A method, device and equipment for determining the scale of a pond, and a storage medium

Abstract

The application discloses a pond reservoir scale determination method, device, equipment and storage medium, the method comprises the following steps: a three-dimensional finite element model of a pond reservoir is established through point cloud data of the pond reservoir, and the effective storage capacity of the region is calculated according to all precipitation data of the pond reservoir within a preset time length, the sensitivity of the non-point source pollution is calculated according to the effective storage capacity, the sensitivity of the non-point source pollution is substituted into the three-dimensional finite element model for assignment, the scale parameters of the last non-point source pollution simulation are iteratively updated based on the results of each non-point source pollution simulation, the minimum value of the non-point source pollution in all non-point source pollution simulations is obtained, and finally it is determined that the scale parameter corresponding to the minimum value of the non-point source pollution is the best parameter, and the best parameter is output in the three-dimensional finite element model to generate the scale layout of the pond reservoir. The application finds the minimum non-point source pollution by model establishment and repeated iterative calculation, so that the pond reservoir layout is generated according to the results of the iterative calculation, and the scale of the pond reservoir is accurate.

Description

Technical Field

[0001] This application relates to the field of geological disaster prevention and control technology, and in particular to a method, apparatus, equipment and storage medium for determining the scale of a pond or reservoir. Background Technology

[0002] In recent years, with the increase in urban population and tourist population along lakes, coasts, and rivers, and rapid economic development, the quality of natural water has shown a downward trend (e.g., from Class I to Class II, and from Class II to Class III). Currently, water quality is mainly cleaned up through watershed optimization of the economic structure and the coastal lake-encircling sewage interception and treatment system, which cannot guarantee the effective collection of non-point source pollution from farmland and villages. This is mainly reflected in the fact that agricultural non-point source pollution is an important source of water pollution. Agricultural irrigation wastewater, rural domestic sewage, and decentralized livestock and poultry breeding wastewater are discharged directly into rural irrigation and drainage ditches without treatment, or flow into receiving rivers, lakes, and reservoirs through surface runoff, leading to organic pollution, eutrophication, and other forms of pollution.

[0003] Currently, in the construction of ponds and reservoirs, the scale of ponds and reservoirs is usually classified according to the water storage capacity level. The method of determining the size of ponds and reservoirs by excessively redundant water storage capacity requires an infrastructure area that matches the redundant water storage capacity, resulting in excessive redundancy in the size of the ponds and reservoirs. This means that the construction of ponds and reservoirs requires a large area of ​​land, and some of the effective storage capacity is never put into use after the ponds and reservoirs are built, resulting in a waste of resources. Alternatively, the water storage capacity is close to the threshold value of the level, so the total storage capacity of the ponds and reservoirs is close to the water storage capacity. When facing natural factors such as heavy rainfall, the amount of water that the ponds and reservoirs need to handle exceeds the total storage capacity, causing non-point source pollution. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, equipment, and storage medium for determining the size of a pond or reservoir, in order to solve the problem that the size of a pond or reservoir in the prior art relies on hierarchical classification, which cannot accurately determine the actual required size of the pond or reservoir.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A method for determining the size of a pond or reservoir, the method comprising:

[0007] Obtain point cloud data of the area where the pond / reservoir is located, and establish a three-dimensional finite element model of the pond / reservoir based on the point cloud data;

[0008] Obtain several precipitation data points for the region within a preset time period;

[0009] The effective reservoir capacity of the region is obtained based on all precipitation data and according to the first preset algorithm.

[0010] Based on the effective storage capacity, the non-point source pollution sensitivity of the region is obtained according to the second preset algorithm;

[0011] Based on the sensitivity of non-point source pollution, scale parameters are assigned to the three-dimensional finite element model, and non-point source pollution simulations are performed on the three-dimensional finite element model after parameter assignment a preset number of times.

[0012] The third preset algorithm iteratively updates the scale parameter of the previous non-point source pollution simulation based on the results of each non-point source pollution simulation, and obtains the minimum non-point source pollution value in all non-point source pollution simulations.

[0013] Determine whether the minimum value of non-point source pollution is within a preset range. If so, determine that the scale parameter corresponding to the minimum value of non-point source pollution is the optimal parameter.

[0014] The optimal parameters are output in the three-dimensional finite element model to generate the scale layout of the pond.

[0015] As a further improvement to this application, the effective reservoir capacity of the region is obtained based on all precipitation data according to a first preset algorithm, including:

[0016] Obtain the minimum and maximum precipitation from all precipitation data;

[0017] The minimum theoretical capacity of the pond / reservoir is defined based on the minimum precipitation and the first preset weighting factor.

[0018] The maximum theoretical capacity of the pond / reservoir is defined based on the maximum precipitation and the second preset weighting factor.

[0019] Storage capacity intervals are generated with the minimum theoretical storage capacity and the maximum theoretical storage capacity as the two ends, respectively.

[0020] At least three theoretical storage capacities are obtained at preset intervals within the storage capacity range;

[0021] Calculate the effective water interception volume for each selected theoretical reservoir capacity according to equation (1):

[0022]

[0023] Among them, V ep,i Let ΔW be the effective water interception volume of the theoretical reservoir capacity selected on day i. i To determine the increase in water volume to the theoretical reservoir capacity on day i, W np,i For the isosource runoff of the theoretical reservoir capacity on day i, W irw,i W represents the return water volume of the theoretical reservoir capacity selected on day i. tw,i For the design tailwater volume of the theoretical reservoir capacity selected on day i, WE i WP represents the evaporation rate of the pond surface within the theoretical storage capacity on day i. iWR represents the leakage rate of the theoretical reservoir capacity on day i. i WO represents the irrigation water consumption based on the theoretical reservoir capacity selected on day i. i The design discharge volume for the theoretical reservoir capacity on day i;

[0024] The reservoir capacity consumption of the region within the preset time period is obtained, and the effective water interception and storage volume that is greater than or equal to the reservoir capacity consumption and closest to the reservoir capacity consumption is selected as the optimal water interception and storage volume from all effective water interception and storage volumes.

[0025] Obtain a selected theoretical reservoir capacity that matches the optimal water interception volume and define it as the effective reservoir capacity.

[0026] As a further improvement to this application, the non-point source pollution sensitivity of the region is obtained based on the effective storage capacity according to a second preset algorithm, including:

[0027] A preset number of non-point source pollution samples were taken from the effective storage capacity, and the pollution effect of each non-point source pollution sample was calculated according to equation (2):

[0028]

[0029] Among them, EE i For the pollution effect of the i-th non-point source pollution sample, f(x1,x2,…,x) i +Δ i ,x n ) is the parameter x i The state function after disturbance, f(x) is the initial state function, and n is the parameter x. i The quantity, Δ i For parameter x i The amount of disturbance change;

[0030] The total sequence impact of all pollution effects is calculated according to equation (3):

[0031]

[0032] Where, μ i Let represent the total order effect of all pollution effects, and j represent the number of pollution effects.

[0033] As a further improvement to this application, scale parameters are assigned to the three-dimensional finite element model based on the aforementioned area source pollution sensitivity, and area source pollution simulations are performed on the parameter-assigned three-dimensional finite element model a preset number of times, including:

[0034] The three-dimensional finite element model is imported into the three-dimensional finite difference numerical calculation software through a preset interface program;

[0035] According to the preset strategy, all total sequence effects are assigned parameter values ​​to the three-dimensional finite element model respectively;

[0036] Based on the aforementioned three-dimensional finite difference numerical calculation software, the surface source pollution simulation is performed on all three-dimensional finite element models after parameter assignment for the preset number of times.

[0037] The third preset algorithm iteratively updates the scale parameters of the previous non-point source pollution simulation based on the results of each non-point source pollution simulation.

[0038] As a further improvement to this application, the scale parameter of the previous non-point source pollution simulation is iteratively updated based on the results of each non-point source pollution simulation according to the third preset algorithm, and the minimum non-point source pollution value in all non-point source pollution simulations is obtained, including:

[0039] Based on the minimum non-point source pollution value, at least two random solutions are assigned according to equation (4), and the result of all random solutions is defined as the minimum non-point source pollution value.

[0040]

[0041] Among them, X i Let x be the set of all random solutions. i1 ,x i2 ,...,x in V represents each random solution, and N is the total number of random solutions; i v is the set of velocities for all random solutions. i1 ,v i2 ,...,v in The speed of each random solution;

[0042] Based on the same random solution, the position and velocity of each random solution are updated at preset time intervals according to equation (5):

[0043]

[0044] Among them, v id Let w·v be the velocity of the current random solution at step d. id-1 Let c1·random()·(p) be the velocity inertia of the current random solution at step d-1, ω be the inertia coefficient, and c1·random()·(p) best,i -x i ) represents the self-awareness of the current random solution, c2·random()·(g best,i -x i ) represents the social cognitive representation of the current random solution; c1 and c2 are both learning factors, random() is a random number within a preset range, and p best,i For the current random solution, the optimal solution obtained so far is g. best,i This is the optimal solution obtained for all random solutions;

[0045] Iterate a preset number of times according to equation (5) to update each p. best,i And each g best,i ;

[0046] Judge each p separately best,i Is the first difference compared to the previous iteration less than or equal to the first preset adaptation threshold?

[0047] If so, then determine each g separately. best,i Is the second difference compared to the previous iteration less than or equal to the second preset adaptation threshold?

[0048] If so, it is determined that the minimum value of non-point source pollution has been obtained.

[0049] As a further improvement to this application, each p is updated by iterating a preset number of times according to equation (5). best,i and each g best,i ,include:

[0050] The inertia coefficient ω is optimized according to equation (6):

[0051]

[0052] Where, ω i The optimized inertia coefficient, ω ini Let ω be the initial inertia coefficient. end G is the inertia coefficient after the maximum number of iterations. k This represents the maximum number of iterations.

[0053] As a further improvement to this application, outputting the optimal parameters in the three-dimensional finite element model to generate the scale layout of the reservoir includes:

[0054] Calculate the total capacity of the pond / reservoir according to formula (7):

[0055] V sum =V ep +V0(7);

[0056] Among them, V sum V represents the total storage capacity. ep V0 represents the effective storage capacity, and V0 represents the dead storage capacity.

[0057] The total storage capacity is output in the three-dimensional finite element model to generate the scale layout.

[0058] To achieve the above objectives, this application also provides the following technical solutions:

[0059] A device for determining the size of a pond or reservoir, wherein the device is applied to a method for determining the size of a pond or reservoir as described above, and the device comprises:

[0060] The three-dimensional finite element model building module is used to acquire point cloud data of the area where the pond is located, and to build a three-dimensional finite element model of the pond based on the point cloud data.

[0061] The precipitation data acquisition module is used to acquire several precipitation data points for the region within a preset time period.

[0062] The effective storage capacity calculation module is used to obtain the effective storage capacity of the region based on all precipitation data and a first preset algorithm.

[0063] The non-point source pollution sensitivity calculation module is used to obtain the non-point source pollution sensitivity of the region based on the effective reservoir capacity and a second preset algorithm.

[0064] The non-point source pollution simulation module is used to assign scale parameters to the three-dimensional finite element model based on the non-point source pollution sensitivity, and to perform a preset number of non-point source pollution simulations on the three-dimensional finite element model after parameter assignment.

[0065] The non-point source pollution minimum value iteration module is used to iteratively update the scale parameter of the previous non-point source pollution simulation based on the result of each non-point source pollution simulation according to the third preset algorithm, and to obtain the minimum non-point source pollution value in all non-point source pollution simulations.

[0066] The non-point source pollution minimum value determination module is used to determine whether the non-point source pollution minimum value is within a preset range. If so, the scale parameter corresponding to the non-point source pollution minimum value is determined to be the optimal parameter.

[0067] The scale layout generation module is used to output the optimal parameters in the three-dimensional finite element model to generate the scale layout of the pond.

[0068] To achieve the above objectives, this application also provides the following technical solutions:

[0069] An electronic device includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements a method for determining the size of a pond as described above.

[0070] To achieve the above objectives, this application also provides the following technical solutions:

[0071] A storage medium storing program instructions, which, when executed by a processor, implement a method for determining the size of a pond as described above.

[0072] This application establishes a three-dimensional finite element model of a reservoir using point cloud data. Based on all precipitation data within a preset time period, the effective reservoir capacity is calculated. The non-point source pollution sensitivity is then calculated based on the effective capacity and assigned to the three-dimensional finite element model. The scale parameters of the previous non-point source pollution simulation are iteratively updated based on the results of each simulation. The minimum non-point source pollution value is obtained from all simulations, and the scale parameter corresponding to the minimum non-point source pollution value is determined as the optimal parameter. This optimal parameter is then output in the three-dimensional finite element model to generate the reservoir's scale layout. This application establishes a model and iteratively calculates it to find the minimum non-point source pollution. The results of these iterative calculations are then used to generate the reservoir layout, ensuring accurate reservoir scale without relying on hierarchical classification. Attached Figure Description

[0073] Figure 1 A schematic diagram of the process steps for one embodiment of the method for determining the scale of the pond / reservoir in this application;

[0074] Figure 2 A schematic diagram of the functional modules of one embodiment of the pond / reservoir size determination device of this application;

[0075] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of this application;

[0076] Figure 4 This is a schematic diagram of the structure of one embodiment of the storage medium of this application. Detailed Implementation

[0077] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0078] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0079] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0080] like Figure 1 As shown, this embodiment provides an example of a method for determining the size of a pond or reservoir. In this embodiment, the method for determining the size of a pond or reservoir includes the following steps:

[0081] Step S1: Obtain point cloud data of the area where the pond / reservoir is located, and establish a three-dimensional finite element model of the pond / reservoir based on the point cloud data.

[0082] Step S2: Obtain several precipitation data points for the region within a preset time period.

[0083] Step S3: Based on all precipitation data, obtain the effective reservoir capacity of the region according to the first preset algorithm.

[0084] Step S4: Based on the effective storage capacity, obtain the regional non-point source pollution sensitivity according to the second preset algorithm.

[0085] Step S5: Assign scale parameters to the three-dimensional finite element model based on its sensitivity to non-point source pollution, and perform a preset number of non-point source pollution simulations on the three-dimensional finite element model after parameter assignment.

[0086] Step S6: Based on the results of each non-point source pollution simulation, iteratively update the scale parameter of the previous non-point source pollution simulation according to the third preset algorithm, and obtain the minimum non-point source pollution value in all non-point source pollution simulations.

[0087] Step S7: Determine whether the minimum value of non-point source pollution is within the preset range. If the minimum value of non-point source pollution is within the preset range, then proceed to step S8.

[0088] Step S8: Determine the scale parameter corresponding to the minimum value of non-point source pollution as the optimal parameter.

[0089] Step S9: Output the optimal parameters in the three-dimensional finite element model to generate the scale and layout of the pond.

[0090] Further, step S3 specifically includes the following steps:

[0091] Step S31: Obtain the minimum and maximum precipitation from all precipitation data.

[0092] Step S32: Define the minimum theoretical capacity of the pond / reservoir based on the minimum precipitation and the first preset weighting factor.

[0093] Step S33: Define the maximum theoretical capacity of the pond / reservoir based on the maximum rainfall and the second preset weighting factor.

[0094] Preferably, the first preset weighting factor and the second preset weighting factor can both be determined based on the abundance of rainfall in the area where the pond or reservoir is located, combined with the water demand of the project area, to determine the horizontal year used for calculating the water collection volume of the pond or reservoir. Generally, three horizontal years, namely abundant, normal, and dry, are used for calculation. Among them, the frequency of abundant water years (corresponding to the irrigation guarantee rate) can be taken as 10% to 25%, normal water years can be taken as 50%, and dry water years can be taken as 75% to 90%.

[0095] Preferably, the first and second preset weighting factors can also collect rainfall data from rain gauges or meteorological stations in neighboring areas with similar precipitation causes, and determine the typical years of three level years according to the principle of selecting typical years in hydrology, as a reference for calculating the runoff volume of ponds and reservoirs.

[0096] Step S34: Generate storage capacity intervals with the minimum theoretical storage capacity and the maximum theoretical storage capacity as the two ends, respectively.

[0097] Step S35: Obtain at least three selected theoretical storage capacities within the storage capacity range at preset intervals.

[0098] Step S36: Calculate the effective water interception capacity for each selected theoretical reservoir according to equation (1):

[0099]

[0100] Among them, Vep,i Let ΔW be the effective water interception volume of the theoretical reservoir capacity selected on day i. i To determine the increase in water volume to the theoretical reservoir capacity on day i, W np,i For the isosource runoff of the theoretical reservoir capacity on day i, W irw,i W represents the return water volume of the theoretical reservoir capacity selected on day i. tw,i For the design tailwater volume of the theoretical reservoir capacity selected on day i, WE i WP represents the evaporation rate of the pond surface within the theoretical storage capacity on day i. i WR represents the leakage rate of the theoretical reservoir capacity on day i. i WO represents the irrigation water consumption based on the theoretical reservoir capacity selected on day i. i The design discharge volume is the theoretical reservoir capacity selected on day i.

[0101] Preferably, W np,i The specific method of obtaining it can be calculated according to formula (1):

[0102]

[0103] Among them, P i Let P be the precipitation on day i. α The design precipitation interception threshold for ponds and reservoirs when the annual precipitation control rate for the region is α, where α is the threshold value. i Let A be the runoff coefficient on day i. Ca It refers to the area of ​​the catchment area of ​​a region.

[0104] Preferably, for daily precipitation less than 2 mm, it is generally difficult to form surface runoff, so it is generally considered invalid rainfall during data processing; for daily precipitation greater than 2 mm, it is compared with (the meaning is given in the following formula), and the smaller value is taken as the precipitation depth for interception in ecological ponds and reservoirs, that is, the part of the precipitation exceeding the threshold is not intercepted by ecological ponds and reservoirs, and is discharged through its original channels; then, W is calculated based on the daily runoff coefficient and catchment area parameters. np,i .

[0105] Preferably, the annual runoff control rate is defined as the percentage of the total annual rainfall that is controlled (not discharged) within the site through natural and artificially enhanced infiltration, storage, evaporation (transpiration), etc., calculated based on multi-year daily rainfall statistics.

[0106] Preferably, for the design precipitation interception threshold P αBased on climate data from ground-based international exchange stations, select daily rainfall (excluding snowfall) data from at least the past 30 years (reflecting long-term rainfall patterns and recent climate changes). Subtract rainfall events with a value of 2 mm or less. Sort the daily rainfall values ​​from smallest to largest. Calculate the ratio of the total rainfall below a certain threshold (the total rainfall below this threshold is calculated based on the actual rainfall, and the total rainfall above this threshold is calculated based on the actual rainfall; the sum of both is added together) to the total rainfall. The rainfall (daily value) corresponding to this ratio (i.e., the annual precipitation control rate) is the design precipitation interception threshold P. α .

[0107] Preferably, the runoff coefficient α i It can be calculated using equation (1``):

[0108]

[0109] Where a and b are time-varying runoff coefficient parameters related to the underlying surface characteristics and climate characteristics of the watershed, P a,i The amount of rainfall affecting the i-th day in the early stages.

[0110] Preferably, P a,i It can be calculated using equation (1):

[0111] P a.i =K(P a,i-1 +P i-1 -R i-1 (1```).

[0112] Where K is the recession coefficient, used to reflect the characteristic of the reduction in watershed storage due to evaporation, and is calculated from water surface evaporation and the maximum watershed storage. a,i-1 For the initial impact rainfall on day i-1, P i-1 R represents the precipitation on day i-1. i-1 The precipitation runoff depth is on day i-1.

[0113] Preferably, WE i It can be calculated using equation (1):

[0114] WE i =E water -E land (1````).

[0115] Among them, E water E represents the long-term average evaporation from the water surface. land E represents the long-term average land evaporation. land The difference between the long-term average precipitation and the multi-year average runoff depth in the region is generally used for calculation.

[0116] Preferably, based on the pond / reservoir design concept and the aforementioned water balance calculation formula, different effective storage capacities (corresponding to different retention times) are initially proposed, and daily effective water storage volume regulation calculations are performed for typical years of abundant, normal, and dry seasons, respectively, to obtain the daily effective water storage volume series {Wep,i} (i=1~365 / 366) of the ecological pond / reservoir. The regulation process of the ecological pond / reservoir is as follows: for the intercepted water volume, if the farmland in the area needs irrigation, it is prioritized for outflow for irrigation; excess water is stored in the pond / reservoir as much as possible without being discarded; when the pond / reservoir is full and irrigation is not required, it is appropriately purified by the pond / reservoir before being outflowed; water that cannot enter the pond / reservoir is discharged through its original channels.

[0117] It should be noted that the initial effective storage capacity is closely related to the residence time of intercepted water in the ecological reservoir. In order to achieve the appropriate purification function of the ecological reservoir, the residence time of low-pollution water in the ecological reservoir should not be less than the minimum residence time specified in relevant regulations. At the same time, the size should be determined in accordance with the principle of moderation and economy, taking into account the requirements of the ecological reservoir function, purification process, and pollutant concentration reduction indicators.

[0118] Preferably, economic benefit indicators such as total precipitation control rate / number of control days, clean water replacement rate, tailwater utilization rate, water resource reuse rate, retention time, and construction cost are statistically analyzed for ecological ponds and reservoirs under various effective water storage volume conditions. Relationship curves between effective volume and each indicator are established, and the reservoir capacity that maximizes the marginal benefit of each indicator is found from them.

[0119] Preferably, taking into account various factors, the effective storage capacity of the ecological pond or reservoir is determined in an economically reasonable manner to meet the design objectives and maximize water resource utilization and ecological environmental benefits.

[0120] Step S37: Obtain the reservoir capacity consumption of the region within a preset time period, and select the effective water interception and storage volume that is greater than or equal to the reservoir capacity consumption and closest to the reservoir capacity consumption volume from all effective water interception and storage volumes as the optimal water interception and storage volume.

[0121] Step S38: Obtain the selected theoretical reservoir capacity that matches the optimal water interception and storage volume and define it as the effective reservoir capacity.

[0122] Furthermore, step S4 specifically includes the following steps:

[0123] Step S41: Perform a preset number of non-point source pollution samplings on the effective storage capacity, and calculate the pollution effect of each non-point source pollution sampling according to equation (2):

[0124]

[0125] Among them, EE i For the pollution effect of the i-th non-point source pollution sample, f(x1,x2,…,x) i +Δ i ,x n) is the parameter x i The state function after disturbance, f(x) is the initial state function, and n is the parameter x. i The quantity, Δ i For parameter x i The amount of disturbance change.

[0126] Step S42, calculate the total sequence effect of all pollution effects according to equation (3):

[0127]

[0128] Where, μ i Let represent the total order effect of all pollution effects, and j represent the number of pollution effects.

[0129] Furthermore, step S5 specifically includes the following steps:

[0130] Step S51: Import the three-dimensional finite element model into the three-dimensional finite difference numerical calculation software through the preset interface program.

[0131] Step S52: Assign parameter values ​​to the three-dimensional finite element model according to the preset strategy for all total sequence influences.

[0132] Step S53: Based on the three-dimensional finite difference numerical calculation software, perform a preset number of surface source pollution simulations on all three-dimensional finite element models after parameter assignment.

[0133] Step S54: Based on the results of each non-point source pollution simulation, the scale parameters of the previous non-point source pollution simulation are iteratively updated according to the third preset algorithm.

[0134] Furthermore, step S6 specifically includes the following steps:

[0135] Step S61: Based on the minimum non-point source pollution, assign at least two random solutions according to equation (4), and define the result of all random solutions as the minimum non-point source pollution.

[0136]

[0137] Among them, X i Let x be the set of all random solutions. i1 ,x i2 ,...,x in V represents each random solution, and N is the total number of random solutions; i v is the set of velocities for all random solutions. i1 ,v i2 ,...,v in The speeds for each random solution are respectively.

[0138] Step S62, based on the same random solution, update the position and velocity of each random solution at preset time intervals according to equation (5):

[0139]

[0140] Among them, v id Let w·v be the velocity of the current random solution at step d. id-1 Let c1·random()·(p) be the velocity inertia of the current random solution at step d-1, ω be the inertia coefficient, and c1·random()·(p) best,i -x i ) represents the self-awareness of the current random solution, c2·random()·(g best,i -x i ) represents the social cognitive representation of the current random solution; c1 and c2 are both learning factors, random() is a random number within a preset range, and p best,i For the current random solution, the optimal solution obtained so far is g. best,i This is the optimal solution obtained for all random solutions.

[0141] Step S63, iterate a preset number of times according to equation (5) to update each p best,i And each g best,i .

[0142] Step S64, determine each p best,i Compared to the first difference in the previous iteration, is it less than or equal to the first preset adaptation threshold? If p best,i If the first difference compared to the previous iteration is less than or equal to the first preset adaptation threshold, then step S65 is executed.

[0143] Step S65, determine each g respectively best,i Compared to the previous iteration, is the second difference less than or equal to the second preset adaptation threshold? If g best,i If the second difference compared to the previous iteration is less than or equal to the second preset adaptation threshold, then step S66 is executed.

[0144] Step S66 determines that the minimum value of non-point source pollution has been obtained.

[0145] Furthermore, step S63 specifically includes the following steps:

[0146] Step S631, optimize the inertia coefficient ω according to equation (6):

[0147]

[0148] Where, ω i The optimized inertia coefficient, ω ini Let ω be the initial inertia coefficient. endG is the inertia coefficient after the maximum number of iterations. k This represents the maximum number of iterations.

[0149] Furthermore, step S9 specifically includes the following steps:

[0150] Step S91, calculate the total storage capacity of the pond according to equation (7):

[0151] V sum =V ep +V0(7).

[0152] Among them, V sum For the total storage capacity, V ep V0 represents the effective storage capacity, while V0 represents the dead storage capacity.

[0153] Step S92: Output the total storage capacity in the three-dimensional finite element model to generate a scaled layout.

[0154] Preferably, to maintain the normal growth of aquatic plants in ecological ponds and reservoirs, ensure the health of the ecosystem, and maintain a certain landscape effect, ecological ponds and reservoirs need to maintain a certain water surface during the dry season when the inflow is low and the irrigation reuse volume is high. The elevation corresponding to this water surface is the dead water level of the ecological pond and reservoir, and the corresponding reservoir volume is the dead storage capacity. The determination of the dead storage capacity of ecological ponds and reservoirs should consider factors such as the thickness of the impermeable layer of the pond and reservoir, the thickness of the planting base layer for aquatic plants, and the minimum water depth required to maintain the survival of aquatic animals and plants. Among these, the minimum water depth varies depending on the purification process of different ponds (aerobic, anaerobic, facultative), the plants planted, and the requirements of animals and microorganisms needed to construct the food chain.

[0155] This embodiment establishes a three-dimensional finite element model of the reservoir using point cloud data. The effective reservoir capacity is calculated based on all precipitation data within a preset time period. The non-point source pollution sensitivity is then calculated based on the effective capacity and assigned to the three-dimensional finite element model. The scale parameters of the previous non-point source pollution simulation are iteratively updated based on the results of each simulation. The minimum non-point source pollution value is obtained from all simulations, and the scale parameter corresponding to the minimum non-point source pollution value is determined as the optimal parameter. This optimal parameter is then output in the three-dimensional finite element model to generate the reservoir's scale layout. This application establishes a model and iteratively calculates it to find the minimum non-point source pollution. The results of the iterative calculations are then used to generate the reservoir layout, ensuring accurate reservoir scale without relying on hierarchical classification.

[0156] like Figure 2As shown, this embodiment provides an example of a pond / reservoir scale determination device. In this embodiment, the pond / reservoir scale determination device is applied to the pond / reservoir scale determination method as described in the above embodiment. The pond / reservoir scale determination device includes a point cloud data acquisition module 1, a three-dimensional finite element model establishment module 2, a precipitation data acquisition module 3, an effective reservoir capacity calculation module 4, a non-point source pollution sensitivity calculation module 5, a non-point source pollution simulation module 6, a non-point source pollution minimum value iteration module 7, a non-point source pollution minimum value judgment module 8, and a scale layout generation module 9, which are connected in sequence.

[0157] The system includes: a point cloud data acquisition module 1 for acquiring point cloud data of the area where the pond / reservoir is located; a 3D finite element model building module 2 for building a 3D finite element model of the pond / reservoir based on the point cloud data; a precipitation data acquisition module 3 for acquiring several precipitation data points of the area within a preset time period; an effective storage capacity calculation module 4 for calculating the effective storage capacity of the area based on all precipitation data according to a first preset algorithm; a non-point source pollution sensitivity calculation module 5 for calculating the non-point source pollution sensitivity of the area based on the effective storage capacity according to a second preset algorithm; and a non-point source pollution simulation module 6 for assigning scale parameters to the 3D finite element model based on the non-point source pollution sensitivity, and for... The three-dimensional finite element model after parameter assignment performs a preset number of area source pollution simulations; the area source pollution minimum value iteration module 7 is used to iteratively update the scale parameters of the previous area source pollution simulation based on the results of each area source pollution simulation according to the third preset algorithm, and obtain the minimum area source pollution value in all area source pollution simulations; the area source pollution minimum value judgment module 8 is used to determine whether the minimum area source pollution value is within a preset interval. If the minimum area source pollution value is within the preset interval, the scale parameter corresponding to the minimum area source pollution value is determined to be the optimal parameter; the scale layout generation module 9 is used to output the optimal parameters in the three-dimensional finite element model to generate the scale layout of the pond.

[0158] Furthermore, the precipitation data acquisition module 3 specifically includes a first precipitation data acquisition submodule, a second precipitation data acquisition submodule, a third precipitation data acquisition submodule, a fourth precipitation data acquisition submodule, a fifth precipitation data acquisition submodule, a sixth precipitation data acquisition submodule, a seventh precipitation data acquisition submodule, and an eighth precipitation data acquisition submodule that are electrically connected in sequence.

[0159] The first precipitation data acquisition submodule is used to acquire the minimum and maximum precipitation amounts from all precipitation data.

[0160] The second precipitation data acquisition submodule is used to define the minimum theoretical capacity of ponds and reservoirs based on the minimum precipitation and the first preset weighting factor.

[0161] The third precipitation data acquisition submodule is used to define the maximum theoretical capacity of the pond / reservoir based on the maximum precipitation and the second preset weighting factor.

[0162] The fourth precipitation data acquisition submodule is used to generate reservoir capacity intervals with the minimum theoretical reservoir capacity and the maximum theoretical reservoir capacity as the two ends, respectively.

[0163] The fifth precipitation data acquisition submodule is used to acquire at least three selected theoretical reservoir capacities within the reservoir capacity range at preset intervals.

[0164] The sixth precipitation data acquisition submodule is used to calculate the effective water interception volume of each selected theoretical reservoir capacity according to equation (1):

[0165]

[0166] Among them, V ep,i Let ΔW be the effective water interception volume of the theoretical reservoir capacity selected on day i. i To determine the increase in water volume to the theoretical reservoir capacity on day i, W np,i For the isosource runoff of the theoretical reservoir capacity on day i, W irw,i W represents the return water volume of the theoretical reservoir capacity selected on day i. tw,i For the design tailwater volume of the theoretical reservoir capacity selected on day i, WE i WP represents the evaporation rate of the pond surface within the theoretical storage capacity on day i. i WR represents the leakage rate of the theoretical reservoir capacity on day i. i WO represents the irrigation water consumption based on the theoretical reservoir capacity selected on day i. i The design discharge volume is the theoretical reservoir capacity selected on day i.

[0167] The seventh precipitation data acquisition submodule is used to obtain the reservoir capacity consumption of a region within a preset time period, and select the effective interception and storage volume that is greater than or equal to the reservoir capacity consumption and closest to the reservoir capacity consumption volume from all effective interception and storage volumes as the optimal interception and storage volume.

[0168] The eighth precipitation data acquisition submodule is used to acquire a selection of theoretical reservoir capacity that matches the optimal water interception and storage capacity and define it as the effective reservoir capacity.

[0169] Furthermore, the effective storage capacity calculation module 4 includes a first effective storage capacity calculation submodule and a second effective storage capacity calculation submodule that are electrically connected in sequence.

[0170] The first effective storage capacity calculation submodule is used to perform a preset number of area source pollution samplings on the effective storage capacity, and calculate the pollution effect of each area source pollution sampling according to equation (2):

[0171]

[0172] Among them, EE i For the pollution effect of the i-th non-point source pollution sample, f(x1,x2,…,x) i +Δi ,x n ) is the parameter x i The state function after disturbance, f(x) is the initial state function, and n is the parameter x. i The quantity, Δ i For parameter x i The amount of disturbance change.

[0173] The second effective storage capacity calculation submodule is used to calculate the total sequence impact of all pollution effects according to equation (3):

[0174]

[0175] Where, μ i Let represent the total order effect of all pollution effects, and j represent the number of pollution effects.

[0176] Furthermore, the area source pollution sensitivity calculation module 5 includes a first area source pollution sensitivity calculation submodule, a second area source pollution sensitivity calculation submodule, a third area source pollution sensitivity calculation submodule, and a fourth area source pollution sensitivity calculation submodule that are connected in sequence.

[0177] The first area source pollution sensitivity calculation submodule is used to import the three-dimensional finite element model into the three-dimensional finite difference numerical calculation software through a preset interface program; the second area source pollution sensitivity calculation submodule is used to assign parameter values ​​to the three-dimensional finite element model according to a preset strategy for all total order effects; the third area source pollution sensitivity calculation submodule is used to perform a preset number of area source pollution simulations on all three-dimensional finite element models after parameter assignment based on the three-dimensional finite difference numerical calculation software; and the fourth area source pollution sensitivity calculation submodule is used to iteratively update the scale parameters of the previous area source pollution simulation based on the results of each area source pollution simulation according to the third preset algorithm.

[0178] Furthermore, the non-point source pollution simulation module 6 includes a first non-point source pollution simulation sub-module, a second non-point source pollution simulation sub-module, a third non-point source pollution simulation sub-module, and a fourth non-point source pollution simulation sub-module that are electrically connected in sequence.

[0179] Among them, the first non-point source pollution simulation submodule is used to assign at least two random solutions based on the minimum non-point source pollution according to equation (4), and the result of all random solutions is defined as the minimum non-point source pollution.

[0180]

[0181] Among them, X i Let x be the set of all random solutions. i1 ,x i2 ,…,x in V represents each random solution, and N is the total number of random solutions; iv is the set of velocities for all random solutions. i1 ,v i2 ,…,v in The speeds for each random solution are respectively.

[0182] The second area source pollution simulation submodule is used to update the position and velocity of each random solution according to equation (5) at preset time intervals based on the same random solution:

[0183]

[0184] Among them, v id Let w·v be the velocity of the current random solution at step d. id-1 Let c1·random()·(p) be the velocity inertia of the current random solution at step d-1, ω be the inertia coefficient, and c1·random()·(p) best,i -x i ) represents the self-awareness of the current random solution, c2·random()·(g best,i -x i ) represents the social cognitive representation of the current random solution; c1 and c2 are both learning factors, random() is a random number within a preset range, and p best,i For the current random solution, the optimal solution obtained so far is g. best,i This is the optimal solution obtained for all random solutions.

[0185] The third surface source pollution simulation submodule is used to iterate a preset number of times according to equation (5) to update each p best,i And each g best,i .

[0186] The fourth area source pollution simulation submodule is used to determine each p best,i Compared to the first difference in the previous iteration, is it less than or equal to the first preset adaptation threshold? If p best,i If the first difference compared to the previous iteration is less than or equal to the first preset adaptation threshold, then each g is judged separately. best,i Compared to the previous iteration, is the second difference less than or equal to the second preset adaptation threshold? If g best,i If the second difference compared to the previous iteration is less than or equal to the second preset adaptation threshold, then it is determined that the minimum value of non-point source pollution has been obtained.

[0187] Furthermore, the non-point source pollution simulation module 6 also includes a fifth non-point source pollution simulation sub-module that is electrically connected to the fourth non-point source pollution simulation sub-module.

[0188] The fifth surface source pollution simulation submodule is used to optimize the inertia coefficient ω according to equation (6):

[0189]

[0190] Where, ω i The optimized inertia coefficient, ω ini Let ω be the initial inertia coefficient. end G is the inertia coefficient after the maximum number of iterations. k This represents the maximum number of iterations.

[0191] Furthermore, the scale layout generation module includes a first scale layout generation submodule and a second scale layout generation submodule that are electrically connected in sequence.

[0192] The first scale layout generation submodule is used to calculate the total reservoir capacity according to equation (7):

[0193] V sum =V ep +V0(7).

[0194] Among them, V sum For the total storage capacity, V ep V0 represents the effective storage capacity, while V0 represents the dead storage capacity.

[0195] The second scale layout generation submodule is used to output the total library capacity in the three-dimensional finite element model to generate the scale layout.

[0196] It should be noted that the functional modules in this embodiment are all based on the process steps in the above embodiments. The extension and limitation of the functional modules in this embodiment can be referred to the process steps in the above embodiments, and will not be repeated in this embodiment.

[0197] This embodiment establishes a three-dimensional finite element model of the reservoir using point cloud data. The effective reservoir capacity is calculated based on all precipitation data within a preset time period. The non-point source pollution sensitivity is then calculated based on the effective capacity and assigned to the three-dimensional finite element model. The scale parameters of the previous non-point source pollution simulation are iteratively updated based on the results of each simulation. The minimum non-point source pollution value is obtained from all simulations, and the scale parameter corresponding to the minimum non-point source pollution value is determined as the optimal parameter. This optimal parameter is then output in the three-dimensional finite element model to generate the reservoir's scale layout. This application establishes a model and iteratively calculates it to find the minimum non-point source pollution. The results of the iterative calculations are then used to generate the reservoir layout, ensuring accurate reservoir scale without relying on hierarchical classification.

[0198] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Figure 3 As shown, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101.

[0199] The memory 102 stores program instructions for implementing a fault detection method for an oil-immersed transformer according to any of the above embodiments.

[0200] The processor 101 is used to execute program instructions stored in the memory 102 to perform fault detection of the oil-immersed transformer.

[0201] The processor 101 can also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip with signal processing capabilities. The processor 101 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0202] Furthermore, Figure 4 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. See also: Figure 4 The storage medium 11 in this embodiment stores program instructions 111 capable of implementing all the above methods. These program instructions 111 can be stored in the storage medium as a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods in each embodiment of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0203] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0204] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for determining the size of a pond or reservoir, characterized in that, The methods for determining the size of the pond / reservoir include: Obtain point cloud data of the area where the pond / reservoir is located, and establish a three-dimensional finite element model of the pond / reservoir based on the point cloud data; Obtain several precipitation data points for the region within a preset time period; The effective reservoir capacity of the region is obtained based on all precipitation data and according to the first preset algorithm. Based on the effective storage capacity, the non-point source pollution sensitivity of the region is obtained according to the second preset algorithm; Based on the sensitivity of non-point source pollution, scale parameters are assigned to the three-dimensional finite element model, and non-point source pollution simulations are performed on the three-dimensional finite element model after parameter assignment a preset number of times. The third preset algorithm iteratively updates the scale parameter of the previous non-point source pollution simulation based on the results of each non-point source pollution simulation, and obtains the minimum non-point source pollution value in all non-point source pollution simulations. Determine whether the minimum value of non-point source pollution is within a preset range. If so, determine that the scale parameter corresponding to the minimum value of non-point source pollution is the optimal parameter. The optimal parameters are output in the three-dimensional finite element model to generate the scale and layout of the pond; The third preset algorithm iteratively updates the scale parameters of the previous non-point source pollution simulation based on the results of each simulation, and obtains the minimum non-point source pollution value among all simulations, including: Based on the minimum non-point source pollution value, at least two random solutions are assigned according to equation (4), and the result of all random solutions is defined as the minimum non-point source pollution value; （4）； in, Let be the set of all random solutions. For each random solution, The number of all random solutions; The set of velocities for all random solutions. The speed of each random solution; Based on the same random solution, the position and velocity of each random solution are updated at preset time intervals according to equation (5): （5）； in, For the current random solution at the th Step speed, For the current random solution at the th The velocity inertia of the step, The inertia coefficient, For the self-cognitive representation of the current random solution, This represents the social cognitive representation of the current random solution. and All are learning factors. A random number within a preset value range. This is the optimal solution obtained for the current random solution. This is the optimal solution obtained for all random solutions; Iterate a preset number of times according to equation (5) to update each And each ; Judge each Is the first difference compared to the previous iteration less than or equal to the first preset adaptation threshold? If so, then determine each one separately. Is the second difference compared to the previous iteration less than or equal to the second preset adaptation threshold? If so, it is determined that the minimum value of non-point source pollution has been obtained.

2. The method for determining the scale of a pond or reservoir according to claim 1, characterized in that, Based on all precipitation data, the effective reservoir capacity of the region is obtained according to a first preset algorithm, including: Obtain the minimum and maximum precipitation from all precipitation data; The minimum theoretical capacity of the pond / reservoir is defined based on the minimum precipitation and the first preset weighting factor. The maximum theoretical capacity of the pond / reservoir is defined based on the maximum precipitation and the second preset weighting factor. Storage capacity intervals are generated with the minimum theoretical storage capacity and the maximum theoretical storage capacity as the two ends, respectively. At least three theoretical storage capacities are obtained at preset intervals within the storage capacity range; Calculate the effective water interception volume for each selected theoretical reservoir capacity according to formula (1): （1）； in, For the first The effective water interception capacity of the Tianjie reservoir is selected based on its theoretical capacity. For the first The increase in water volume based on the theoretical reservoir capacity of the Tianjie selection method. For the first The non-point source runoff of the selected theoretical reservoir capacity. For the first The return water volume of the Tianjie theoretical reservoir capacity. For the first The design tailwater volume of the Tianjie theoretical reservoir capacity For the first The evaporation rate of the pond surface within the theoretical storage capacity of the Tianjie reservoir. For the first The leakage rate of the Tianjie theoretical reservoir capacity. For the first The irrigation water consumption of the Tianjie theoretical reservoir capacity. For the first The design output of the theoretical reservoir capacity is selected by Tianjie; The reservoir capacity consumption of the region within the preset time period is obtained, and the effective water interception and storage volume that is greater than or equal to the reservoir capacity consumption and closest to the reservoir capacity consumption is selected as the optimal water interception and storage volume from all effective water interception and storage volumes. Obtain a selected theoretical reservoir capacity that matches the optimal water interception volume and define it as the effective reservoir capacity.

3. The method for determining the scale of a pond or reservoir according to claim 1, characterized in that, Based on the effective storage capacity, the non-point source pollution sensitivity of the region is obtained according to a second preset algorithm, including: A preset number of non-point source pollution samples were taken from the effective storage capacity, and the pollution effect of each non-point source pollution sample was calculated according to equation (2): （2）； in, For the first The pollution effect of sampling from a non-point source pollution source. For parameters The state function after being disturbed The initial state function, For parameters Quantity, For parameters The amount of disturbance change; The total sequence impact of all pollution effects is calculated according to equation (3): （3）； in, For the total sequence effect of all pollution effects, This represents the quantity of pollution effects.

4. The method for determining the scale of a pond or reservoir according to claim 3, characterized in that, Based on the aforementioned area source pollution sensitivity, scale parameters are assigned to the three-dimensional finite element model, and area source pollution simulations are performed on the parameter-assigned three-dimensional finite element model a preset number of times, including: The three-dimensional finite element model is imported into the three-dimensional finite difference numerical calculation software through a preset interface program; According to the preset strategy, all total sequence effects are assigned parameter values ​​to the three-dimensional finite element model respectively; Based on the aforementioned three-dimensional finite difference numerical calculation software, the area source pollution simulation is performed on all three-dimensional finite element models after parameter assignment for the preset number of times. The third preset algorithm iteratively updates the scale parameters of the previous non-point source pollution simulation based on the results of each non-point source pollution simulation.

5. The method for determining the scale of a pond or reservoir according to claim 1, characterized in that, Iterate a preset number of times according to equation (5) to update each and each ,include: The inertia coefficient is optimized according to equation (6). : （6）； in, The optimized inertia coefficient, The initial inertia coefficient, The inertia coefficient after the maximum number of iterations. This represents the maximum number of iterations.

6. The method for determining the scale of a pond or reservoir according to claim 1, characterized in that, Outputting the optimal parameters in the three-dimensional finite element model to generate the scale layout of the reservoir includes: Calculate the total capacity of the pond according to formula (7): （7）； in, The total storage capacity is [missing information]. For the effective storage capacity, For dead storage capacity; The total storage capacity is output in the three-dimensional finite element model to generate the scale layout.

7. A device for determining the size of a pond or reservoir, wherein the device is applied to a method for determining the size of a pond or reservoir as described in any one of claims 1 to 6, characterized in that, The device for determining the size of the pond / reservoir includes: The three-dimensional finite element model building module is used to acquire point cloud data of the area where the pond is located, and to build a three-dimensional finite element model of the pond based on the point cloud data. The precipitation data acquisition module is used to acquire several precipitation data points for the region within a preset time period. The effective storage capacity calculation module is used to obtain the effective storage capacity of the region based on all precipitation data and a first preset algorithm. The non-point source pollution sensitivity calculation module is used to obtain the non-point source pollution sensitivity of the region based on the effective reservoir capacity and a second preset algorithm. The non-point source pollution simulation module is used to assign scale parameters to the three-dimensional finite element model based on the non-point source pollution sensitivity, and to perform a preset number of non-point source pollution simulations on the three-dimensional finite element model after parameter assignment. The non-point source pollution minimum value iteration module is used to iteratively update the scale parameter of the previous non-point source pollution simulation based on the result of each non-point source pollution simulation according to the third preset algorithm, and to obtain the minimum non-point source pollution value in all non-point source pollution simulations. The non-point source pollution minimum value determination module is used to determine whether the non-point source pollution minimum value is within a preset range. If so, the scale parameter corresponding to the non-point source pollution minimum value is determined to be the optimal parameter. The scale layout generation module is used to output the optimal parameters in the three-dimensional finite element model to generate the scale layout of the pond.

8. An electronic device, characterized in that, The device includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements a method for determining the size of a pond as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores program instructions, which, when executed by a processor, implement a method for determining the size of a pond as described in any one of claims 1 to 6.

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