Multi-dimensional stress-strength interference analysis and modeling method, system and equipment for vacuum breaking type GIS (Geographic Information System) and medium

Abstract

The invention discloses a multi-dimensional stress-strength interference analysis and modeling method, system and device for a vacuum breaking type GIS and a medium. The method comprises the steps that multi-source basic data of key components of the vacuum breaking type GIS device are collected and preprocessed; defining each stress component based on the preprocessed data, introducing a nonlinear index to establish a nonlinear interference factor model, and quantifying the coupling interference effect of each stress component; establishing a three-dimensional finite element model of the key part, and performing dynamic simulation on the three-dimensional finite element model to obtain a simulation analysis result; and based on the nonlinear interference factor model and the simulation analysis result, performing damage accumulation calculation by adopting a first fatigue analysis rule, and estimating the residual life of the part through a life prediction algorithm. The model and the algorithm have wide application prospects in GIS equipment design and maintenance, and are beneficial for improving the reliability of the equipment, reducing the maintenance cost and prolonging the service life of the equipment.

Description

Technical Field

[0001] This invention relates to the field of multibody dynamics optimization technology for operating mechanisms of high-voltage gas-insulated switchgear, and particularly to a multidimensional stress-intensity interference analysis and modeling method, system, equipment, and medium for vacuum-breaking GIS. Background Technology

[0002] At present, with the rapid development of ultra-high voltage power transmission technology and extra-high voltage power transmission technology, high-voltage gas-insulated switchgear (GIS) is widely used in the main power grid system due to its advantages such as high insulation performance, small footprint, and strong operational reliability.

[0003] However, in actual operation, the key operating, transmission, and support components of vacuum-operated GIS are subjected to complex multi-source stresses over long periods, including mechanical impact stress, thermal stress, vibration stress, and electromagnetic coupling stress. These multi-source stresses superimpose, couple, and interfere with each other, causing the strength distribution of the components to exhibit significant nonlinear characteristics and multidimensional randomness. Existing research and engineering design typically employ single-dimensional stress intensity analysis methods, such as static strength methods or unidirectional fatigue life estimation methods based on S-N curves. While these methods are computationally simple, they have significant limitations. For example, existing technologies ignore stress coupling effects, thus failing to reflect the true failure mechanism under the combined action of multiple stress sources. Furthermore, most existing technical solutions lack quantitative models of interference factors, failing to characterize the interaction strength between different stress components. Therefore, the evaluation results deviate from actual failure characteristics, especially under dynamic impact or high-frequency operating conditions, where traditional models exhibit significant errors.

[0004] Therefore, it is urgent to propose a new theoretical system and calculation method to accurately analyze the strength distribution and failure probability of key components of vacuum-breaking GIS under the combined action of multiple stress sources. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a multidimensional stress-intensity interference analysis and modeling method, system, equipment, and medium for vacuum-severable GIS, solving the problem that existing technologies, which typically employ single-dimensional stress intensity analysis methods, cannot accurately reflect the true failure mechanism and lifespan distribution of key components in vacuum-severable GIS under the coupling effect of multiple stress sources.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS, including: Collect and preprocess multi-source basic data of key components of vacuum-switching GIS equipment; Based on the preprocessed data, each stress component is defined, and a nonlinear exponent is introduced to establish a nonlinear interference factor model to quantify the coupling interference effect of various stress components. A three-dimensional finite element model of the key components is established, and dynamic simulation is performed on the three-dimensional finite element model to obtain simulation analysis results; Based on the nonlinear interference factor model and the simulation analysis results, the first fatigue analysis rule is used to calculate the cumulative damage, and the remaining life of the component is estimated by the life prediction algorithm.

[0008] As a preferred embodiment of the multidimensional stress-strength interferometric analysis and modeling method for vacuum-suspension GIS described in this invention, the step of calculating damage accumulation using the first fatigue analysis rule includes: Based on the nonlinear interference factor model and the simulation analysis results, the actual damage increment of the key components under each load condition is calculated. Based on the fatigue life of the key component and the actual damage increment, the damage value of the key component under different working conditions is cumulatively calculated based on the first fatigue analysis rule. A dynamic damage progression model is used to track the update process of the cumulative damage value of the critical component under each new load condition. The cumulative damage value is compared with a first damage threshold to assess the damage status of the critical component.

[0009] As a preferred embodiment of the multidimensional stress-strength interferometry analysis and modeling method for vacuum-suspension GIS described in this invention, the estimation of the remaining life of the component using a life prediction algorithm includes: The predicted remaining life of the critical component is calculated based on the component's initial life and current damage state. The first simulation algorithm is used to randomly simulate various load changes in the working condition data to construct a large number of random working condition scenarios. The damage accumulation calculation and the remaining life prediction are repeatedly executed under the random working condition scenarios to generate the life distribution of the key components under different working conditions. Based on the statistical results of the lifespan distribution, the predicted range of the remaining lifespan of the critical component is determined.

[0010] The beneficial effects of this preferred technical solution are: to realize online, efficient and accurate theoretical analysis and model establishment of the strength distribution, failure probability and reliability of key components of vacuum-splitting GIS under multi-source stress, and to provide a scientific basis for the reliability design and life assessment of GIS operating mechanism equipment.

[0011] As a preferred embodiment of the multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS described in this invention, it further includes: A target optimization model is constructed using a first optimization algorithm, the target optimization model aiming to minimize the relative error between the model's predicted remaining lifetime and the actual lifetime; The weighting coefficient and nonlinear exponent are used as parameters to be optimized. The parameter combination of each particle in the particle swarm is randomly initialized, and the fitness value of each particle corresponding to the parameter combination is calculated based on the simulation results. The individual optimal solution and the global optimal solution of each particle in the particle swarm are updated based on the fitness value, and the parameter combination is iteratively optimized until the convergence condition is met. The optimized weighting coefficients and the nonlinear exponent are substituted into the nonlinear interference factor model to improve the quantification accuracy of stress coupling effect and the prediction accuracy of remaining lifetime.

[0012] As a preferred embodiment of the multidimensional stress-intensity interferometry analysis and modeling method for vacuum-splitting GIS described in this invention, the collection of multi-source basic data of key components of vacuum-splitting GIS equipment includes: The key components of the vacuum interruption type GIS equipment include the operating mechanism, support structure, arc-extinguishing chamber and connecting parts; the multi-source basic data includes geometric parameters, material properties and operating condition data. The geometric parameters include the size, shape, thickness, and geometric structure parameters of the component; the material properties are the mechanical property data of the material used in the component, including yield strength, tensile strength, elastic modulus, and coefficient of thermal expansion; and the operating condition data are the environmental conditions in which the component is located, including temperature, pressure, vibration frequency, and impact load.

[0013] As a preferred embodiment of the multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severable GIS described in this invention, the step of defining each stress component based on the preprocessed data and introducing a nonlinear exponent to establish a nonlinear interferometry factor model includes: Define each stress component, which includes static stress, dynamic stress, and thermal stress; Based on the contribution of each stress component to component failure and the material constitutive relationship, weighting coefficients are determined, and a nonlinear exponent is introduced to characterize the coupling amplification effect when multiple stress components act together. A nonlinear interference factor model is constructed based on the weighting coefficients and the nonlinear exponents to quantify the comprehensive interference effect of various stress components under their combined action. Based on the operating condition data, the weighting coefficients and nonlinear exponents are calibrated using experimental data to establish a stress-intensity interference relationship that reflects the actual operating conditions.

[0014] The beneficial effects of this preferred technical solution are: it enables quantitative analysis of the overall strength and reliability of components by combining theoretical derivation, numerical simulation and experimental calibration.

[0015] As a preferred embodiment of the multidimensional stress-strength interferometry analysis and modeling method for vacuum-suspension GIS described in this invention, the dynamic simulation of the three-dimensional finite element model to obtain simulation analysis results includes: The three-dimensional finite element model is meshed to generate a discretized model composed of several finite elements; Define the corresponding material mechanical property parameters based on the material properties; Based on the operating condition data, apply corresponding boundary conditions and working loads to the discretized model; Based on the applied boundary conditions and working loads, multibody dynamics simulation is performed on the discretized model. By considering the vibration modes, impact loads, and operating frequencies of the components, the instantaneous stress and deformation of the key components under different dynamic working conditions are obtained as the simulation analysis results.

[0016] Secondly, the present invention provides a multidimensional stress-intensity interferometry analysis and modeling system for vacuum-breaking GIS, comprising: The data processing module is used to collect multi-source basic data of key components of vacuum-switching GIS equipment and perform preprocessing. The interference theory derivation module is used to define each stress component based on the preprocessed data, introduce a nonlinear exponent to establish a nonlinear interference factor model, and quantify the coupling interference effect of various stress components. The finite element modeling and multibody dynamics analysis module is used to establish a three-dimensional finite element model of key components and perform dynamic simulation on the three-dimensional finite element model to obtain simulation analysis results. The component life prediction module is used to calculate damage accumulation based on the nonlinear interference factor model and the simulation analysis results, using the first fatigue analysis rule, and to estimate the remaining life of the component through the life prediction algorithm.

[0017] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to implement the steps of a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-breaking GIS.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-breaking GIS.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention aims to systematically model and calculate the strength distribution, failure probability, and reliability of key components in vacuum-splitting GIS under complex stress fields. Through multi-dimensional stress-strength interference theory analysis, this invention can accurately assess the strength and reliability of key GIS components under different operating conditions, predict potential failures in advance, and ensure the safety of equipment operation. By combining damage quantification and life prediction algorithms, this invention can monitor the damage status of components in real time, improving the health management capabilities of the equipment. The models and algorithms of this invention have broad application prospects in the design and maintenance of GIS equipment, helping to improve equipment reliability, reduce maintenance costs, and extend equipment service life. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the overall process logic of a multidimensional stress-strength interferometry analysis and modeling method for vacuum-severed GIS provided in one embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram illustrating the interference factor calculation and algorithm optimization process of a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-breaking GIS, provided as an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the convergence process of the particle swarm optimization algorithm for a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS, provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0025] Example 1, referring to Figure 1 As an embodiment of the present invention, a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS is provided, such as... Figure 1 The specific steps shown are as follows: S100: Collect multi-source basic data of key components of vacuum-switching GIS equipment and perform preprocessing; S200: Define each stress component based on the preprocessed data, introduce a nonlinear exponent to establish a nonlinear interference factor model, and quantify the coupling interference effect of various stress components; S300: Establish a three-dimensional finite element model of the key components, and perform dynamic simulation on the three-dimensional finite element model to obtain simulation analysis results; S400: Based on the nonlinear interference factor model and simulation analysis results, the first fatigue analysis rule is used to calculate the damage accumulation, and the remaining life of the component is estimated by the life prediction algorithm.

[0026] It should be noted that, to address the problem that existing technologies, which typically employ single-dimensional stress intensity analysis methods, cannot accurately reflect the true failure mechanism and lifespan distribution of key components in vacuum-breaking GIS systems under the coupling of multiple stress sources, this invention utilizes multi-dimensional stress-intensity interference theory analysis. This enables precise assessment of the strength and reliability of key GIS components under different operating conditions, early prediction of potential failures, and ensures the safety of equipment operation. Furthermore, by combining damage quantification and lifespan prediction algorithms, this invention can monitor the damage status of components in real time, improving equipment health management capabilities. The model and algorithm of this invention have broad application prospects in GIS equipment design and maintenance, helping to improve equipment reliability, reduce maintenance costs, and extend equipment lifespan.

[0027] Example 2, refer to Figure 2 and Figure 3 Based on the previous embodiment, this embodiment provides a specific implementation method for multidimensional stress-intensity interferometry analysis and modeling of vacuum-severed GIS, to illustrate the technical means used in this method.

[0028] In this embodiment of the invention, step S100, which involves collecting and preprocessing multi-source basic data of key components of a vacuum-breaking GIS device, includes: Specifically, key components of vacuum interruption type GIS equipment include operating mechanism, support structure, arc-extinguishing chamber and connectors, and multi-source basic data include geometric parameters, material properties and operating condition data; Among them, geometric parameters include the size, shape, thickness, and geometric structure parameters of the component; material properties are the mechanical property data of the materials used in the component, including yield strength, tensile strength, elastic modulus, and coefficient of thermal expansion; and operating condition data are the environmental conditions in which the component is located, including temperature, pressure, vibration frequency, and impact load.

[0029] Specifically, the preprocessing steps include cleaning, normalizing, and structuring the data on geometric parameters, material properties, and operating conditions; removing invalid and abnormal data; unifying the data format and dimensions; and performing data integrity verification and correlation integration to construct a standardized multi-source dataset suitable for subsequent stress analysis, finite element modeling, and life prediction.

[0030] In this embodiment of the invention, step S200 defines each stress component based on the preprocessed data, introduces a nonlinear exponent to establish a nonlinear interference factor model, and quantifies the coupling interference effect of various stress components, including: Define each stress component, which includes static stress, dynamic stress, and thermal stress. Based on the contribution of each stress component to component failure and the material constitutive relationship, weighting coefficients are determined, and a nonlinear exponent is introduced to characterize the coupling amplification effect when multiple stress components act together. A nonlinear interference factor model is constructed based on weighting coefficients and nonlinear exponents to quantify the comprehensive interference effect of various stress components under their combined action. Based on the working condition data, the weighting coefficients and nonlinear exponents were calibrated using experimental data to establish a stress-intensity interference relationship that reflects the actual working conditions.

[0031] Specifically, such as Figure 2 As shown, the interference factor in this embodiment The interference between different stress types is reflected by weighting coefficients, which are determined based on the constitutive relation of the material and experimental data. The formula is as follows: Where n represents the number of stress components. The weighting coefficient for each stress component reflects the degree to which that stress type contributes to component failure. This represents the i-th type of stress component.

[0032] Specifically, a nonlinear interference factor model is constructed based on weighting coefficients and nonlinear exponents to quantify the comprehensive interference effect of various stress components under their combined action. The formula is expressed as follows: Here, m represents the nonlinearity exponent, reflecting the degree of nonlinearity in the interference between stress and strength. This model can accurately describe the amplification effect of the interaction between multiple stress sources on the strength of a component.

[0033] In an optional embodiment, the step of quantifying the coupling interference effect of various stress components may further include: constructing a high-dimensional mapping relationship between stress components and component response by introducing a multi-stress coupling response surface method, so as to fit the nonlinear strength degradation behavior under the combined action of multiple stress sources.

[0034] In an optional embodiment, the step of quantifying the coupling interference effect of various stress components may further include: using a physical information-based neural network to embed the material constitutive equation and stress coupling mechanism into the network structure, and learning the implicit interaction law and intensity interference effect between stress components through a data-driven approach.

[0035] It should be noted that step S200 above, by defining multiple stress components and constructing a nonlinear interference factor model, achieves the scientific quantification of the multi-source stress coupling effect, including static, dynamic, and thermal stresses. This overcomes the limitation of traditional methods that ignore stress interaction and can more realistically reflect the comprehensive impact of stress on component strength under complex working conditions, thereby improving the analytical ability of component failure mechanisms and the accuracy of reliability assessment.

[0036] In this embodiment of the invention, step S300 establishes a three-dimensional finite element model of the key component and performs dynamic simulation on the three-dimensional finite element model to obtain simulation analysis results, including: Mesh the three-dimensional finite element model to generate a discretized model composed of several finite elements; Define the corresponding mechanical property parameters of the material based on its properties; Apply corresponding boundary conditions and working loads to the discretized model based on the operating condition data; Based on the applied boundary conditions and working loads, multibody dynamics simulation is performed on the discretized model. By considering the vibration modes, impact loads and operating frequencies of the components, the instantaneous stress and deformation of the key components under different dynamic working conditions are obtained as simulation analysis results.

[0037] It should be noted that a three-dimensional finite element model of the key GIS components is established based on the CAD model and the interference factor, stress intensity relationship, and nonlinear effect model in step S200. The modeling process includes: geometric modeling, mesh generation, material property definition, and boundary conditions and load application.

[0038] Specifically, based on the collected geometric parameters, a finite element model of the three-dimensional component is constructed. The three-dimensional component model is discretized into a finite number of small elements, typically tetrahedral or hexahedral elements. In the finite element model, corresponding material mechanical property parameters are defined according to material characteristics, including elastic modulus, Poisson's ratio, yield strength, and fatigue limit parameters. Finally, boundary conditions and external loads are applied according to actual working conditions.

[0039] Specifically, multibody dynamics simulation of the discretized model mainly includes vibration analysis, impact load simulation, and response analysis. By considering the vibration modes, impact loads, and operating frequencies of the components, the instantaneous stress and deformation of the components under different dynamic conditions can be obtained.

[0040] It should be noted that step S300 above, by establishing a three-dimensional finite element model and conducting dynamic simulation, achieves a refined simulation of the transient stress and deformation state of key components under actual loads. The simulation results not only provide the mechanical response of the components under dynamic working conditions, but also provide high-precision stress input for subsequent damage analysis, enhancing the model's realism in describing the component behavior under actual working conditions and its engineering applicability.

[0041] In this embodiment of the invention, step S400, based on the nonlinear interference factor model and simulation analysis results, uses the first fatigue analysis rule to calculate damage accumulation and estimates the remaining life of the component using a life prediction algorithm, including the following sub-steps A1~A3: In A1: Damage accumulation is calculated using the first fatigue analysis rule; detailed steps: Based on the nonlinear interference factor model and simulation analysis results, the actual damage increment of key components under various load conditions is calculated. Based on the fatigue life and actual damage increment of key components, the damage values ​​of key components under different working conditions are cumulatively calculated according to the first fatigue analysis rule. A dynamic damage progression model is used to track the update process of the cumulative damage value of critical components under each new load condition. The cumulative damage value is compared with the first damage threshold to assess the damage status of critical components.

[0042] Specifically, this embodiment combines the results of stress-strength interference theory and finite element and multibody dynamics analysis to accurately quantify the damage of key GIS components and predict their remaining lifespan. Assuming that the relationship between the stress intensity and strength interference factor of the component is nonlinear under each working condition, the damage values ​​of the key component under different working conditions are cumulatively calculated based on the fatigue life and actual damage increment of the key component, using the first fatigue analysis rule (Miner's rule). The formula is as follows: in, Indicates the total degree of damage. Indicates the number of load cycles under the load condition. This indicates the fatigue life of a component under load conditions.

[0043] In an alternative embodiment, the first fatigue analysis rule can also be a nonlinear cumulative damage model based on continuous damage mechanics, which considers the nonlinear characteristics of damage evolution within the material and the stress history dependence, and replaces the linear accumulation rule by introducing a damage evolution equation.

[0044] In an optional embodiment, the first fatigue analysis method may also employ an energy-based fatigue life prediction method, which comprehensively considers the influence of mean stress, stress amplitude and multiaxial stress state, and achieves a damage assessment that better reflects the actual material behavior by calculating cyclic plastic strain energy or specific stress parameters.

[0045] In this embodiment of the invention, the damage to the GIS operating mechanism increases with the continuous accumulation of stress cycles during operation. A dynamic damage progression model is used to track the update process of the cumulative damage value of key components under each new load condition. Whenever a component experiences a new load condition, the cumulative damage value D is updated, and the update formula is as follows: in, This represents the incremental damage caused under the current load condition. Over time, the damage accumulates, eventually leading to component failure. Once the damage reaches a critical value, D... total =1 indicates that the component's lifespan is about to end.

[0046] It should be noted that the setting of the first damage threshold is based on the physical mechanism of material fatigue failure and engineering safety criteria. It is determined by combining the material's fatigue limit, fracture toughness and the safety factor of the component under actual service conditions. Usually, the threshold is set to 1 based on the results of standard fatigue tests, industry specifications or reliability design requirements, indicating that the accumulated damage has reached the theoretical failure threshold and the component has entered the end-of-life state.

[0047] In A2: The remaining lifespan of the component is estimated using a lifespan prediction algorithm; detailed steps include: The predicted remaining life of critical components is calculated based on their initial life and current damage state, expressed by the following formula: in, This is the theoretical maximum lifespan of the component. It is the load duration under the i-th operating condition. interference It is the stress interference factor, which reflects the actual effect of stress on materials; The first simulation algorithm is used to randomly simulate various load changes in the working condition data to construct a large number of random working condition scenarios. Damage accumulation calculation and remaining life prediction are repeatedly performed under random working condition scenarios to generate the life distribution of key components under different working conditions. Based on the statistical results of lifespan distribution, the prediction range of the remaining lifespan of critical components is determined.

[0048] Specifically, in this embodiment, to further improve the accuracy of lifetime prediction and considering the randomness of factors such as load spectrum and operating condition changes, a Monte Carlo simulation method is adopted. This involves a first simulation algorithm that randomly simulates various load changes in the operating condition data, constructing a large number of random operating condition scenarios. Damage accumulation calculation and remaining lifetime prediction are repeatedly performed under these random operating condition scenarios to generate the lifetime distribution of key components under different operating conditions. The Monte Carlo simulation algorithm can generate a large number of different simulation scenarios and derive a reliable remaining lifetime prediction range through statistical methods.

[0049] In an optional embodiment, the first simulation algorithm can also be a random load process simulation method based on Markov chain Monte Carlo, which simulates the random evolution sequence of the working condition by constructing a transition probability matrix of the load state.

[0050] In an optional embodiment, the first simulation algorithm may also employ an efficient random simulation method that combines Latin hypercube sampling with importance sampling techniques, which significantly improves the simulation efficiency and accuracy for predicting extreme operating conditions and long-tailed lifetime distributions while ensuring sampling coverage and statistical representativeness.

[0051] In A3: Optimize the weighting coefficients and nonlinear exponents in the nonlinear interference factor model; detailed steps include: The first optimization algorithm is used to construct a target optimization model, which aims to minimize the relative error between the model's predicted remaining lifetime and the actual lifetime. The weighting coefficient and nonlinear exponent are used as parameters to be optimized. The parameter combination of each particle in the particle swarm is randomly initialized, and the fitness value of each particle corresponding to the parameter combination is calculated based on the simulation results. Update the individual optimal solution and the global optimal solution of each particle in the particle swarm based on the fitness value, and iteratively optimize the parameter combination until the convergence condition is met; The optimized weighting coefficients and nonlinear exponents are substituted into the nonlinear interference factor model to improve the quantification accuracy of stress coupling effects and the prediction accuracy of remaining lifetime.

[0052] It should be noted that, to improve the accuracy of lifetime prediction, this embodiment utilizes the Particle Swarm Optimization (PSO) algorithm to adjust the parameters in the model. By optimizing the weights of the stress interference factor, damage accumulation factor, and other model parameters through PSO, more accurate lifetime prediction results are obtained, further enhancing the reliability and practicality of the prediction.

[0053] In an optional embodiment, the first optimization algorithm may also be a genetic algorithm, which simulates natural selection and genetic mechanisms to iteratively evolve the parameter population through selection, crossover and mutation operations, thereby achieving efficient global optimization of complex nonlinear parameter spaces.

[0054] In an optional embodiment, the first optimization algorithm can also be a gradient-based optimization method, which uses a loss function to iteratively update the gradient information of the parameters within a differentiable model framework. This method is suitable for large-scale parameter optimization and has a relatively fast convergence speed.

[0055] Specifically, in this embodiment, the objective optimization model aims to minimize the relative error between the model's predicted remaining lifetime and the actual lifetime, as expressed by the formula: in, Let be the parameter vector to be optimized. Based on parameters Predicted lifespan, This refers to the actual lifespan of the component.

[0056] In a feasible implementation, each "particle" in the particle swarm optimization algorithm corresponds to a complete set of model parameters. The initialization process must ensure both the randomness of particle distribution and constraint satisfaction. The particle swarm size is set to N = 50~100, balancing optimization efficiency and global search capability. Particle positions are randomly generated. For each particle's 6-dimensional parameters, values ​​are randomly selected within the corresponding constraint range. Stress weights w1, w2, and w3 are generated using random number normalization, and the nonlinear exponent m is randomly generated using a uniform distribution, where m ~ U(1.2, 3.5). Particle velocity determines the step size for parameter updates; the initial velocity is set to 10%~20% of the corresponding parameter constraint range. Fitness function calculation is performed. The fitness value of each particle directly reflects the prediction accuracy of its corresponding parameter combination. The calculation process needs to be combined with Monte Carlo simulation, which modulates the parameter vector of the current particle... Substituting the stress interference factor formula and damage accumulation formula, and based on this parameter combination, 1000~5000 random operating condition simulations were performed, simulating different combinations of temperature, pressure, and vibration frequency, and the mean of the life distribution was output. .like Figure 3 As shown, the relative error is calculated based on the objective function. The smaller the fitness value, the better the parameter combination.

[0057] In one feasible implementation, the maximum number of iterations T of the particle swarm optimization algorithm is... max The number of cycles is set to 200, and the inertia weight ω adopts a linear decreasing strategy. The initial inertia weight =0.9, the purpose is to enhance the global search and terminate the inertial weight. =0.4, the purpose is to improve local convergence. Regarding the optimization speed setting, the speed update formula is: in, Let be the velocity vector of the i-th particle in generation t. This represents the individual optimal solution for the i-th particle, i.e., the optimal parameter combination for that particle up to the current iteration. The swarm optimal solution is the optimal combination of parameters among all particles up to the current iteration. The random number is set to the interval [0, 1] to increase the randomness of the optimization and avoid local optima. The learning factors c1 and c2 are both set to 2.0.

[0058] In a feasible implementation, after each iteration, the individual optimal and the group optimal are updated, and it is determined whether to terminate the optimization search. The individual optimal solution is then updated. If the fitness value of the i-th particle in generation t+1 is... Then Updated to Otherwise, it will remain unchanged. Update the population optimum. Iterate through the individual optimal solutions of all particles, if a certain particle's The corresponding fitness value is less than the current The fitness value will then be Updated to this Otherwise, it remains unchanged. After the iteration terminates, the population optimal solution is output. This is used as the final parameter of the model. The optimized stress weight, nonlinear exponent, and other parameters are substituted into the stress interference factor formula to improve the quantification accuracy of multi-stress coupling effects. Combined with the optimized damage accumulation factor, the linear deviation of Miner's rule is corrected, and the damage accumulation under dynamic load is quantified more accurately.

[0059] It should be noted that the above step S400 can dynamically track the damage evolution of components, realize real-time assessment of their health status and reliable prediction of their lifespan, thereby providing a scientific basis for preventive maintenance and life cycle management of equipment, improving operational safety and optimizing maintenance strategies.

[0060] Example 3: This example provides a multidimensional stress-intensity interferometry analysis and modeling system for vacuum-breaking GIS, including: The data processing module is used to collect multi-source basic data of key components of vacuum-switching GIS equipment and perform preprocessing. The interference theory derivation module is used to define each stress component based on the preprocessed data, introduce a nonlinear exponent to establish a nonlinear interference factor model, and quantify the coupling interference effect of various stress components. The finite element modeling and multibody dynamics analysis module is used to build three-dimensional finite element models of key components and perform dynamic simulations on the three-dimensional finite element models to obtain simulation analysis results. The component life prediction module is used to calculate damage accumulation based on the nonlinear interference factor model and simulation analysis results, using the first fatigue analysis rule, and to estimate the remaining life of the component through the life prediction algorithm.

[0061] It should be noted that the technical solution of the multidimensional stress-intensity interferometric analysis and modeling system for vacuum-fractured GIS is based on the same concept as the technical solution of the multidimensional stress-intensity interferometric analysis and modeling method for vacuum-fractured GIS described above. For details not described in detail in the technical solution of the multidimensional stress-intensity interferometric analysis and modeling system for vacuum-fractured GIS in this embodiment, please refer to the description of the technical solution of the multidimensional stress-intensity interferometric analysis and modeling method for vacuum-fractured GIS described above.

[0062] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0063] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a multidimensional stress-intensity interferometry analysis and modeling method for vacuum-breaking GIS. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0064] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0065] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0066] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory, random access memory, flash memory, hard disk, or optical disk, and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multidimensional stress-strength interferometry analysis and modeling method for vacuum-severed GIS, characterized in that, include: Collect and preprocess multi-source basic data of key components of vacuum-switching GIS equipment; Based on the preprocessed data, each stress component is defined, and a nonlinear exponent is introduced to establish a nonlinear interference factor model to quantify the coupling interference effect of various stress components. A three-dimensional finite element model of the key components is established, and dynamic simulation is performed on the three-dimensional finite element model to obtain simulation analysis results; Based on the nonlinear interference factor model and the simulation analysis results, the first fatigue analysis rule is used to calculate the cumulative damage, and the remaining life of the component is estimated by the life prediction algorithm.

2. The multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS as described in claim 1, characterized in that, The calculation of cumulative damage using the first fatigue analysis rule includes: Based on the nonlinear interference factor model and the simulation analysis results, the actual damage increment of the key components under each load condition is calculated. Based on the fatigue life of the key component and the actual damage increment, the damage value of the key component under different working conditions is cumulatively calculated based on the first fatigue analysis rule. A dynamic damage progression model is used to track the update process of the cumulative damage value of the critical component under each new load condition. The cumulative damage value is compared with a first damage threshold to assess the damage status of the critical component.

3. The multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS as described in claim 2, characterized in that, The estimation of the remaining lifespan of a component using a lifespan prediction algorithm includes: The predicted remaining life of the critical component is calculated based on the component's initial life and current damage state. The first simulation algorithm is used to randomly simulate various load changes in the working condition data to construct a large number of random working condition scenarios. The damage accumulation calculation and the remaining life prediction are repeatedly executed under the random working condition scenarios to generate the life distribution of the key components under different working conditions. Based on the statistical results of the lifespan distribution, the predicted range of the remaining lifespan of the critical component is determined.

4. The multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS as described in claim 3, characterized in that, Also includes: A target optimization model is constructed using a first optimization algorithm, the target optimization model aiming to minimize the relative error between the model's predicted remaining lifetime and the actual lifetime; The weighting coefficient and nonlinear exponent are used as parameters to be optimized. The parameter combination of each particle in the particle swarm is randomly initialized, and the fitness value of each particle corresponding to the parameter combination is calculated based on the simulation results. The individual optimal solution and the global optimal solution of each particle in the particle swarm are updated based on the fitness value, and the parameter combination is iteratively optimized until the convergence condition is met. The optimized weighting coefficients and the nonlinear exponent are substituted into the nonlinear interference factor model to improve the quantification accuracy of stress coupling effect and the prediction accuracy of remaining lifetime.

5. The multidimensional stress-strength interferometry analysis and modeling method for vacuum-severed GIS as described in claim 1, characterized in that, The multi-source basic data collected for key components of the vacuum-interruptible GIS equipment includes: The key components of the vacuum interruption type GIS equipment include the operating mechanism, support structure, arc-extinguishing chamber and connecting parts; the multi-source basic data includes geometric parameters, material properties and operating condition data. The geometric parameters include the size, shape, thickness, and geometric structure parameters of the component; the material properties are the mechanical property data of the material used in the component, including yield strength, tensile strength, elastic modulus, and coefficient of thermal expansion; and the operating condition data are the environmental conditions in which the component is located, including temperature, pressure, vibration frequency, and impact load.

6. The multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS as described in claim 5, characterized in that, The step of defining each stress component based on the preprocessed data and establishing a nonlinear interference factor model by introducing a nonlinear exponent includes: Define each stress component, which includes static stress, dynamic stress, and thermal stress; Based on the contribution of each stress component to component failure and the material constitutive relationship, weighting coefficients are determined, and a nonlinear exponent is introduced to characterize the coupling amplification effect when multiple stress components act together. A nonlinear interference factor model is constructed based on the weighting coefficients and the nonlinear exponents to quantify the comprehensive interference effect of various stress components under their combined action. Based on the operating condition data, the weighting coefficients and nonlinear exponents are calibrated using experimental data to establish a stress-intensity interference relationship that reflects the actual operating conditions.

7. The multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severed GIS as described in claim 6, characterized in that, The dynamic simulation of the three-dimensional finite element model yields the following simulation analysis results: The three-dimensional finite element model is meshed to generate a discretized model composed of several finite elements; Define the corresponding material mechanical property parameters based on the material properties; Based on the operating condition data, apply corresponding boundary conditions and working loads to the discretized model; Based on the applied boundary conditions and working loads, multibody dynamics simulation is performed on the discretized model. By considering the vibration modes, impact loads, and operating frequencies of the components, the instantaneous stress and deformation of the key components under different dynamic working conditions are obtained as the simulation analysis results.

8. A multidimensional stress-strength interferometric analysis and modeling system for vacuum-severable GIS, employing the multidimensional stress-strength interferometric analysis and modeling method for vacuum-severable GIS as described in any one of claims 1 to 7, characterized in that, include: The data processing module is used to collect multi-source basic data of key components of vacuum-switching GIS equipment and perform preprocessing. The interference theory derivation module is used to define each stress component based on the preprocessed data, introduce a nonlinear exponent to establish a nonlinear interference factor model, and quantify the coupling interference effect of various stress components. The finite element modeling and multibody dynamics analysis module is used to establish a three-dimensional finite element model of key components and perform dynamic simulation on the three-dimensional finite element model to obtain simulation analysis results. The component life prediction module is used to calculate damage accumulation based on the nonlinear interference factor model and the simulation analysis results, using the first fatigue analysis rule, and to estimate the remaining life of the component through the life prediction algorithm.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the multidimensional stress-intensity interferometry analysis and modeling method for vacuum-severable GIS as described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the multidimensional stress-strength interferometric analysis and modeling method for vacuum-severable GIS as described in any one of claims 1 to 7.

Images