Seepage optimization method and system for pumped storage power station based on water level fluctuation data

Abstract

The present invention proposes a method and system for optimizing seepage in a pumped-storage power station based on water level fluctuation data. This method relates to the technical field of seepage field analysis. First, a three-dimensional structural model of the upper reservoir is constructed based on satellite geographic maps and water depth data. Water particles are then arranged in the model, and permeation holes are set according to the permeability coefficient to form a seepage field simulation model. Next, through seepage simulation at different water levels, the flow characteristics of water particles at the edge of the reservoir surface are extracted. The simulated characteristics are further compared with the actual water flow characteristics, and the model credibility is evaluated based on the degree of fit. If the credibility does not meet the standard, the model is optimized. Finally, the optimized model is used to conduct seepage early warning analysis. Through precise three-dimensional modeling, dynamic seepage simulation, and feature fit verification, the present invention improves the accuracy of seepage prediction, providing a scientific basis for the safe operation of power stations.

Description

Technical Field

[0001] The present invention relates to the technical field of seepage field analysis, and in particular to a seepage optimization method and system for a pumped storage power station based on water level fluctuation data. Background Art

[0002] Pumped-storage power stations are highly valued for their peak-shaving and valley-filling functions in power systems, but the problem of upper reservoir seepage has always been a key factor affecting the safe operation and economic benefits of power stations. Reservoir seepage is constrained by water level fluctuations, geological structure complexity, and differences in material permeability characteristics. Traditional seepage analysis methods are difficult to meet the needs of high-precision prediction and dynamic optimization. Existing technologies usually rely on simplified two-dimensional seepage models or static geological parameter assumptions, and fail to fully consider the dynamic impact of the three-dimensional structural characteristics of the reservoir and water level changes on the seepage field, resulting in large deviations between simulation results and actual seepage behavior. At the same time, existing methods lack systematicity in model verification, and there is insufficient comparison between simulation data and actual water flow characteristics, making it difficult to evaluate the reliability and practicality of the model. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for optimizing the performance of a seepage field that can be predicted relatively accurately.

[0004] The present invention discloses a seepage optimization method for a pumped storage power station based on water level fluctuation data, comprising:

[0005] Step S100: Obtain a satellite geographic map of the pumped-storage power station, construct an upper reservoir surface structure map based on the satellite geographic map, and simultaneously collect water depth data of nodes at different locations of the upper reservoir. Map the water depth data on the reservoir surface structure map, and construct a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped depth data. The three-dimensional reservoir bottom structure and the upper reservoir surface structure map are combined into a three-dimensional reservoir structure.

[0006] Step S200: Filling a three-dimensional reservoir structure with water particles, collecting the permeability coefficients of nodes at different locations of the reservoir, and setting water permeability holes on corresponding structural surfaces in the three-dimensional reservoir structure based on the permeability coefficients to construct a reservoir permeability field simulation model;

[0007] Step S300: driving the reservoir seepage field simulation model to perform seepage field simulation at different water levels and collecting water particle flow characteristics at the edge of the reservoir surface structure;

[0008] Step S400: collecting water flow characteristics at the edge of the upper surface of the reservoir at different water levels, and comparing the water flow characteristics with the water particle flow characteristics at the same water level. Based on the degree of agreement, the credibility of the reservoir infiltration field simulation model is determined. If the credibility is less than or equal to a preset value, the reservoir infiltration simulation model is optimized and adjusted until the credibility is greater than the preset value.

[0009] Step S500: Performing a reservoir seepage early warning analysis using the optimized reservoir seepage simulation model.

[0010] In an embodiment disclosed in the present invention, a method for constructing a three-dimensional reservoir bottom structure below a reservoir surface structure diagram based on mapped marked water depth data includes:

[0011] Step S101, based on the water depth data marked at different position nodes on the reservoir surface structure map, configure the reservoir bottom mapping point for the corresponding position node below the reservoir surface structure map;

[0012] Step S102: Connect adjacent reservoir bottom mapping points to form several structural surfaces of the reservoir bottom.

[0013] In an embodiment disclosed in the present invention, a method for setting water permeability holes on corresponding structural surfaces in a three-dimensional reservoir structure based on a permeability coefficient includes:

[0014] Step S201: constructing a plurality of permeability coefficient intervals connected end to end, uniformly collecting a plurality of test permeability coefficients within the permeability coefficient intervals, conducting a test permeability coefficient experiment, and determining the water seepage rate of the material corresponding to the test permeability coefficient under different water pressures. If the difference between the water seepage rates corresponding to the test permeability coefficients is greater than or equal to a preset value, further narrowing and segmenting the permeability coefficient interval;

[0015] Step S202, calculate the average value of the water seepage rate corresponding to the permeability coefficient interval, record it as the average water seepage rate, and determine the number of water seepage holes per unit area of ​​the structural surface based on the average water seepage rate corresponding to the permeability coefficient permeability interval.

[0016] In an embodiment disclosed in the present invention, a method for collecting water particle flow characteristics at the edge of a reservoir surface structure includes:

[0017] Step S301: configuring physical parameters for each water particle, including mass and initial position, and boundary conditions, and using finite element analysis technology to drive each water particle to perform simulated motion;

[0018] In step S302, the particle orientation change and particle motion rate change of water particles at different position nodes on the edge of the reservoir surface structure are determined, the combination of the particle orientation change and the particle motion rate change is recorded as a particle flow feature group, and the particle flow feature group mark is recorded at the corresponding position node.

[0019] In an embodiment disclosed in the present invention, a method for determining water flow characteristics at the upper edge of a reservoir includes:

[0020] Step S401: aligning the edge of the reservoir surface structure with the edge of the upper surface of the reservoir, and marking the position nodes marked with the particle flow feature group on the edge of the reservoir surface structure at the corresponding positions on the edge of the upper surface of the reservoir;

[0021] Step S402: collecting water flow characteristics of the position nodes marked on the upper surface edge of the reservoir. The water flow characteristics include changes in water flow direction and water flow rate. The combination of water flow direction and water flow rate is recorded as a water flow characteristic group.

[0022] In an embodiment disclosed in the present invention, a method for comparing the flow characteristics of water bodies and the flow characteristics of water particles at the same water level includes:

[0023] Step S403: Compare the particle flow feature group and the water flow feature group of nodes at the same position, serialize the changes in particle direction and water flow direction in time, obtain particle direction sequence and water flow direction sequence, gradually transform the sequence correspondence, calculate the direction sub-matching degree after each transformation, and retain the direction sub-matching degree with the highest value.

[0024] Step S404: Sequencing the particle motion rate changes and the water flow rate changes in time to obtain a particle motion rate sequence and a water flow rate sequence, gradually transforming the sequence correspondence, and calculating the rate sub-fit after each transformation, and retaining the maximum rate sub-fit;

[0025] Step S405 : Based on the direction sub-fitness and the rate sub-fitness, the node sub-fitness corresponding to the position node is determined. Based on the node sub-fitness of each position node, the fit between the water flow characteristics and the water particle flow characteristics is determined.

[0026] In the embodiment disclosed in the present invention, the expression for calculating the degree of agreement between the water flow characteristics and the water particle flow characteristics is: ;

[0027] in, For the degree of fit, For the The sub-fit analysis function of the position node, is the total number of position nodes involved in the comparison of the reservoir surface structure edge and the reservoir upper surface edge;

[0028] Among them, the expression of the sub-fit analysis function is: ;

[0029] in, The first The time node corresponds to the unit matching parameter. If only the direction angle is less than or equal to the preset value, then Output the first unit matching parameter. If the direction angle is less than or equal to the preset value, and the speed difference is less than or equal to the preset value, then Output the second unit matching parameter, the first unit matching parameter < the second unit matching parameter, is the total number of time nodes in the sequence.

[0030] In an embodiment disclosed in the present invention, a method for determining the credibility of a reservoir seepage field simulation model based on the degree of fit includes:

[0031] Step S406: a degree of fit interval is set for the degree of fit, and the credibility of the reservoir seepage field simulation model is determined based on the degree of fit interval to which the degree of fit belongs.

[0032] In an embodiment disclosed in the present invention, the method for optimizing and adjusting the reservoir seepage simulation model includes:

[0033] Step S407 : increasing the density of collecting the permeability coefficient, and adaptively adjusting the number of water permeability holes set on the corresponding structural surface in the three-dimensional reservoir structure as the distribution density of the permeability coefficient in the three-dimensional reservoir increases.

[0034] In the embodiment disclosed in the present invention, a seepage optimization system for a pumped storage power station based on water level fluctuation data is also disclosed, including:

[0035] The first module is used to obtain a satellite geographic map of the pumped storage power station, and construct an upper reservoir surface structure map based on the satellite geographic map, and simultaneously collect water depth data of nodes at different locations of the upper reservoir, map and mark the water depth data on the reservoir surface structure map, and construct a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped water depth data, and combine the three-dimensional reservoir bottom structure and the upper reservoir surface structure map into a three-dimensional reservoir structure;

[0036] The second module is used to fill the three-dimensional reservoir structure with water particles, collect the permeability coefficients of nodes at different locations in the reservoir, and set water permeability holes on the corresponding structural surfaces in the three-dimensional reservoir structure based on the permeability coefficients to construct a reservoir permeability field simulation model;

[0037] The third module is used to drive the reservoir seepage field simulation model to perform seepage field simulation at different water levels and collect the flow characteristics of water particles at the edge of the reservoir surface structure;

[0038] The fourth module is used to collect water flow characteristics at the edge of the upper surface of the reservoir at different water levels, and compare the water flow characteristics with the water particle flow characteristics at the same water level. The credibility of the reservoir infiltration field simulation model is determined based on the degree of fit. If the credibility is less than or equal to the preset value, the reservoir infiltration simulation model is optimized and adjusted until the credibility is greater than the preset value.

[0039] The fifth module is used to conduct reservoir seepage early warning analysis using the optimized reservoir seepage simulation model.

[0040] The present invention proposes a method and system for optimizing seepage in a pumped-storage power station based on water level fluctuation data. This method relates to the technical field of seepage field analysis. First, a three-dimensional structural model of the upper reservoir is constructed based on satellite geographic maps and water depth data. Water particles are then arranged in the model, and permeation holes are set according to the permeability coefficient to form a seepage field simulation model. Next, through seepage simulation at different water levels, the flow characteristics of water particles at the edge of the reservoir surface are extracted. The simulated characteristics are further compared with the actual water flow characteristics, and the model credibility is evaluated based on the degree of fit. If the credibility does not meet the standard, the model is optimized. Finally, the optimized model is used to conduct seepage early warning analysis. Through precise three-dimensional modeling, dynamic seepage simulation, and feature fit verification, the present invention improves the accuracy of seepage prediction, providing a scientific basis for the safe operation of power stations.

[0041] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a method step diagram of a pumped storage power station seepage optimization method based on water level fluctuation data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0044] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solutions of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should have the common meanings understood by those skilled in the art of the present invention.

[0045] Example:

[0046] The purpose of the present invention is to provide a method and system for optimizing the performance of a seepage field that can be predicted relatively accurately.

[0047] The present invention discloses a method for optimizing the seepage of a pumped storage power station based on water level fluctuation data. Figure 1 ,include:

[0048] Step S100: obtain a satellite geographic map of the pumped-storage power station, and construct an upper reservoir surface structure map based on the satellite geographic map, and simultaneously collect water depth data of nodes at different locations of the upper reservoir, map and mark the water depth data in the reservoir surface structure map, and construct a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped and marked water depth data, and combine the three-dimensional reservoir bottom structure and the upper reservoir surface structure map into a three-dimensional reservoir structure.

[0049] This step aims to construct a three-dimensional structural model of the upper reservoir of a pumped-storage power station using high-precision geographic data and water depth information, providing a realistic spatial framework for subsequent seepage analysis. First, satellite remote sensing technology is used to obtain a geographic map of the reservoir, extract the reservoir's planar outline and boundary features, and generate a surface structure map of the upper reservoir. For example, for a particular reservoir, its elliptical boundary can be extracted from high-resolution satellite imagery to form a two-dimensional surface model. Simultaneously, water depth data is collected at various nodes within the reservoir using sonar or laser depth sounders. For example, 100 nodes are selected at the center and edges of the reservoir, and the water depth values ​​are recorded. This water depth data is mapped onto the surface structure map, forming a two-dimensional model with depth markers. Based on the mapped water depth data, the three-dimensional reservoir bottom structure is generated by placing reservoir bottom mapping points below the surface structure map and connecting adjacent mapping points to form a structural surface (steps S101-S102). For example, if the water depth at a node is 50 meters, bottom points are marked below the corresponding location and connected to form a smooth bottom surface. Finally, the bottom structure and surface structure images are combined to form a complete three-dimensional reservoir structural model. The principle of this process is to use remote sensing data and spatial analysis of water depth information to accurately restore the reservoir's geometry and depth distribution, ensuring that the model reflects the actual terrain characteristics and provides a reliable structural foundation for seepage simulation.

[0050] In step S200, water particles are arranged in the three-dimensional reservoir structure, the permeability coefficients of nodes at different positions of the reservoir are collected, and based on the permeability coefficients, water permeability holes are set on the corresponding structural surfaces in the three-dimensional reservoir structure to construct a reservoir permeability field simulation model.

[0051] This step constructs a permeability field simulation model that can simulate seepage behavior by arranging water particles in a three-dimensional reservoir structure and combining them with the permeability coefficient. In the three-dimensional reservoir structure, water particles are evenly arranged using numerical simulation methods to simulate the distribution of water in the reservoir. For example, 1,000 particles are arranged per cubic meter to represent the microscopic motion units of the water body. At the same time, the permeability coefficients of nodes at different locations in the reservoir are collected to reflect the water seepage capacity of the geological material. For example, the permeability coefficient of a sandstone area may be 10 ﹣5 m / s, while the clay layer is 10 ﹣8 To deal with the spatial heterogeneity of permeability coefficient, several end-to-end permeability coefficient intervals (e.g. 10 ﹣6 to 10 ﹣5 m / s is an interval), and the test permeability coefficient is evenly selected in each interval, and a water pressure test is performed to determine the seepage rate. If the difference in the test seepage rate exceeds a preset value (such as 0.1 m³ / s), the interval is further subdivided to improve accuracy (step S201). Based on the average seepage rate of each interval, the number of water permeability holes per unit area of ​​the structural surface is calculated. For example, in areas with high average seepage rate, 10 permeability holes are set per square meter, and in areas with low average seepage rate, 2 are set (step S202). By setting permeability holes on the structural surface corresponding to the three-dimensional structure, the seepage path of water through the geological structure is simulated, and a permeability field simulation model is constructed. The principle of this step is to accurately describe the seepage behavior of water in complex geological environments through particle simulation and fine partitioning of the permeability coefficient, combined with the dynamic setting of permeability holes, laying a high-precision foundation for dynamic seepage analysis.

[0052] Step S300 : driving the reservoir seepage field simulation model to perform seepage field simulation at different water levels, and collecting water particle flow characteristics at the edge of the reservoir surface structure.

[0053] This step numerically simulates the seepage field simulation model at different water levels, analyzing seepage behavior and collecting flow characteristics of water particles at the reservoir surface edge to provide data support for model validation. Finite element analysis (FEA) is used to drive the seepage field simulation model to simulate the reservoir's seepage process at different water levels (e.g., a maximum water level of 100 meters and a minimum water level of 80 meters), calculating the motion trajectories and seepage characteristics of water particles within the structure. Each water particle is assigned physical parameters (e.g., mass 1g, initial position coordinates) and boundary conditions (e.g., a fixed boundary at the reservoir edge), and its motion trajectory is simulated using the finite element method (step S301). For example, as the water level drops, particles may flow down a slope, resulting in specific changes in direction and velocity. The focus is on collecting water particle flow characteristics at the edges of the reservoir's surface structures, including changes in particle direction (e.g., from horizontal flow to a 45-degree inclination) and velocity (e.g., from 0.1 m / s to 0.3 m / s). These characteristics are combined and recorded as particle flow feature groups and labeled at corresponding nodes, such as the feature group for a node at the edge being "45 degrees, 0.3 m / s" (step S302). This step aims to capture the impact of water level fluctuations on the seepage field through dynamic numerical simulation and edge feature extraction, generating simulated data reflecting seepage behavior, which provides a key basis for subsequent comparison and verification with actual data.

[0054] Step S400: Collect the water flow characteristics at the edge of the upper surface of the reservoir at different water levels, and compare the water flow characteristics and water particle flow characteristics at the same water level. Determine the credibility of the reservoir infiltration field simulation model based on the degree of fit. If the credibility is less than or equal to a preset value, optimize and adjust the reservoir infiltration simulation model until the credibility is greater than the preset value.

[0055] This step evaluates the credibility of the infiltration field simulation model by comparing the simulated particle flow characteristics with actual water flow characteristics, and optimizes and adjusts them as needed to improve accuracy. First, actual water flow characteristics at the reservoir's upper surface edge at different water levels are collected using field monitoring equipment (such as current meters and direction sensors). These characteristics include changes in flow direction (e.g., a 30-degree deflection from north to northeast) and velocity (e.g., a decrease from 0.2 m / s to 0.1 m / s). These characteristics are recorded as a water flow characteristic set. For example, at a water level of 90 meters, a certain edge node records a northeast flow direction and a velocity of 0.15 m / s. The reservoir surface structure edge is aligned with the upper surface edge, and the simulated particle flow characteristic set is compared with the actual water flow characteristic set at the same water level and node. Specifically, the particle direction and flow direction are serialized to calculate the direction sub-match; the particle velocity and flow velocity are serialized to calculate the velocity sub-match (steps S403-S404). Based on the sub-fits for direction and rate, the node sub-fits are calculated and the overall fit W is derived using a weighted formula. For example, if the fit W = 0.85, while the preset threshold is 0.9, the model's credibility is insufficient and requires optimization. By increasing the permeability coefficient acquisition frequency (e.g., from once every 100 meters to once every 50 meters) and adjusting the number of permeable pores, the model is rebuilt until the fit meets the requirements (step S406). The principle of this step is to ensure that the model accurately reflects actual seepage behavior and improve prediction reliability through quantitative comparison and verification of simulated and actual data.

[0056] Step S500: Performing a reservoir seepage early warning analysis using the optimized reservoir seepage simulation model.

[0057] This step uses the optimized seepage field simulation model to predict reservoir seepage behavior under different operating conditions, identify risks, and generate early warning information to support the safe operation of the power plant. Based on the high-precision model constructed in the previous step, the reservoir's seepage rate, path, and distribution are simulated under various water level fluctuation scenarios (such as a sudden drop in water level due to rapid pumping), identifying potential risk areas. For example, a seepage rate exceeding 0.5 m³ / s in a particular area may indicate a leakage risk. By integrating the model's output of seepage data, abnormal seepage points (such as an abnormal deflection in the seepage direction at a node on the edge) are identified and early warning information is generated, prompting management to implement reinforcement or drainage measures. This step leverages the accuracy of the 3D structural model, the adaptability of the seepage hole placement, and the reliability of the goodness-of-fit verification to accurately predict seepage behavior and promptly identify risk points that could lead to reservoir leakage or structural instability. The principle behind this is to conduct forward-looking analysis based on the high-precision model, providing a scientific basis for safe reservoir operation and maintenance decisions, significantly reducing safety hazards caused by seepage.

[0058] In an embodiment disclosed in the present invention, a method for constructing a three-dimensional reservoir bottom structure below a reservoir surface structure diagram based on mapped marked water depth data includes:

[0059] Step S101 : Based on the water depth data marked at different position nodes on the reservoir surface structure diagram, a reservoir bottom mapping point is configured for the corresponding position nodes below the reservoir surface structure diagram.

[0060] Step S102: Connect adjacent reservoir bottom mapping points to form several structural surfaces of the reservoir bottom.

[0061] In an embodiment disclosed in the present invention, a method for setting water permeability holes on corresponding structural surfaces in a three-dimensional reservoir structure based on a permeability coefficient includes:

[0062] Step S201: construct several permeability coefficient intervals connected end to end, evenly collect several test permeability coefficients in the permeability coefficient intervals, conduct test permeability coefficient experiments, and determine the water seepage rate of the material corresponding to the test permeability coefficient under different water pressures. If the difference between the water seepage rates corresponding to the test permeability coefficients is greater than or equal to a preset value, the permeability coefficient interval is further narrowed and segmented.

[0063] Step S202, calculate the average value of the water seepage rate corresponding to the permeability coefficient interval, record it as the average water seepage rate, and determine the number of water seepage holes per unit area of ​​the structural surface based on the average water seepage rate corresponding to the permeability coefficient permeability interval.

[0064] In an embodiment disclosed in the present invention, a method for collecting water particle flow characteristics at the edge of a reservoir surface structure includes:

[0065] In step S301 , physical parameters including mass and initial position are configured for each water particle, and boundary conditions are configured. Finite element analysis technology is used to drive each water particle to perform simulated motion.

[0066] This step aims to simulate the motion of water particles within the reservoir using finite element analysis (FEM) techniques, providing a data foundation for subsequent flow characterization. First, physical parameters are assigned to each water particle, including mass (e.g., assuming each particle has a mass of 1g, representing a tiny water unit) and initial position (e.g., a particle is located at the reservoir edge at coordinates (x, y, z) = (100, 50, 90)). Furthermore, boundary conditions are configured, such as fixed boundaries at the edge of the reservoir structure (to restrict particle escape) or pressure boundaries at the water level (to reflect the effects of water level fluctuations). Based on these parameters and conditions, FEM techniques are used to numerically solve fluid dynamics equations (such as the Navier-Stokes equations) to drive the simulated motion of each water particle within the three-dimensional reservoir structure. For example, as the water level drops from 100 to 90 meters, a particle might be driven down a slope by gravity and the flow field induced by permeable pores, resulting in a specific trajectory. The principle of this process is to discretize complex fluid motion into particle motion through the finite element method, combine physical parameters and boundary conditions, accurately simulate the dynamic behavior of water in the reservoir, and provide high-precision simulation data for collecting flow characteristics at the edge.

[0067] In step S302, the particle orientation change and particle motion rate change of water particles at different position nodes on the edge of the reservoir surface structure are determined, the combination of the particle orientation change and the particle motion rate change is recorded as a particle flow feature group, and the particle flow feature group mark is recorded at the corresponding position node.

[0068] In an embodiment disclosed in the present invention, a method for determining water flow characteristics at the upper edge of a reservoir includes:

[0069] Step S401: align the edge of the reservoir surface structure and the edge of the upper surface of the reservoir, and mark the position nodes marked with the particle flow feature group on the edge of the reservoir surface structure at the corresponding positions on the edge of the upper surface of the reservoir.

[0070] Step S402: collecting water flow characteristics of the position nodes marked on the upper surface edge of the reservoir. The water flow characteristics include changes in water flow direction and water flow rate. The combination of water flow direction and water flow rate is recorded as a water flow characteristic group.

[0071] In an embodiment disclosed in the present invention, a method for comparing the flow characteristics of water bodies and the flow characteristics of water particles at the same water level includes:

[0072] In step S403, the particle flow feature group and the water flow feature group of the nodes at the same position are compared, and the changes in the particle direction and the changes in the water flow direction are serialized in time to obtain the particle direction sequence and the water flow direction sequence. The sequence correspondence is gradually transformed, and the direction sub-matching degree after each transformation is calculated, and the maximum direction sub-matching degree is retained.

[0073] In step S404, the particle motion rate changes and the water flow rate changes are serialized in time to obtain a particle motion rate sequence and a water flow rate sequence, the sequence correspondence is gradually transformed, and the rate sub-matching degree after each transformation is calculated, and the maximum rate sub-matching degree is retained.

[0074] Step S405 : Based on the direction sub-fitness and the rate sub-fitness, the node sub-fitness corresponding to the position node is determined. Based on the node sub-fitness of each position node, the fit between the water flow characteristics and the water particle flow characteristics is determined.

[0075] In the embodiment disclosed in the present invention, the expression for calculating the degree of agreement between the water flow characteristics and the water particle flow characteristics is: .

[0076] in, For the degree of fit, For the The sub-fit analysis function of the position node, It is the total number of position nodes involved in the comparison of the reservoir surface structure edge and the reservoir upper surface edge.

[0077] Among them, the expression of the sub-fit analysis function is: .

[0078] in, The first The time node corresponds to the unit matching parameter. If only the direction angle is less than or equal to the preset value, then Output the first unit matching parameter. If the direction angle is less than or equal to the preset value, and the speed difference is less than or equal to the preset value, then Output the second unit matching parameter, the first unit matching parameter < the second unit matching parameter, is the total number of time nodes in the sequence.

[0079] In an embodiment disclosed in the present invention, a method for determining the credibility of a reservoir seepage field simulation model based on the degree of fit includes:

[0080] Step S406: a degree of fit interval is set for the degree of fit, and the credibility of the reservoir seepage field simulation model is determined based on the degree of fit interval to which the degree of fit belongs.

[0081] In an embodiment disclosed in the present invention, the method for optimizing and adjusting the reservoir seepage simulation model includes:

[0082] Step S407 : increasing the density of collecting the permeability coefficient, and adaptively adjusting the number of water permeability holes set on the corresponding structural surface in the three-dimensional reservoir structure as the distribution density of the permeability coefficient in the three-dimensional reservoir increases.

[0083] In the embodiment disclosed in the present invention, a seepage optimization system for a pumped storage power station based on water level fluctuation data is also disclosed, including:

[0084] The first module is used to obtain a satellite geographic map of the pumped storage power station, and construct an upper reservoir surface structure map based on the satellite geographic map, and simultaneously collect water depth data of nodes at different locations of the upper reservoir, map and mark the water depth data on the reservoir surface structure map, and construct a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped water depth data, and combine the three-dimensional reservoir bottom structure and the upper reservoir surface structure map into a three-dimensional reservoir structure;

[0085] The second module is used to fill the three-dimensional reservoir structure with water particles, collect the permeability coefficients of nodes at different locations in the reservoir, and set water permeability holes on the corresponding structural surfaces in the three-dimensional reservoir structure based on the permeability coefficients to construct a reservoir permeability field simulation model;

[0086] The third module is used to drive the reservoir seepage field simulation model to perform seepage field simulation at different water levels and collect the flow characteristics of water particles at the edge of the reservoir surface structure;

[0087] The fourth module is used to collect water flow characteristics at the edge of the upper surface of the reservoir at different water levels, and compare the water flow characteristics with the water particle flow characteristics at the same water level. The credibility of the reservoir infiltration field simulation model is determined based on the degree of fit. If the credibility is less than or equal to the preset value, the reservoir infiltration simulation model is optimized and adjusted until the credibility is greater than the preset value.

[0088] The fifth module is used to conduct reservoir seepage early warning analysis using the optimized reservoir seepage simulation model.

[0089] The present invention proposes a method and system for optimizing seepage in a pumped-storage power station based on water level fluctuation data. This method relates to the technical field of seepage field analysis. First, a three-dimensional structural model of the upper reservoir is constructed based on satellite geographic maps and water depth data. Water particles are then arranged in the model, and permeation holes are set according to the permeability coefficient to form a seepage field simulation model. Next, through seepage simulation at different water levels, the flow characteristics of water particles at the edge of the reservoir surface are extracted. The simulated characteristics are further compared with the actual water flow characteristics, and the model credibility is evaluated based on the degree of fit. If the credibility does not meet the standard, the model is optimized. Finally, the optimized model is used to conduct seepage early warning analysis. Through precise three-dimensional modeling, dynamic seepage simulation, and feature fit verification, the present invention improves the accuracy of seepage prediction, providing a scientific basis for the safe operation of power stations.

[0090] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A seepage optimization method for a pumped storage power station based on water level fluctuation data, characterized in that: include: Step S100: Obtain a satellite geographic map of the pumped-storage power station, construct an upper reservoir surface structure map based on the satellite geographic map, and simultaneously collect water depth data of nodes at different locations of the upper reservoir. Map the water depth data on the reservoir surface structure map, and construct a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped depth data. The three-dimensional reservoir bottom structure and the upper reservoir surface structure map are combined into a three-dimensional reservoir structure. Step S200: Filling a three-dimensional reservoir structure with water particles, collecting the permeability coefficients of nodes at different locations of the reservoir, and setting water permeability holes on corresponding structural surfaces in the three-dimensional reservoir structure based on the permeability coefficients to construct a reservoir permeability field simulation model; Step S300: driving the reservoir seepage field simulation model to perform seepage field simulation at different water levels and collecting water particle flow characteristics at the edge of the reservoir surface structure; Step S400: collecting water flow characteristics at the edge of the upper surface of the reservoir at different water levels, and comparing the water flow characteristics with the water particle flow characteristics at the same water level. Based on the degree of agreement, the credibility of the reservoir infiltration field simulation model is determined. If the credibility is less than or equal to a preset value, the reservoir infiltration simulation model is optimized and adjusted until the credibility is greater than the preset value. Step S500, using the optimized reservoir seepage simulation model to perform a reservoir seepage early warning analysis; Methods for comparing the flow characteristics of water bodies and the flow characteristics of water particles at the same water level include: Step S403: Compare the particle flow feature group and the water flow feature group of nodes at the same position, serialize the changes in particle direction and water flow direction in time, obtain particle direction sequence and water flow direction sequence, gradually transform the sequence correspondence, calculate the direction sub-matching degree after each transformation, and retain the direction sub-matching degree with the highest value. Step S404: Sequencing the particle motion rate changes and the water flow rate changes in time to obtain a particle motion rate sequence and a water flow rate sequence, gradually transforming the sequence correspondence, and calculating the rate sub-fit after each transformation, and retaining the maximum rate sub-fit; Step S405: Determine the node sub-fitness corresponding to the position node based on the direction sub-fitness and the rate sub-fitness, and determine the fit between the water flow characteristics and the water particle flow characteristics based on the node sub-fitness of each position node; The expression for calculating the degree of agreement between water flow characteristics and water particle flow characteristics is: ； in, For the degree of fit, For the The sub-fit analysis function of the position node, is the total number of position nodes involved in the comparison of the reservoir surface structure edge and the reservoir upper surface edge; Among them, the expression of the sub-fit analysis function is: ； in, The first The time node corresponds to the unit matching parameter. If only the direction angle is less than or equal to the preset value, then Output the first unit matching parameter. If the direction angle is less than or equal to the preset value, and the speed difference is less than or equal to the preset value, then Output the second unit matching parameter, the first unit matching parameter < the second unit matching parameter, is the total number of time nodes in the sequence.

2. The method for optimizing seepage flow in a pumped storage power station based on water level fluctuation data according to claim 1, characterized in that: The method for constructing a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped marked water depth data includes: Step S101, based on the water depth data marked at different position nodes on the reservoir surface structure map, configure the reservoir bottom mapping point for the corresponding position node below the reservoir surface structure map; Step S102: Connect adjacent reservoir bottom mapping points to form several structural surfaces of the reservoir bottom.

3. The method for optimizing seepage flow in a pumped storage power station based on water level fluctuation data according to claim 1, characterized in that: Methods for setting water permeability holes on corresponding structural surfaces in a three-dimensional reservoir structure based on the permeability coefficient include: Step S201: constructing a plurality of permeability coefficient intervals connected end to end, uniformly collecting a plurality of test permeability coefficients within the permeability coefficient intervals, conducting a test permeability coefficient experiment, and determining the water seepage rate of the material corresponding to the test permeability coefficient under different water pressures. If the difference between the water seepage rates corresponding to the test permeability coefficients is greater than or equal to a preset value, further narrowing and segmenting the permeability coefficient interval; Step S202, calculate the average value of the water seepage rate corresponding to the permeability coefficient interval, record it as the average water seepage rate, and determine the number of water seepage holes per unit area of ​​the structural surface based on the average water seepage rate corresponding to the permeability coefficient permeability interval.

4. The method for optimizing seepage flow in a pumped storage power station based on water level fluctuation data according to claim 1, characterized in that: Methods for collecting water particle flow characteristics at the edge of reservoir surface structures include: Step S301: configuring physical parameters for each water particle, including mass and initial position, and boundary conditions, and using finite element analysis technology to drive each water particle to perform simulated motion; In step S302, the particle orientation change and particle motion rate change of water particles at different position nodes on the edge of the reservoir surface structure are determined, the combination of the particle orientation change and the particle motion rate change is recorded as a particle flow feature group, and the particle flow feature group mark is recorded at the corresponding position node.

5. The method for optimizing seepage flow in a pumped storage power station based on water level fluctuation data according to claim 4, characterized in that: Methods for determining the flow characteristics of water at the upper edge of a reservoir include: Step S401: aligning the edge of the reservoir surface structure with the edge of the upper surface of the reservoir, and marking the position nodes marked with the particle flow feature group on the edge of the reservoir surface structure at the corresponding positions on the edge of the upper surface of the reservoir; Step S402: collecting water flow characteristics of the position nodes marked on the upper surface edge of the reservoir. The water flow characteristics include changes in water flow direction and water flow rate. The combination of water flow direction and water flow rate is recorded as a water flow characteristic group.

6. The method for optimizing seepage flow in a pumped storage power station based on water level fluctuation data according to claim 1, characterized in that: Methods for determining the credibility of reservoir infiltration field simulation models based on the degree of agreement include: Step S406: a degree of fit interval is set for the degree of fit, and the credibility of the reservoir seepage field simulation model is determined based on the degree of fit interval to which the degree of fit belongs.

7. The method for optimizing seepage flow in a pumped storage power station based on water level fluctuation data according to claim 1, characterized in that: The methods for optimizing and adjusting the reservoir seepage simulation model include: Step S407 : increasing the density of collecting the permeability coefficient, and adaptively adjusting the number of water permeability holes set on the corresponding structural surface in the three-dimensional reservoir structure as the distribution density of the permeability coefficient in the three-dimensional reservoir increases.

8. The seepage optimization system of pumped storage power station based on water level fluctuation data is characterized by: A method for optimizing seepage in a pumped storage power station for executing any one of claims 1 to 7, comprising: The first module is used to obtain a satellite geographic map of the pumped storage power station, and construct an upper reservoir surface structure map based on the satellite geographic map, and simultaneously collect water depth data of nodes at different locations of the upper reservoir, map and mark the water depth data on the reservoir surface structure map, and construct a three-dimensional reservoir bottom structure below the reservoir surface structure map based on the mapped water depth data, and combine the three-dimensional reservoir bottom structure and the upper reservoir surface structure map into a three-dimensional reservoir structure; The second module is used to fill the three-dimensional reservoir structure with water particles, collect the permeability coefficients of nodes at different locations in the reservoir, and set water permeability holes on the corresponding structural surfaces in the three-dimensional reservoir structure based on the permeability coefficients to construct a reservoir permeability field simulation model; The third module is used to drive the reservoir seepage field simulation model to perform seepage field simulation at different water levels and collect the flow characteristics of water particles at the edge of the reservoir surface structure; The fourth module is used to collect water flow characteristics at the edge of the upper surface of the reservoir at different water levels, and compare the water flow characteristics with the water particle flow characteristics at the same water level. The credibility of the reservoir infiltration field simulation model is determined based on the degree of fit. If the credibility is less than or equal to the preset value, the reservoir infiltration simulation model is optimized and adjusted until the credibility is greater than the preset value. The fifth module is used to conduct reservoir seepage early warning analysis using the optimized reservoir seepage simulation model.

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