An underground water resource deployment and decision analysis method for water-deficient mining areas

Abstract

The application provides a kind of water resource deployment and decision analysis method in water-deficient mining area underground, relating to the technical field of mining area water resource management and intelligent decision-making, comprising: obtaining sensing data in a specified area in a mine; constructing a steady-state seepage flow field of the mine goaf; constructing a water resource carrying capacity evaluation model; constructing a water demand model for different areas in the mine; obtaining a deployment demand distribution vector and a deployment demand sequence for different areas in the mine according to the water demand model; determining a priority deployment vector for each mine area in combination with the deployment priority of different areas in the mine; constructing a multi-objective optimization model, and calculating the optimal water resource deployment strategy based on a preselected optimization algorithm; based on the optimal water resource deployment strategy, controlling the action of water storage and drainage equipment, and modifying the aforementioned model or parameters according to the execution result data. The application scheme realizes dynamic modification and real-time response of the deployment strategy, ensuring accurate supply and scheduling optimization of underground water resources in water-deficient mining areas.

Description

Technical Field

[0001] This application relates to the field of water resource management and intelligent decision-making technology in mining areas, specifically, to a method for the allocation and decision analysis of underground water resources in water-deficient mining areas. Background Technology

[0002] In typical water-scarce mining areas such as western my country and energy bases, water resources are not only a fundamental guarantee for safe production and domestic water use in coal mines, but also a core element for maintaining regional ecosystems and social development. However, with the continuous increase in the intensity of coal mining, the structure of underground aquifers is prone to non-uniform damage, and the water conductivity of fractures is significantly enhanced, leading to an imbalance in the original water resource occurrence, a decline in storage and drainage capacity, and an increasingly prominent contradiction between supply and demand. Frequent hydrological problems such as water accumulation in goaf areas and the risk of water inrush exacerbate the difficulty of underground water resource regulation.

[0003] Currently, underground water supply in mines mainly relies on a combination of surface water supply, local recycling, and zoned scheduling. However, these methods generally have the following shortcomings: (1) The water resource allocation process lacks systematicness and intelligence, mainly relying on manual experience or static scheduling plans, which makes it difficult to meet the precise water supply requirements of multiple working faces, multiple time periods, and multiple needs; (2) There is a lack of information closed-loop mechanism based on feedback from actual operating status, which makes it impossible to realize real-time correction and adaptive adjustment of water resource regulation strategies; (3) With the accelerated development trend of intelligent coal mines, the mine production system has put forward higher requirements for the real-time performance, safety, and intelligence level of water resource allocation. Traditional fixed water transfer paths and static zoned management methods are difficult to adapt to the instability of the hydrodynamic system caused by underground mining disturbances, resulting in problems such as water accumulation at the bottom of the mine, local water shortage, and frequent start-stop of water pumps, which seriously affect the safe operation of the mine and the efficiency of the water supply and drainage system. Summary of the Invention

[0004] This application aims to address key technical problems in the management of underground water resources in the Kuishui mining area, such as inaccurate identification of storage and discharge capacity, static and rigid allocation schemes, and lack of intelligent feedback in the decision-making mechanism. In turn, it provides a method for underground water resource allocation and decision analysis in the Kuishui mining area.

[0005] This application provides a method for the allocation and decision analysis of underground water resources in a water-deficient mining area, including:

[0006] Step 1: Obtain hydrological data, structural disturbance parameters, and operating parameters of storage and drainage equipment in a designated area of ​​the mine to generate sensing data;

[0007] Step 2: Based on the sensed data and combined with mine survey information, construct a steady-state seepage flow field in the mine goaf; identify at least one dominant water path formed by fractures according to the steady-state seepage flow field, and obtain the effective water storage capacity, maximum water delivery, and path response lag time of all dominant water paths;

[0008] Step 3: Based on the effective water storage capacity, the maximum water conveyance, and the path response lag time, and combined with the water conveyance efficiency coefficient and rated capacity of the water storage and drainage equipment, and the expected allocation time, a water resource carrying capacity assessment model is constructed. The water resource carrying capacity assessment model is used to determine the water resource carrying capacity of different areas of the mine and the mine under the expected allocation time.

[0009] Step 4: Based on the preset work plan, mining equipment operating parameters, and historical water usage records, construct water demand models for different areas of the mine; obtain the allocation demand distribution vector and allocation demand sequence for different areas of the mine based on the water demand models; determine the priority allocation vector for each area of ​​the mine by combining the allocation priority of different areas of the mine.

[0010] Step 5: Based on the water resource carrying capacity assessment model and the water demand model for different areas of the mine, determine multiple objective optimization functions and constraints, construct a multi-objective optimization model, and calculate the optimal water resource allocation strategy based on the pre-selected optimization algorithm. The multiple objective optimization functions include: a prediction deviation minimization objective function, an allocation energy consumption minimization objective function, and an allocation priority weighted deviation minimization objective function. The constraints include: water resource carrying capacity boundary conditions for different areas of the mine, total allocation volume boundary conditions for the mine, dominant water path capacity constraints, and water balance constraints for the water diversion path.

[0011] Step 6: Based on the optimal water resource allocation strategy, control the operation of the storage and drainage equipment; during the execution of the optimal water resource allocation strategy, collect execution result data in real time, and after correcting the water resource carrying capacity assessment model, the water demand model, the maximum water delivery volume and / or the priority allocation vector of each mine area based on the execution result data, recalculate the optimal water resource allocation strategy.

[0012] Preferably, in step one, the hydrological data includes: water level and water pressure in the water source area;

[0013] The structural disturbance parameters include: the crack opening in crack-prone areas and micro-seismic events caused by mining in the working face;

[0014] The operating parameters of the storage and drainage equipment include: the operating status of the storage and drainage equipment, the water flow rate, the water volume, and the scheduling actions;

[0015] The hydrological data, structural disturbance parameters, and operating parameters of the storage and drainage equipment are structured and processed to obtain the sensing data based on the location and acquisition time of the data.

[0016] Preferably, in step two: after constructing the steady-state seepage flow field of the goaf area of ​​the mine based on the sensing information and mine survey information obtained in step one, the discrete element method is used to deploy tracer particles in the steady-state seepage flow field, and a reverse tracer operation is performed based on the steady-state seepage flow field to obtain the spatiotemporal distribution of the migration trajectory of the tracer particles in the steady-state seepage flow field.

[0017] Based on the spatiotemporal distribution of the tracer particle migration trajectories, the dominant water path in the steady-state seepage flow field is determined. and the path response lag time of the dominant water path ;

[0018] Around the dominant water path, using high-fracture-density finite elements as water storage units, the effective water storage capacity is calculated using the following formula: ,in: The effective water storage capacity is... For the first The effective water content of a high-crack-density finite element For the volume of high-crack-density lamellae, k =1,2,…, n The high-crack-density fragments refer to fragments with a crack density higher than a set density threshold.

[0019] The maximum water delivery capacity is calculated using the following formula: ;in, The maximum water delivery capacity, The water flux per unit area of ​​the water-conducting channel between two fractured granular elements. Let be the cross-sectional area of ​​the water-conducting channel between the two fractured granular elements. , This is the index number of the fracture fragment. express Time of the first Water transport flux of the main water pathway.

[0020] Preferably, in step three, the water resource carrying capacity assessment model is expressed as follows:

[0021] ;

[0022] in, For the water resource carrying capacity of all dominant water pathways, For the first The dominant water pathway during the expected allocation time Water resource carrying capacity Not less than η i The water conveyance efficiency coefficient of the storage and drainage equipment. For the first The main water path corresponds to the maximum drainage capacity of the storage and drainage equipment during stable operation; For the first The effective water storage capacity of the main water pathway For the first The equivalent head propagation length of the dominant water path, The velocity of water head propagation.

[0023] Preferably, in step four, for the first For each mining area, the pre-set work plan includes: work intensity. The operating parameters of the mining equipment include: the number of operating devices. and water consumption intensity per unit of equipment The historical water usage records include: historical average water usage. and historical adjustment weighting coefficient ω ;

[0024] The water demand model for different areas of the mine includes:

[0025] Construct the first Each mining area Water demand model at any given time: ;in, ; β it For the first The allocation and response coefficient for each mine area;

[0026] according to A water demand model for each mining area at any given time was constructed. Distribution vector of allocation demand in each mine area at any given time: ;

[0027] by The starting time is [time], and the prediction time window is [time]. The sequence of allocation needs for each mine area: ;

[0028] For the first Priority factor for allocation and distribution in individual mining areas Construct priority allocation vectors for each mine area: .

[0029] Preferably, in step five:

[0030] The objective function for minimizing the prediction bias is expressed as: ; For the first The actual water allocation volume for each mining area;

[0031] The objective function for minimizing energy consumption during allocation is expressed as: ; To allocate path energy consumption weights for different paths;

[0032] The objective function for minimizing the weighted deviation of allocation priorities is expressed as: .

[0033] Preferably, in step five:

[0034] The water resource carrying capacity boundary conditions for different areas of the mine are expressed as follows: ;

[0035] The boundary conditions for the total allocation of the mine are expressed as follows: ;in, The demand for water allocation in mines is calculated as follows:

[0036] Construct a mine water demand forecasting model: N represents the total number of mining areas;

[0037] After calculating the emergency redundancy of the aforementioned mine water demand prediction model, the mine water allocation demand calculation model is obtained: ;in, ; The length of the sliding window. for Mine water demand forecast for a given period This is the redundancy adjustment coefficient;

[0038] The dominant water path capacity constraint is expressed as follows: ;

[0039] The water balance constraint condition of the water diversion path is expressed as follows: .

[0040] Preferably, the construction of the multi-objective optimization model in step five includes:

[0041] by The multi-objective optimization function is represented by the following, which is then normalized: ; For the first The weight coefficients of each objective function. Indicates the first The maximum expected value of an objective function;

[0042] The multi-objective optimization model is expressed as follows:

[0043] .

[0044] Preferably, in step five, the pre-selected optimization algorithm includes: a swarm intelligence algorithm or a multi-objective optimization algorithm;

[0045] After inputting the multi-objective optimization model into the pre-selected optimization algorithm for iterative computation, the control objective is obtained: the first... The optimal water allocation volume for each mining area, the optimal path flow for each water allocation path, the objective function allocation performance index, the optimal priority allocation priority factor, and the predicted allocation energy consumption.

[0046] The optimal water resource allocation strategy is generated based on the control objective. The optimal water resource allocation strategy includes: valve opening degree, start / stop status of storage and drainage equipment, and operating parameters on each water allocation path.

[0047] Preferably, in step six, the execution result data includes: the actual water allocation volume in the mine area, the actual flow rate of the water allocation path, the actual allocation energy consumption, the completion rate of water supply demand in the priority mine area and the deviation of the objective function allocation performance index, and the water pressure / level of the water allocation path.

[0048] Based on the execution result data, the water resource carrying capacity assessment model, the water demand model, the maximum water delivery volume, and / or the regional priority allocation vectors for each mine are modified, including:

[0049] The water demand model for the mining area is dynamically adjusted based on the execution result data.

[0050] If the water pressure / water level along the water diversion route exceeds the set limit, the water resource carrying capacity assessment model will be adjusted.

[0051] If the deviation between the actual flow rate and the optimal flow rate of the water diversion path exceeds a set deviation threshold and the duration is longer than a set duration, the maximum water delivery volume is adjusted.

[0052] If the water supply demand completion rate of the priority mining area does not reach the target completion rate, the priority allocation vector of each mining area will be adjusted.

[0053] The technical solution provided in this application has the following technical effects compared with the prior art:

[0054] The method for underground water resource allocation and decision analysis in the Kuishui mining area provided in this application generates sensing data based on hydrological data, structural disturbance parameters, and operating parameters of storage and drainage equipment in a designated area of ​​the mine. Combined with mine survey information, a steady-state seepage flow field is constructed in the goaf area of ​​the mine, thereby obtaining the fracture network evolution results and identifying at least one dominant water path formed by fractures. After obtaining the effective storage capacity, maximum water conveyance, and path response lag time of all dominant water paths, a water resource carrying capacity assessment model is constructed by combining the water conveyance efficiency coefficient and rated capacity of the drainage equipment with the expected allocation duration. This model determines the water resource carrying capacity of different areas of the mine and the mine itself under the expected allocation duration. By pre-setting work plans, operating parameters of mining equipment, and historical water usage records, a water demand model for different areas of the mine is constructed, thereby determining the water demand of each area in the mine and the priority of water allocation for each area. Finally, based on the water resource carrying capacity assessment model and the water demand model of different areas of the mine, multiple objective optimization functions and constraints are determined to construct a multi-objective optimization model. The optimal water resource allocation strategy is then calculated using a pre-selected optimization algorithm. After controlling the operation of the storage and drainage equipment based on the optimal water resource allocation strategy, the system judges whether the optimal water resource allocation strategy meets the requirements by collecting execution result data in real time. If the requirements are not met, one or more of the aforementioned models or parameters can be revised, and the optimal water resource allocation strategy can be recalculated to ensure that the final optimal water resource allocation strategy meets all the requirements for underground water resource allocation in the Kuishui mining area. The above-mentioned scheme of this application couples the fracture network evolution, water conduction path, and various water demand areas to construct a dynamic control scheme for underground water allocation in the Kuishui mining area. This effectively solves the problems of easy water loss and low water storage utilization, realizes dynamic correction and real-time response of the allocation strategy, and improves the adaptive and intelligent level of underground water resource management in the Kuishui mining area. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the process steps of the method for allocating and making decisions on underground water resources in the Kuishui mining area according to an embodiment of this application;

[0056] Figure 2 This is a schematic diagram of the distribution of water head and seepage trajectory in the fracture network obtained during the steady-state seepage flow field simulation process according to one embodiment of this application;

[0057] Figure 3 This is a schematic diagram of the downward seepage volume distribution per unit area at different locations obtained during the steady-state seepage flow field simulation process according to an embodiment of this application;

[0058] Figure 4 This is an embodiment of the present application showing the distribution of seepage intensity at different locations in a goaf, determined based on the steady-state seepage field.

[0059] Figure 5 This is a schematic diagram illustrating the time distribution of groundwater transport based on the use of different fracture channels, according to one embodiment of this application.

[0060] Figure 6 This is a schematic diagram of the allocation path according to one embodiment of this application. Detailed Implementation

[0061] The specific embodiments of this application will be further described below with reference to the accompanying drawings.

[0062] It is readily understood that, based on the technical solution of this application, various structural and implementation methods can be interchanged by those skilled in the art without altering the essential spirit of this application. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this application and should not be considered as the entirety of this application or as limitations or restrictions on the technical solution of the application.

[0063] This embodiment provides a method for the allocation and decision analysis of underground water resources in a water-deficient mining area, such as... Figure 1 As shown, it includes:

[0064] Step 1: Obtain hydrological data, structural disturbance parameters, and operating parameters of storage and drainage equipment in a designated area of ​​the mine to generate sensing data.

[0065] This step involves multi-source information sensing and data fusion. The defined areas include aquifers, goaf areas, drainage tunnels, the front edge of the mining face, and roadways. Storage and drainage equipment may include artificial dams, reservoirs, and pumping stations. Specifically, this is achieved by deploying various types of sensing devices in these areas to collect data. For example, water level sensors and pore water pressure gauges are deployed in major water source areas such as aquifers, goaf areas, drainage tunnels, the front edge of the mining face, and roadways to acquire changes in water level and pressure. Fracture propagation sensors, such as fissure gauges, distributed optical fibers, or microseismic monitoring devices, are installed in fracture-prone areas to identify the fracture opening status. Flow monitors and valve status recording devices are installed in artificial dams, reservoirs, and pumping stations to collect water flow velocity, water volume, and scheduling behavior. Local disturbance monitoring instruments, such as stress gauges and displacement gauges, are installed at the mining face to sense structural disturbances caused by mining activities. It should be noted that, since this scheme is designed for water resource allocation, the various sensors are deployed in areas with water flow exceeding a certain value to ensure the economic efficiency of the subsequent control measures. The data collected by the aforementioned sensors are categorized according to their physical properties, including: hydrological data, such as water level and pressure in the water source area; structural disturbance parameters, such as the crack opening in crack-prone areas and micro-seismic events caused by mining in the working face; and operating parameters of storage and drainage equipment, including the operating status of the equipment, water flow velocity, water volume, and scheduling actions (such as pump station operating status, valve opening and closing, and flow rate in the water supply pipeline).

[0066] All types of sensors collect data according to a sampling period, which can be set according to requirements. The collected data is multi-source data. Preprocessing operations such as unit unification, anomaly removal, and missing measurement interpolation are performed on the above multi-source data. Furthermore, structured processing is performed to obtain the perceived data based on the data's location and acquisition time; that is, a standardized time-series input dataset is formed according to the order of acquisition time. , Corresponding to the aforementioned position, Corresponding to the sampling time, the standardized time-series input dataset includes, but is not limited to: aquifer dynamic head field dataset (the numerical distribution of head recorded in three-dimensional space and time series), fracture conduction probability field dataset (recording the conduction probability of adjacent fractures in three-dimensional space in numerical form from 0 to 1), storage and discharge node flow status dataset (a sequence of records of flow changes in storage and discharge equipment such as reservoirs, pumping stations, and valve chambers, composed of node numbers and time series), and local disturbance intensity index field dataset (obtained by normalized weighted calculation based on monitoring values ​​such as microseismic events, stress changes, and displacement changes). All of the above data are structured numerical data in a unified format.

[0067] Step 2: Based on the sensed data and combined with mine survey information, construct a steady-state seepage flow field in the goaf of the mine; identify at least one dominant water path formed by fractures according to the steady-state seepage flow field, and obtain the effective water storage capacity, maximum water delivery, and path response lag time of all dominant water paths.

[0068] This step, which identifies fracture water-conducting paths and analyzes storage and drainage capacity, is based on discrete element analysis software such as UDEC (Universal Distinct Element Code) and 3DEC (Three-Dimensional Distinct Element Code) to construct the aforementioned steady-state seepage field. Using the sensing data and mine survey information obtained in step one, a steady-state seepage field including aquifers, coal seams, surrounding rock composite layers, and mining-induced fractures can be constructed in the discrete element analysis software. This allows for simulation and analysis of fracture network evolution and water pressure response during mining. The sensing data may include water levels in each layer, and the mine survey information may include aquifer distribution and depth, rock strata structure determined by geological logging, permeability coefficients of each stratum, and coal seam mining height and advance step distance obtained based on mine design and field records. This step identifies fracture connectivity, water-conducting channels formed by fractures, and dominant water paths that serve as hydraulic transmission channels. It then assesses the actual storage and drainage capacity of storage and drainage equipment such as water tanks and pumps within the mine area, providing a basis for water resource carrying capacity and allocation constraints.

[0069] Step 3: Based on the effective water storage capacity, the maximum water conveyance, and the path response lag time, and combined with the water conveyance efficiency coefficient and rated capacity of the water storage and drainage equipment, and the expected allocation time, a water resource carrying capacity assessment model is constructed. The water resource carrying capacity assessment model is used to determine the water resource carrying capacity of different areas of the mine and the mine under the expected allocation time.

[0070] This step enables dynamic assessment of water resource carrying capacity by comprehensively considering the flux and storage capacity of the dominant water pathway, as well as the operating parameters of each storage and drainage system. Time-segmented forecasting can be achieved by setting different expected allocation durations.

[0071] Step 4: Based on the preset work plan, mining equipment operating parameters, and historical water usage records, construct a water demand model for different areas of the mine; obtain the allocation demand distribution vector and allocation demand sequence for different areas of the mine based on the water demand model; and determine the priority allocation vector for each area of ​​the mine by combining the allocation priority of different areas of the mine.

[0072] This step enables the prediction and allocation demand analysis of water load in different areas of the mine. Based on the underground operation plan, equipment distribution, operating load and historical water use records, a water demand prediction model for multiple working faces and time periods is constructed to determine the allocation volume and priority distribution for different times and areas.

[0073] Step 5: Based on the water resource carrying capacity assessment model and the water demand model for different areas of the mine, determine multiple objective optimization functions and constraints, construct a multi-objective optimization model, and calculate the optimal water resource allocation strategy based on the pre-selected optimization algorithm. The multiple objective optimization functions include: a prediction deviation minimization objective function, an allocation energy consumption minimization objective function, and an allocation priority weighted deviation minimization objective function. The constraints include: water resource carrying capacity boundary conditions for different areas of the mine, total allocation volume boundary conditions for the mine, dominant water path capacity constraints, and water balance constraints for the water diversion path.

[0074] This step optimizes the allocation path and intelligent decision-making, proposing a group of objective functions with the goals of supply and demand matching degree, water transfer energy consumption, water source security and scheduling economy. Combining the water resource carrying capacity obtained in step three and the water transfer parameters of the dominant water path obtained in step two, the constraints are determined, and the optimal water resource allocation path and allocation scheme are solved by a pre-selection optimization algorithm.

[0075] Step 6: Based on the optimal water resource allocation strategy, control the operation of the storage and drainage equipment; during the execution of the optimal water resource allocation strategy, collect execution result data in real time, and after correcting the water resource carrying capacity assessment model, the water demand model, the maximum water delivery volume and / or the priority allocation vector of each mine area according to the execution result data, regenerate the optimal water resource allocation strategy.

[0076] This step implements control execution and feedback correction. Based on the optimal water resource allocation strategy obtained in step five, allocation commands are issued to control the actions of various storage and drainage equipment, such as pump station start-up and shutdown, water valve switching, and path switching. During the allocation process, execution result data is collected in real time and compared with the optimal predicted value obtained in step five. The function parameters in the aforementioned models or boundary conditions are adjusted to achieve self-correction and strategy iteration, forming a closed-loop control system of perception, decision-making, and feedback.

[0077] The above-mentioned scheme of this application generates sensing data based on hydrological data, structural disturbance parameters, and operating parameters of storage and drainage equipment in a designated area of ​​the mine. Combined with mine survey information, a steady-state seepage flow field is constructed in the goaf of the mine, thereby obtaining the fracture network evolution results and identifying at least one dominant water path formed by the fractures. After obtaining the effective storage capacity, maximum water conveyance, and path response lag time of all dominant water paths, a water resource carrying capacity assessment model is constructed by combining the water conveyance efficiency coefficient and rated capacity of the drainage equipment with the expected allocation duration. This model determines the water resource carrying capacity of different areas of the mine and the mine itself under the expected allocation duration. By pre-setting work plans, operating parameters of mining equipment, and historical water usage records, a water demand model for different areas of the mine is constructed, thereby determining the water demand of each area and the priority of each area during water allocation. Finally, based on the water resource carrying capacity assessment model and the water demand model for different areas of the mine, multiple objective optimization functions and constraints are determined, a multi-objective optimization model is constructed, and the optimal water resource allocation strategy is obtained by calculating the multi-objective optimization model based on a pre-selected optimization algorithm. After controlling the operation of the storage and drainage equipment based on the optimal water resource allocation strategy, the system judges whether the optimal water resource allocation strategy meets the requirements by collecting execution result data in real time. If the requirements are not met, one or more of the aforementioned models or parameters can be revised, and the optimal water resource allocation strategy can be recalculated to ensure that the final optimal water resource allocation strategy can meet the various requirements of underground water resource allocation in the Kuishui mining area. The above-mentioned scheme of this application couples the fracture network evolution, water conduction path, and various water demand areas to construct an underground water allocation scheme for Kuishui mining area that can be dynamically controlled. This effectively solves the problems of easy water loss and low water storage utilization rate, realizes dynamic correction and real-time response of the allocation strategy, and improves the adaptive and intelligent level of underground water resource management in Kuishui mining area.

[0078] Preferably, step two above includes:

[0079] S21: Construct the steady-state seepage flow field of the goaf area of ​​the mine based on the sensing information and mine survey information obtained in step one;

[0080] Specifically, based on the assumption that similar materials in the rock strata are continuous media, groundwater transport follows Darcy's law. However, flow within fractures is limited by compaction; the space is small and the flow velocity is slow, thus not entirely conforming to Darcy's law. After compaction, delamination fractures and microfractures close and are filled with particles, making the flow more consistent with Darcy's law. Therefore, an equivalent treatment method is used, assigning a large permeability coefficient to the fractured region, equating the overlying rock fractures and strata to a porous medium model. Water level is obtained from the aforementioned sensing information. The permeability coefficients of each layer are obtained from the mine exploration information, and the steady-state seepage flow field is described by the following mathematical equation:

[0081] ;

[0082] Where: K x K z K xL K zL K xY K zY These are the horizontal and vertical permeability coefficients of the corresponding rock strata, respectively, in m / d. Here, K... xL K zL The value is greater than K xY K zY Numerical values; H and L represent the height and width of the selected area during model construction, respectively, in meters; l1 represents the width of both sides of the underground water storage area in the mine, in meters; D L and D Y The region is defined as containing fissures and rock strata; Q represents the recharge intensity from the overlying fissures to the goaf, in meters (m). 2 / d.

[0083] S22: Using the discrete element method, tracer particles are deployed in the steady-state seepage flow field. A reverse tracer operation is performed based on the steady-state seepage flow field to obtain the spatiotemporal distribution of the tracer particle migration trajectories in the steady-state seepage flow field. Based on the spatiotemporal distribution of the tracer particle migration trajectories, the dominant water path in the steady-state seepage flow field is determined. and the path response lag time of the dominant water path ;

[0084] Taking a real mine as an example, to analyze the migration characteristics of groundwater in a fractured network, the discrete element method (DEM) was used to deploy equally spaced tracer particles above and to both sides of the goaf. Based on the obtained seepage model, reverse tracer calculations were performed to obtain the particle migration trajectory distribution. Areas with dense trajectories correspond to water-conducting channels with large seepage flows, while areas with almost no particles indicate weak seepage capacity at that location. Figure 2 The particle trajectory distribution shown (higher water head indicates greater seepage intensity) can intuitively identify the dominant water paths, lateral recharge paths, and weak seepage zones in different fracture zones of the overburden, and can be used to partition the spatial differences in the seepage field. To further quantify the seepage capacity of different regions, the steady-state seepage field is subjected to vertical difference processing, which yields the downward seepage flow distribution per unit area at each location in the model. The calculation results are as follows: Figure 3 The data shows that the seepage flow in the vertical fracture areas on both sides of the goaf is significantly higher than in other locations, with a typical range of approximately 3.8 × 10⁻⁶. -6 Up to 38×10 -6 m 3 / d; the seepage flow in the delamination fractures and adjacent water storage areas is less than 3.8×10. -6 m 3 / d, while the seepage flow in the undeveloped fracture zone is the smallest, close to zero.

[0085] Further simulations were conducted under different inflow conditions, such as... Figure 4 As shown, the steady-state seepage field can further output the seepage intensity distribution at different locations in the goaf. The calculation results show that the goaf is located approximately in the 50–170 cm region, with almost zero seepage in the rock mass on both sides; the figure shows the inflow rate Q as 136.8 × 10⁻⁶. -6 m 3 / d、91.2×10 -6 m 3 / d、45.6×10 -6 m 3 The diagram illustrates the distribution of seepage intensity at different locations in the goaf under three conditions. As the inflow increases, the seepage intensity at each location on the goaf roof increases proportionally, with the seepage at the vertical fractures on the left and right sides being the most significant. Under typical conditions, the vertical fractures on the left and right sides account for approximately 44.25% and 53.74% of the total seepage, respectively, totaling approximately 97.99%. The consistent proportion of seepage intensity at each location under different inflow conditions indicates that the main recharge source for the reservoir is concentrated in the vertical fracture areas on both sides. After solving the steady-state seepage field, the groundwater transport time distribution in different fracture channels can be obtained using tracer particle transport calculations. The calculation results are as follows... Figure 5 As shown, the time for groundwater to seep from the overlying aquifer to the goaf through the vertical fissures on both sides of the goaf is usually less than 6 minutes; however, the groundwater transport time through the delamination fissures and the mixed area of ​​undisturbed rock mass is significantly longer, exceeding 17 days.

[0086] During the simulation, the fractures and hydraulic state are dynamically monitored and analyzed in a steady-state seepage field. Monitoring lines are set up in the lower part of the aquifer, the lower part of the aquitard, and the upper part of the coal seam to track the changes in water head and water level over time during mining. By monitoring the spatiotemporal evolution of water head, the seepage characteristics of the aquifer and the conductivity of fractures can be captured in real time, and the changes in aquifer water level and the distribution range of the water-conducting zone can be inverted accordingly. Specifically, based on the water head distribution cloud map output by the simulation, the spatial distribution characteristics of the high water head gradient area are analyzed, and the water head concentration area formed at the end of the post-mining working face is identified as a candidate area for the main recharge source of the mine's underground water storage area. When the local water head is significantly higher than the surrounding average level, the water head gradient exceeds a preset threshold, and the seepage flow direction of adjacent fracture fragments shows good consistency or high concentration, this area is identified as a water head concentration zone and used as the starting point of the dominant water path. Furthermore, a graph structure is constructed in its neighboring high-fracture-density fragments (the high-fracture-density fragments refer to fragments with fracture density higher than a set density threshold) as the basis for subsequent water-conducting path search and water storage unit division. Further, graph theory modeling is used to identify water-conducting channels. The high-fracture-density nodes output from the simulation are used as nodes in the graph. High-fracture-density nodes can be determined according to a preset density threshold; nodes greater than the density threshold are used to form a node set. V ={ v i For any pair of fracture fragments, if the following connectivity condition is satisfied, then an edge is established between the corresponding nodes. e ij , forming an edge set E ={ e ij It should be noted that the edges here... e ij For graph theory purposes, a connection unit is used to represent a potential water-conducting channel between two fracture fragments, and subsequent paths are composed of multiple such edges connected in series.

[0087] Constructing a potential water-conducting network:

[0088] ;

[0089] in, The distance between the fractured fragments; The criterion for the connectivity distance of fractured fragments is determined based on the continuity of rock mechanics and is used to determine whether there is a potential hydraulic connectivity relationship between two fractured fragments. , The opening degree of the fracture element is extracted based on simulation data; The minimum opening degree should be selected based on the actual situation; generally, =0.1mm.

[0090] Introducing hydrodynamic viscosity μ Each edge e ij impedance weight Based on the Cubic Law calculation, the relationship between the flow resistance between fractured fragments and geometric parameters is characterized, and values ​​are assigned as follows:

[0091] ;

[0092] Using Dijkstra's algorithm, starting from the aquifer initiation node... S To the end point of the goaf T Extract the path with the lowest impedance and identify the dominant water path, which is the path with the lowest impedance and the strongest water conduction capacity, and use it as the main water supply channel for the water storage area.

[0093] S23: Around the dominant water path, using high-fracture-density finite elements as water storage units, the effective water storage capacity is calculated using the following formula: ,in: The effective water storage capacity is... For the first The effective water content of each high-fracture-density lamellae is derived from the water saturation or effective porosity calculations performed in the later stages of the simulation. The volume of the high-crack-density fragment is directly obtained from the mesh element volume in the simulation software. k =1,2,…, n ;

[0094] The maximum water conveyance of the dominant water path is obtained by summing the seepage contributions between all fractured granular elements along the path, and the maximum water conveyance is calculated using the following formula: ;in, The maximum water delivery capacity, The water flux per unit area of ​​the water-conducting channel between two fractured granular elements. Let be the cross-sectional area of ​​the water-conducting channel between the two fractured granular elements. , The index number of the fracture fragment. express Time of the first Water transport flux of the main water pathway.

[0095] This step also calculates the maximum drainage capacity of the dominant water path. Limited by both the local water diversion capacity of the path and the rated capacity of the storage and drainage equipment (pumping station): , T0 represents the effective water supply of the water-conducting path within a continuous T0-segment segment; T0 is the effective segment length of the path's sustainable transmission capacity, used to characterize the minimum water supply capacity of the path within a continuous segment. η is the rated water discharge capacity of the storage and drainage equipment (pump station), based on the mine design or measured value; η is the system water conveyance efficiency (0~1), obtained according to the actual pump station, generally taken as 0.7–0.9.

[0096] Furthermore, in the above scheme, the water resource carrying capacity assessment model in step three is expressed as follows:

[0097] ;

[0098] in, For the water resource carrying capacity of all dominant water pathways, For the first The dominant water pathway during the expected allocation time Water resource carrying capacity Not less than η i The water conveyance efficiency coefficient of the aforementioned storage and drainage equipment (determined based on the operation monitoring data of mine storage and drainage equipment such as pump stations or equipment nameplate parameters). For the first The maximum drainage capacity of the storage and drainage equipment corresponding to the main water path during stable operation (obtained from the mine design documents, pump station nameplate parameters or actual flow rate). For the first The effective water storage capacity of the main water pathway For the first The equivalent head propagation length of the dominant water path, The propagation velocity of the water head (obtained from the sensing data in step one).

[0099] The Water Resource Carrying Capacity (WRCC) assessment model defines the maximum water allocation capacity that an underground water storage system can stably provide or withstand under specific downhole structures, hydraulic channels, and scheduling conditions, expressed in m³ / d. Its magnitude is influenced by factors such as the transport capacity of the water diversion path, storage capacity, discharge capacity of storage and drainage equipment, and response time. The goal of WRCC calculation is to assess the maximum allocateable capacity of a specific path and region, forming a dynamically adjustable scheduling boundary; and to mitigate the geological and safety risks caused by overcapacity pumping or overloaded water injection.

[0100] Preferably, in the aforementioned method for underground water resource allocation and decision analysis in water-deficient mining areas, step four is a process for determining water resource allocation needs. This process needs to be coupled with the water resource carrying capacity obtained in step three to provide data support for subsequent optimization of scheduling paths and allocation schemes. The model aims to predict the water demand and priority distribution in different areas of the mine in the future. After obtaining the water demand in different areas of the mine, it can also obtain the overall water demand of the mine.

[0101] Specifically, groundwater is mainly distributed in areas below the mine shaft and in working units:

[0102] The mining working face area is numbered as follows: i =1... n 1. Used for coal seam mining, blasting drilling, dust removal and cooling, support grouting, etc., in areas with large water demand and strong fluctuations;

[0103] Transportation and support area, numbered i = n 1+1... n 2. Used for dust suppression, cooling, etc., such as belt conveyor tunnels, transport tunnels and main and auxiliary shaft tunnel areas;

[0104] Drainage pumping station and water storage tank, numbered as follows i = n 2+1... n 3. Used for drainage reuse and water source balancing and regulation;

[0105] Equipment operation and maintenance area, numbered as i = n 3+1...N, such as downhole air compressor stations, cooling systems, drilling equipment, etc.

[0106] Furthermore, regarding the first The main input parameters for constructing a water demand model for a mining area include:

[0107] Work intensity (m³ / h), derived from the operation scheduling system, such as mining length, drilling rate, advancing area, shotcrete volume, etc.

[0108] Number of operating equipment in the area (Units), from equipment operation monitoring systems, such as coal mining machines, tunneling machines, drilling rigs, spray devices, pumping stations, etc.;

[0109] Unit equipment water consumption intensity (m³ / (unit·h)) is set according to equipment specifications and process manuals, with different empirical coefficients set for different types of equipment;

[0110] Regional historical average water consumption (m³ / h) is the average water consumption record of the same area under similar operating conditions in the past during a similar period, extracted by the monitoring system;

[0111] Historical adjustment weighting coefficient ω (Dimensionless, 0~1), empirically set, increase the weight when data is missing, decrease it when data is sufficient (usually 0.1~0.3).

[0112] The water demand model for different areas of the mine includes:

[0113] S41: Constructing the... Each mining area Water demand model at any given time: ;in, The first term in the formula is the theoretical water demand under the combined effect of real-time operations and running equipment, and the second term is historical data compensation to make up for the impact of sudden and unobservable factors. β it For the first The allocation response coefficient for each mine area is defined as the rigidity of the area allocation, where 1 indicates rigidity and <1 indicates flexibility and buffering. Different areas within a mine have different functions, and their water demand is affected differently. Based on allocation needs and directions, three types of area logic are constructed: critical operational areas (such as main mining faces and main tunneling faces): must be 100% satisfied and cannot be delayed; high-priority auxiliary areas (such as support and drilling): must be prioritized; and areas that can be delayed (such as dust suppression watering and cooling in transport roadways): can enter the allocation buffer pool for later allocation or reduced supply.

[0114] S42: According to A water demand model for each mining area at any given time was constructed. Distribution vector of allocation demand in each mine area at any given time: This vector is generated by combining the actual allocation needs of different areas of the mine according to the area number.

[0115] by The starting time is [time], and the prediction time window is [time]. The sequence of allocation needs for each mine area: ;

[0116] For the first Priority factor for allocation and distribution in individual mining areas (Dimensionless, value 0~1, set by the scheduling procedure, such as 1 for the main mining area, 0.5 for auxiliary areas, and 0.2 for non-critical areas, etc.), construct the priority allocation vector for each mine area: .

[0117] Based on the water resource carrying capacity obtained in step three and the allocation demand obtained in step four, step five achieves optimal allocation of groundwater. An optimization algorithm is used to solve for the optimal water resource allocation path and regional distribution strategy, achieving synergistic optimization of dynamic supply and demand matching and overall system operating efficiency. The objective of this step is to achieve on-demand water supply in multiple regions and time periods underground under the constraint of limited water resource carrying capacity, while minimizing allocation energy consumption, maximizing priority matching, and ensuring water source security in key areas. Therefore, the multiple objective optimization functions in this step are as follows:

[0118] (1) The objective function for minimizing the prediction bias is expressed as: ; For the first The actual water allocation volume for each mining area; this function represents the sum of squared errors between the actual allocated water supply and the predicted water demand, and is used to measure the difference between the predicted water demand and the actual allocated water supply volume for a mining area.

[0119] (2) The objective function for minimizing the energy consumption of the allocation is expressed as: ; To allocate the path energy consumption weight, factors such as water conveyance distance, head, and pipe resistance are considered, and the weight is obtained based on the mine water pump system.

[0120] (3) The objective function for minimizing the weighted deviation of allocation priority is expressed as: The objective function is used to enhance the water supply satisfaction of key areas. The smaller the function value, the better the allocation strategy performs in meeting the water needs of high-priority areas.

[0121] The constraints include:

[0122] (1) The water resource carrying capacity boundary conditions in different areas of the mine are expressed as follows: This means that the maximum amount of water that can be allocated in each mining area must not exceed its current water resource carrying capacity.

[0123] (2) The boundary conditions for the total allocation of the mine are expressed as follows: ;in, The demand for water allocation in mines is calculated as follows:

[0124] Construct a mine water demand forecasting model: N represents the total number of mining areas;

[0125] After calculating the emergency redundancy of the aforementioned mine water demand prediction model, the mine water allocation demand calculation model is obtained: ;in, ; The length of the sliding window. Forecast of mine water demand in period b. This is the redundancy adjustment coefficient.

[0126] This constraint limits the total allocation amount to no more than the overall supply capacity.

[0127] (3) The dominant water path capacity constraint is expressed as: This constraint is used to limit the maximum allowable flow rate per unit time on the water transfer path to not exceed the maximum water transfer capacity of the main path.

[0128] (4) The water balance constraint condition of the water diversion path is expressed as: The significance of this constraint is that the water demand and expenditure of the region are conserved, ensuring the physical feasibility of the route water transfer network.

[0129] Furthermore, the construction of the multi-objective optimization model in step five includes:

[0130] by The multi-objective optimization function is represented by the following, which is then normalized: ; For the first The weight coefficients of each objective function. Indicates the first The maximum expected value of an objective function;

[0131] The multi-objective optimization model is expressed as follows:

[0132] .

[0133] The pre-selected optimization algorithm includes: swarm intelligence algorithms (such as particle swarm optimization, genetic algorithm, simulated annealing, etc.) or multi-objective optimization algorithms (such as NSGA-II, etc.); the control objective is obtained by iteratively inputting the multi-objective optimization model into the pre-selected optimization algorithm: the first... The optimal water allocation volume for each mining area, the optimal path flow for each water allocation path, the objective function allocation performance index, the optimal priority allocation priority factor, and the predicted allocation energy consumption, where the optimal priority allocation priority factor satisfies the priority index. .

[0134] The optimal water resource allocation strategy is generated based on the control objective. This optimal strategy includes: valve opening degree, start / stop status of storage and drainage equipment, and operating parameters on each water allocation path. The allocation paths can be as follows: Figure 6 As shown, allocation paths 1, 2, ... L are equipped with water storage and drainage devices such as valves, pumping stations, and water tanks. The allocation path and each mine area can have a one-to-one relationship. Valves can be installed between allocation paths, allowing for various combinations of water supply paths between allocation paths and mine areas. The optimal water resource allocation strategy can specifically include: controlling the start-up and shutdown strategies and operating periods of pumping stations in different allocation paths; controlling the valve status between different allocation paths to achieve individual or combined water supply for each allocation path; controlling the working time and duration of different allocation paths according to the priority of different mine areas, dynamically supplying water to mine areas according to priority; if a potential water shortage is detected in a critical area, other allocation paths are activated first, and the strategy mode is switched based on energy consumption indicators and the priority area satisfaction rate.

[0135] In specific implementation, the execution result data in step six includes: the actual water allocation volume in the mine area, the actual flow rate of the water allocation path, the actual allocation energy consumption, the completion rate of water supply demand in the priority mine area and the deviation of the objective function allocation performance index, and the water pressure / level of the water allocation path.

[0136] Based on the execution result data, the water resource carrying capacity assessment model, the water demand model, the maximum water delivery volume, and / or the regional priority allocation vectors for each mine are modified, including:

[0137] (1) Dynamically adjust the water demand model for the mining area based on the execution result data:

[0138] Based on historical execution data, the water demand in step four is revised using moving average or machine learning methods, that is, the water demand is re-predicted.

[0139] (2) If the water pressure / water level of the water diversion path exceeds the set limit, adjust the water resource carrying capacity assessment model.

[0140] That is, if the water pressure / water level exceeds the limit abnormally, the water resource carrying capacity assessment model in step three is modified, and the dynamic water resource carrying capacity is converged or relaxed in stages.

[0141] (3) If the deviation between the actual flow rate and the optimal flow rate of the water diversion path exceeds the set deviation threshold and the duration is longer than the set duration, the maximum water delivery volume shall be adjusted.

[0142] That is, the steady-state seepage flow field in step two is corrected, and the capacity parameters of the water guiding path are recalculated.

[0143] (4) If the water supply demand completion rate of the priority mining area does not reach the target completion rate, adjust the priority allocation vector of each mining area.

[0144] The water supply demand fulfillment rate can be obtained by dividing the number of areas meeting water supply demand by the total number of mining areas. The target fulfillment rate can be determined based on demand, i.e., adjusting the regional priority weights based on the actual water supply fulfillment rate. λ i Optimize the objective function f The parameters in 3.

[0145] If the optimal water resource allocation strategy obtained in step five can ensure that the water demand of each mining area is met, and that energy consumption and safety also meet the requirements, then the above correction process is unnecessary.

[0146] The above-mentioned scheme in this application integrates technologies such as underground hydrological status perception, mining-induced fracture water conduction modeling and analysis, dynamic assessment of storage and drainage capacity, water demand prediction and multi-objective optimization decision-making. The proposed scheme is applicable to the whole-process regulation of water resources in the Kuishui mining area, and improves the regulation accuracy and utilization efficiency of water resources in the mining area.

[0147] As needed, the above technical solutions can be combined to achieve the best technical effect.

[0148] The above are merely the principles and preferred embodiments of this application. It should be noted that, for those skilled in the art, several other modifications can be made based on the principles of this application, and these modifications should also be considered within the scope of protection of this application.

Claims

1. A method for water resource allocation and decision analysis in a water scarce mine, characterized in that, The method comprises the following steps: Step 1: Obtain hydrological data, structural disturbance parameters and storage and drainage equipment operation parameters of a set area in a mine to generate sensing data; Step 2: Based on the sensing data, combine mine survey information to construct a steady-state seepage flow field of the goaf in the mine; identify at least one dominant water path formed by fissures according to the steady-state seepage flow field, and obtain the effective water storage capacity, maximum water transport capacity and path response lag time of all the dominant water paths; Step 3: Based on the effective water storage capacity, the maximum water transport capacity, the path response lag time, combine the water transport efficiency coefficient and the rated capacity of the storage and drainage equipment, and the expected allocation time to construct a water resource carrying capacity evaluation model, which is used to determine the water resource carrying capacity of different areas of the mine and the mine under the expected allocation time; Step 4: According to the pre-set operation plan, the mining equipment operation parameters and the historical water consumption record, a water demand model of different areas of the mine is constructed; according to the water demand model, the allocation demand distribution vector and the allocation demand sequence of different areas of the mine are obtained; and the priority allocation vector of each mine area is determined according to the allocation priority of different areas of the mine; Step 5: According to the water resource carrying capacity evaluation model and the water demand model of different areas of the mine, a plurality of target optimization functions and constraint conditions are determined to construct a multi-objective optimization model, and the optimal water resource allocation strategy is obtained by calculating the multi-objective optimization model based on a pre-selected optimization algorithm; The plurality of target optimization functions include: a prediction bias minimization objective function, a allocation energy consumption minimization objective function and an allocation priority weighted bias minimization objective function; the constraint conditions include: a mine area water resource carrying boundary condition, a mine total allocation amount boundary condition, a dominant water path capacity constraint condition and a water path water amount balance constraint condition; Step 6: Based on the optimal water resource allocation strategy, the storage and drainage equipment is controlled to act; during the execution of the optimal water resource allocation strategy, the execution result data is collected in real time, and after the water resource carrying capacity evaluation model, the water demand model, the maximum water transport capacity and / or the priority allocation vector of each mine area are corrected according to the execution result data, the optimal water resource allocation strategy is regenerated.

2. The method of water resource allocation and decision analysis for underground mines in water scarce regions according to claim 1, characterized in that, In the step 1: The hydrological data includes: water level and water pressure of the water source area; The structural disturbance parameters include: crack opening degree of the crack-prone area and microseismic events caused by mining in the working face; The storage and drainage equipment operation parameters include: operation state, water transport flow rate, water amount and scheduling action of the storage and drainage equipment; The hydrological data, the structural disturbance parameters and the storage and drainage equipment operation parameters are structured and processed to obtain the sensing data according to the position and collection time corresponding to the data.

3. The method of water resource allocation and decision analysis for underground mines in water scarce regions according to claim 2, characterized in that, In the step 2: After the steady-state seepage flow field of the goaf in the mine is constructed based on the sensing information and mine survey information obtained in step 1, tracer particles are arranged in the steady-state seepage flow field by using the discrete element method, and the migration trajectory space-time distribution of the tracer particles in the steady-state seepage flow field is obtained by performing reverse tracing operation based on the steady-state seepage flow field; determining the dominant water path in the steady-state flow field from the spatiotemporal distribution of the migration trajectories of the tracer particles and the path response lag time of the dominant water path ; Around the main water path, the high-fracture-density patch is taken as a water storage unit, and the effective water storage capacity is calculated by the following formula: wherein: is the effective water storage capacity, is the effective water content of the first high-fracture-density patch, is the volume of the high-fracture-density patch, k = 1, 2, …, n ; the high-fracture-density patch refers to a patch with a fracture density higher than a set density threshold. The maximum water delivery amount is calculated by the following equation: ; wherein, is the maximum water delivery amount, is the water flux per unit area of the water-conducting pathway between two fracture elements, is the cross-sectional area of the water-conducting pathway between two fracture elements, , is the index number of the fracture element, represents the water flux of the i-th main water-conducting pathway at time t. the water flux of the i-th main water-conducting pathway at time t.

4. The method of water resource allocation and decision analysis for underground mines in water scarce regions according to claim 3, characterized in that, In the third step, the water resource carrying capacity evaluation model is represented as: ； wherein, is the water resources carrying capacity of the whole dominant water path, is the water resources carrying capacity of the first dominant water path under the expected allocation time length is not less than is the water resources carrying capacity of the second dominant water path under the expected allocation time length i is the water delivery efficiency coefficient of the water storage and drainage equipment, is the maximum drainage capacity of the water storage and drainage equipment corresponding to the first dominant water path under stable operation; is the effective water storage capacity of the first dominant water path, is the equivalent water head propagation length of the first dominant water path, is the water head propagation speed.

5. The method of water resource allocation and decision analysis for underground mines in arid areas according to claim 4, characterized in that, In the fourth step: Regarding the first For each mining area, the pre-set work plan includes: work intensity. The operating parameters of the mining equipment include: the number of operating devices. and water consumption intensity per unit of equipment The historical water usage records include: historical average water usage. and historical adjustment weighting coefficient ω ; The water demand model of different mine areas is constructed, including: Construct the first Each mining area Water demand model at any given time: ;in, ; β it For the first The allocation and response coefficient for each mine area; According to The water demand model of each mine area at each time is constructed The distribution vector of the allocation demand of each mine area at each time is constructed ; At the starting moment, generate the predicted time window as the mine area allocation demand sequence of each mine: ; allocating a dispatch priority factor for each mine area , constructing a priority dispatch vector for each mine area , constructing a priority dispatch vector for each mine area .

6. The method of water resource allocation and decision analysis for underground mines in arid areas according to claim 5, characterized in that, In the fifth step: The objective function for minimizing the prediction bias is expressed as: ; For the first The actual amount of water allocated to each mining area; The deployment energy minimization objective function is expressed as: ; is a path energy weight for the deployment path. The deployment priority weighted deviation minimization objective function is expressed as: .

7. The method of water resource allocation and decision analysis for underground mines in arid areas according to claim 6, characterized in that, In the fifth step: The water resource bearing boundary condition of different areas of the mine is expressed as: ; The total mine allocation boundary condition is expressed as: ; wherein, is the mine water allocation demand, calculated by: A mine water demand prediction model is constructed: N is the total number of mine areas; The mine water demand prediction model is subjected to emergency redundancy calculation to obtain a mine water allocation demand calculation model: ; wherein, ; is a sliding window length, is a mine water demand prediction of a time period, is a redundancy adjustment coefficient; The master water path capacity constraint is expressed as: ; The water routing path water volume balance constraint is expressed as: .

8. The method of water resource allocation and decision analysis for underground mines in arid areas according to claim 7, characterized in that, The multi-objective optimization model is constructed, including: Let be a multi-objective optimization function, and its normalized form is ; is the weight coefficient of the th objective function, is the maximum expected value of the th objective function; The multi-objective optimization model is represented as: 。 9. The method of water resource allocation and decision analysis for underground mines in arid areas according to claim 8, characterized in that, In the fifth step: The preselected optimization algorithm includes a swarm intelligence algorithm or a multi-objective optimization algorithm. The multi-objective optimization model is input into the preselected optimization algorithm for iterative operation, and control targets are obtained, including the optimal water allocation amount of the first mine area, the optimal path flow of each water allocation path, the target function allocation performance index, the optimal priority allocation priority factor, and the allocation energy consumption prediction value. The multi-objective optimization model is input into the preselected optimization algorithm for iterative operation, and control targets are obtained, including the optimal water allocation amount of the first mine area, the optimal path flow of each water allocation path, the target function allocation performance index, the optimal priority allocation priority factor, and the allocation energy consumption prediction value. Based on the control target, the optimal water resource allocation strategy is generated, including the valve opening on each water transfer path, the start-stop state and operating parameters of the water storage and discharge equipment.

10. The method of water resource allocation and decision analysis for underground mines in arid areas according to claim 6, characterized in that, In the sixth step: The execution result data includes the actual allocation water quantity of the mine area, the actual path flow of the water transfer path, the actual allocation energy consumption, the water supply demand completion degree of the priority mine area, and the deviation degree of the target function allocation performance index, and the water pressure / water level of the water transfer path. According to the execution result data, the water resource carrying capacity evaluation model, the water demand model, the maximum water transfer quantity, and / or the priority allocation vector of each mine area are corrected, including: The water demand model of the mine area is dynamically adjusted according to the execution result data; If the water pressure / water level of the water transfer path exceeds the set limit value, the water resource carrying capacity evaluation model is adjusted; If the deviation between the actual path flow of the water transfer path and the optimal path flow exceeds the set deviation threshold and the duration is greater than the set time length, the maximum water transfer quantity is adjusted; If the water supply demand completion degree of the priority mine area does not reach the target completion degree, the priority allocation vector of each mine area is adjusted.

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