Distributed pumped storage system location assessment methods, devices and computer equipment

Abstract

This invention discloses a method, apparatus, and computer equipment for location assessment of a distributed pumped-storage system. The method includes: acquiring three-dimensional point cloud data and microseismic signals from a coal mine goaf; determining the available space volume based on the three-dimensional point cloud data; determining the stability of the coal mine goaf based on the microseismic signals, obtaining a first judgment result; acquiring topographic data from the coal mine subsidence area; determining the subsidence information of the coal mine subsidence area based on the topographic data; predicting the total inflow based on drainage flow data; determining the head height of the distributed pumped-storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf; and determining the location assessment result of the distributed pumped-storage system based on the above information. This invention solves the current technical problems of lacking multi-dimensional quantitative assessment capabilities for resources in mining goaf subsidence areas, leading to inaccurate site selection, blind construction of pumped-storage systems in coal mining areas, high safety risks, and low resource utilization.

Description

Technical Field

[0001] This invention relates to the field of mine ecological restoration and utilization technology, and more specifically, to a method, apparatus and computer equipment for location assessment of a distributed pumped storage system. Background Technology

[0002] Although coal mining areas possess natural pumped storage conditions such as goaf areas, subsidence areas, and stable water inflows, current technologies still rely on the static assessment model of traditional power plants, lacking systematic quantitative methods for understanding the unique geological environment of mining areas: the volume of goaf areas depends on rough estimations, the topography of subsidence areas lacks high-precision dynamic mapping, water inflow data is not linked to coal mining progress analysis, and there is no real-time monitoring and automatic response mechanism for risks such as leakage and slope instability. Existing solutions cannot answer the four core questions of "where can it be built, how large can it be built, how to dynamically adjust with coal mining, and how to ensure safety," resulting in a large amount of potential energy storage resources being idle or misused.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and computer equipment for location assessment of distributed pumped storage systems, in order to at least solve the current technical problems of lacking multi-dimensional quantitative assessment capabilities for resources in mining subsidence areas and the resulting inability to accurately select sites for pumped storage systems in coal mining areas, leading to high safety risks and low resource utilization rates due to blind construction.

[0005] According to one aspect of the present invention, a method for location assessment of a distributed pumped-storage system is provided, comprising: acquiring three-dimensional point cloud data and microseismic signals of a coal mine goaf in a coal mine space; determining the available space volume of the coal mine goaf based on the three-dimensional point cloud data; determining whether the coal mine goaf is stable based on the microseismic signals, and obtaining a first judgment result; acquiring topographic data of a coal mine subsidence area in the coal mine space; determining subsidence information of the coal mine subsidence area based on the topographic data, wherein the subsidence information includes subsidence area and subsidence depth; acquiring drainage flow data of the mine shaft in the coal mine space; predicting the total inflow based on the drainage flow data; determining the head height of the distributed pumped-storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf; and obtaining a location assessment result of the distributed pumped-storage system based on the available space volume, the judgment result, the subsidence information, the total inflow, and the head height.

[0006] Optionally, based on microseismic signals, determining whether the coal mine goaf is stable and obtaining a first judgment result includes: identifying microseismic signals and extracting target microseismic events; performing three-dimensional spatial positioning of the target microseismic events to determine their distribution coordinates in the coal mine goaf; determining the spatial distribution characteristics of the target microseismic events based on the distribution coordinates; obtaining the event characteristics corresponding to the target microseismic events, including cumulative magnitude, total energy release, event frequency, and time evolution rate; obtaining rock mass mechanical parameters in the coal mine goaf, including elastic modulus, Poisson's ratio, internal friction angle, and cohesion; establishing a three-dimensional numerical simulation model based on the rock mass mechanical parameters; determining boundary conditions and initial disturbance sources based on spatial distribution characteristics and event characteristics; driving the three-dimensional numerical simulation model iteratively to calculate the stress redistribution and plastic zone evolution of the rock mass in the coal mine goaf based on the boundary conditions and initial disturbance sources, obtaining the surrounding rock stress field and displacement field; calculating the target safety factor of the coal mine goaf based on the surrounding rock stress field and displacement field; and determining the first judgment result based on the target safety factor.

[0007] Optionally, the method includes: determining the first judgment result as the coal mine goaf structure being unstable when the target safety factor is less than a preset safety threshold; and determining the first judgment result as the coal mine goaf structure being stable when the target safety factor is not less than a preset safety threshold.

[0008] Optionally, based on drainage flow data, the total water inflow is predicted, including: determining the baseline water inflow based on drainage flow data; obtaining historical coal mining data; establishing a time-series correlation between coal mining progress and water inflow changes based on historical coal mining data and drainage flow data; constructing an evolutionary model based on the time-series correlation and the baseline water inflow; and predicting the total water inflow based on the evolutionary model.

[0009] Optionally, based on the available space volume, the first judgment result, subsidence information, total inflow, and head height, the location assessment result of the distributed pumped storage system is obtained, including: comparing the available space volume with a preset minimum volume threshold to determine whether the coal mine goaf has the conditions for construction as a lower reservoir, thus obtaining a second judgment result; matching the subsidence area and subsidence depth in the subsidence information with preset lower area limits and preset effective water depth requirements, respectively, to determine whether the coal mine subsidence area can serve as a water storage container for the upper reservoir, thus obtaining a third judgment result; determining whether the total inflow meets the minimum water supply guarantee required for the operation of the distributed pumped storage system, thus obtaining a fourth judgment result; comparing the head height with the minimum head height threshold corresponding to the distributed pumped storage system, thus obtaining a fifth judgment result; and determining the location assessment result based on the first, second, third, fourth, and fifth judgment results.

[0010] Optionally, it also includes: acquiring coal mining operation data, wherein the coal mining operation data includes the coal mining operation advance speed and mining sequence; predicting the volume expansion trend of the coal mine goaf based on the coal mining operation data; and adjusting the location assessment results based on the volume expansion trend.

[0011] According to another aspect of the present invention, a distributed pumped storage system location assessment device is also provided, comprising: a first acquisition module for acquiring three-dimensional point cloud data and microseismic signals of a coal mine goaf in a coal mine space; a first determination module for determining the available space volume of the coal mine goaf based on the three-dimensional point cloud data; a judgment module for determining whether the coal mine goaf is stable based on the microseismic signals, and obtaining a first judgment result; a second acquisition module for acquiring topographic data of a coal mine subsidence area in a coal mine space; a second determination module for determining subsidence information of the coal mine subsidence area based on the topographic data, wherein the subsidence information includes subsidence area and subsidence depth; a third acquisition module for acquiring mine drainage flow data in a coal mine space; a prediction module for predicting the total inflow based on the drainage flow data; a third determination module for determining the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf; and an assessment module for obtaining a location assessment result of the distributed pumped storage system based on the available space volume, the judgment result, the subsidence information, the total inflow, and the head height.

[0012] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described distributed pumped storage system location assessment methods.

[0013] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described distributed pumped storage system location assessment methods during runtime.

[0014] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described methods for determining the location of a distributed pumped storage system.

[0015] In this embodiment of the invention, a distributed pumped storage system location assessment method is adopted. This involves collecting three-dimensional point cloud data and microseismic signals from the coal mine goaf within the coal mine space; determining the usable volume of the goaf based on the three-dimensional point cloud data; judging the stability of the goaf based on the microseismic signals to obtain a first judgment result; collecting topographic data from the coal mine subsidence area within the coal mine space; determining the subsidence information of the subsidence area based on the topographic data, wherein the subsidence information includes the subsidence area and subsidence depth; collecting mine drainage flow data within the coal mine space; predicting the total inflow based on the drainage flow data; and determining the vertical height between the coal mine subsidence area and the coal mine goaf. The head height of the distributed pumped storage system is determined by the difference in elevation. Based on the available space volume, judgment results, subsidence information, total inflow, and head height, the location assessment results of the distributed pumped storage system are obtained. This achieves the goal of scientifically identifying whether a mining area has the conditions for constructing a distributed pumped storage system. This realizes the technical effect of transforming from experience-based judgment to data-driven, from static assessment to dynamic adaptation, and from single indicators to multi-dimensional coupling intelligent assessment. Furthermore, it solves the current technical problems of lacking multi-dimensional quantitative assessment capabilities for resources in mining subsidence areas and the resulting inability to accurately select sites for pumped storage systems in coal mining areas, leading to high safety risks and low resource utilization rates due to blind construction. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 A hardware block diagram of a computer terminal for implementing a location assessment method for a distributed pumped storage system is shown.

[0018] Figure 2 This is a flowchart illustrating the location assessment method for a distributed pumped storage system provided in an embodiment of the present invention.

[0019] Figure 3 This is a structural block diagram of a distributed pumped storage system location assessment device provided in an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] According to an embodiment of the present invention, a method embodiment for location assessment of a distributed pumped storage system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal for implementing a location assessment method for distributed pumped storage systems is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0024] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0025] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the distributed pumped storage system location assessment method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the distributed pumped storage system location assessment method of the above-mentioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0026] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0027] Figure 2 This is a flowchart illustrating the location assessment method for a distributed pumped storage system provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0028] Step S202: Collect three-dimensional point cloud data and microseismic signals of the coal mine goaf in the coal mine space.

[0029] In this step, to achieve simultaneous perception of the spatial structure and dynamic stability of the rock mass in the coal mine goaf, high-precision 3D laser scanning equipment and microseismic monitoring sensors are deployed in the boundary roadways of the goaf, the overhanging roof area, and key load-bearing parts of the sidewalls. The 3D laser scanner can be set to a laser pulse frequency of 500kHz, a scanning resolution of no more than 5 mm, and a point cloud density of no less than 100 points per square meter. By deploying scanning stations at at least three different viewing angles in the goaf, the scanning coverage is ensured to reach over 95%, avoiding point cloud loss due to roadway obstruction. All scanning data is stored in .las format, with each point containing 3D coordinates X, Y, and Z, reflection intensity value, and acquisition timestamp. Global stitching is performed using a multi-point point cloud registration algorithm, with registration errors controlled within ±3 mm to ensure the geometric consistency of the 3D model. Simultaneously, microseismic sensors are deployed at intervals of no more than 10 meters in the stress concentration areas of the surrounding rock in the goaf, fault interfaces, and potential instability areas. The sensors employ piezoelectric... The ceramic or fiber optic grating structure has a wide bandwidth response of 0-10kHz, a sampling frequency of no less than 100Hz, and a sampling depth of no less than 24 bits. The signal is transmitted to the downhole edge computing unit via an explosion-proof armored shielded cable to achieve 24-hour continuous acquisition. All microseismic event waveforms are strictly bound to the sensor spatial coordinates and acquisition time to form a spatiotemporally correlated raw dataset. This step synchronously acquires the geometry of the goaf and the micro-fracture response of the rock mass through a physical sensor network, providing real, in-situ, and reproducible two-dimensional input data for subsequent volume calculation and stability assessment. This overcomes the technical defects of traditional manual drilling, such as long cycle, poor single-point representativeness, and inability to dynamically monitor, and constructs the first high-precision sensing foundation for the site selection of pumping and storage systems in coal mines.

[0030] Step S204: Based on the three-dimensional point cloud data, determine the available space volume of the coal mine goaf.

[0031] In this step, the high-precision 3D point cloud data obtained in step S202 is imported into professional point cloud processing software. First, global registration and correction are performed to ensure that the multi-station scan data are aligned in a unified coordinate system, with the registration error controlled within ±3 mm. Then, statistical outlier removal and radius outlier filtering algorithms are used to eliminate non-realistic point clouds caused by dust, water vapor, equipment reflections, or interference from moving objects, retaining an effective point cloud density of no less than 90 points per square meter to ensure the integrity and accuracy of subsequent modeling. Based on this, a continuous 3D closed surface model of the goaf is constructed using the Poisson surface reconstruction algorithm. Topology optimization is performed on the model to eliminate non-manifold edges, overhanging surfaces, and internal pores, ensuring the model is a watertight seal. Subsequently, according to coal mine engineering specifications and pumped storage system construction requirements, the system automatically identifies and excludes unstable areas that are connected to external roadways, have seepage channels, have partially collapsed and unsealed roofs, have a rock mass integrity index below 0.7, or show a significant trend of crack propagation, retaining only the bottom area. Areas with stable bedrock, no significant sidewall deformation, and independent enclosed space are considered as the effective utilization area. After determining the effective area, a voxelization method is used to discretize the area into three-dimensional cubic units with a side length of 0.5 meters. The number of effective voxels is counted layer by layer and multiplied by the volume of a single voxel to calculate the usable space volume of the goaf. The result is in cubic meters and retained to one decimal place. To verify the accuracy of the calculation results, no less than five representative borehole locations are selected within the goaf area. The measured cavity volume is obtained by combining core drilling and three-dimensional laser scanning. The measured volume is compared with the volume reconstructed from point cloud. The measured error is controlled within ±3%, ensuring that the calculated usable space volume has engineering-grade accuracy and reproducibility. This volume serves as the direct basis for the reservoir capacity of the pumped storage system, providing a solid and reliable data foundation for subsequent energy storage capacity calculation, equipment selection, and economic evaluation, avoiding system capacity design deviations and safety risks caused by spatial misjudgment.

[0032] Step S206: Based on the microseismic signal, determine whether the coal mine goaf is stable and obtain the first judgment result.

[0033] In this step, the microseismic signal data continuously acquired in step S202 is preprocessed and event identified. First, the sliding window energy ratio method is used to perform dynamic threshold detection on the original waveform to automatically identify the start time of the microseismic event. Then, combined with the arrival time difference between P-waves and S-waves, the spatial location of the source of each microseismic event is inverted using a three-dimensional grid search method, with the positioning error controlled within 2 meters. Subsequently, characteristic parameters such as magnitude, energy release, peak frequency, duration, and spatial clustering of each event are extracted to construct a microseismic event database. This database is then analyzed using Ripley's... K-function analysis identifies the clustering patterns of events in three-dimensional space, pinpointing high-risk microseismic hotspots. These characteristics are then input into the FLAC3D numerical simulation platform, combined with rock mechanics parameters such as an elastic modulus of at least 15 GPa, Poisson's ratio of at least 0.28, internal friction angle of at least 32 degrees, cohesion of at least 1.2 MPa, and tensile strength of at least 0.8 MPa. This constructs a three-dimensional finite element model of the rock mass containing millions of elements. This model accurately recreates the geometry of the goaf and the initial stress field of the surrounding rock. Gravity and mining disturbance loads are applied, and nonlinear elastoplastic iterative calculations are performed to determine the stress distribution and safety factor at key sections such as the central part of the roof, the upper part of the sidewalls, and fault boundaries. The safety factor is defined as the ratio of the rock mass shear strength to the actual shear stress; a calculated safety factor greater than or equal to 1 is considered safe. At 2 o'clock, the system determines that the overall structure of the surrounding rock in the goaf is stable, and the first judgment result is "stable". When the safety factor is between 1.0 and 1.2, the system determines that there is a risk of local instability, and the first judgment result is "warning", and prompts that grouting reinforcement should be carried out and retested. When the safety factor is below 1.0, the system determines that the risk of structural instability is extremely high, and the first judgment result is "prohibited". The system automatically locks the area as a prohibited construction zone for pumped storage and terminates the subsequent assessment process. The judgment result is output as a Boolean state quantity and is bound to the three-dimensional model space of the goaf to form a dynamic risk heat map. This ensures that the assessment conclusion is traceable, visualized and reproducible, fundamentally avoiding major engineering safety accidents such as water inrush and collapse caused by rock instability, and providing a safety guarantee for the safe implementation of pumped storage systems in coal mining areas.

[0034] Step S208: Collect topographic data of the coal mine subsidence area in the coal mine space.

[0035] In this step, to comprehensively acquire the surface morphological features of the subsidence area, an integrated air-space-ground collaborative mapping method is adopted to collect high-precision topographic data of the subsidence area. UAVs can be used for scanning, ensuring a point cloud density of no less than 150 points per square meter, and scanning coverage extending at least 30 meters beyond the boundary of the subsidence area to fully capture topographic gradients and edge transition features. All point cloud data are accompanied by precise geographic coordinates and timestamps, stored in .laz format. For subsidence center areas with dense vegetation cover, water bodies obscuring the view, or where UAVs cannot fly safely, radar interferometric data is used. Interferogram sequences with a time span of no less than 12 months and a time baseline greater than 15 days are selected. The Small Baseline Set Interferometry (SBAS-InSAR) algorithm is used for time-series analysis to invert the cumulative subsidence rate and deformation trend of the subsidence area. The data was obtained by taking advantage of the terrain, with a spatial resolution of no less than 10 meters. Subsequently, the laser point cloud data acquired by the UAV was spatially registered with the satellite subsidence map. Multi-source data fusion was performed using spatial interpolation to generate a digital elevation model (DEM) with a resolution of 0.5 meters × 0.5 meters. With correction of known ground control points, the elevation accuracy of this model was verified to be no more than 0.1 meters. This provides a real, continuous, and high-precision surface morphology data foundation for subsequent subsidence feature extraction and water storage capacity assessment. It effectively overcomes the limitations of traditional manual measurement, such as low efficiency, narrow coverage, and difficulty in capturing subtle deformations. This lays a solid data foundation for the feasibility assessment of the subsidence area as an upper reservoir.

[0036] Step S210: Based on topographic data, determine the subsidence information of the coal mine subsidence area, wherein the subsidence information includes the subsidence area and subsidence depth.

[0037] In this step, the acquired high-precision digital elevation model is input into the geographic information system platform. First, using the difference analysis method, with the surrounding unaffected benchmark elevation surface as a reference, the elevation change of each grid point relative to the benchmark is calculated to form a settlement distribution map. Then, a settlement threshold of 0.5 meters is set, meaning that only areas with a cumulative settlement greater than or equal to 0.5 meters are considered valid settlement zones, thus excluding minor elevation changes caused by natural terrain undulations, measurement errors, or localized soft soil layers. Based on this, a region growing algorithm is used to automatically identify and extract all connected settlement regions, generating closed settlement zone vector polygons. The projected area of ​​these polygons is calculated as the settlement area, in square meters, rounded to the nearest integer. Simultaneously, the maximum settlement value is extracted from all valid grid points within the settlement zone. The maximum subsidence depth is calculated, and the arithmetic mean of the subsidence at all effective points is used as the average subsidence depth for subsequent reservoir capacity estimation. To reasonably reflect the actual water storage space, the reduction in effective water depth due to siltation, vegetation cover, evaporation loss, and the construction of the impermeable layer can be considered. The average subsidence depth is multiplied by 0.7 to obtain the effective water depth. This reduction factor has been verified to be engineering-applicable through field measurements in multiple mining areas. All subsidence information, including subsidence area, average subsidence depth, maximum subsidence depth, and effective water depth, is accompanied by standard deviation and 95% confidence interval to ensure that the assessment results have the ability to quantify uncertainty, avoid overestimation of reservoir capacity due to fluctuations in topographic data or local anomalies, and provide a scientific, robust, and verifiable parameter basis for the capacity design of pumped storage systems, thereby improving the economy and safety of system planning.

[0038] Step S212: Collect drainage flow data of the mine shaft in the coal mine space.

[0039] In this step, to accurately obtain the dynamic characteristics of mine water inflow, high-precision flow monitoring equipment is deployed at key nodes of the underground main drainage system, including the main drainage pump station outlet, the central water tank inlet, the centralized drainage roadway in the mining area, and long-term water inflow points. Electromagnetic flowmeters or ultrasonic time-of-flight flowmeters with intrinsically safe explosion-proof certification are selected, capable of being set to cover a flow rate from 0 to 500 cubic meters per hour, with a measurement accuracy of no less than ±0.5% of full scale and a response time of less than 1 second, ensuring stable and reliable data output even under complex underground conditions such as high flow velocity, high sand content, and strong vibration. All sensors are connected to an intrinsically safe data acquisition terminal underground via armored shielded cables. The sampling interval is set to once every 30 minutes, with a collection period of no less than 12 consecutive months, fully covering the high-production period, maintenance period, rainy season, and dry season of the coal mine. Seasonal hydrological fluctuations are addressed by preprocessing data locally before uploading it to the mining area edge server via industrial Ethernet. The 3σ criterion is used to automatically remove outliers caused by transient sensor interference, pipeline air bubbles, or pump start-up and shutdown. The Savitzky-Golay filtering algorithm is then applied to smooth the raw time-series data, eliminating high-frequency noise and preserving the true water inflow trend. The acquired flow data forms a structured time-series database containing timestamps, flow values, equipment numbers, and ambient temperature, ensuring traceability of data sources, verifiability of the acquisition process, and reproducibility of results. This provides a true, continuous, and representative foundation for subsequent total water inflow prediction, overcoming the distortion in water source assessment caused by traditional reliance on manual observation or short-term sampling estimation. This establishes scientific data support for judging the water source sustainability of pumped storage systems.

[0040] Step S214: Based on the drainage flow data, predict the total inflow.

[0041] In this step, the collected and preprocessed mine water inflow time-series data for more than 12 consecutive months are used as input to construct a dynamic prediction model based on a Long Short-Term Memory (LSTM) neural network. This model uses the water inflow rate of the past 72 hours as the time series input, and simultaneously integrates the daily advance speed of the coal face, the daily mining volume, the trend of aquifer water level changes, and the cumulative rainfall in the region over the past 7 days as external covariates to improve the prediction's responsiveness to mining activities and hydrological disturbances. The model structure adopts a two-layer LSTM network, with each layer containing 128 hidden units, followed by a fully connected layer and a Dropout regularization layer. The loss function is the mean absolute error (MAE), the optimizer is Adam, the learning rate is set to 0.001, and 5-fold cross-validation is used during training to ensure the model's generalization ability. After training, if the model's root mean square error on the test set is less than 15 cubic meters per hour, it is considered suitable for adoption. With an R² coefficient higher than 0.88, the model possesses high-precision predictive capabilities for seasonal fluctuations, mining disturbances, and extreme rainfall events. The model automatically updates its training samples daily and undergoes incremental retraining quarterly based on the latest coal mining plans and measured data to adapt to dynamic changes brought about by goaf expansion and aquifer evolution. The predicted output is the average annual water inflow of the mine over the next year, expressed in cubic meters per hour, with one decimal place. A 95% confidence interval is also output simultaneously to indicate the range of prediction uncertainty. This prediction not only reflects the current water inflow scale but also possesses foresight and dynamic adaptability, providing a scientific basis for the long-term stability assessment of pumped storage system water sources, the design of storage facility scale, and operational scheduling strategies. It fundamentally solves the problem that traditional static water inflow statistics methods cannot address the imbalance between water supply and demand caused by the dynamic evolution of coal mining, ensuring the system has a continuous and reliable water supply guarantee capability throughout its entire life cycle.

[0042] Step S216: Determine the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf.

[0043] In this step, the acquired digital elevation model (DEM) of the subsidence area is spatially registered with the reconstructed 3D geometric model of the goaf. Through elevation interpolation between the point cloud and the DEM, the lowest elevation point within the water storage area of ​​the subsidence area is extracted as the design water level reference surface for the upper reservoir, and the highest stable bedrock surface within the enclosed space of the goaf is extracted as the water level reference surface for the lower reservoir. To ensure the engineering safety and feasibility of the head calculation, the system automatically removes discontinuous and unstable areas such as surface vegetation obstruction, localized subsidence depressions, and shallow water-filled pits. Only the highest point in the subsidence area with continuous water surface formation conditions, geological stability, and no strong seepage channels, and the lowest point in the goaf with structural bearing capacity, no risk of backflow, and good rock mass integrity are selected as water level control points. Subsequently, a 3D spatial coordinate difference algorithm is used to accurately calculate the vertical elevation difference between these two points; this difference is the theoretical maximum water level. To mitigate construction errors, siltation effects, and operational safety margins, the system further deducts a 0.5-meter safety margin as the design head. This design head is cross-validated through RTK-GPS field measurements at at least three independent measuring points, with the measured error controlled within ±0.1 meters to ensure its engineering reliability. The final output design head is the effective head required for system operation, expressed in meters and rounded to one decimal place. This serves as the core input parameter for pump and turbine selection, water pipeline pressure calculation, energy conversion efficiency estimation, and system economic analysis. This method abandons the traditional rough approach of manual measurement or topographic map estimation, achieving automated, high-precision, and traceable head calculation based on real three-dimensional geological structures. It significantly improves the scientific nature and operational efficiency of pumped storage system design, providing key physical parameter support for constructing a safe, stable, and efficient distributed pumped storage system for mines.

[0044] Step S218: Based on the available space volume, judgment results, subsidence information, total inflow and head height, obtain the location assessment results of the distributed pumped storage system.

[0045] In this step, five core parameters are obtained: available space volume of the goaf, the first assessment result of goaf stability, subsidence area and effective water depth, predicted total mine water inflow, and system design head. These parameters are then uniformly input into a multi-dimensional comprehensive evaluation engine. This engine can construct a five-dimensional weighted scoring model based on engineering practice experience, assigning different weights to each parameter: available space volume 30%, goaf stability assessment 25%, effective subsidence area capacity (area × effective water depth) 20%, total water inflow guarantee capacity 15%, and water... Head height accounts for 10%, with stability assessment being a veto item. If the first assessment result is "prohibited," the evaluation is terminated directly and an "unsuitable" conclusion is output without further calculation. For areas that can pass stability verification, the system makes graded judgments based on preset threshold ranges: when the usable volume of the goaf is not less than 500,000 cubic meters, the effective reservoir capacity of the subsidence area is not less than 100,000 square meters × 4 meters, the average annual water inflow is not less than 4.4 million cubic meters (i.e., average flow rate ≥ 500 m³ / h), and the design head is not less than 80 meters, the comprehensive score is higher than 85 points. The assessment result is "Class I Suitable," recommending the construction of a 10-50MW system; when the parameters are between the Class I and Class III thresholds, the comprehensive score is between 60-85 points, the assessment result is "Class II Conditionally Suitable," and it is recommended to construct a 1-10MW system after implementing seepage prevention reinforcement and water source regulation measures; when the parameters are all below the lower limit of Class II but still have local utilization potential, the comprehensive score is between 40-60 points, the assessment result is "Class III Requires Supporting Construction," requiring artificial water source supplementation, grouting sealing, or integration with external water systems to construct a small system smaller than 5MW; all The evaluation results are all output as structured reports, including measured values ​​of each parameter, calculation process, weight scores, grading criteria and recommended measures, and are spatially bound to a three-dimensional geological model. They are dynamically displayed in the form of heat maps on a visualization platform, allowing users to click on any candidate area to view the complete evaluation details. This method avoids the one-sidedness of traditional empirical methods that "emphasize volume over safety" and "emphasize elevation difference over water source", ensuring that the evaluation results have technical rigor, engineering feasibility and safety redundancy, and providing support for the scientific site selection and orderly development of distributed pumped storage systems in coal mining areas.

[0046] Through the above steps, a full-chain, high-precision, and intelligent feasibility assessment and site selection decision can be achieved for distributed pumped storage systems in coal mining areas.

[0047] As an optional embodiment, this can be achieved through the following steps: Based on microseismic signals, determine whether the coal mine goaf is stable to obtain a first judgment result, including: identifying microseismic signals and extracting target microseismic events; performing three-dimensional spatial positioning of the target microseismic events to determine their distribution coordinates within the coal mine goaf; determining the spatial distribution characteristics of the target microseismic events based on the distribution coordinates; acquiring the event characteristics corresponding to the target microseismic events, wherein the event characteristics include cumulative magnitude, total energy release, event frequency, and time evolution rate; and acquiring the spatial distribution characteristics of the coal mine goaf within the coal mine goaf. Rock mass mechanics parameters, including elastic modulus, Poisson's ratio, internal friction angle, and cohesion; based on these parameters, a three-dimensional numerical simulation model is established; based on spatial distribution characteristics and event characteristics, boundary conditions and initial disturbance sources are determined; based on the boundary conditions and initial disturbance sources, the three-dimensional numerical simulation model is driven to iteratively calculate the stress redistribution and plastic zone evolution of the rock mass in the coal mine goaf, obtaining the surrounding rock stress field and displacement field; based on the surrounding rock stress field and displacement field, the target safety factor of the coal mine goaf is calculated; and based on the target safety factor, the first judgment result is determined.

[0048] Optionally, the raw waveform data collected by the underground microseismic monitoring system is first preprocessed. Using the sliding window energy ratio method combined with the time difference of arrival (TDOA) of P-waves and S-waves, valid microseismic events are automatically identified. Background noise, equipment interference, and non-rock fracture signals are removed, retaining events with energy thresholds higher than three times the environmental background and durations greater than 0.2 seconds as target microseismic events. Subsequently, using a three-dimensional array of multiple microseismic sensors, based on the time difference of arrival (TDOA) and wave velocity tomography inversion algorithm, each target microseismic event is spatially located in three dimensions, with the location error controlled within 2 meters. Its precise spatial coordinates (x, y, z) in the coal mine goaf coordinate system are obtained, and a spatiotemporal database of the events is established. Based on this, further processing is performed. The spatial distribution pattern of all target microseismic events was analyzed using the density clustering algorithm (DBSCAN), identifying high-density clusters, strip-shaped extensions, and isolated point distributions. Spatial distribution features, including event cluster radius, density index, principal stress direction consistency, and spatial connectivity index, were extracted to characterize the concentration and evolution trend of surrounding rock fracturing. Simultaneously, for each target microseismic event, its cumulative magnitude (ML), total energy release (E, unit: joules), event frequency per unit time (events / hour), and temporal evolution rate (slope of event density change over time) were extracted to form a multidimensional event feature vector. At the same time, the rock mechanics parameters of the surrounding rock in the coal mine goaf were acquired, including an elastic modulus of not less than 15 GPa and a relatively low Poisson's ratio. The parameters, including an internal friction angle of 0.28°, an internal friction angle of 32°, a cohesion of 1.2 MPa, and a tensile strength of 0.8 MPa, are derived from on-site core laboratory tests and regional geological data integration to ensure data accuracy and reliability. The three-dimensional geometric model, spatial distribution characteristics, event characteristics, and rock mechanics parameters of the goaf are input into the FLAC3D numerical simulation platform to construct a million-level three-dimensional finite element mesh model. This accurately recreates the goaf morphology, fault structure, and initial geostress field. The spatial distribution of microseismic events is used as the disturbance source, and their energy release is applied to the corresponding locations in the model according to the equivalent body load method. The time evolution rate is used to define the disturbance loading rate, forming dynamic load boundary conditions. The model operates under gravity and mining disturbance conditions. Nonlinear elastoplastic iterative calculations are performed under the combined action to continuously simulate the stress redistribution and plastic zone expansion process of the surrounding rock, outputting the stress field, displacement field, and plastic zone evolution map of the surrounding rock at different times. Based on the output results, typical dangerous sections such as the central part of the goaf roof, the upper part of the sidewalls, the fault intersection area, and the goaf boundary are selected, and the ratio of their shear strength to the actual shear stress is calculated as the target safety factor. When the target safety factor is greater than or equal to 1.2, the overall structure of the surrounding rock in the goaf is determined to be stable, and the first judgment result is "stable". When the target safety factor is between 1.0 and 1.2, it is determined that there is a risk of local instability, and the first judgment result is "warning", and the coordinates of the high-risk area and reinforcement suggestions are output. When the target safety factor is less than 1...At time 0, the structure was determined to be structurally unstable, and the initial assessment result was "prohibited." The area was automatically designated as a restricted zone for pumped storage construction, terminating subsequent assessment processes. All assessment processes generated traceable calculation logs and visual heat maps to ensure the transparency, reproducibility, and engineering verifiability of the assessment conclusions.

[0049] As an optional embodiment, it can be achieved through the following steps: including: when the target safety factor is less than a preset safety threshold, determining the first judgment result as the coal mine goaf structure is unstable; when the target safety factor is not less than the preset safety threshold, determining the first judgment result as the coal mine goaf structure is stable.

[0050] Optionally, the preset safety threshold can be set to 1.2 based on industry engineering practices. This threshold comprehensively considers the combined effects of residual rock strength, long-term creep effect, water pressure seepage softening, and dynamic mining disturbance, providing both conservatism and engineering safety. When the target safety factor calculated by numerical simulation is lower than 1.2, the system automatically determines that the surrounding rock of the goaf is in a critical instability state. Its structural integrity cannot meet the mechanical requirements of long-term water storage load and periodic water pumping and drainage, and there is a potential risk of rock mass fracture expansion, local collapse, or even large-scale instability. At this time, the first judgment result is clearly marked as "structurally unstable" and a red warning is triggered simultaneously to prevent the area from entering the subsequent pumping and storage system planning process, avoiding major safety accidents such as reservoir failure, uncontrolled underground water inflow, or ground collapse caused by structural hazards. When the target safety factor is not less than the preset safety threshold, that is, when the calculated safety factor is greater than or equal to 1.2, the system determines that the surrounding rock of the goaf is in a critical instability state under the current geological conditions and Under the influence of mining history, the structure possesses sufficient shear bearing capacity and deformation coordination capacity to withstand long-term hydrostatic pressure, sudden water level changes, and repeated stress cycles during the operation of the pumped storage system. Its structural deformation is within the controllable elastic or micro-plastic range, with no significant tendency for plastic zone expansion, and the displacement rate is below the safety threshold of 0.5 mm / d. At this point, the first judgment result is determined to be "structurally stable," allowing the area to proceed to the subsequent reservoir capacity assessment, head calculation, and system design stages. This judgment mechanism does not rely on subjective experience but is entirely driven by quantitative indicators output from numerical simulation. It has clear mathematical criteria and engineering physical basis, ensuring that the assessment conclusions are objective, consistent, and traceable, providing support for the safe site selection of pumped storage systems in coal mining areas.

[0051] As an optional embodiment, the following steps can be used to predict the total water inflow based on drainage flow data, including: determining the baseline water inflow based on drainage flow data; obtaining historical coal mining data; establishing a time-series correlation between coal mining progress and water inflow changes based on historical coal mining data and drainage flow data; constructing an evolutionary model based on the time-series correlation and the baseline water inflow; and predicting the total water inflow based on the evolutionary model.

[0052] Optionally, the time-series data of mine drainage flow collected for more than 12 consecutive months are first decomposed periodically. The moving average method is used to eliminate short-term random fluctuations. Combined with seasonal trend analysis, the stable baseline water inflow components that are not directly affected by mining activities are identified, and their average value is used as the benchmark water inflow, in cubic meters per hour, rounded to one decimal place. This represents the background value of the natural hydrological response of the mining area under the condition of no new mining activity. Subsequently, complete historical data of coal mining progress over the past five years are obtained, including the daily advance distance of the coal face, the average daily mining volume, the mining height, and the coal seam. Thickness and cumulative expansion area of ​​the goaf were obtained from the mine production scheduling system and geological survey records, ensuring accurate alignment between the time series and drainage data. Based on this time alignment, a nonlinear time-series correlation model was constructed with coal mining progress as the independent variable and drainage flow increment as the dependent variable. Sliding window correlation analysis and Granger causality tests were used to identify the lag response characteristics of coal mining disturbance to water inflow. It was found that the water inflow increment typically peaks 3 to 7 days after the mining face advances and exhibits an approximately power-law relationship with the unit mining volume. Based on this correlation, the cumulative mining volume was introduced as a... Using the benchmark water inflow as the core driving factor, an autoregressive integral moving average-exogenous variable model (ARIMAX) with memory effect is constructed, and an exponentially decaying weight function is embedded to characterize the decay law of mining impact over time, forming a coal mining-water inflow evolution model. The model automatically updates the training samples daily and performs online optimization by minimizing the prediction error (MAE) to ensure that it can dynamically adapt to long-term disturbances such as adjustments in coal mining methods, changes in coal seam thickness, and aquifer evolution. After the model stabilizes and converges, the daily advance speed and number of working faces in the coal mining plan for the next 5 years are input. According to reports, the driving model continuously predicts the total annual mine water inflow, and the output results include the average annual water inflow, monthly distribution curves, and 95% confidence intervals. This prediction method breaks through the traditional linear extrapolation mode of "substituting static for dynamic" and establishes a physical mechanism-driven prediction chain between mining disturbance and water inflow response in the pumped storage scenario. This upgrades water source assessment from "historical statistics" to "process prediction", significantly improving the foresight, accuracy, and engineering adaptability of the system's water supply capacity judgment, and providing a solid and reliable dynamic water source basis for the long-term operational reliability and large-scale design of pumped storage systems.

[0053] As an optional embodiment, the following steps can be used to obtain the location assessment result of the distributed pumped storage system based on the available space volume, the first judgment result, subsidence information, total inflow, and head height. This includes: comparing the available space volume with a preset minimum volume threshold to determine whether the coal mine goaf has the conditions for construction as a lower reservoir, thus obtaining a second judgment result; matching the subsidence area and subsidence depth in the subsidence information with preset lower area limits and preset effective water depth requirements, respectively, to determine whether the coal mine subsidence area can serve as a water storage container for the upper reservoir, thus obtaining a third judgment result; determining whether the total inflow meets the minimum water supply guarantee required for the operation of the distributed pumped storage system, thus obtaining a fourth judgment result; comparing the head height with the minimum head height threshold corresponding to the distributed pumped storage system, thus obtaining a fifth judgment result; and determining the location assessment result based on the first, second, third, fourth, and fifth judgment results.

[0054] Optionally, the calculated usable volume of the mined-out area is first compared with a preset minimum volume threshold of 500,000 cubic meters. If the volume is less than this threshold, the mined-out area is deemed unable to provide sufficient energy storage capacity, and the second judgment result is "not meeting the conditions for lower reservoir construction." If the volume is not less than 500,000 cubic meters, the second judgment result is "meeting the conditions for lower reservoir construction," allowing the process to proceed to the next evaluation stage. Subsequently, the subsidence information obtained in step S208 is analyzed, and the effective area and maximum subsidence depth of the subsidence area are extracted and matched with a preset lower limit of 100,000 square meters and an effective water depth requirement of 4 meters, respectively. If the subsidence area is less than 100,000 square meters or the effective water depth is less than 4 meters, the third judgment result is "not meeting the requirements for upper reservoir storage container," indicating that it cannot form a stable and sufficient water storage space. If both indicators meet the requirements, the third judgment result is "meeting the conditions for upper reservoir construction." Next, the predicted annual average water inflow from step S214 is compared with the minimum water supply guarantee required for system operation, 4.4 million cubic meters per year (i.e., average flow rate ≥ 500 m³ / h). If the predicted water inflow is lower than this value, the fourth judgment result is "insufficient water supply guarantee," indicating that the system may face the risk of water supply interruption during the dry season or periods of intensified mining. If the water supply guarantee is met or exceeded, the fourth judgment result is "sufficient water supply guarantee," which can support the continuous operation of the system. Then, the design head determined in step S216 is compared with the minimum head height threshold of 80 meters required for the distributed pumped storage system. If the head is lower than 80 meters, the system's energy conversion efficiency will decrease significantly, making it economically infeasible, and the fifth judgment result is "insufficient head." If the head is not lower than 80 meters, the fifth judgment result is "head meets requirements." After obtaining the first to fifth judgment results, the system activates the logical decision engine: if the first judgment result is "structurally unstable" or any of the second, third, fourth, or fifth judgment results is "not satisfied", the evaluation is immediately terminated, and the location evaluation result is "unsuitable for construction"; only when the first judgment result is "structurally stable" and all the second, third, fourth, and fifth judgment results are "satisfied", the system determines that the location meets the comprehensive construction conditions, outputs the location evaluation result as "Class I suitable for construction", and automatically generates a complete evaluation report including the compliance status of each parameter, the margin of deviation from the critical value, risk warnings, and recommended construction scale; this evaluation logic abandons the fuzzy evaluation method of single indicator weighted average, ensuring that the basic conditions for the safety and operation of each project are strictly verified, building a safety bottom line for the scientific site selection of the mine pumping system, and guaranteeing the technical feasibility, operational safety, and economic sustainability of the project.

[0055] As an optional embodiment, this can be achieved through the following steps: It further includes: acquiring coal mining operation data, wherein the coal mining operation data includes the coal mining operation advance speed and mining sequence; predicting the volume expansion trend of the coal mine goaf based on the coal mining operation data; and adjusting the location assessment results based on the volume expansion trend.

[0056] Optionally, coal mining operation data is acquired, including the average daily advance speed of the coal face, monthly mining plans, coal seam stratification mining sequence, and mining area succession layout for the next three years. The data originates from the mine's intelligent scheduling system, geological exploration reports, and production plan ledgers, ensuring accuracy to the day in the time dimension and refinement to the mining area unit in the spatial dimension. Based on the aforementioned coal mining operation data, a dynamic prediction model for the expansion of the goaf volume is constructed. This model uses the coal face advance speed as the time-driven variable and the mining cross-sectional area and goaf porosity (taken as 0.35-0.48) as geometric parameters, and is obtained through integration. The system continuously accumulates the newly added goaf volume in each future time period to form a time-volume expansion curve. The model simultaneously incorporates geological structural constraints; when the predicted expansion path intersects with faults, collapse columns, or strong aquifers, a spatial avoidance radius is automatically introduced to ensure that the predicted volume only includes structurally usable and safe areas. Based on this volume expansion trend, the system dynamically updates the location assessment results generated in step S218: in areas initially assessed as "Class I suitable" or "Class II conditionally suitable," if the predicted goaf volume will increase by more than 20% of the current assessed volume within the next 12 months, the system automatically triggers a "..." The system will issue an "Expansion Potential Early Warning" warning, upgrading the assessment level to "Class I Preferred Expansion Area" and marking it as a future expansion reserve area. It will also recommend reserving space for water pipeline interfaces, unit foundations, and control signal channels during the initial construction phase. If the predicted volume exceeds 1.5 million cubic meters within three years, the system will automatically generate a "Medium-Sized System Expansion Path Map," planning to add pump-turbine unit modules in stages during the second and third years to achieve a smooth evolution of pumped storage capacity from 10MW to 20MW. Conversely, if the predicted expansion of the goaf tends to stagnate or the effective volume shrinks due to changes in geological conditions, the system will downgrade the assessment level from "Class I Suitable Expansion Area" to "Class I Preferred Expansion Area." The "suitable" category was adjusted to "Class II with conditions," and it was suggested that a regulating reservoir or external water source should be configured in advance to maintain the stability of the system operation. This dynamic adjustment process is automatically executed once a quarter, integrating the latest coal mining plan and measured goaf expansion data to form a closed-loop optimization mechanism of "assessment-prediction-feedback-revision." This makes the location assessment results no longer static conclusions, but dynamic decision-making basis that evolves continuously with the coal mining process. This method greatly improves the adaptability, economy, and engineering feasibility of the system throughout its entire life cycle, providing a new type of energy storage solution that is scalable, iterative, and sustainable for the energy transformation of coal mining areas.

[0057] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0058] Through the above description of the embodiments, those skilled in the art can clearly understand that the location assessment method for distributed pumped storage systems according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0059] According to embodiments of the present invention, a distributed pumped storage system location assessment device is also provided for implementing the above-described distributed pumped storage system location assessment method. Figure 3 This is a structural block diagram of a distributed pumped storage system location assessment device provided according to an embodiment of the present invention, such as... Figure 3 As shown, the distributed pumped storage system location assessment device includes: a first acquisition module 302, a first determination module 304, a judgment module 306, a second acquisition module 308, a second determination module 310, a third acquisition module 312, a prediction module 314, a third determination module 316, and an assessment module 318. The distributed pumped storage system location assessment device will be described below.

[0060] The first acquisition module 302 is used to acquire three-dimensional point cloud data and microseismic signals of the coal mine goaf in the coal mine space.

[0061] The first determining module 304 is used to determine the available space volume of the coal mine goaf based on three-dimensional point cloud data.

[0062] This module abandons the traditional rough method of estimation based on geological profiles or empirical formulas. For the first time, it realizes a full three-dimensional, high-precision, and physically constrained automated quantitative assessment of available energy storage space in coal mine goaf. It provides solid, reliable, and traceable spatial baseline data for the capacity design of pumped storage systems, ensuring the scientific nature and safety of engineering scale design and avoiding the risk of insufficient storage capacity or structural instability caused by spatial misjudgment.

[0063] The judgment module 306 is used to determine whether the coal mine goaf is stable based on the microseismic signal and obtain the first judgment result.

[0064] This module establishes a rigid safety criterion based on the actual fracture behavior of rock mass for the safe site selection of pumped storage systems in coal mining areas. It avoids the major engineering risks caused by relying solely on geological experience or static model inferences and is a decision-making module to ensure the safe operation of the system throughout its entire life cycle.

[0065] The second acquisition module 308 is used to acquire topographic data of the coal mine subsidence area in the coal mine space.

[0066] This module overcomes the limitations of traditional manual surveying, which is characterized by low efficiency, narrow coverage, and susceptibility to weather conditions. It achieves fully automated, highly timely, and millimeter-level precision full-domain perception of the topography of coal mine subsidence areas, providing real, continuous, and quantifiable spatial baseline data for reservoir site selection. It is a cornerstone perception unit supporting the feasibility assessment of pumped storage systems, significantly improving the scientific nature and comprehensiveness of resource assessment and the feasibility of engineering implementation.

[0067] The second determining module 310 is used to determine the subsidence information of the coal mine subsidence area based on terrain data, wherein the subsidence information includes the subsidence area and the subsidence depth.

[0068] This module breaks through the traditional extensive mode of relying on manual cross-section mapping or experience to estimate subsidence. It realizes fully automatic subsidence information extraction driven by high-precision three-dimensional terrain data and based on spatial computing and physical constraints. It provides accurate, reliable and traceable input parameters for reservoir capacity calculation, seepage prevention scheme design and water level scheduling strategy formulation. It fundamentally ensures the scientificity and safety of reservoir site selection for distributed pumped storage systems and greatly improves the conversion efficiency of abandoned space resources in mining areas and the success rate of project implementation.

[0069] The third acquisition module 312 is used to acquire drainage flow data of the mine shaft in the coal mine space.

[0070] This module does not rely on manual inspections and records, completely abandoning the traditional extensive model of "sampling flow measurement and monthly summary". It realizes automated, digital and structured collection of coal mine water inflow throughout the entire process, all elements and all time periods. It provides a real, continuous and traceable original data foundation for the reliability assessment of water sources in pumped storage systems, ensuring that the water resource basis of the system design no longer depends on assumptions or empirical estimates, but is based on real, dynamic and verifiable engineering data.

[0071] The prediction module 314 is used to predict the total inflow based on drainage flow data.

[0072] This module breaks through the inherent limitations of traditional pumping and storage systems that rely on natural hydrological conditions and cannot be adapted to mining areas. It achieves accurate water source prediction based on actual mining behavior and hydrological response patterns, providing a stable, reliable, and quantifiable basis for water resource security for mine pumping and storage systems. It significantly improves the scientific, forward-looking, and engineering feasibility of resource utilization of abandoned coal mine spaces.

[0073] The third determining module 316 is used to determine the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf.

[0074] This module breaks through the geographical limitations of traditional pumped storage systems that rely on natural mountain elevation differences. It precisely transforms the man-made abandoned spaces of coal mine goaf and subsidence areas into quantifiable hydraulic potential energy resources, giving distributed pumped storage systems the physical basis for engineering implementation in mining areas without natural terrain advantages.

[0075] The evaluation module 318 is used to obtain the location evaluation results of the distributed pumped storage system based on the available space volume, judgment results, subsidence information, total inflow and head height.

[0076] This module comprehensively weighs safety, water resources, scale, efficiency, and maintainability, and outputs a system-level assessment conclusion that is truly applicable to the actual conditions of coal mining areas, feasible, constructable, and operable in the long term. It provides executable engineering criteria for the scientific deployment of distributed pumped storage systems in coal mining areas.

[0077] It should be noted that the first acquisition module 302, the first determination module 304, the judgment module 306, the second acquisition module 308, the second determination module 310, the third acquisition module 312, the prediction module 314, the third determination module 316, and the evaluation module 318 mentioned above correspond to steps S202 to S218 in the embodiments. Multiple modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0078] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0079] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the distributed pumped storage system location assessment method and device in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned distributed pumped storage system location assessment method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0080] The processor can access information and application programs stored in the memory via a transmission device to execute the following steps: acquiring three-dimensional point cloud data and microseismic signals of the coal mine goaf in the coal mine space; determining the available space volume of the coal mine goaf based on the three-dimensional point cloud data; determining whether the coal mine goaf is stable based on the microseismic signals, and obtaining a first judgment result; acquiring topographic data of the coal mine subsidence area in the coal mine space; determining the subsidence information of the coal mine subsidence area based on the topographic data, wherein the subsidence information includes the subsidence area and subsidence depth; acquiring drainage flow data of the mine shaft in the coal mine space; predicting the total inflow based on the drainage flow data; determining the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf; and obtaining the location assessment result of the distributed pumped storage system based on the available space volume, judgment result, subsidence information, total inflow, and head height.

[0081] Optionally, the processor may also execute program code for the following steps: based on microseismic signals, determining whether the coal mine goaf is stable and obtaining a first determination result, including: identifying microseismic signals and extracting target microseismic events; performing three-dimensional spatial positioning of the target microseismic events to determine their distribution coordinates in the coal mine goaf; determining the spatial distribution characteristics of the target microseismic events based on the distribution coordinates; obtaining the event characteristics corresponding to the target microseismic events, wherein the event characteristics include cumulative magnitude, total energy release, event frequency, and time evolution rate; and obtaining the spatial distribution characteristics of the coal mine goaf. The rock mass mechanics parameters, including elastic modulus, Poisson's ratio, internal friction angle, and cohesion, are used to establish a three-dimensional numerical simulation model. Boundary conditions and initial disturbance sources are determined based on spatial distribution characteristics and event characteristics. Based on the boundary conditions and initial disturbance sources, the three-dimensional numerical simulation model is used to iteratively calculate the stress redistribution and plastic zone evolution of the rock mass in the coal mine goaf, obtaining the surrounding rock stress field and displacement field. Based on the surrounding rock stress field and displacement field, the target safety factor of the coal mine goaf is calculated. The first judgment result is determined according to the target safety factor.

[0082] Optionally, the processor may also execute program code that includes: determining that the first judgment result is that the coal mine goaf structure is unstable when the target safety factor is less than the preset safety threshold; and determining that the first judgment result is that the coal mine goaf structure is stable when the target safety factor is not less than the preset safety threshold.

[0083] Optionally, the processor may also execute program code for the following steps: predicting the total inflow based on drainage flow data, including: determining the baseline inflow based on drainage flow data; acquiring historical coal mining data; establishing a time-series correlation between coal mining progress and changes in inflow based on historical coal mining data and drainage flow data; constructing an evolutionary model based on the time-series correlation and the baseline inflow; and predicting the total inflow based on the evolutionary model.

[0084] Optionally, the processor may also execute program code for the following steps: based on the available space volume, the first judgment result, subsidence information, total inflow, and head height, obtain the location assessment result of the distributed pumped storage system, including: comparing the available space volume with a preset minimum volume threshold to determine whether the coal mine goaf has the conditions for construction as a lower reservoir, and obtaining the second judgment result; matching the subsidence area and subsidence depth in the subsidence information with the preset lower limit of area and the preset effective water depth requirement, respectively, to determine whether the coal mine subsidence area can serve as a water storage container for the upper reservoir, and obtaining the third judgment result; determining whether the total inflow meets the minimum water supply guarantee required for the operation of the distributed pumped storage system, and obtaining the fourth judgment result; comparing the head height with the minimum head height threshold corresponding to the distributed pumped storage system, and obtaining the fifth judgment result; and determining the location assessment result based on the first judgment result, the second judgment result, the third judgment result, the fourth judgment result, and the fifth judgment result.

[0085] Optionally, the processor may also execute program code that includes the following steps: acquiring coal mining operation data, wherein the coal mining operation data includes the coal mining operation advance speed and mining sequence; predicting the volume expansion trend of the coal mine goaf based on the coal mining operation data; and adjusting the location assessment results based on the volume expansion trend.

[0086] This invention provides a scheme for intelligent site selection and feasibility assessment of distributed pumped storage systems in coal mines, integrating multi-source geological sensing, dynamic mining prediction, and closed-loop decision-making with safety constraints. The scheme involves: collecting three-dimensional point cloud data and microseismic signals from the coal mine goaf; determining the usable volume of the goaf based on the three-dimensional point cloud data; determining the stability of the goaf based on the microseismic signals; collecting topographic data from the coal mine subsidence area; determining the subsidence information of the subsidence area, including subsidence area and depth, based on the topographic data; collecting mine drainage flow data; predicting the total inflow based on the drainage flow data; and determining the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the goaf. Based on available space volume, judgment results, subsidence information, total water inflow, and water head height, the location assessment results of the distributed pumped storage system are obtained. This achieves the goal of scientifically identifying whether a mining area has the conditions for constructing a distributed pumped storage system. This realizes the technical effect of transforming from experience-based judgment to data-driven, from static assessment to dynamic adaptation, and from single indicators to multi-dimensional coupling intelligent assessment. Furthermore, it solves the current technical problems of lacking multi-dimensional quantitative assessment capabilities for resources in mining subsidence areas and the resulting inability to accurately select sites for pumped storage systems in coal mining areas, leading to high safety risks and low resource utilization rates due to blind construction.

[0087] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0088] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the distributed pumped storage system location assessment method provided in the above embodiments.

[0089] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0090] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring three-dimensional point cloud data and microseismic signals of the coal mine goaf in the coal mine space; determining the available space volume of the coal mine goaf based on the three-dimensional point cloud data; determining whether the coal mine goaf is stable based on the microseismic signals, and obtaining a first judgment result; acquiring topographic data of the coal mine subsidence area in the coal mine space; determining the subsidence information of the coal mine subsidence area based on the topographic data, wherein the subsidence information includes the subsidence area and subsidence depth; acquiring drainage flow data of the mine in the coal mine space; predicting the total inflow based on the drainage flow data; determining the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf; and obtaining the location assessment result of the distributed pumped storage system based on the available space volume, judgment result, subsidence information, total inflow, and head height.

[0091] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining whether the coal mine goaf is stable based on microseismic signals and obtaining a first determination result, including: identifying microseismic signals and extracting target microseismic events; performing three-dimensional spatial positioning of the target microseismic events to determine the distribution coordinates of the target microseismic events in the coal mine goaf; determining the spatial distribution characteristics of the target microseismic events based on the distribution coordinates; obtaining the event characteristics corresponding to the target microseismic events, wherein the event characteristics include cumulative magnitude, total energy release, event frequency, and time evolution rate; obtaining Rock mechanics parameters from the coal mine goaf are obtained, including elastic modulus, Poisson's ratio, internal friction angle, and cohesion. A three-dimensional numerical simulation model is established based on these parameters. Boundary conditions and initial disturbance sources are determined based on spatial distribution and event characteristics. The three-dimensional numerical simulation model is then used to iteratively calculate the stress redistribution and plastic zone evolution of the rock mass in the coal mine goaf, obtaining the surrounding rock stress field and displacement field. Based on the surrounding rock stress field and displacement field, the target safety factor for the coal mine goaf is calculated. Finally, the first judgment result is determined according to the target safety factor.

[0092] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: including: determining that the structure of the coal mine goaf is unstable when the target safety factor is less than a preset safety threshold; and determining that the structure of the coal mine goaf is stable when the target safety factor is not less than the preset safety threshold.

[0093] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: predicting the total inflow based on drainage flow data, including: determining a baseline inflow based on drainage flow data; acquiring historical coal mining data; establishing a temporal correlation between coal mining progress and changes in inflow based on historical coal mining data and drainage flow data; constructing an evolutionary model based on the temporal correlation and the baseline inflow; and predicting the total inflow based on the evolutionary model.

[0094] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining the location assessment result of the distributed pumped storage system based on the available space volume, the first judgment result, subsidence information, total inflow, and head height, including: comparing the available space volume with a preset minimum volume threshold to determine whether the coal mine goaf has the conditions for construction as a lower reservoir, and obtaining the second judgment result; matching the subsidence area and subsidence depth in the subsidence information with a preset lower limit of area and a preset effective water depth requirement, respectively, to determine whether the coal mine subsidence area can serve as a water storage container for the upper reservoir, and obtaining the third judgment result; determining whether the total inflow meets the minimum water supply guarantee required for the operation of the distributed pumped storage system, and obtaining the fourth judgment result; comparing the head height with the minimum head height threshold corresponding to the distributed pumped storage system, and obtaining the fifth judgment result; and determining the location assessment result based on the first judgment result, the second judgment result, the third judgment result, the fourth judgment result, and the fifth judgment result.

[0095] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: further including: acquiring coal mining operation data, wherein the coal mining operation data includes coal mining operation advance speed and mining sequence; predicting the volume expansion trend of the coal mine goaf based on the coal mining operation data; and adjusting the location assessment results based on the volume expansion trend.

[0096] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: collect three-dimensional point cloud data and microseismic signals of a coal mine goaf in a coal mine space; determine the available space volume of the coal mine goaf based on the three-dimensional point cloud data; determine whether the coal mine goaf is stable based on the microseismic signals, and obtain a first judgment result; collect topographic data of a coal mine subsidence area in a coal mine space; determine the subsidence information of the coal mine subsidence area based on the topographic data, wherein the subsidence information includes subsidence area and subsidence depth; collect drainage flow data of the mine shaft in a coal mine space; predict the total inflow based on the drainage flow data; determine the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf; and obtain the location assessment result of the distributed pumped storage system based on the available space volume, the judgment result, the subsidence information, the total inflow, and the head height.

[0097] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0098] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0100] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0103] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for location assessment of a distributed pumped storage system, characterized in that, include: Collect three-dimensional point cloud data and microseismic signals of the coal mine goaf in the coal mine space; Based on the three-dimensional point cloud data, the available space volume of the coal mine goaf is determined; Based on the microseismic signal, it is determined whether the coal mine goaf is stable, and a first judgment result is obtained; Collect topographic data of the coal mine subsidence area in the coal mine space; Based on the topographic data, subsidence information of the coal mine subsidence area is determined, wherein the subsidence information includes subsidence area and subsidence depth; Collect drainage flow data of the mine shafts in the coal mine space; Based on the drainage flow data, the total inflow is predicted; The head height of the distributed pumped storage system is determined based on the vertical height difference between the coal mine subsidence area and the coal mine goaf. Based on the available space volume, the judgment result, the subsidence information, the total inflow, and the water head height, the location assessment result of the distributed pumped storage system is obtained.

2. The method according to claim 1, characterized in that, The step of determining whether the coal mine goaf is stable based on the microseismic signal, and obtaining a first determination result, includes: Identify the microseismic signals and extract the target microseismic events; The target microseismic event is located in three-dimensional space to determine its distribution coordinates in the coal mine goaf. Based on the distribution coordinates, the spatial distribution characteristics of the target microseismic event are determined; Obtain the event characteristics corresponding to the target microseismic event, wherein the event characteristics include cumulative magnitude, total energy release, event frequency, and time evolution rate; Obtain the rock mass mechanical parameters in the coal mine goaf, wherein the rock mass mechanical parameters include elastic modulus, Poisson's ratio, internal friction angle and cohesion; Based on the aforementioned rock mass mechanical parameters, a three-dimensional numerical simulation model was established; Based on the spatial distribution characteristics and the event characteristics, the boundary conditions and the initial disturbance source are determined; Based on the boundary conditions and the initial disturbance source, the three-dimensional numerical simulation model is driven to iteratively calculate the stress redistribution and plastic zone evolution of the rock mass in the coal mine goaf, and obtain the surrounding rock stress field and displacement field. Based on the surrounding rock stress field and the displacement field, the target safety factor of the coal mine goaf is calculated; The first judgment result is determined based on the target safety factor.

3. The method according to claim 2, characterized in that, Determining the first judgment result based on the target safety factor includes: If the target safety factor is less than a preset safety threshold, the first judgment result is determined to be that the coal mine goaf structure is unstable. If the target safety factor is not less than the preset safety threshold, the first judgment result is determined to be that the coal mine goaf structure is stable.

4. The method according to claim 1, characterized in that, The prediction of total water inflow based on the drainage flow data includes: Based on the drainage flow data, a baseline inflow rate is determined; Obtain historical coal mining data; Based on the historical coal mining data and the drainage flow data, a time-series correlation between coal mining progress and water inflow changes is established. An evolutionary model is constructed based on the aforementioned temporal correlation and the aforementioned baseline inflow rate; Based on the evolution model, the total inflow is predicted.

5. The method according to claim 1, characterized in that, The location assessment result of the distributed pumped storage system, based on the available space volume, the first judgment result, the subsidence information, the total inflow, and the water head height, includes: The available space volume is compared with a preset minimum volume threshold to determine whether the coal mine goaf has the conditions for construction as a reservoir, and a second judgment result is obtained. The subsidence area and subsidence depth in the subsidence information are matched with the preset lower limit of area and the preset effective water depth requirement, respectively, to determine whether the coal mine subsidence area can be used as a water storage container for the upper reservoir, and a third judgment result is obtained. Determine whether the total inflow meets the minimum water supply guarantee required for the operation of the distributed pumped storage system, and obtain the fourth determination result; The water head height is compared with the minimum water head height threshold corresponding to the distributed pumped storage system to obtain the fifth judgment result; Based on the first judgment result, the second judgment result, the third judgment result, the fourth judgment result, and the fifth judgment result, the location assessment result is determined.

6. The method according to any one of claims 1 to 5, characterized in that, Also includes: Acquire coal mining operation data, wherein the coal mining operation data includes the coal mining operation advance speed and mining sequence; Based on the coal mining data, the volume expansion trend of the goaf in the coal mine is predicted; The location assessment results are adjusted based on the volume expansion trend.

7. A location assessment device for a distributed pumped storage system, characterized in that, include: The first acquisition module is used to acquire three-dimensional point cloud data and microseismic signals of the coal mine goaf in the coal mine space. The first determining module is used to determine the available space volume of the coal mine goaf based on the three-dimensional point cloud data. The judgment module is used to determine whether the coal mine goaf is stable based on the microseismic signal, and obtain a first judgment result; The second acquisition module is used to acquire topographic data of the coal mine subsidence area in the coal mine space; The second determining module is used to determine the subsidence information of the coal mine subsidence area based on the terrain data, wherein the subsidence information includes subsidence area and subsidence depth; The third acquisition module is used to acquire drainage flow data of the mine shaft in the coal mine space; The prediction module is used to predict the total inflow based on the drainage flow data; The third determining module is used to determine the head height of the distributed pumped storage system based on the vertical height difference between the coal mine subsidence area and the coal mine goaf. The evaluation module is used to obtain the location evaluation result of the distributed pumped storage system based on the available space volume, the judgment result, the subsidence information, the total inflow, and the water head height.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the distributed pumped storage system location assessment method according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the location assessment method for a distributed pumped storage system as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the location assessment method for a distributed pumped storage system as described in any one of claims 1 to 6.

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