundary elevation in the terrain data is regarded as the water surface elevation value, and the following formula is used to calculate it:

[0086]

[0087] Where, is the average elevation of the surface sampling points at the water boundary, m; E w (x, y) is the elevation value of each surface sampling point at the water boundary, m; n is the number of water boundary sampling points. In this embodiment, the average elevation value of the surface sampling points at the water boundary is 20.6m.

[0088] S32, water boundary selection;

[0089] Based on the elevation value E of the surface sampling point after mining f (x,y), use drawing software such as Surfer to extract all elevation values ​​less than or equal to the preset water surface elevation value The area around the subsidence area with elevation lower than or equal to the water surface elevation is defined as the water area. Figure 2 ;in, Figure 2 (a) and (b) are the three-dimensional topography and subsidence water area of ​​the study area before mining, respectively. Figure 2 (c) and (d) are the three-dimensional topography and subsidence water area of ​​the study area at mining time 1, Figure 2 (e) and (f) are the three-dimensional topography and subsidence water area of ​​the study area at mining time 2, Figure 2 (g) and (h) are the three-dimensional topography and subsidence water area of ​​the study area at mining time 3, respectively.

[0090] The surface subsidence prediction values ​​at different mining times were calculated using the formulas of S22 and S23, with a subsidence of 1.5 m as the water accumulation boundary. Figure 3 ;in, Figure 3 (a), (b), and (c) are the subsidence water areas in the study area at mining times 1, 2, and 3, respectively.

[0091] S33, calculation of water area expansion and water resources;

[0092] Use the area calculation and volume calculation functions to calculate the expanded water area and the amount of water resources after expansion. And compare the calculation results with the calculation results using only the subsidence prediction method. Figure 4 .

[0093] The comparison results show that the water area increment calculated by the "considering subsidence value and DEM" method is smaller than that of the "considering subsidence value only" method. Specifically, at mining time 3, the difference between the two methods is 0.12 km. 2 The opposite trend was observed for water resource increments. The "simultaneous consideration of subsidence and DEM" method significantly increased water resource increments compared to the "subsidence-only" method. The latter's estimated value at mining time 2 was nearly eight times that of the former, and nearly three times that of mining time 3.

[0094] The mechanism for this discrepancy lies in the fact that the "subsidence-only" method treats each working face as an independent first-time mining face and uses a fixed phreatic level (generally considered to be 1.5 to 2.0 meters) as the criterion for determining the waterlogging boundary. When the subsidence value exceeds this standard, it is determined to be water expansion. However, this method ignores the dynamic spatial relationship between historical subsidence areas and the phreatic level. This leads to a decrease in prediction accuracy, especially when the difference between the elevation of the existing subsidence area and the phreatic level is less than 1.5 meters. In contrast, the "simultaneous consideration of subsidence and DEM" method establishes a more accurate system for predicting subsidence water expansion by fusing historical subsidence water spatial data with a subsidence prediction model. When mining is carried out in an existing waterlogged area, the subsidence impact is primarily manifested as an increase in water depth rather than an expansion of the waterlogged area, which is highly consistent with the prediction results of the fusion model.

[0095] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program. When the program is run by a processor, it can execute the above-mentioned method for dynamic prediction of water area expansion for coal mining subsidence waterlogging areas.

[0096] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0097] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for dynamic prediction of water area expansion in coal mining subsidence and waterlogging areas.

[0098] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned method for dynamic prediction of water area expansion in coal mining subsidence areas.

[0099] Example 2

[0100] Based on the method for dynamically predicting water area expansion in coal mining subsidence waterlogging areas proposed in Example 1, this embodiment proposes a system for dynamically predicting water area expansion in coal mining subsidence waterlogging areas, including:

[0101] Elevation data acquisition module: Based on the monitoring of the elevation data of the water and land areas in the study area by drones and unmanned boats, the full terrain elevation data of the coal mining subsidence area is obtained through data fusion;

[0102] Surface subsidence prediction module: Based on the geological mining conditions and predicted parameters of the expected working face, the surface subsidence at each moment caused by working face mining is estimated;

[0103] In addition, the water area expansion prediction module: determines the water surface elevation based on the full terrain elevation data and orthophoto images of the coal mining subsidence area, and calculates the expansion area of ​​the subsidence water area and the amount of water resources at each moment caused by working face mining based on the amount of surface subsidence.

[0104] The method of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It will be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0105] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for dynamically predicting water area expansion in coal mining subsidence areas, characterized by: The following steps are involved: Using drones and unmanned boats to monitor the elevation data of the water and land areas in the study area, and using data fusion to obtain the full terrain elevation data of the coal mining subsidence area; Based on the geological mining conditions and expected parameters of the working face, the estimated surface subsidence at each moment caused by mining at the working face; The water surface elevation is determined based on the full terrain elevation data and orthophoto images of the coal mining subsidence area, and the expansion area of ​​the subsidence water area and the amount of water resources at each moment caused by working face mining are calculated based on the amount of surface subsidence.

2. A method for dynamically predicting water area expansion in coal mining subsidence areas according to claim 1, characterized in that: The steps to obtain full terrain elevation data of coal mining subsidence area include: S11, using drones to obtain surface elevation data and orthophotos, and using orthophotos to obtain the extent of subsidence waters; S12, using an unmanned vessel to measure the underwater topography of the sunken waters within the range to obtain underwater elevation data; S13, using surface elevation data and underwater elevation data to generate land DEM and water DEM respectively, the land and water boundary is calculated by averaging the elevation data of the two data to generate the full terrain DEM, and the elevation value E of the surface sampling point in the study area is obtained. i (x,y).

3. The method for dynamically predicting water area expansion in coal mining subsidence areas according to claim 1 is characterized in that: The estimated process of surface subsidence at each moment is as follows: S21, collecting geological mining condition information of the working face to be mined and the probability integral method estimated parameters of the adjacent mining area; S22, statically predicting surface subsidence based on the collected geological mining condition information of the working face to be mined and the probability integral method prediction parameters of the adjacent mining area; S23, based on the static prediction of surface subsidence, introduces the Boltzmann time function to perform dynamic prediction of surface subsidence; S24, superimpose the estimated result of the subsidence of the working face to be mined at the estimated time on the current elevation data E of the surface sampling point i In (x, y), the elevation value E of the surface sampling point after the working face is mined is obtained f (x,y).

4. A method for dynamically predicting water area expansion in coal mining subsidence areas according to claim 3, characterized in that: The formula for dynamic prediction of surface subsidence is: Among them, w(x, y, t) is the surface subsidence at the estimated time; w(x, y) is the parameter that affects the final subsidence, t is the time interval from the start of mining to the estimated time; t0 is the time when the maximum subsidence velocity occurs; H0 is the average mining depth, p is the lithology coefficient of the overburden; c is the average mining velocity of the working face.

5. A method for dynamically predicting water area expansion in coal mining subsidence areas according to claim 4, characterized in that: Elevation value E of the surface sampling point after mining f The calculation formula for (x,y) is: AND f (x,y)=E i (x,y)-0.001w(x,y,t)。 6. A method for dynamically predicting water area expansion in coal mining subsidence areas according to claim 3, characterized in that: The calculation process of the expansion area of ​​subsided waters and water resources at each moment is as follows: S31: The average value of the water boundary elevation in the terrain data is regarded as the water surface elevation value. S32, based on the elevation value E of the surface sampling point after mining f (x,y), using drawing software, extract all elevation values ​​less than or equal to the water surface elevation value The area around the subsidence area with elevation lower than or equal to the water surface elevation is defined as the water area; S33, calculating the expanded water area and the expanded water resources.

7. A water area expansion dynamic prediction system for coal mining subsidence areas, characterized by: include: Elevation data acquisition module: Based on the monitoring of the elevation data of the water and land areas in the study area by drones and unmanned boats, the full terrain elevation data of the coal mining subsidence area is obtained through data fusion; Surface subsidence prediction module: Based on the geological mining conditions and predicted parameters of the expected working face, the surface subsidence at each moment caused by working face mining is estimated; In addition, the water area expansion prediction module: determines the water surface elevation based on the full terrain elevation data and orthophoto images of the coal mining subsidence area, and calculates the expansion area of ​​the subsidence water area and the amount of water resources at each moment caused by working face mining based on the amount of surface subsidence.

8. A computer storage medium storing a readable program, characterized in that: When the program is executed by the processor, it can execute the method for dynamic prediction of water area expansion for coal mining subsidence waterlogging areas as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to a dynamic prediction method for water area expansion in coal mining subsidence waterlogging areas as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that The computer instructions instruct the computing device to perform operations corresponding to the method for dynamic prediction of water area expansion in coal mining subsidence areas as described in any one of claims 1-6.

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