Hydropower station dam safety parameter forward modeling-inversion circulation method and related device
By screening key parameters and conducting forward simulation and inverse cycles, the problem of dam digital simulation results deviating from reality was solved, high precision and robustness of dam safety assessment were achieved, and the adaptability and accuracy of the model were improved.
Patent Information
- Application Number
- CN202510770951.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-10
- Publication Date
- 2025-09-19
AI Technical Summary
The digital simulation results of existing dams are restricted by the accuracy of initial data, resulting in low precision and affecting safety and reliability. This is mainly reflected in the difficulty in accurately simulating the uncertainty of geological parameters, the time-varying effects of material properties, and the dynamics of environmental loads.
Key parameters are screened through initial value sensitivity analysis, parameter prior distribution is constructed, and forward simulation and inversion cycles are performed. Parameters are optimized using Sobol index analysis, Monte Carlo sampling, adjoint equation method and Bayesian inversion framework. Parameter calibration is achieved by combining GPU cluster parallel computing and Tikhonov regularization processing.
It improves the accuracy and adaptability of dam safety assessment, enhances the robustness of the model in the face of uncertain factors, ensures the consistency between simulation results and actual results, and improves the accuracy of dam safety assessment.
Smart Images
Figure CN120671244A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of smart hydropower technology and relates to a forward-inverse cycle method for safety parameters of a hydropower station dam and related devices. Background Art
[0002] As critical infrastructure in water conservancy projects, the safe and stable operation of dams is crucial for flood control, power generation, water supply, and other aspects. Dam digital simulation technology has emerged as a key means of ensuring dam safety. Dam digital simulation utilizes computer technology, numerical simulation methods, and related theories to simulate and analyze various operating conditions, mechanical behavior, and flow characteristics of dams during construction and operation. It helps engineers predict dam performance, assess their safety, optimize design options, and develop appropriate operational management strategies. Dam digital simulation often requires the development of complex mathematical models to describe the physical processes within the dam and its surrounding environment.
[0003] Dam digital simulation technology has numerous applications. For engineers, it can help predict dam performance. By simulating dam operation under different operating conditions, engineers can understand how the dam will perform under various conditions in advance, thus gaining a more comprehensive understanding of its performance. In terms of dam safety assessment, digital simulation can simulate stress and deformation under extreme operating conditions, thereby determining whether the dam poses safety risks.
[0004] However, in actual applications, the results of dam digital simulations are often inaccurate due to the limitations of the initial data. This is primarily manifested in three aspects: First, the uncertainty of geological parameters. Parameters such as the permeability coefficient and elastic modulus of the rock and soil are difficult to accurately obtain through limited drilling, resulting in deviations in foundation boundary conditions. Second, the time-varying effects of material properties. Nonlinear characteristics such as concrete aging and crack development are difficult to accurately model, and existing constitutive models exhibit idealized flaws in simulating complex stress paths. Third, the dynamic nature of environmental loads. The temporal and spatial distribution of dynamic loads such as sudden reservoir water level drops and seismic wave inputs is random, making conventional deterministic simulations difficult to cover all operating conditions. These data errors can have a cumulative amplification effect during the calculation process, causing simulation results of key indicators such as the stress field and seepage field to deviate from reality. This can obscure potential sliding surfaces or structural weaknesses, impacting the reliability of safety assessments. Summary of the Invention
[0005] The purpose of the present invention is to provide a forward-inverse cycle method and related devices for the safety parameters of a hydropower station dam, so as to solve the technical problem that the digital simulation results of the dam in the prior art deviate from reality, have low accuracy, and affect its safety and reliability.
[0006] In order to achieve the above object, the present invention adopts the following technical solutions:
[0007] In a first aspect, the present invention provides a forward-inverse cycle method for dam safety parameters of a hydropower station, comprising the following steps:
[0008] Through initial value sensitivity analysis, the key parameters in the dam simulation model are screened and the master control parameter set is obtained;
[0009] Obtain initial parameters according to the master control parameter set, input the initial parameters into the dam simulation model, and obtain simulation results;
[0010] The simulation results are compared with the actual results. If the simulation results do not match the actual results, the initial parameters are obtained by inverting the actual results and re-input into the dam simulation model until the simulation results match the actual results, and the initial parameters are output.
[0011] Furthermore, the step of screening key parameters in the dam simulation model through initial value sensitivity analysis to obtain a master control parameter set specifically includes:
[0012] By performing Sobol index analysis on the input parameters of the dam simulation model, the global sensitivity of each parameter is quantified, and the main control parameter set X = {x1, x2, ..., xn} that needs to be optimized is screened out.
[0013] Furthermore, the step of obtaining initial parameters according to the master control parameter set, inputting the initial parameters into the dam simulation model, and obtaining simulation results specifically includes:
[0014] Based on the master parameter set, according to geological exploration reports, material test data and historical engineering cases, the parameter prior distribution P(X) is constructed, and the initial parameter set X0 is generated through Monte Carlo sampling;
[0015] Based on the initial parameter set X0, a forward simulation calculation is performed to run the dam multi-field coupling model to obtain time series simulation data.
[0016] Y sim =y sim(t1) ,y sim(t2) ,…,y sim(tm)
[0017] Furthermore, the step of obtaining initial parameters by inverting actual results specifically includes:
[0018] Construct an inverse operator and build an inverse problem solver
[0019] X=F-1(Y obs )
[0020] The Y obs For the actual result, we use the adjoint equation method or the Bayesian inversion framework to minimize the objective function:
[0021] J(X)=‖Y sim -Y obs ‖ 2 +λ‖X-X0‖ 2
[0022] Solve to obtain the optimal parameters;
[0023] Perform dynamic error propagation analysis based on the optimal parameters and define time-dependent error indicators:
[0024] ∈(t)=|Y sim(t) -Y obs(t) ∣
[0025] And calculate its cumulative effect:
[0026]
[0027] Determine the error growth pattern through the Lyapunov exponent;
[0028] Based on the error growth model, the quasi-Newton iteration method is used to update the parameters. The specific formula is:
[0029]
[0030] Where H k is the Hessian matrix approximation, α k is the step size adaptive adjustment coefficient;
[0031] Perform multi-time scale verification, requiring the cumulative error to be less than a preset threshold in a short time interval, and verifying that the error does not increase significantly in an extended time interval;
[0032] When multiple consecutive iterations meet the preset conditions of parameter change and maximum error, it is determined to be converged, and the parameters obtained by the final inversion are output as the initial parameters.
[0033] Furthermore, the preset conditions for the parameter variation and the maximum error are:
[0034] ‖X k+1 -X k ‖<δX
[0035] And max∈(t)<ξ2.
[0036] Furthermore, the method also includes: parallelizing forward simulation and inversion tasks based on a GPU cluster, simultaneously calculating several groups of parameter candidate solutions in a single iteration cycle, and improving the convergence speed through asynchronous gradient descent.
[0037] Furthermore, in the inversion process, when some model calculations are irreversible, pathological treatment is adopted to add a Tikhonov regularization term to the Hessian matrix to improve the matrix condition number. The specific formula is:
[0038] H reg =H+γI
[0039] Where H reg represents the Hessian matrix after regularization; γ represents the regularization coefficient; I represents the identity matrix; H represents the Hessian matrix.
[0040] In a second aspect, the present invention provides a forward-inverse cycle system for dam safety parameters of a hydropower station, comprising:
[0041] The master control parameter acquisition module is used to screen the key parameters in the dam simulation model through initial value sensitivity analysis to obtain the master control parameter set;
[0042] The forward simulation module is used to obtain initial parameters according to the master control parameter set, input the initial parameters into the dam simulation model, and obtain simulation results;
[0043] The inversion output module is used to compare the simulation results with the actual results. If the simulation results do not match the actual results, the initial parameters are obtained by inverting the actual results and re-input into the dam simulation model until the simulation results match the actual results, and the initial parameters are output.
[0044] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the forward-inverse cycle method for the safety parameters of a hydropower station dam are implemented.
[0045] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a forward-inverse cycle method for the safety parameters of a hydropower station dam.
[0046] Compared with the prior art, the present invention has the following beneficial effects:
[0047] The present invention discloses a forward-inverse cycle method and related devices for the safety parameters of a hydropower station dam. By analyzing the sensitivity of initial values, key parameters (a set of master control parameters) in a dam simulation model are screened out, and parameters that have a significant impact on the safety performance of the dam can be accurately located. This allows subsequent analysis and calculations to focus on processing these key parameters, avoiding ineffective analysis of a large number of non-critical parameters, thereby improving the accuracy of the dam safety assessment. The simulation results are then compared with the actual results. If they do not match, the initial parameters are obtained by inverting the actual results, and the cycle is repeated until they match. The parameters of the dam simulation model are calibrated so that the model can more accurately reflect the actual operating conditions of the dam, thereby improving the accuracy of the dam safety assessment. The present invention uses long-term error feedback between actual monitoring data and simulation results to reversely correct the initial parameters until the system output is consistent with the real physical process on any time scale, thereby improving the accuracy of the dam digital simulation results.
[0048] Furthermore, the present invention considers geological exploration reports, material test data and historical engineering cases to construct parameter prior distributions, and generates an initial parameter set through Monte Carlo sampling, which can cover a variety of complex geological and material conditions, enabling the dam simulation model to better adapt to different actual working conditions and enhancing the adaptability and robustness of the model in practical applications.
[0049] Furthermore, during the inversion process, the present invention comprehensively analyzes and processes errors by defining time-dependent error metrics, calculating cumulative effects, and determining error growth patterns. This helps the model maintain good performance in the face of various uncertainties and improves its ability to cope with uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS
[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.
[0051] Figure 1 is a flow chart of the method of the present invention;
[0052] Figure 2 is a schematic diagram of the system of the present invention;
[0053] Figure 3 is an overall flow chart of an embodiment of the present invention;
[0054] Figure 4 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION
[0055] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.
[0056] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.
[0057] See also Figure 1 and Figure 3 The embodiment of the present invention discloses a forward-inverse cycle method for the safety parameters of a hydropower station dam, comprising the following steps:
[0058] S1, through initial value sensitivity analysis, screen the key parameters in the dam simulation model and obtain the main control parameter set;
[0059] The key input parameters of the dam simulation model (such as foundation permeability coefficient k and concrete elastic modulus E) have a significant nonlinear effect on the output results (such as displacement δ and seepage pressure p). The Sobol index analysis can quantify the global sensitivity of each parameter and screen out the main control parameter set that needs to be optimized.
[0060] X={x1,x2,…,x n}
[0061] S2, obtaining initial parameters according to the master control parameter set, inputting the initial parameters into the dam simulation model, and obtaining simulation results;
[0062] Based on the master parameter set, according to geological exploration reports, material test data and historical engineering cases, the parameter prior distribution P(X) is constructed, and the initial parameter set X0 is generated through Monte Carlo sampling;
[0063] Based on the initial parameter set X0, a forward simulation is performed to run the dam multi-field coupling model (structure-seepage-temperature field) to obtain time series simulation data
[0064] Y sim =y sim(t1) ,y sim(t2) ,…,y sim(tm)
[0065] S3, compare the simulation results with the actual results. If the simulation results do not match the actual results, the initial parameters are obtained by inverting the actual results and re-input into the dam simulation model until the simulation results match the actual results, and the initial parameters are output.
[0066] Construct the inverse operator and build the inverse problem solver:
[0067] X=F-1(Y obs )
[0068] The Y obs For the actual result, we use the adjoint equation method or the Bayesian inversion framework to minimize the objective function:
[0069] J(X)=‖Y sim -Y obs ‖ 2 +λ‖X-X0‖ 2
[0070] Solve to obtain the optimal parameters;
[0071] Perform dynamic error propagation analysis based on the optimal parameters and define time-dependent error indicators:
[0072] ∈(t)=|Y sim(t) -Y obs(t) ∣
[0073] And calculate its cumulative effect:
[0074]
[0075] Determine the error growth pattern through the Lyapunov exponent;
[0076] Based on the error growth model, the quasi-Newton iteration method is used to update the parameters. The specific formula is:
[0077]
[0078] Where H k is the Hessian matrix approximation, α k is the step size adaptive adjustment coefficient;
[0079] In addition, multi-timescale verification is required. For short-term convergence, Σ∈<ξ1 is required within t∈[0,T1] (e.g., displacement error <2mm). For long-term stability, the error does not increase significantly over an extended time t∈[T1,T2] (dΣ∈ / dt≈0).
[0080] When multiple iterations meet the preset conditions of parameter change and maximum error, it is determined to be converged, and the parameters obtained by the final inversion are output as the initial parameters. For example, the termination condition is set at the end, when 5 consecutive iterations meet
[0081] ‖X k+1 -X k ‖<δX
[0082] And max∈(t)<ξ2, it is judged to be converged.
[0083] In a feasible embodiment of the present invention, during the specific inversion modeling process, parameter space dimensionality reduction is performed, and principal component analysis (PCA) or autoencoder is used to perform low-dimensional embedding on the high-dimensional parameter set X, extract the main controlling feature vector Z, and compress the optimization problem from Rn to Rd (d<<n).
[0084] Uncertainty quantification uses the Markov Chain Monte Carlo (MCMC) method to generate the parameter posterior distribution P(X|Yobs) and calculate confidence intervals. For example, the inversion result of the permeability coefficient k can be expressed as k = 1.2×10-5m / s±15% (95% confidence level).
[0085] Parallel computing acceleration enables parallelization of forward simulation and inversion tasks based on GPU clusters. Hundreds of parameter candidate solutions can be calculated simultaneously within a single iteration, and convergence speed is improved through asynchronous gradient descent.
[0086] The model is also required to satisfy weak formal reversibility: given the complete state data Ytk at any time point tk, there exists a unique parameter set X such that F(X,tk) = Ytk, where F is the forward simulation operator. For irreversible models (such as plastic deformation), a time window truncation technique is required to decompose the long-term simulation into multiple reversible subintervals. Ill-conditioned processing can be used: a Tikhonov regularization term is added to the Hessian matrix to improve the matrix condition number. The specific formula is:
[0087] H reg =H+γI
[0088] Where H reg represents the Hessian matrix after regularization; γ represents the regularization coefficient; I represents the identity matrix; H represents the Hessian matrix.
[0089] This method, through a data-model dual-driven closed-loop optimization, effectively overcomes the over-reliance on initial values in traditional simulations. Future work could further integrate digital twin technology to achieve real-time online parameter calibration and explore the application of quantum optimization algorithms to ultra-high-dimensional problems. This method could also be extended to other areas such as slope stability analysis and identification of surrounding rock parameters in underground caverns, demonstrating its broad engineering value.
[0090] See also Figure 2 The embodiment of the present invention discloses a forward-inverse cycle system for the safety parameters of a hydropower station dam, which includes a main control parameter acquisition module, a forward simulation module and an inverse output module.
[0091] Among them, the main control parameter acquisition module is used to screen the key parameters in the dam simulation model through initial value sensitivity analysis to obtain the main control parameter set; the forward simulation module is used to obtain the initial parameters according to the main control parameter set, input the initial parameters into the dam simulation model to obtain the simulation results; the inversion output module is used to compare the simulation results with the actual results. If the simulation results do not match the actual results, the initial parameters are obtained by inverting the actual results, and re-input them into the dam simulation model until the simulation results match the actual results, and the initial parameters are output.
[0092] In one embodiment of the present invention, see Figure 4 , provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a forward-inverse cycle method for the safety parameters of a hydropower station dam.
[0093] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the forward-inverse cycle method for the safety parameters of a hydropower station dam in the above embodiment.
[0094] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0095] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0096] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.
[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.
[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.
Claims
1. A forward-inverse cycle method for dam safety parameters of a hydropower station, characterized in that: The following steps are involved: Through initial value sensitivity analysis, the key parameters in the dam simulation model are screened and the master control parameter set is obtained; Obtain initial parameters according to the master control parameter set, input the initial parameters into the dam simulation model, and obtain simulation results; The simulation results are compared with the actual results. If the simulation results do not match the actual results, the initial parameters are obtained by inverting the actual results and re-input into the dam simulation model until the simulation results match the actual results, and the initial parameters are output.
2. A forward-inverse cycle method for dam safety parameters of a hydropower station according to claim 1, characterized in that: The step of screening key parameters in the dam simulation model through initial value sensitivity analysis to obtain the master control parameter set specifically includes: By performing Sobol index analysis on the input parameters of the dam simulation model, the global sensitivity of each parameter is quantified, and the main control parameter set that needs to be optimized is selected: X={x1,x2,…,x n }。 3. A forward-inverse cycle method for dam safety parameters of a hydropower station according to claim 1, characterized in that: The step of obtaining initial parameters according to the master control parameter set, inputting the initial parameters into the dam simulation model, and obtaining simulation results specifically includes: Based on the master parameter set, according to geological exploration reports, material test data and historical engineering cases, the parameter prior distribution P(X) is constructed, and the initial parameter set X0 is generated through Monte Carlo sampling; Based on the initial parameter set X0, a forward simulation is performed to run the dam multi-field coupling model and obtain the time series simulation data: AND sim =and sim(t1) ,and sim(t2) ,…,and sim(tm) 。 4. A forward-inverse cycle method for dam safety parameters of a hydropower station according to claim 1, characterized in that: The step of obtaining initial parameters by inverting actual results specifically includes: Construct the inverse operator and build the inverse problem solver: X=F-1(Y obs ) The Y obs For the actual result, we use the adjoint equation method or the Bayesian inversion framework to minimize the objective function: J(X)=‖Y sim -Y obs ‖ 2 +λ‖X-X0‖ 2 Solve to obtain the optimal parameters; Perform dynamic error propagation analysis based on the optimal parameters and define time-dependent error indicators: ∈(t)=∣Y sim(t) -Y obs(t) ∣ And calculate its cumulative effect: Determine the error growth pattern through the Lyapunov exponent; Based on the error growth model, the quasi-Newton iteration method is used to update the parameters. The specific formula is: Where H k is the Hessian matrix approximation, α k is the step size adaptive adjustment coefficient; Perform multi-time scale verification, requiring the cumulative error to be less than a preset threshold in a short time interval, and verifying that the error does not increase significantly in an extended time interval; When multiple consecutive iterations meet the preset conditions of parameter change and maximum error, it is determined to be converged, and the parameters obtained by the final inversion are output as the initial parameters.
5. A forward-inverse cycle method for dam safety parameters of a hydropower station according to claim 4, characterized in that: The preset conditions for the parameter variation and maximum error are: ‖X k+1 -X k ‖<δX And max∈(t)<ξ2.
6. A forward-inverse cycle method for dam safety parameters of a hydropower station according to claim 1, characterized in that: The method also includes: parallelizing forward simulation and inversion tasks based on a GPU cluster, simultaneously calculating several groups of parameter candidate solutions in a single iteration cycle, and improving convergence speed through asynchronous gradient descent.
7. A forward-inverse cycle method for dam safety parameters of a hydropower station according to claim 1, characterized in that: In the inversion process, when some models are irreversible, pathological treatment is adopted to add Tikhonov regularization terms to the Hessian matrix to improve the matrix condition number. The specific formula is: H reg =H+γI Where H reg represents the Hessian matrix after regularization; γ represents the regularization coefficient; I represents the identity matrix; H represents the Hessian matrix.
8. A forward-inverse cycle system for dam safety parameters of a hydropower station, characterized in that: include: The master control parameter acquisition module is used to screen the key parameters in the dam simulation model through initial value sensitivity analysis to obtain the master control parameter set; The forward simulation module is used to obtain initial parameters according to the master control parameter set, input the initial parameters into the dam simulation model, and obtain simulation results; The inversion output module is used to compare the simulation results with the actual results. If the simulation results do not match the actual results, the initial parameters are obtained by inverting the actual results and re-input into the dam simulation model until the simulation results match the actual results, and the initial parameters are output.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the forward-inverse cycle method for the safety parameters of a hydropower station dam are implemented as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the forward-inverse cycle method for the safety parameters of a hydropower station dam are implemented as described in any one of claims 1 to 7.