Method, device and electronic device for determining non-saturation parameter values

Abstract

To provide a method and device for determining an unsaturated parameter numeric value, and an electronic device.SOLUTION: The method of the present invention includes the steps of: determining unsaturated parameters of a first preset number of determination objects and a range of values of the unsaturated parameters in a second preset number of osmosis compartments in accordance with a preset model; generating a third preset number of unsaturated parameter combinations on the basis of the unsaturated parameters and the range of values; obtaining a finite element model and a simulation calculation condition on the basis of acquired observation data of hydroelectric power construction; performing simulation calculation on the basis of the unsaturated parameter combination, the finite element model, and the simulation calculation condition to obtain a simulation calculation result; and determining a target unsaturated parameter combination on the basis of the simulation calculation result and the observation data of the hydroelectric power construction to obtain numeric values of the unsaturated parameters.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to the technical field of hydraulic and hydroelectric power generation engineering, and in particular to a method, device, and electronic device for determining the value of an unsaturated parameter. [Background technology]

[0002] In large-scale water conservancy and hydroelectric power engineering, the problem of seepage is the key to the safety of dams, and seepage analysis is a very important issue in dam design. Seepage and seepage control are very important issues for water conservancy and hydroelectric power engineering. Traditional seepage field analysis is mostly saturated or stable seepage field analysis, and seepage analysis under unsaturated conditions such as rainfall infiltration and flood discharge has rarely been carried out. Although such unsaturated situations are common in actual construction, it is very difficult to obtain unsaturated parameters with existing field experiment methods, and most of them have large errors. In recent years, in the field of water conservancy and hydroelectric power engineering, there has been a gradual increase in seepage back analysis research, which obtains actual data such as seepage pressure and head, and uses specific back analysis methods to rationally derive unsaturated parameters of the seepage field.

[0003] The currently commonly used method of obtaining unsaturated parameters through indoor tests and back analysis has the following main shortcomings: During the actual construction and operation of water conservancy and hydropower projects, the seepage flow field of rock mass often changes dynamically due to the influence of boundary head changes, excavation and filling of dam foundations, rainfall, flood discharge, etc., and it is difficult to accurately simulate this dynamic change process in indoor tests. Current back analysis methods mainly use the equal division method, which has an extremely large number of parameter combinations and low inversion efficiency.

[0004] Therefore, the conventional technology is unable to simulate the dynamic change process of the bedrock seepage flow field, and has problems in that it is difficult to determine accurate unsaturated parameter values ​​and the acquisition efficiency is low. Summary of the Invention [Problem to be solved by the invention]

[0005] The present application provides a method, device, and electronic device for determining unsaturated parameter values ​​to at least solve the problems existing in the related art, such as the inability to simulate the dynamic change process of rock seepage flow field, the inability to determine accurate unsaturated parameter values, and low acquisition efficiency. [Means for solving the problem]

[0006] According to one aspect of the embodiment of the present application, determining a first pre-defined number of unsaturated parameters to be determined and a range of values ​​of said unsaturated parameters for a second pre-defined number of infiltration compartments according to a pre-defined model; generating a third preset number of unsaturated parameter combinations based on the unsaturated parameters and the value ranges; Obtaining a finite element model and simulation calculation conditions based on the obtained observation data of the hydroelectric power generation construction; performing a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; determining a target unsaturated parameter combination based on the simulation calculation results and observation data of the hydroelectric power generation construction, and obtaining the values ​​of the unsaturated parameters.

[0007] According to another aspect of the present embodiment, a determination module for determining a first pre-defined number of unsaturated parameters to be determined and a range of values ​​of said unsaturated parameters for a second pre-defined number of infiltration sections according to a pre-defined model; a generation module that generates a third preset number of unsaturated parameter combinations based on the unsaturated parameters and the value ranges; A first acquisition module for acquiring a finite element model and a simulation calculation condition based on the acquired observation data of the hydroelectric power generation construction; a calculation module that performs a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; The present invention further provides an unsaturated parameter value determining device, comprising: a second acquisition module for determining a target unsaturated parameter combination based on the simulation calculation result and the observation data of the hydroelectric power generation construction, and obtaining the value of the unsaturated parameter.

[0008] Optionally, the generation module: a first determination unit for determining a first unsaturated parameter, a second unsaturated parameter, and a remaining unsaturated parameter from the first preset number of unsaturated parameters to be determined; A first acquisition unit for acquiring a relationship equation between the first unsaturated parameter and a second unsaturated parameter, the relationship equation and the first unsaturated parameter representing the second unsaturated parameter; and a generating unit for obtaining candidate values ​​of each of the unsaturated parameters in the permeation section according to the first unsaturated parameter, the relationship equation, the remaining unsaturated parameters, the value range, and a preset method, and generating a third preset number of combinations of the unsaturated parameters.

[0009] Optionally, the first acquisition module comprises: A first acquisition unit for acquiring profile data points based on observation data of the hydroelectric power construction; a second acquisition unit for acquiring the finite element model based on the profile data points and a pre-defined grid type; a division unit for generalizing the finite element model to divide the finite element model into a second preset number of the permeable compartments; a second acquisition unit for acquiring a permeation compartment parameter of the permeation compartment; a third acquisition unit for acquiring boundary conditions and initial water level conditions of the finite element model according to observed data of the hydroelectric power generation project; and a fourth acquisition unit for obtaining the simulation calculation conditions based on the permeation compartment parameters, the boundary conditions and the initial water level conditions.

[0010] Optionally, the third acquisition unit comprises: A first acquisition submodule for acquiring a water level line for a preset period based on observation data of a hydroelectric power generation construction project; a first determination sub-module for determining a slope head boundary of the finite element model based on the water level line; A second determination submodule that determines a water level value of the head boundary based on observation data of the hydroelectric power generation construction project, using a left boundary of the finite element model as a head boundary; A second acquisition submodule for acquiring an actual observed head value based on the observation data of the hydroelectric power generation construction; and a third acquisition sub-module for inputting the measured observed head values ​​into a trained neural network to obtain the initial water level conditions.

[0011] Optionally, the third acquisition unit comprises: a fourth acquisition submodule for acquiring a predetermined number of watershed level values; A fourth acquisition sub-module, for obtaining a head calculation value and a corresponding boundary head value of each borehole position according to a preset seepage analysis method and the watershed water level value; and a training sub-module for training an initial neural network using the head calculation value and the boundary head value to obtain the trained neural network.

[0012] Optionally, the calculation module: a selection unit for selecting one of the third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated; a first calculation unit that performs a simulation calculation based on the unsaturated parameter combination of the calculation target, the finite element model, and the simulation calculation conditions to obtain a single combination simulation calculation result corresponding to the unsaturated parameter combination of the calculation target; A circulation unit that starts to execute the step of selecting one of the unsaturated parameter combinations from the third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated, and executes the subsequent steps until a single combination single combination simulation calculation result corresponding to each of the unsaturated parameter combinations is obtained; and an integration unit for integrating all the single combination simulation calculation results to obtain the simulation calculation result.

[0013] Optionally, the second acquisition module comprises: a second determining unit for determining a fifth predetermined number of detected data points; A fifth obtaining unit obtains a zero pressure surface position at a preset time corresponding to each of the unsaturated parameter combinations according to the simulation calculation result; a sixth acquisition unit for obtaining predicted data at each of the sensed data points for each of the unsaturated parameter combinations based on the zero pressure surface position and the sensed data points; A seventh acquisition unit that acquires actual measurement data corresponding to the prediction data based on the observation data of the hydroelectric power generation construction; a second calculation unit for calculating an error at each of the detected data points for each of the non-saturation parameter combinations based on the predicted data and the actual data; a third calculation unit for calculating a total error corresponding to each of the non-saturated parameter combinations based on the errors; and an identification unit for identifying the unsaturated parameter combination corresponding to the smallest total error as the target unsaturated parameter combination including the numerical values ​​of the unsaturated parameters.

[0014] According to a further aspect of the present embodiment, there is provided an electronic device including a processor, a communication interface, a memory, and a communication bus, the processor, the communication interface, and the memory communicating with each other via the communication bus, the electronic device comprising: The memory stores a computer program; The present invention further provides an electronic device, wherein the processor is adapted to execute the method steps according to any one of the above embodiments by operating the computer program stored in the memory.

[0015] According to a further aspect of an embodiment of the present application, there is further provided a computer-readable storage medium having a computer program stored thereon, the computer program being configured to perform the method steps according to any one of the above embodiments when executed by a processor.

[0016] In the embodiment of the present application, a first preset number of unsaturated parameters to be determined and a second preset number of unsaturated parameter value ranges in the infiltration section are determined according to a preset model, and a third preset number of unsaturated parameter combinations are generated according to the unsaturated parameters and value ranges, and a finite element model and simulation calculation conditions are obtained according to the obtained observation data of the hydroelectric power construction, and a simulation calculation is performed according to the unsaturated parameter combination, the finite element model and the simulation calculation conditions to obtain the simulation calculation result, and a target unsaturated parameter combination is determined according to the simulation calculation result and the observation data of the hydroelectric power construction, and the numerical value of the unsaturated parameter is obtained. Through the above method, a finite element model is constructed, simulation calculation conditions are set, and a simulation calculation is performed on the water level change process in the water storage, thereby effectively considering the influence of each geological factor, and improving the accuracy of parameter acquisition. By combining the unsaturated parameters to determine the target unsaturated parameter combination, the numerical value of each unsaturated parameter is obtained, which effectively reduces the amount of calculation and improves the efficiency of obtaining the optimal unsaturated parameter combination. By performing error analysis of the simulation calculation results based on observed data from hydroelectric power generation construction and selecting the target unsaturated parameter combination, it is possible to simply, conveniently, and efficiently obtain a local optimal solution. This solves the problems existing in the related art, such as the inability to simulate the dynamic change process of the rock seepage flow field, the inability to determine accurate unsaturated parameter values, and low acquisition efficiency. [Brief description of the drawings]

[0017] The drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the description, serve to explain the principles of the invention. In order to more clearly describe the embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings that need to be used in the description of the embodiments or the prior art, and it is obvious to those skilled in the art that they can obtain other drawings based on these drawings without any creative efforts. [Figure 1] FIG. 2 is a flow diagram of a method for determining an optional numerical desaturation parameter according to an embodiment of the present application. [Diagram 2] FIG. 2 is a schematic diagram of an optional finite element model and permeation compartment according to an embodiment of the present application. [Diagram 3] FIG. 13 illustrates the relationship between optional upstream water level and date change according to an embodiment of the present application. [Figure 4] FIG. 13 is a result of a seepage flow forward analysis of the parameters used in the optional unsaturated parameter combination according to an embodiment of the present application. [Diagram 5] FIG. 2 is a flow diagram of another optional method for determining a numerical desaturation parameter according to an embodiment of the present application. [Figure 6] FIG. 2 is a structural block diagram of an optional non-saturation parameter value determining device according to an embodiment of the present application; [Figure 7] FIG. 2 is a structural block diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] In order to make those skilled in the art better understand the aspects of the present application, the technical solutions in the embodiments of the present application will be described below clearly and completely with reference to the drawings in the embodiments of the present application, but it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the protection scope of the present application.

[0019] In addition, the terms "first", "second", and the like in the specification and claims of the present application and in the drawings are used to distinguish between similar objects and are not used to describe a particular order or priority. It should be understood that the data used in this manner are interchangeable where appropriate, and that the embodiments of the present application described herein can be performed in an order other than that shown or described herein. Furthermore, the terms "comprise" and "have" and any variations thereof are intended to cover an exclusive inclusion, e.g., a process, method, system, product, or apparatus that includes a series of steps or units need not be limited to the steps or units expressly described, but may include other steps or units not expressly described or inherent to the process, method, product, or apparatus.

[0020] According to one aspect of the embodiment of the present application, there is provided a method for determining an unsaturation parameter value, and as shown in FIG. 1, the flow of the method may include the following steps S101 to S105.

[0021] Step S101: determining a first preset number of unsaturated parameters to be determined and a value range of the unsaturated parameters in a second preset number of infiltration compartments according to a preset model.

[0022] Optionally, in this embodiment, the pre-set model may be the van Genuchten (VG) model, which is a smooth continuous water retention curve model based on the Mualem theory. JPEG0007640789000001.jpg45145

[0023] s is a model variable, y and n are parameters related to the pore distribution, α is a parameter related to the intake value, θ is the moisture content, and θ s is the saturated volumetric water content, and θ r is the residual volumetric water content, and k r is the soil unsaturated permeability coefficient, s eis an intermediate variable, and k s is the saturated permeability coefficient.

[0024] First, the unsaturated parameters to be determined in the van Genuchten (VG) model are determined. The unsaturated parameters are selected using α, y, n, and θ r In general, analysis is performed using the following four parameters: s and saturated permeability coefficient k s In general, data obtained from laboratory tests are used for the calculation of α, y, n, θ in a second preset number of infiltration sections according to previous laboratory tests and construction experience. r A range of values ​​is determined. Since the nature of the permeation flow is different for each permeation section, the first preset number and the second preset number are both plural, and the first preset number may be 4 or other values.

[0025] Step S102: Generate a third preset number of unsaturated parameter combinations based on the unsaturated parameters and value ranges.

[0026] Optionally, the desaturation parameters (α, y, n, θ) determined in step S101 are r Based on the range of values ​​of the second pre-set number of infiltration sections, the schemes for the unsaturated parameters are rationally combined by an orthogonal design method to generate a third pre-set number of unsaturated parameter combinations. For each unsaturated parameter combination, α, y, n, θ r There is a specific numerical value of , and the third preset number is a plurality of numbers, but is not particularly limited.

[0027] Step S103: A finite element model and simulation calculation conditions are obtained based on the acquired observation data of the hydroelectric power generation construction.

[0028] Optionally, observation data of a hydroelectric project, for example, observation data from an observation point with a long observation hole in a hydroelectric project in southwest China, generate a riverbank profile based on the observation data of the hydroelectric project, perform grid division on the riverbank profile, and create a two-dimensional or three-dimensional finite element model. Determine the initial water level conditions of the riverbank from the observation data of the hydroelectric project, set boundary conditions and seepage compartment parameters for each seepage compartment, and obtain simulation calculation conditions.

[0029] Step S104: A simulation calculation is performed based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result.

[0030] Optionally, the SEEP / W module of the GEO-studio software is used to perform a simulation calculation, such as a seepage flow simulation analysis, for each unsaturated parameter combination based on the above finite element model and simulation calculation conditions, and obtain a simulation calculation result corresponding to each unsaturated parameter combination, where the simulation calculation result includes the upstream water level based on the water level change in the reservoir and the distribution of the head (seepage pressure) at the measurement point position.

[0031] Step S105: Based on the simulation calculation results and the observation data of the hydroelectric power generation construction, a target unsaturated parameter combination is determined, and the values ​​of the unsaturated parameters are obtained.

[0032] Optionally, actual measurement data is obtained based on observation data of hydroelectric power generation construction. Based on the simulation calculation results and actual measurement data obtained in step S104, the error sum of squares between the actual measurement data and the simulation calculation results is calculated, and after the calculation of the error sum of squares of all unsaturated parameter combinations is completed, the unsaturated parameter combination with the smallest error sum of squares is set as the target unsaturated parameter combination, and the numerical value of the unsaturated parameter of the target unsaturated parameter combination is set as the optimal value of the unsaturated parameter to be determined above.

[0033] In the embodiment of the present application, a first preset number of unsaturated parameters to be determined and a second preset number of unsaturated parameter value ranges in the infiltration section are determined according to a preset model, and a third preset number of unsaturated parameter combinations are generated according to the unsaturated parameters and value ranges, and a finite element model and simulation calculation conditions are obtained according to the obtained observation data of the hydroelectric power construction, and a simulation calculation is performed according to the unsaturated parameter combination, the finite element model and the simulation calculation conditions to obtain the simulation calculation result, and a target unsaturated parameter combination is determined according to the simulation calculation result and the observation data of the hydroelectric power construction, and the numerical value of the unsaturated parameter is obtained. Through the above method, a finite element model is constructed, simulation calculation conditions are set, and a simulation calculation is performed on the water level change process in the water storage, and the influence of each geological factor is effectively considered, so that the accuracy of parameter acquisition is improved. By combining the unsaturated parameters and determining the target unsaturated parameter combination, the numerical value of each unsaturated parameter is obtained, which effectively reduces the amount of calculation and improves the efficiency of obtaining the optimal unsaturated parameter combination. By performing error analysis of the simulation calculation results based on observed data from hydroelectric power generation construction and selecting the target unsaturated parameter combination, it is possible to simply, conveniently, and efficiently obtain a local optimal solution. This solves the problems existing in the related art, such as the inability to simulate the dynamic change process of the rock seepage flow field, the inability to determine accurate unsaturated parameter values, and low acquisition efficiency.

[0034] As an optional example, the step of generating a third preset number of unsaturated parameter combinations based on the unsaturated parameters and the value ranges may include: determining a first unsaturated parameter, a second unsaturated parameter, and a remaining unsaturated parameter from a first preset number of unsaturated parameters to be determined; Obtaining a relationship equation between a first unsaturated parameter and a second unsaturated parameter, the relationship equation and the first unsaturated parameter being representative of the second unsaturated parameter; and obtaining candidate values ​​for each unsaturated parameter in the infiltration section according to the first unsaturated parameter, the relationship equation, the remaining unsaturated parameters, the value range and the preset method, to generate a third preset number of unsaturated parameter combinations.

[0035] Optionally, in this embodiment, the desaturation parameters are α, y, n, θ r In this case, the first preset number is 4. r From the above, a first unsaturated parameter, e.g., n, and a second unsaturated parameter, e.g., y, are selected, and the remaining unsaturated parameters are selected from α, θ r Includes:

[0036] A relational equation between n and y, for example, y=1-1 / n, may be obtained, and y may be expressed by n according to the relational equation. Therefore, once the value of n is determined, the value of y can be obtained according to the value of n and the relational equation, and the value of y does not need to be taken into account in generating the unsaturated parameter combination, thereby reducing the amount of calculation. Next, the first unsaturated parameter n, the remaining unsaturated parameters α and θ are determined. r Based on the value ranges of the above unsaturated parameters in the second preset number of infiltration compartments, the schemes for the unsaturated parameters are rationally combined by a preset method (e.g., orthogonal design method) to generate a third preset number of unsaturated parameter combinations. Taking the case where there are three types of infiltration compartments as an example, the value range of α in the infiltration compartment 1 is, for example, 0.02 to 0.14, the value range of n in the infiltration compartment 2 is, for example, 0 to 4.00, and the ranges of other values ​​are not described in detail, and the detailed value ranges are shown in the following table. The generated unsaturated parameter combinations are shown in the following table.

[0037] JPEG0007640789000002.jpg61170

[0038] In this case, the second preset number is three, the non-saturated parameter combinations include groups 1 to 7, and the third preset number is seven.

[0039] In the embodiment of the present application, the unsaturated parameters are combined by orthogonal design, which can effectively reduce the calculation of the combination and improve the efficiency of obtaining the optimal unsaturated parameter combination.

[0040] As an optional embodiment, the step of obtaining a finite element model and a simulation calculation condition based on the obtained observation data of the hydroelectric power generation construction includes: obtaining profile data points based on observed data of the hydroelectric construction; obtaining a finite element model based on the profile data points and a pre-defined grid type; generalizing the finite element model to divide the finite element model into a second predetermined number of permeable compartments; obtaining permeation compartment parameters for the permeation compartment; obtaining boundary conditions and initial water level conditions for a finite element model based on observed data of a hydroelectric power generation project; Obtaining simulation calculation conditions based on the infiltration compartment parameters, boundary conditions and initial water level conditions.

[0041] Optionally, in this embodiment, the observation data of an observation point with a long observation hole in a hydroelectric power project in southwest China is used as the observation data of the hydroelectric power project to create a two-dimensional finite element model. Based on the observation data of the hydroelectric power project, obtain profile data points, import the profile data points into the SEEP / W module of GEO-studio software, generate a riverbank profile with a left side above sea level of 1010 meters (m), a right side above sea level of 575 m, and a bottom of 1100 m, and then use a preset grid type to perform grid division on the riverbank profile graph to create a two-dimensional finite element model, where the preset grid type can be a 10 m square grid. After the model is appropriately generalized, the finite element model is divided into a second preset number, for example, three seepage compartments, and the generalization is a normal method, that is, ignoring some factors such as faults and structural surfaces that have little impact. In this case, the finite element model includes infiltration section 1, infiltration section 2, and infiltration section 3, as shown in Figure 2, the upper right corner of Figure 2 is the river valley, the two-dimensional finite element model in Figure 2 has an initial groundwater level line, and also has monitoring point 1 and monitoring point 2, the two monitoring points are for selecting and comparing data from the observation data and simulation calculation results of the hydroelectric power generation construction, please refer to Figure 2 for details.

[0042] In addition, the boundary conditions, initial water level conditions and infiltration compartment parameters can be set in the SEEP / W module of the GEO-studio software as follows: The method includes obtaining the infiltration compartment parameters of the infiltration compartment, where the model is adjusted according to the infiltration capacity of each zone, and the saturated infiltration coefficient is adjusted according to the infiltration capacity of each zone. -5 m / s, 3.00×10 -6 m / s, 9.00 x 10 -7 The saturated volumetric water content of the permeation compartment 1 is determined as 0.30, and the saturated volumetric water content of the permeation compartments 2 and 3 is determined as 0.40. The specific permeation compartment parameters are shown in the table below.

[0043] JPEG0007640789000003.jpg27170

[0044] The initial water level conditions of the riverbank may be determined from the observation data of the hydroelectric power generation construction work, boundary conditions may be set, the above-mentioned infiltration section parameters, boundary conditions and initial water level conditions may be integrated to obtain simulation calculation conditions, and a simulation calculation may be performed based on the simulation calculation conditions and the above-mentioned finite element model.

[0045] In the embodiment of the present application, a finite element model is created based on the observation data of hydroelectric power generation engineering, and together with the simulation calculation conditions, a basis for subsequent simulation calculation is provided. Based on the above finite element model and the simulation calculation conditions, an inversion analysis is carried out according to the relationship between the water level and the measurement point seepage pressure and head during the water level change process in the reservoir, so as to effectively consider the influence of each geological factor, and improve the accuracy of parameter acquisition.

[0046] As an optional example, the step of obtaining boundary conditions and initial water level conditions for the finite element model based on observed data of the hydroelectric power generation construction includes: A step of obtaining a water level line for a preset period based on observation data of a hydroelectric power generation construction project; determining a slope head boundary of the finite element model based on the waterline; A step of determining a water level value of the head boundary based on observation data of a hydroelectric power generation construction project, with the left boundary of the finite element model being a head boundary; Obtaining an actual observed head value based on observation data of a hydroelectric power generation construction project; and inputting the measured observed head values ​​into the trained neural network to obtain initial water level conditions.

[0047] Optionally, in this embodiment, the observation data of an observation point with a long observation hole in a hydroelectric power plant in southwest China is used as the observation data of the hydroelectric power plant, and the change in the upstream water level over time (i.e., the water level line) in a hydroelectric power plant in southwest China is obtained based on the observation data of the hydroelectric power plant. This water level line is shown in FIG. 3, which includes the change in the upstream water level (m) between 2021 / 4 / 3 and 2021 / 10 / 3. From FIG. 3, the water level line for a preset period is obtained. The preset period may be from April 23, 2021 to May 7, 2021, for a total of 15 days.

[0048] Based on this water level line, the valley side of the river in the finite element model is set as the slope head boundary (i.e., the upper right corner in FIG. 2), and the water level value of the slope head boundary is determined based on the water level line.

[0049] The boundary water level on the left side of the finite element model (the boundary on the mountain side of the right bank, i.e., the side away from the river valley) is determined as the head boundary, and the water level value of the head boundary is determined based on observation data from hydroelectric power generation construction work.For example, the water level value of the head boundary is determined by inversion using borehole water level observation data in the observation data from hydroelectric power generation construction work.

[0050] Based on the observation data of hydroelectric power generation construction, the actual observation head value is obtained, and the actual observation head value is input into the trained neural network to obtain the optimal watershed boundary water level condition, i.e., the initial water level condition.

[0051] In an embodiment of the present application, the boundary conditions of a finite element model are determined based on observational data of hydroelectric power generation construction, and a trained neural network is used to obtain the initial water level conditions, thereby reducing the time it takes to determine the initial water level conditions.

[0052] As an optional example, prior to inputting the actual measured head values ​​into the trained neural network, the method includes: obtaining a fourth preset number of watershed level values; obtaining head calculation values ​​and corresponding boundary head values ​​for each borehole location according to a preset seepage analysis method and a watershed water level value; and training an initial neural network using the head calculations and the boundary head values ​​to obtain a trained neural network.

[0053] Optionally, a plurality of proposed watershed water level values ​​are obtained, and the fourth preset number is a plurality of values. Using a stable seepage analysis method (i.e., a preset seepage analysis method), the seepage flow field of the left bank mountain body and the calculated head values ​​at each drilling position are calculated from the above watershed water level values, and corresponding boundary head values ​​are obtained. The calculated head values ​​are used as input samples and the boundary head values ​​are used as output samples to train an initial neural network, and a trained neural network is obtained. The initial neural network may be a genetic neural network.

[0054] In the embodiment of the present application, the head calculation value and the boundary head value are used to train an initial neural network and obtain a trained neural network, thereby reducing the time it takes to determine the initial water level condition and improving the efficiency of determining the desaturation parameter value.

[0055] As an optional embodiment, the step of performing a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result includes: Selecting one unsaturated parameter combination from a third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated; A step of performing a simulation calculation based on the unsaturated parameter combination to be calculated, the finite element model, and the simulation calculation conditions to obtain a single combination simulation calculation result corresponding to the unsaturated parameter combination to be calculated; A third step of selecting one unsaturated parameter combination from the preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated, and starting to execute the subsequent steps to obtain a single combination simulation calculation result corresponding to each unsaturated parameter combination; and combining all the single combination simulation calculation results to obtain a simulation calculation result.

[0056] Optionally, in this embodiment, it is necessary to carry out simulation calculation for each unsaturated parameter combination, and obtain a single combination simulation calculation result corresponding to this unsaturated group. This process is repeated until the simulation calculation for all unsaturated parameter combinations is completed, and specifically includes:

[0057] One unsaturated parameter combination is selected from the third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated, and a combination simulation calculation is performed on the unsaturated parameters of the calculation target based on the above finite element model and simulation calculation conditions, to obtain a single combination simulation calculation result corresponding to the unsaturated parameter combination to be calculated. An unsaturated parameter combination for which a simulation calculation has not been performed is selected from the third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated, and the above process is repeated to obtain a single combination simulation calculation result for the unsaturated parameter combination to be calculated. The process ends when a single combination simulation calculation result corresponding to each unsaturated parameter combination is obtained.

[0058] All the single combination simulation calculation results may be integrated to obtain a simulation calculation result, and a target unsaturated parameter combination may be determined based on the simulation calculation result and the observation data of the hydroelectric power generation construction.

[0059] In the embodiment of the present application, a simulation calculation is performed for each unsaturated parameter combination, and a simulation calculation result corresponding to each unsaturated parameter combination is obtained, which later provides a basis for selecting a target unsaturated parameter combination from the unsaturated parameter combinations based on the simulation calculation result and the observation data of the hydroelectric power generation project.

[0060] As an optional embodiment, the step of determining a target unsaturated parameter combination and obtaining a value of the unsaturated parameter based on the simulation calculation result and the observation data of the hydroelectric power generation construction includes: determining a fifth preset number of detected data points; Obtaining a zero pressure surface position at a preset time corresponding to each unsaturated parameter combination based on a simulation calculation result; obtaining predicted data at each sensed data point for each unsaturated parameter combination based on the zero pressure surface location and the sensed data points; obtaining actual measurement data corresponding to the prediction data based on observation data of the hydroelectric power generation construction; calculating an error at each detected data point for each desaturation parameter combination based on the predicted data and the actual data; calculating a total error corresponding to each unsaturated parameter combination based on the errors; Identifying the unsaturated parameter combination that corresponds to the smallest total error as a target unsaturated parameter combination that includes the numerical values ​​of the unsaturated parameters.

[0061] Optionally, a fifth preset number of detection data points is determined, and the fifth preset number is a plurality of detection data points. In this embodiment, two detection data points, i.e., monitoring point 1 and monitoring point 2 in FIG. 2, are used to explain a total of seven non-saturation parameter combinations of groups 1 to 7 as an example.

[0062] Based on the simulation calculation results, the seepage flow forward analysis result diagram shown in Figure 4 of the parameters used for each unsaturated parameter combination is obtained. Figure 4 shows the distribution of the head of different pressures in the finite element model, and from this seepage flow forward analysis result diagram, the zero pressure surface position at the preset time corresponding to each unsaturated parameter combination can be obtained. The zero pressure surface position is the position where the pressure head is 0m, and the preset times are 1, 5, 7, 11, and 15 days. Based on the above zero pressure surface position, monitoring point 1 and monitoring point 2, the predicted data at each detection data point of each unsaturated parameter combination is obtained. The predicted data is the sea level (m) at the two detection data points of the zero pressure surface position. Based on the observation data of the hydroelectric power generation construction, the actual measurement data (m) corresponding to the predicted data is obtained. The above predicted data and actual measurement data are shown in the following table.

[0063] JPEG0007640789000004.jpg80170

[0064] Based on the above predicted data and actual measured data, the standard deviation (i.e., error) at each detected data point for each desaturation parameter combination is calculated, and based on the error, the population standard deviation sum (i.e., total error) corresponding to each desaturation parameter combination is calculated. The errors and population standard deviation sum (i.e., total error) at monitoring point 1 and monitoring point 2 for each desaturation parameter combination are shown in the table below. JPEG0007640789000005.jpg53170

[0065] As a result of the comparison, the data calculated for group 5 has a population standard deviation of 0.04166 when compared with the data at monitoring point 1, and a population standard deviation of 0.04470 when compared with the data at monitoring point 2. The sum of the population standard deviations (i.e., the sum of the errors at monitoring point 1 and monitoring point 2) is the desaturation parameter combination with the smallest population standard deviation sum among all the groups. Therefore, group 5 is determined as the target desaturation parameter combination. rThe specific value of n in each infiltration section is obtained, and the specific value of y in each infiltration section is calculated using the specific value of n according to the relationship equation between n and y: y=1-1 / n.

[0066] In the embodiment of the present application, error analysis is performed by employing the sum of squares of the error between the calculated measured data and the numerical simulation calculation results, thereby making it possible to efficiently obtain a local optimum solution in a simple, easy manner.

[0067] According to one aspect of the embodiment of the present application, there is provided another method for determining an unsaturation parameter value, as shown in FIG. 5, the flow of the method includes the following steps:

[0068] The method includes the steps of determining the number and approximate range of VG model inversion analysis parameters, rationally combining schemes for unsaturated parameters using an orthogonal design method, creating a three-dimensional finite element model and performing an unsaturated seepage flow forward analysis, calculating the sum of squares of the error between the actual measured data and the numerical simulation calculation results to find the optimal combination, and inverting the results.

[0069] In the embodiment of the present application, an inversion analysis is performed according to the relationship between the water level and the measurement point osmotic pressure and hydraulic head during the water level change process in the reservoir, and the influence of each geological factor is effectively considered, so that the accuracy of parameter acquisition is improved. The unsaturated parameters are combined by orthogonal design, which effectively reduces the combination calculation and improves the efficiency of obtaining the optimal unsaturated parameter combination. The error analysis is performed using the error sum of squares between the calculated actual data and the numerical simulation calculation results, which is simple and convenient, and the local optimal solution can be efficiently obtained.

[0070] According to another aspect of the embodiment of the present application, there is also provided an unsaturated parameter value determining device for implementing the above-mentioned unsaturated parameter value determining method. Figure 6 is a structural block diagram of an optional unsaturated parameter value determining device according to the embodiment of the present application. As shown in Figure 6, the device includes: a determining module 601 for determining a first preset number of unsaturated parameters to be determined and a value range of the unsaturated parameters in a second preset number of infiltration sections according to a preset model; a generation module 602 for generating a third preset number of combinations of unsaturated parameters based on the unsaturated parameters and the range of values; A first acquisition module 603 for acquiring a finite element model and a simulation calculation condition based on the acquired observation data of the hydroelectric power generation construction; A calculation module 604 performs a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; It may also include a second acquisition module 605 that determines a target unsaturated parameter combination based on the simulation calculation results and observation data of the hydroelectric power generation construction, and obtains the numerical values ​​of the unsaturated parameters.

[0071] In addition, the determination module 601 in this embodiment may perform the above step S101, the generation module 602 in this embodiment may perform the above step S102, the first acquisition module 603 in this embodiment may perform the above step S103, the calculation module 604 in this embodiment may perform the above step S104, and the second acquisition module 605 in this embodiment may perform the above step S105.

[0072] By using the above modules, a finite element model is constructed, simulation calculation conditions are set, and simulation calculations are performed on the water level change process in the reservoir, and the influence of each geological factor is effectively considered, thereby improving the accuracy of parameter acquisition. By combining unsaturated parameters and determining the target unsaturated parameter combination, the numerical value of each unsaturated parameter is obtained, thereby effectively reducing the amount of calculation and improving the efficiency of obtaining the optimal unsaturated parameter combination. By performing error analysis of the simulation calculation result based on the observation data of the hydroelectric power generation project and selecting the target unsaturated parameter combination, a local optimal solution can be obtained simply and easily and efficiently. The problem existing in the related art that the dynamic change process of the rock seepage flow field cannot be simulated, accurate unsaturated parameter numerical values ​​cannot be determined, and the acquisition efficiency is low is solved.

[0073] As an optional example, the generation module may: A first determination unit for determining a first unsaturated parameter, a second unsaturated parameter and a remaining unsaturated parameter from a first preset number of unsaturated parameters to be determined; A first acquisition unit for acquiring a relationship equation between a first unsaturated parameter and a second unsaturated parameter, the relationship equation and the first unsaturated parameter representing the second unsaturated parameter; and a generation unit for obtaining candidate values ​​for each unsaturated parameter in the infiltration section according to the first unsaturated parameter, the relationship equation, the remaining unsaturated parameters, the value range and the preset method, and generating a third preset number of unsaturated parameter combinations.

[0074] As an optional example, the first acquisition module includes: A first acquisition unit for acquiring profile data points based on observation data of the hydroelectric power generation construction; a second acquisition unit for acquiring a finite element model based on the profile data points and a pre-set grid type; a partitioning unit for generalizing the finite element model to partition the finite element model into a second pre-defined number of permeable compartments; a second acquisition unit for acquiring a permeation compartment parameter of the permeation compartment; a third acquisition unit for acquiring boundary conditions and initial water level conditions of the finite element model based on observed data of the hydroelectric power generation project; and a fourth acquisition unit for obtaining a simulation calculation condition based on the infiltration compartment parameters, the boundary conditions and the initial water level condition.

[0075] As an optional example, the third acquisition unit: A first acquisition submodule for acquiring a water level line for a preset period based on observation data of a hydroelectric power generation construction project; a first determination submodule for determining a slope head boundary of the finite element model based on the water level line; a second determination submodule for determining a water level value of the head boundary based on observation data of the hydroelectric power generation construction, with the left boundary of the finite element model as the head boundary; a second acquisition submodule for acquiring a measured head value based on the observation data of the hydroelectric power plant construction; and a third acquisition sub-module for inputting the measured observed head values ​​into the trained neural network to obtain initial water level conditions.

[0076] As an optional example, the third acquisition unit: a fourth acquisition submodule for acquiring a predetermined number of watershed level values; a fourth acquisition sub-module for obtaining a head calculation value and a corresponding boundary head value of each borehole position according to a preset seepage analysis method and a watershed water level value; and a training sub-module for training the initial neural network using the head calculation values ​​and the boundary head values ​​to obtain a trained neural network.

[0077] As an optional example, the calculation module may: a selection unit for selecting one unsaturated parameter combination from a third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated; A first calculation unit that performs a simulation calculation based on the unsaturated parameter combination of the calculation target, the finite element model, and a simulation calculation condition to obtain a single combination simulation calculation result corresponding to the unsaturated parameter combination of the calculation target; a third circulation unit for selecting one unsaturated parameter combination from the preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated, and starting to execute subsequent steps to obtain a single combination simulation calculation result corresponding to each unsaturated parameter combination; and an integration unit for integrating all the single combination simulation calculation results to obtain a simulation calculation result.

[0078] As an optional example, the second acquisition module may include: a second determining unit for determining a fifth predetermined number of detected data points; A fifth obtaining unit obtains a zero pressure surface position at a preset time corresponding to each unsaturated parameter combination according to the simulation calculation result; a sixth acquisition unit for obtaining predicted data at each detected data point for each unsaturated parameter combination according to the zero pressure surface position and the detected data point; a seventh acquisition unit for acquiring actual measurement data corresponding to the predicted data based on the observation data of the hydroelectric power generation construction; a second calculation unit for calculating an error at each detected data point for each desaturation parameter combination based on the predicted data and the actual measured data; a third calculation unit for calculating a total error corresponding to each unsaturated parameter combination based on the error; and a determination unit for determining the unsaturated parameter combination corresponding to the minimum total error as a target unsaturated parameter combination including the numerical values ​​of the unsaturated parameters.

[0079] Here, the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the contents disclosed in the above embodiments.

[0080] According to a further aspect of the embodiment of the present application, there is also provided an electronic device for implementing the above-mentioned method for determining a desaturation parameter value, and the electronic device may be a server, a terminal, or a combination thereof.

[0081] FIG. 7 is a structural block diagram of an optional electronic device according to an embodiment of the present application. As shown in FIG. 7, the electronic device includes: a processor 701, a communication interface 702, a memory 703, and a communication bus 704, and the processor 701, the communication interface 702, and the memory 703 communicate with each other via the communication bus 704; The memory 703 stores computer programs. When the processor 701 executes a computer program stored in the memory 703, determining a range of values ​​of the unsaturated parameter for a first predetermined number of determined unsaturated parameters and a second predetermined number of infiltration sections according to a predetermined model; generating a third preset number of unsaturated parameter combinations based on the unsaturated parameters and value ranges; Obtaining a finite element model and simulation calculation conditions based on the obtained observation data of the hydroelectric power generation construction; performing a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; The method also includes a step of determining a target unsaturated parameter combination based on the simulation calculation results and the observation data of the hydroelectric power generation construction work, and obtaining the numerical values ​​of the unsaturated parameters.

[0082] Optionally, in this embodiment, the above communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus may be classified into an address bus, a data bus, a control bus, etc. In FIG. 7, only one thick line is shown for ease of illustration, but this does not indicate that there is only one bus or only one type of bus.

[0083] The communication interface is used for communication between the electronic device and other devices.

[0084] The memory may include RAM and may include non-volatile memory, such as at least one magnetic disk memory. Optionally, the memory may be at least one storage device remote from the processor.

[0085] 7, the memory 703 may include, but is not limited to, the determining module 601, the generating module 602, the first obtaining module 603, the calculating module 604, and the second obtaining module 605 of the device for determining the non-saturation parameter value. In addition, the memory 703 may include, but is not limited to, other module units of the device for determining the non-saturation parameter value, which are not described in detail in this example.

[0086] The processor may be a general-purpose processor, including but not limited to a Central Processing Unit (CPU), a Network Processor (NP), etc., a Digital Signal Processing (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0087] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, so they will not be described in detail in this embodiment.

[0088] As can be understood by those skilled in the art, the structure shown in FIG. 7 is schematic, and the device implementing the above-mentioned method for determining the desaturation parameter value may be a terminal device, and the terminal device may be a terminal device such as a smartphone (e.g., Android mobile phone, iOS mobile phone, etc.), a tablet terminal, a handheld computer, a mobile Internet device (MID: Mobile Internet Devices), a PAD, etc. FIG. 7 does not limit the structure of the above-mentioned electronic device. For example, the terminal device may include more or less components (e.g., a network interface, a display device, etc.) than those shown in FIG. 7, or may have a different configuration from that shown in FIG. 7.

[0089] As can be understood by those skilled in the art, all or part of the steps in the various methods of the above embodiments may be performed by instructing a program to hardware associated with the terminal equipment, and the program may be stored in a computer-readable storage medium including a flash memory, a ROM, a RAM, a magnetic disk, an optical disk, etc.

[0090] According to a further aspect of the present embodiment, a storage medium is also provided. Optionally, in this embodiment, the storage medium may store a program code for executing the method for determining the desaturation parameter value.

[0091] Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in the network shown in the above embodiment.

[0092] Optionally, in this embodiment, the storage medium comprises: determining a range of values ​​of the unsaturated parameter for a first predetermined number of determined unsaturated parameters and a second predetermined number of infiltration sections according to a predetermined model; generating a third preset number of unsaturated parameter combinations based on the unsaturated parameters and value ranges; Obtaining a finite element model and simulation calculation conditions based on the obtained observation data of the hydroelectric power generation construction; performing a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; The computer-implemented storage device is configured to store a program code for executing a step of determining a target unsaturated parameter combination and obtaining numerical values ​​of the unsaturated parameters based on the simulation calculation results and observation data of the hydroelectric power generation construction work.

[0093] Optionally, the specific examples in this embodiment can refer to the examples described in the above embodiments, but are not described in detail in this embodiment.

[0094] Optionally, in this embodiment, the above storage medium may include various media capable of storing program code, such as, but not limited to, a USB memory, a ROM, a RAM, a removable hard disk, a magnetic disk, an optical disk, etc.

[0095] In the description of this specification, a description that refers to "this embodiment", "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, general expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. In addition, a person skilled in the art may combine different embodiments or examples described herein, as well as features of different embodiments or examples, without mutual contradiction. In the description of this disclosure, "plurality" means at least two, for example, two, three, etc., unless otherwise expressly specified.

[0096] It is clear that the above embodiment is merely an example for clear explanation, and is not intended to limit the embodiment. Those skilled in the art can make other different forms of changes or modifications based on the above description. A complete list of all implementations is not necessary or possible here. Any obvious changes or modifications derived thereby are within the protection scope of the present invention.

Claims

1. 1. A method for determining an unsaturation parameter value, comprising the steps of: Determining a first predetermined number of unsaturated parameters to be determined and a range of values ​​of the unsaturated parameters in a second predetermined number of permeation compartments according to a predetermined model, the predetermined model being a VG model, the predetermined model being represented by the following formula: s is a model variable, y and n are parameters related to the pore distribution, α is a parameter related to the intake value, θ is the water content, and θ ｓ is the saturated volumetric water content, and θ ｒ is the remaining volumetric water content, k ｒ is the soil unsaturated permeability coefficient, s ｅ is an intermediate variable, and generating a third preset number of unsaturated parameter combinations based on the unsaturated parameters and the value ranges; Obtaining a finite element model and simulation calculation conditions based on the obtained observation data of the hydroelectric power generation construction; performing a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; determining a target unsaturated parameter combination based on the simulation calculation result and the observation data of the hydroelectric power generation construction, and obtaining a value of the unsaturated parameter; generating a third preset number of unsaturated parameter combinations based on the unsaturated parameters and the ranges of values; determining a first unsaturated parameter, a second unsaturated parameter, and a remaining unsaturated parameter from the first preset number of unsaturated parameters to be determined; obtaining a relationship equation between the first unsaturation parameter and a second unsaturation parameter, the relationship equation and the first unsaturation parameter being representative of the second unsaturation parameter; and obtaining candidate values ​​of each of the unsaturated parameters in the permeable section by a preset method, which is an orthogonal design method, using the first unsaturated parameter, the relationship equation, the remaining unsaturated parameters, and the range of values, to generate a third preset number of combinations of the unsaturated parameters; The step of obtaining a finite element model and simulation calculation conditions based on the obtained observation data of the hydroelectric power generation construction, obtaining profile data points based on observed data of the hydroelectric construction; obtaining the finite element model based on the profile data points and a pre-defined grid type; generalizing the finite element model to divide the finite element model into a second predetermined number of the permeable compartments; obtaining permeation compartment parameters for the permeation compartment; obtaining boundary conditions and initial water level conditions for the finite element model based on observed data of the hydroelectric power generation construction; obtaining the simulation calculation conditions based on the infiltration compartment parameters, the boundary conditions, and the initial water level conditions; The step of obtaining boundary conditions and initial water level conditions of the finite element model based on observation data of the hydroelectric power generation construction includes: A step of obtaining a water level line for a preset period based on observation data of a hydroelectric power generation construction project; determining a slope head boundary of the finite element model based on the waterline; A step of determining a water level value of the head boundary based on observation data of the hydroelectric power generation construction project, with the left boundary of the finite element model being a water head boundary, the step of determining the water level value of the head boundary based on the observation data of the hydroelectric power generation construction project includes: A step including a step of determining the water level value of the head boundary by inversion based on borehole water level observation data in the observation data of the hydroelectric power generation construction; Obtaining an actual observed head value based on observation data of the hydroelectric power generation construction work; obtaining a fourth preset number of watershed level values; Obtaining head calculation values ​​and corresponding boundary head values ​​for each borehole position according to a preset seepage analysis method and the watershed water level value; training an initial neural network using the head calculation values ​​and the boundary head values ​​to obtain a trained neural network; inputting the measured observed head values ​​into the trained neural network to obtain the initial water level conditions; Each step is executed by a processor A method for determining an unsaturation parameter value, comprising:

2. The step of performing a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result includes: selecting one of the third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated; A step of performing a simulation calculation based on the unsaturated parameter combination of the calculation target, the finite element model, and the simulation calculation conditions, and obtaining a single combination simulation calculation result corresponding to the unsaturated parameter combination of the calculation target; A step of selecting one of the third preset number of unsaturated parameter combinations as an unsaturated parameter combination to be calculated, and performing subsequent steps until a single combination simulation calculation result corresponding to each of the unsaturated parameter combinations is obtained; and integrating all the single combination simulation calculation results to obtain the simulation calculation result. Each step is executed by a processor 2. The method of claim 1 .

3. The step of determining a target unsaturated parameter combination and obtaining a value of the unsaturated parameter based on the simulation calculation result and the observation data of the hydroelectric power generation construction, determining a fifth preset number of detected data points; obtaining a zero pressure surface position at a preset time corresponding to each of the unsaturated parameter combinations based on the simulation calculation results; obtaining predicted data at each of the sensed data points for each of the unsaturated parameter combinations based on the zero pressure surface location and the sensed data points; obtaining actual measurement data corresponding to the prediction data based on observation data of the hydroelectric power generation construction work; calculating an error at each of the detected data points for each of the non-saturated parameter combinations based on the predicted data and the measured data; calculating a total error corresponding to each of the unsaturated parameter combinations based on the errors; identifying the unsaturated parameter combination corresponding to the smallest total error as the target unsaturated parameter combination including the numerical values ​​of the unsaturated parameters; Each step is executed by a processor 2. The method of claim 1 .

4. 1. An apparatus for determining an unsaturation parameter value, comprising: a determination module for determining a first predetermined number of unsaturated parameters to be determined and a range of values ​​of said unsaturated parameters at a second predetermined number of permeation compartments according to a predetermined model, The preset model is a VG model, and the preset model is expressed by the following formula: s is a model variable, y and n are parameters related to the pore distribution, α is a parameter related to the intake value, θ is the water content, and θ ｓ is the saturated volumetric water content, and θ ｒ is the remaining volumetric water content, k ｒ is the soil unsaturated permeability coefficient, s ｅ is an intermediate variable, the decision module, a generation module that generates a third preset number of unsaturated parameter combinations based on the unsaturated parameters and the value ranges; A first acquisition module for acquiring a finite element model and a simulation calculation condition based on the acquired observation data of the hydroelectric power generation construction; a calculation module that performs a simulation calculation based on the unsaturated parameter combination, the finite element model, and the simulation calculation conditions to obtain a simulation calculation result; A second acquisition module is configured to determine a target unsaturated parameter combination based on the simulation calculation result and the observation data of the hydroelectric power generation construction, and obtain a value of the unsaturated parameter; The generating module includes: a first determination unit for determining a first unsaturated parameter, a second unsaturated parameter, and a remaining unsaturated parameter from the first preset number of unsaturated parameters to be determined; a first acquisition unit for acquiring a relationship equation between the first unsaturation parameter and a second unsaturation parameter, the relationship equation and the first unsaturation parameter representing the second unsaturation parameter; a generating unit for obtaining candidate values ​​of each of the unsaturated parameters in the permeation section according to the first unsaturated parameter, the relationship equation, the remaining unsaturated parameters, the value range, and a preset method, to generate a third preset number of combinations of the unsaturated parameters; The first acquisition module includes: A first acquisition unit for acquiring profile data points based on observation data of the hydroelectric power construction; a second acquisition unit for acquiring the finite element model based on the profile data points and a pre-defined grid type; a division unit for generalizing the finite element model to divide the finite element model into a second preset number of the permeation compartments; a second acquisition unit for acquiring a permeation compartment parameter of the permeation compartment; a third acquisition unit for acquiring boundary conditions and initial water level conditions of the finite element model based on observed data of the hydroelectric power generation project; a fourth acquisition unit for obtaining the simulation calculation conditions according to the permeation compartment parameters, the boundary conditions and the initial water level conditions; The third acquisition unit includes: A first acquisition submodule for acquiring a water level line for a preset period based on observation data of a hydroelectric power generation construction project; a first determination sub-module for determining a slope head boundary of the finite element model based on the waterline; A second determination submodule for determining a water level value of the head boundary based on observation data of a hydroelectric power generation project, with the left boundary of the finite element model as the head boundary, and determining the water level value of the head boundary based on the observation data of the hydroelectric power generation project includes a step of determining the water level value of the head boundary by inversion based on borehole water level observation data in the observation data of the hydroelectric power generation project; A second acquisition submodule for acquiring an actual observed head value based on the observation data of the hydroelectric power generation construction; a fourth pre-defined number of watershed level values; and a fourth acquisition sub-module for obtaining a head calculation value and a corresponding boundary head value of each borehole position according to a preset seepage analysis method and a watershed water level value; a training submodule for training an initial neural network using the head calculation value and the boundary head value to obtain a trained neural network; and a third acquisition sub-module that inputs the measured observed head values ​​into the trained neural network to obtain initial water level conditions.

5. A processor, a communication interface, a memory, and a communication bus; The processor, the communication interface, and the memory are in communication with each other via the communication bus; The memory stores a computer program; The electronic device, wherein the processor executes the method steps according to any one of claims 1 to 3 by operating the computer program stored in the memory.

6. A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method steps according to any one of claims 1 to 3.

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