Software operation and maintenance method and system and storage medium

Abstract

The invention relates to the technical field of data mining, in particular to a software operation and maintenance method and system. The method comprises the following steps: acquiring engineering simulation heterogeneous hardware data, and performing multi-physics field coupling tensor compression to obtain a multi-physics field topology snapshot; finite element grid nonlinear constraint is executed based on the multi-physics field topology snapshot, and a nonlinear contact strategy tensor is obtained; according to a non-linear contact strategy tensor coding structure dynamics intelligent contract, simulation software dynamics credibility verification is carried out, and a dynamics credible chain is obtained; constructing a hydrodynamic vortex model based on the dynamic trusted chain, and performing boundary layer separation simulation flow optimization on the hydrodynamic vortex model to obtain a vortex flow field map; performing explicit dynamic explicit integration on the vortex flow field atlas to obtain a convergence optimization field; and performing phase field damage evolution according to the convergence optimization field to obtain a multi-physics field evolution twinborn body, and deploying the multi-physics field evolution twinborn body. The method can effectively improve the utilization rate of software computing resources.

Description

Technical Field

[0001] The present invention relates to the technical field of data mining, and in particular, to a software operation and maintenance method, system and storage medium. Background Art

[0002] Engineering simulation software assists engineers in complex system modeling, optimization and verification through numerical calculation, finite element analysis, multi-physics field coupling simulation, etc. With the improvement of computer hardware performance and the development of high-performance computing (HPC) technology, engineering simulation software has gradually developed from a single-machine operation mode to distributed computing, cloud simulation and parallel computing directions. However, limited by factors such as high computing load, complex data interaction and cross-platform compatibility, the operation and maintenance management of engineering simulation software faces many technical challenges. Traditional engineering simulation software operation and maintenance methods mainly rely on manual management and static rule configuration. For example, operation and maintenance management is carried out by manually monitoring server status, regularly maintaining the computing cluster, manually adjusting computing resource allocation and offline analyzing system logs. Engineering simulation software usually involves large-scale numerical calculation and complex data processing, with long execution time of computing tasks and high demand for computing resources. Traditional operation and maintenance methods mainly rely on manual configuration of computing nodes and storage resources, which are difficult to meet the requirements of dynamic computing load, resulting in uneven resource allocation and affecting computing efficiency. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a software operation and maintenance method, system and storage medium to solve at least one of the above technical problems.

[0004] To achieve the above object, a software operation and maintenance method includes the following steps:

[0005] Step S1: Obtain engineering simulation heterogeneous hardware data, and perform multi-physics field coupling tensor compression on the engineering simulation heterogeneous hardware data to obtain a multi-physics field topology snapshot;

[0006] Step S2: Execute finite element mesh nonlinear constraints based on the multi-physics field topology snapshot to obtain a nonlinear contact strategy tensor;

[0007] Step S3: Encode a structural dynamics smart contract according to the nonlinear contact strategy tensor, and perform dynamic trust verification on the structural dynamics smart contract for the simulation software to obtain a dynamic trust chain;

[0008] Step S4: Construct a hydrodynamic vortex model based on the dynamic trust chain, and perform boundary layer separation simulation flow optimization on the hydrodynamic vortex model to obtain a vortex flow field map;

[0009] Step S5: Perform explicit dynamics explicit integration on the vortex flow field atlas to obtain a converged and optimized field; perform phase field damage evolution based on the converged and optimized field to obtain a multi-physical field evolution twin, and deploy the multi-physical field evolution twin through a preset engineering simulation cloud architecture.

[0010] The present invention effectively reduces data redundancy and improves computational efficiency by acquiring engineering simulation heterogeneous hardware data and performing multi-physical field coupling tensor compression on it, while ensuring that the interaction information between multi-physical fields is retained, providing high-quality input data for subsequent calculations. Performing finite element mesh nonlinear constraints based on multi-physical field topological snapshots can accurately describe the contact behavior of complex structures, improve the stability and accuracy of contact calculations, and thus enhance the physical realism of the simulation. Through the generation of a nonlinear contact strategy tensor and intelligent contract coding, the automated management of structural dynamics constraint conditions is realized, and combined with trusted verification technology, the credibility and traceability of simulation calculations are enhanced, ensuring the reliability of calculation results. Based on the construction of a dynamics trusted chain, a hydrodynamic vortex model is built, enabling the vortex characteristics to be optimized under dynamic constraints, and optimizing the flow distribution through boundary layer separation simulation, improving the convergence and accuracy of fluid calculations. The explicit dynamics integration method can efficiently solve the dynamic characteristics of the vortex flow field, making the calculation process more stable, and finally obtaining a converged and optimized field, providing accurate dynamic input for subsequent phase field damage evolution. Phase field damage evolution can capture the material evolution process in a multi-physical field system, making the simulation results closer to the actual working conditions, and combined with the engineering simulation cloud architecture for deployment, improving the utilization rate of computing resources, supporting large-scale parallel computing and remote simulation, and realizing the efficient operation and intelligent management of engineering simulation software.

[0011] Optionally, step S1 is specifically as follows:

[0012] Step S11: Acquire multi-physical field data of engineering simulation heterogeneous hardware, and perform time-sensitive synchronization processing on the multi-physical field data to generate an initial multi-physical field data set;

[0013] Step S12: Perform non-Euclidean topological mapping on the initial multi-physical field data set, and perform data standardization and temporal consistency correction to generate standardized multi-physical field data;

[0014] Step S13: Based on the standardized multi-physical field data, perform tensor principal component analysis to extract material nonlinear characteristics including strain-stress coupling parameters, non-uniform thermal expansion coefficients, and contact stiffness evolution parameters, and generate a nonlinear material characteristic matrix;

[0015] Step S14: Construct a multi-physical field topological structure across space-time scales based on the nonlinear material characteristic matrix to generate an initial multi-physical field topological model;

[0016] Step S15: Perform non - linear tensor low - rank approximation dimensionality reduction on the multi - physical - field topology initial model, compress and retain the physical interaction relationships, and generate a multi - physical - field topology snapshot.

[0017] The present invention performs time - sensitive synchronization processing on multi - physical - field data of engineering simulation heterogeneous hardware, ensures the consistency of different physical - field data on the time axis, eliminates the timing deviation in the data acquisition process, and improves the accuracy of simulation data. The non - Euclidean topology mapping can adapt to complex multi - physical - field interaction relationships, making the data structure more in line with the actual physical characteristics. At the same time, combined with data standardization and timing consistency correction, it improves the data input quality and reduces the error accumulation caused by data scale mismatch. Using tensor principal component analysis to extract key material non - linear features enables accurate modeling of important parameters such as strain - stress coupling, non - uniform thermal expansion, and contact stiffness, providing a high - precision description of material behavior for subsequent simulation calculations. Based on the non - linear material feature matrix, a multi - physical - field topology structure across space - time scales is constructed, enabling the simulation model to take into account both local physical characteristics and global evolution relationships, thereby enhancing the model's expressive ability. Through non - linear tensor low - rank approximation dimensionality reduction, the redundant calculation amount is effectively reduced, and at the same time, the physical interaction relationships are compressed and retained. The finally generated multi - physical - field topology snapshot can maintain high - fidelity to the key characteristics of multi - physical fields while reducing the computational complexity, providing an efficient and accurate data basis for subsequent engineering simulation calculations.

[0018] Optionally, step S12 is specifically as follows:

[0019] Step S121: Align the discrete - time - point data of the initial multi - physical - field data set, interpolate the missing data at a time step Δt = 10 -3 s, and set the maximum offset correction threshold to ±5×10 -3 s to adjust the time offset of adjacent measurement points, and generate a time - series - aligned multi - physical - field data set;

[0020] Step S122: Perform non - Euclidean graph topology mapping on the time - series - aligned multi - physical - field data set, and construct a local physical interaction graph to generate a non - Euclidean multi - physical - field initial graph;

[0021] Step S123: Perform hierarchical spectral clustering analysis on the non - Euclidean multi - physical - field initial graph, select the first N = 10 eigenvectors for Laplacian eigen - decomposition, and use a threshold ε = 10 -4 for feature truncation to generate a spectro - smoothed multi - physical - field graph;

[0022] Step S124: Perform normalized data standardization on the spectro - smoothed multi - physical - field graph, normalize the data to the interval [-3σ, 3σ], and perform time - series trend correction based on a sliding window length L = 50 to generate standardized multi - physical - field data;

[0023] Step S125: Perform Bayesian optimal interpolation prediction based on the standardized multi-physical field data, set the anomaly threshold δ = 3σ to eliminate anomaly points, and perform interpolation and completion under the confidence interval α = 95% to generate the standardized multi-physical field data.

[0024] In the present invention, by aligning the discrete time point data of the initial multi-physical field data set, it is ensured that the data of different physical fields are synchronized within the time step range. By setting the maximum offset correction threshold, the measurement error is effectively reduced, and the consistency and reliability of the time series data are improved. The use of non-Euclidean graph topology mapping makes the data structure more adaptable to the complex interaction relationships of multi-physical fields. At the same time, by constructing a local physical interaction graph, the influence of local features on the overall topological structure is enhanced, thereby improving the simulation accuracy. Combining hierarchical spectral clustering analysis helps to extract the global features of multi-physical field data, and effectively reduces the dimension through Laplacian eigen-decomposition, reduces the redundant calculation amount, improves the calculation efficiency. At the same time, the feature selection of the data is optimized by the threshold truncation method to ensure the integrity of key features. Normalized data standardization can eliminate the scale difference between physical quantities, make the data distribution more uniform, and at the same time perform time series trend correction based on the sliding window length, effectively eliminate non-stationary factors, and improve the consistency of the data. Finally, through Bayesian optimal interpolation prediction, the problem of missing abnormal data is effectively compensated, the anomaly threshold is set to eliminate anomaly points, the data quality is improved, and interpolation and completion are performed under the confidence interval to ensure the continuity and accuracy of the data, thereby providing high-quality and reliable standardized multi-physical field data for subsequent simulation calculations.

[0025] Optionally, step S2 is specifically as follows:

[0026] Step S21: Perform finite element mesh division on the multi-physical field topology snapshot and adjust the element size to generate an initial finite element mesh data set;

[0027] Step S22: Identify the non-linear contact area based on the initial finite element mesh data set and construct a contact stiffness distribution matrix to generate a non-linear contact area feature matrix;

[0028] Step S23: Implement contact constraint optimization on the non-linear contact area feature matrix, calculate the contact mechanical response to obtain a non-linear contact pair stiffness matrix; dynamically adjust the stiffness distribution based on the non-linear contact pair stiffness matrix to generate a contact-optimized finite element mesh;

[0029] Step S24: Perform multi-agent reinforcement learning strategy training based on the contact-optimized finite element mesh and optimize the strategy convergence to generate a non-linear contact strategy model;

[0030] Step S25: Perform policy tensor encoding on the non-linear contact strategy model to generate a non-linear contact strategy tensor.

[0031] Through finite element mesh generation and adjustment of element sizes, the present invention improves the mesh quality and calculation accuracy, enabling subsequent simulation calculations to be carried out on a better discretization basis. The identification of non-linear contact areas can accurately extract complex contact relationships and construct a contact stiffness distribution matrix, enabling the contact characteristics to be accurately quantified and enhancing the reliability of simulation calculations. For the calculation of the contact mechanical response in non-linear contact areas, dynamic optimization and adjustment are carried out in combination with the contact stiffness matrix to ensure the rationality of the stiffness distribution, enhance the physical authenticity of the model, and at the same time, the optimized finite element mesh can better adapt to the stress-strain transfer of complex structures and improve the simulation accuracy. Training with a multi-agent reinforcement learning strategy enables the non-linear contact optimization to have stronger adaptability and generalization ability, and by optimizing the strategy convergence, it is ensured that the model can still converge quickly and stably in complex contact environments, thereby improving the simulation calculation efficiency. Finally, the non-linear contact optimization strategy is quantitatively expressed by means of policy tensor encoding, enabling the contact optimization results to be directly applied to subsequent calculations in tensor form, improving the data transmission efficiency and compatibility, and providing an efficient and intelligent contact optimization method for complex engineering simulations.

[0032] Optionally, the encoded structural dynamics smart contract described in step S3 is specifically:

[0033] Perform tensor low-rank decomposition on the non-linear contact policy tensor, set the tensor rank threshold r ∈ [5, 15], and obtain the structural dynamics feature tensor;

[0034] Based on the structural dynamics feature tensor, construct a Lagrangian constraint optimization model, set the optimization iteration step size η = 0.01 and the convergence error threshold ε ≤ 10 -5 , and calculate the generalized coordinate transformation matrix to obtain the set of structural mechanics optimization parameters;

[0035] Establish a dynamic state transition equation based on the set of structural dynamics optimization parameters, and construct a state-control coupling model in combination with the preset multi-body system constraint equations, set the constraint weight matrix W c ∈ [0.1, 1.0], and generate the basic framework of the smart contract;

[0036] Perform verifiable calculation conversion on the basic framework of the smart contract, set the state consistency verification threshold δ s ≤ 0.05, construct a state consistency verification sub-module, and generate a dynamics trusted smart contract;

[0037] Conduct a consensus mechanism test on the dynamics trusted smart contract, set the number of consensus nodes N c = 10 and the number of consensus verification rounds R ∈ [50, 100], and optimize the execution time of the smart contract based on the results of the consensus mechanism test to obtain the structural dynamics smart contract.

[0038] Through the low-rank decomposition of the non-linear contact strategy tensor, the present invention extracts key contact feature information, sets a tensor rank threshold, effectively reduces data redundancy, ensures computational efficiency and data compactness, while retaining important structural dynamics information. Based on the extracted structural dynamics feature tensor, a Lagrangian constraint optimization model is constructed, and an optimization iteration step size and a convergence error threshold are set, thereby improving the convergence speed and stability of the optimization calculation. The calculation of the generalized coordinate transformation matrix ensures the consistency of dynamic parameters in different reference frames, improves the calculation accuracy and the rationality of system modeling. On this basis, a dynamic state transition equation is established, and combined with the preset multi-body system constraint equation, a state-control coupling model is constructed to ensure that the dynamic system can achieve optimal performance under controlled conditions. At the same time, by setting the constraint weight matrix, the system constraint conditions can be more flexibly adapted to different working conditions. The construction of the intelligent contract basic framework makes the dynamic optimization process verifiable, and through the state consistency verification sub-module, improves the reliability and data security of the intelligent contract execution. The consensus mechanism test further enhances the stability and anti-interference ability of the dynamic trusted intelligent contract, ensures the execution consistency of the intelligent contract, and at the same time, by optimizing the number of consensus verification rounds and the number of nodes, improves the computational efficiency and reduces the consumption of computing resources, finally realizing an efficient, trusted and stable structural dynamics intelligent contract, providing a reliable guarantee for the automated operation and maintenance of engineering simulation software.

[0039] Optionally, the dynamic trust verification of the simulation software in step S3 is specifically as follows:

[0040] Deploy the intelligent contract environment for the structural dynamics intelligent contract, set the calculation accuracy threshold ∈ = 10 -5 and the maximum number of iterations I max = 500, to generate the initial environment for the dynamic verification of the simulation software;

[0041] Based on the initial environment for the dynamic verification of the simulation software, extract the dynamic state variables of the contract execution, so as to construct a state observation matrix;

[0042] Conduct a numerical stability analysis on the state observation matrix to generate a state stability evaluation report;

[0043] According to the state stability evaluation report, adaptively adjust the time step of the dynamic state variables, set the initial time step Δt0 = 0.001s, the maximum step size Δt max = 0.1s, and dynamically update the step size in combination with the preset error control factor γ ∈ [0.8, 1.2] to generate time step optimized dynamic data

[0044] Calculate the credibility of the time step optimized dynamic data, set the lower limit of credibility P mim= 95%, calculate the consistency error of the execution of the smart contract, and set the consistency error control criterion E cons ≤ 10 -4 Optimize the credibility of the calculation result of the consistency error to generate a kinetic credibility verification report;

[0045] Verify the kinetic credibility verification report through a consensus mechanism, and set the number of consensus nodes N c = 10, the number of consensus verification rounds R = 100, to generate a kinetic credibility chain.

[0046] Through the deployment of the smart contract environment, this invention sets the calculation accuracy threshold and the maximum number of iterations to ensure the stability and numerical accuracy of the simulation calculation, while improving the calculation efficiency and reducing the waste of computing resources. Based on the initial environment of the kinetic verification of the simulation software, the kinetic state variables of the contract execution are extracted, and a state observation matrix is constructed to provide an accurate data basis for the numerical analysis of the subsequent kinetic system. Through numerical stability analysis, the stability of the state variables is evaluated, potential numerical divergence problems are identified, and the reliability and data controllability of the kinetic simulation are improved. According to the stability evaluation report, a time step adaptive adjustment strategy is adopted, combined with an error control factor, to realize the dynamic update of the step size, improve the calculation efficiency while ensuring the calculation accuracy, and avoid the decrease in accuracy caused by too large a step size or the excessive calculation time caused by too small a step size. The credibility calculation ensures the credibility of the smart contract execution process, calculates the consistency error, and optimizes the credibility according to the set error control criterion, improving the reliability and stability of the smart contract calculation result. Finally, the kinetic credibility verification report is verified through the consensus mechanism to ensure the consistency of the kinetic data in the distributed computing environment, enhance the credibility and traceability of the simulation calculation, generate a kinetic credibility chain, and provide a solid guarantee for the efficient, stable and credible operation and maintenance of the engineering simulation software.

[0047] Optionally, step S4 is specifically as follows:

[0048] Step S41: Extract the structural kinetic state variables based on the kinetic credibility chain, and combine the contact stress distribution data in the non - linear contact strategy tensor to construct an initial kinetic - contact characteristic model;

[0049] Step S42: Conduct an energy conservation analysis on the initial kinetic - contact characteristic model, and calculate the stress gradient and deformation response in the contact area, thereby establishing a dynamic adjustment function for the contact stiffness;

[0050] Step S43: Optimize the contact kinetic parameters according to the dynamic adjustment function of the contact stiffness to obtain an optimized set of contact kinetic parameters;

[0051] Step S44: Obtain historical fluid simulation data, and extract the fluid - structure coupling relationship from the historical fluid simulation data to obtain a set of fluid coupling parameters;

[0052] Step S45: Construct a hydrodynamic vortex model based on the optimized contact dynamics parameter set and the fluid coupling parameter set;

[0053] Step S46: Conduct implementation simulation credibility verification on the hydrodynamic vortex model, optimize the dynamic-fluid interaction characteristic parameters based on the credibility verification results, and finally generate a vortex flow field atlas.

[0054] In the present invention, by extracting structural dynamics state variables based on the dynamics credibility chain and combining the contact stress distribution data in the non-linear contact strategy tensor, an initial dynamic-contact characteristic model is constructed, thereby enhancing the modeling accuracy of the dynamic behavior in the contact area and improving the adaptability of dynamic simulation to actual working conditions. Perform energy conservation analysis on the initial dynamic-contact characteristic model, calculate the stress gradient and deformation response in the contact area, ensure that the contact process conforms to physical laws, and establish a dynamic adjustment function for contact stiffness, so that the contact stiffness is adaptively adjusted according to the changes in external load and deformation state, improving the accuracy of simulation calculation. Optimize the contact dynamics parameters according to the dynamic adjustment function of contact stiffness, making the contact mechanics calculation more in line with actual physical characteristics, and enhancing the stability and calculation efficiency of simulation calculation. By obtaining historical fluid simulation data and extracting the fluid-structure coupling relationship, a fluid coupling parameter set is established, thereby enhancing the simulation system's ability to simulate the structural response under fluid action and improving the accuracy of multi-physics field coupling modeling. Use the optimized contact dynamics parameter set and fluid coupling parameter set to construct a hydrodynamic vortex model, enhancing the physical consistency and calculation accuracy of vortex modeling, making the fluid dynamics simulation closer to real engineering applications. Finally, conduct simulation credibility verification on the hydrodynamic vortex model, optimize the dynamic-fluid interaction characteristic parameters based on the verification results, ensure the credibility and accuracy of the vortex flow field simulation results, enhance the simulation ability of engineering simulation software in complex flow environments, and provide reliable numerical support for multi-physics field coupling analysis.

[0055] Optionally, the phase field damage evolution described in step S5 is specifically as follows:

[0056] Extract the initial damage field based on the convergence optimization field and conduct time evolution analysis of the damage field to generate a preliminary damage evolution model;

[0057] Perform finite element discretization on the preliminary damage evolution model to obtain a local damage evolution characteristic map;

[0058] Perform time evolution analysis based on the local damage evolution characteristic map to obtain a damage propagation path;

[0059] Optimize the damage field parameters of the preliminary damage evolution model according to the damage propagation path, update the damage evolution process, and obtain an updated damage evolution model;

[0060] Integrate the multi - physical - field interaction based on the multi - physical - field topology snapshot and the updated damage evolution model to obtain the multi - physical - field evolution twin.

[0061] In the present invention, the initial damage field is extracted based on the convergence - optimized field, and the time - evolution analysis of the damage field is carried out. It can accurately capture the initial damage state inside the material or structure, and combined with time - series analysis, reveal the dynamic trend of damage evolution, improving the timeliness and accuracy of damage modeling. The preliminary damage evolution model is discretized by finite - element method, enabling the damage evolution process to be numerically solved on a high - resolution spatial scale, obtaining the local damage evolution feature map, thereby refining the distribution characteristics of the damaged area, improving the fineness of the simulation and the local response simulation ability. Performing time - evolution analysis based on the local damage evolution feature map can effectively predict the propagation path of damage, providing reliable damage - propagation information for subsequent optimization and enhancing the accuracy of damage prediction. Optimizing the damage - field parameters of the preliminary damage evolution model according to the damage propagation path can adjust the key parameters for different damage mechanisms, improving the physical rationality and simulation credibility of damage modeling, and by updating the damage evolution process, enabling the model to better adapt to complex working conditions and expanding the applicable range of engineering simulation. Finally, by combining the multi - physical - field topology snapshot with the updated damage evolution model, the multi - physical - field interaction integration is realized, obtaining the multi - physical - field evolution twin, enabling the simulation to not only reflect the damage evolution inside the structure but also couple the mutual influences between multi - physical fields, providing high - precision, multi - scale, and multi - field - coupled simulation support for engineering simulation, and improving the reliability and accuracy of engineering decisions.

[0062] Optionally, a software operation and maintenance system in this specification is used to execute the software operation and maintenance method as described above. The software operation and maintenance system includes:

[0063] A coupled tensor compression module, which is used to obtain the heterogeneous hardware data of engineering simulation and perform multi - physical - field coupled tensor compression on the heterogeneous hardware data of engineering simulation to obtain the multi - physical - field topology snapshot;

[0064] A non - linear constraint module, which is used to perform finite - element mesh non - linear constraints based on the multi - physical - field topology snapshot to obtain the non - linear contact strategy tensor;

[0065] A dynamics credibility verification module, which is used to encode the structural dynamics smart contract according to the non - linear contact strategy tensor and perform simulation software dynamics credibility verification on the structural dynamics smart contract to obtain the dynamics credibility chain;

[0066] A vortex flow - field construction module, which is used to construct a hydrodynamic vortex model based on the dynamics credibility chain and perform boundary - layer separation simulation flow optimization on the hydrodynamic vortex model to obtain the vortex flow - field atlas;

[0067] The multi-physical field interaction evolution module is used to perform explicit dynamic explicit integration on the vortex flow field atlas to obtain a converged and optimized field; perform phase field damage evolution based on the converged and optimized field to obtain a multi-physical field evolution twin, and deploy the multi-physical field evolution twin through a preset engineering simulation cloud architecture.

[0068] The software operation and maintenance system of the present invention can implement any software operation and maintenance method of the present invention. It is a medium for combining operations and signal transmissions between various modules to complete the software operation and maintenance method. The internal modules of the system cooperate with each other, thereby effectively improving the utilization rate of software computing resources.

[0069] Optionally, this specification also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed, the software operation and maintenance method described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects, and advantages of the present invention will become more apparent:

[0071] Figure 1 It is a schematic flowchart of the steps of the software operation and maintenance method of the present invention;

[0072] Figure 2 It is a detailed schematic flowchart of step S1 in the present invention;

[0073] Figure 3 It is a detailed schematic flowchart of step S2 in the present invention;

[0074] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0076] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0077] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a software operation and maintenance method, and the method includes the following steps:

[0079] Step S1: Obtain engineering simulation heterogeneous hardware data, and perform multi-physical field coupling tensor compression on the engineering simulation heterogeneous hardware data to obtain a multi-physical field topology snapshot;

[0080] In this embodiment, an engineering simulation software including a GPU cluster, embedded sensors, and an FPGA accelerator is used to realize real-time acquisition and preprocessing of engineering simulation data. Through the IEEE1588 clock synchronization technology, a sampling rate of 1 kHz is achieved, and multi-physical field data such as temperature, stress, vibration, and electromagnetic are obtained, and the data resolution reaches 16 bits; subsequently, the high-order singular value decomposition (HOSVD) algorithm is used to perform tensor compression processing on the original data, and the tensor rank threshold is set to 10, effectively reducing data redundancy while retaining the coupling characteristics between physical fields, and finally forming a multi-physical field topology snapshot with high precision and low redundancy, providing a solid data basis for subsequent simulation analysis.

[0081] Step S2: Execute finite element mesh nonlinear constraints based on the multi-physical field topology snapshot to obtain a nonlinear contact strategy tensor;

[0082] In this embodiment, finite element software such as ABAQUS or ANSYS is used for adaptive mesh generation and nonlinear constraint modeling. In specific implementation, the initial mesh element size is set to 2 mm, the key contact areas are identified through a nonlinear contact constraint algorithm based on the variational principle, and the stress field distribution of each area is calculated; at the same time, a contact stiffness distribution matrix is constructed, and local mesh density fine-tuning is adopted to achieve an accurate description of the contact behavior, and finally a nonlinear contact strategy tensor that can truly reflect complex contact characteristics is obtained, ensuring that the model has good numerical stability and physical authenticity under high load conditions.

[0083] Step S3: Encode a structural dynamics smart contract according to the nonlinear contact strategy tensor, and perform dynamics trusted verification on the structural dynamics smart contract by a simulation software to obtain a dynamics trusted chain;

[0084] In this embodiment, a non-linear contact strategy tensor is used as the core input, and a smart contract is developed using the Solidity language and deployed on the Ethereum private chain platform. In the specific process, the Lagrangian constraint optimization method is adopted, and the iteration step size is set to 0.01 seconds and the convergence error threshold is 1×10 -5 , and at the same time, the generalized coordinate transformation matrix is calculated to ensure the consistency of parameters in different reference frames; through the simulation environment, the execution of the smart contract is verified in real time and security tested, and a dynamic trusted chain with traceability, automatic feedback and fault monitoring capabilities is constructed to provide highly reliable data support for complex engineering simulations.

[0085] Step S4: Based on the dynamic trusted chain, a hydrodynamic vortex model is constructed, and the boundary layer separation simulation flow rate of the hydrodynamic vortex model is optimized to obtain a vortex flow field map;

[0086] In this embodiment, a hydrodynamic vortex model is constructed in ANSYS Fluent, and the large eddy simulation (LES) method and the boundary layer separation technology are used to solve the flow field. Specifically, the flow field sampling step size is set to 0.005 seconds, and an empirical correction coefficient of 0.85 is introduced to correct the position of the boundary layer separation point; through the iterative solution of the pressure and velocity distributions in the flow field and the correction of the boundary conditions, a high-precision vortex flow field map is obtained, which fully reflects the coupling effect between the fluid and the structure and provides key hydrodynamic parameters for subsequent dynamic integration and damage evolution.

[0087] Step S5: Perform explicit dynamics explicit integration on the vortex flow field map to obtain a converged and optimized field; perform phase field damage evolution based on the converged and optimized field to obtain a multi-physical field evolution twin, and deploy the multi-physical field evolution twin through a preset engineering simulation cloud architecture.

[0088] In this embodiment, the explicit solver in LS-DYNA is used to perform explicit dynamics integration on the vortex flow field map, and the integration step size is accurately set to 0.001 seconds. The system monitors the dynamic response of each time step in real time during the iterative solution process, and the residual between two consecutive iterations drops to 1×10 -4When the following condition is met, it is determined that convergence has occurred, thereby generating a stable convergence optimization field. This convergence optimization field not only fully reflects the coupling effect between the fluid and the structure, but also provides accurate initial conditions for subsequent phase-field damage evolution. During the phase-field damage evolution process, an improved phase-field model is combined with the finite element discretization method to model the internal damage propagation and crack evolution of the structure. In specific operations, an adaptive mesh refinement technique is adopted in the critical damage area, controlling the mesh element size within 0.5 mm, and setting the simulation time step to 0.01 s to ensure that subtle crack propagation changes can be captured. After multiple iterative simulations, an updated damage evolution model is generated, which can accurately predict the crack propagation trend of the structure during the loading process. Finally, the updated damage evolution model is integrated with the multi-physical field topology snapshot data to construct a multi-physical field evolution twin, and it is deployed online in real time through a preset engineering simulation cloud architecture (such as deployed based on the AWS EC2 container cluster), refreshing the data once per second to ensure the real-time nature of online monitoring and data feedback, thereby providing efficient and accurate technical support for the intelligent operation and maintenance and decision-making support of engineering simulation software.

[0089] Optionally, step S1 is specifically as follows:

[0090] Step S11: Obtain multi-physical field data of heterogeneous hardware for engineering simulation, and perform time-sensitive synchronization processing on the multi-physical field data to generate an initial multi-physical field data set;

[0091] In this embodiment, through a heterogeneous hardware platform composed of a high-performance GPU cluster, embedded sensors, and an FPGA accelerator deployed in the engineering simulation software, multi-physical field data such as temperature, stress, vibration, and electromagnetic field during the engineering simulation process are collected in real time. The distributed time synchronization protocol (IEEE 1588) is used to calibrate the time of the data, and the synchronization accuracy is set to 1 microsecond to ensure the consistency of the multi-physical field data. Subsequently, noise filtering is performed on the data, and the fifth-order Savitzky-Golay filter is used to smooth the measurement signal to eliminate high-frequency noise and maintain key physical characteristics, and finally an initial multi-physical field data set for simulation calculation is generated.

[0092] Step S12: Perform non-Euclidean topological mapping on the initial multi-physical field data set, and perform data standardization and temporal consistency correction to generate standardized multi-physical field data;

[0093] In this embodiment, a non-Euclidean topological mapping algorithm based on graph theory is adopted to embed data points into a high-dimensional topological space, thereby depicting the complex spatial relationships between physical fields. At the same time, the Z-score normalization method is used to standardize the data, converting all data values into the range of a standard normal distribution (mean of 0 and standard deviation of 1), and an error threshold of ±0.001 is set to eliminate outliers. Meanwhile, to ensure the accuracy of time series data, a sliding window autoregressive method (window length 50 steps) is used to correct the temporal consistency of the data, making the local change trend of the time series signal smooth, and finally generating standardized multi-physical field data.

[0094] Step S13: Based on the standardized multi-physical field data, perform tensor principal component analysis to extract material nonlinear characteristics including strain-stress coupling parameters, non-uniform thermal expansion coefficients, and contact stiffness evolution parameters, and generate a nonlinear material characteristic matrix.

[0095] In this embodiment, tensor principal component analysis (TPCA) technology is used to extract material nonlinear characteristics. By constructing a third-order tensor (space-time-physical variables), the principal component relationship between physical variables is analyzed. A retention criterion with a cumulative contribution rate of principal components greater than 95% is set to extract key characteristics, including strain-stress coupling parameters (range 0.2–0.9), non-uniform thermal expansion coefficients (1.2×10 -5 –5.8×10 -5 K -1 ) and contact stiffness evolution parameters (500–5000 MPa). At the same time, the QR decomposition method is used to reduce the dimension of the characteristic matrix to reduce computational redundancy, and finally a nonlinear material characteristic matrix is obtained.

[0096] Step S14: According to the nonlinear material characteristic matrix, construct a multi-physical field topological structure across space-time scales to generate an initial multi-physical field topological model.

[0097] In this embodiment, a multi-layer graph neural network technology is further used to construct a multi-physical field topological structure across space-time scales. This method utilizes the coupling relationship between physical fields to construct a multi-layer connection graph, sets the edge weight threshold in the graph to 0.05 to filter out weak interactions, and ensures that key coupling relationships are retained. Then, a graph attention network (GAT) is used to calculate the interaction weights between physical fields, and the dynamic time warping (DTW) method is used to analyze the dynamic characteristics across space-time, enabling the topological structure to adapt to the multi-physical field interaction relationships at different scales, and finally generating an initial multi-physical field topological model, laying a solid foundation for subsequent simulation analysis and optimization.

[0098] Step S15: Perform nonlinear tensor low-rank approximation dimensionality reduction on the initial multi-physical field topological model, and compress and retain physical interaction relationships to generate a multi-physical field topological snapshot.

[0099] In this embodiment, a non-linear tensor low-rank approximation method such as Tucker decomposition is used to reduce the dimension of the initial multi-physics field topology model. The decomposition rank threshold is set to 10 to compress redundant data and simultaneously retain the core interaction features between physical fields. The alternating least squares (ALS) method is used to optimize the tensor decomposition process to improve the convergence speed and computational stability. Ensure that the compressed model still accurately expresses complex coupling relationships, thereby generating an efficient, low-redundancy, and high-fidelity multi-physics field topology snapshot, providing accurate data support for the efficient operation and maintenance of engineering simulation software and subsequent multi-physics field coupling simulations.

[0100] Optionally, step S12 is specifically as follows:

[0101] Step S121: Align the discrete time point data of the initial multi-physics field data set, interpolate missing data at a time step of Δt = 10 -3 s, and set the maximum offset correction threshold to ±5×10 -3 s to adjust the time offset of adjacent measurement points to generate a time-series aligned multi-physics field data set;

[0102] In this embodiment, the discrete time point data of the initial multi-physics field data set is aligned. Engineering simulation data includes measurement data of multiple physical fields (such as temperature field, stress field, and displacement field) at different time points, and due to the accuracy limitations of hardware or sensors, some data points may be missing. In this case, a linear interpolation method is used, and the time step is set to 10 -3 seconds (s = 10 -3 s), and the missing data is interpolated and supplemented according to this time step. For each pair of adjacent measurement points, we set the maximum offset correction threshold to ±5×10 -3 s to ensure that the supplemented data within the time step does not cause excessive time offset. In this way, the generated time-series aligned data set ensures the consistency and synchronization of multi-physics field data on the same time axis.

[0103] Step S122: Perform a non-Euclidean graph topology mapping on the time-series aligned multi-physics field data set, construct a local physical interaction graph, and generate a non-Euclidean multi-physics field initial graph;

[0104] In this embodiment, a non-Euclidean graph topological mapping is performed on the multi-physical field dataset after temporal alignment. A graph embedding method based on the nearest neighbor is used for topological mapping. Specifically, the K-nearest neighbor (KNN) algorithm is adopted to construct the graph topology, with K set to 8, that is, each data point is connected to its 8 nearest neighbor nodes. Through the normalization process of the adjacency matrix, the weight threshold W_th is set to 0.05 to ensure that the interaction relationships above this threshold are effectively retained, while the edges below this threshold are removed, finally generating the initial non-Euclidean multi-physical field graph (MP-GraphInit). This mapping method effectively captures the non-linear connections between complex physical fields and provides a structured graphical representation for subsequent analysis.

[0105] Step S123: Perform hierarchical spectral clustering analysis on the initial non-Euclidean multi-physical field graph, select the first N = 10 eigenvectors for Laplacian eigen-decomposition, and use the threshold ε = 10 -4 for feature truncation to generate a spectral smooth multi-physical field graph;

[0106] In this embodiment, hierarchical spectral clustering analysis is performed on the initial non-Euclidean multi-physical field graph. In this step, the spectral clustering algorithm is used to cluster the multi-physical field dataset, with the number of clusters set to 10 (N = 10), and the eigenvectors of each cluster are extracted. Then, the Laplacian eigen-decomposition method is applied for eigenvalue decomposition, the most important first N eigenvectors are selected from them, and the feature truncation method is used to set the threshold to 10 -4 , and only the components with eigenvalues greater than this threshold are retained. This process effectively reduces the data dimension, extracts the main physical patterns and structural information in the multi-physical field data, lays a foundation for subsequent analysis, and generates a spectral smooth multi-physical field graph.

[0107] Step S124: Perform normalized data standardization on the spectral smooth multi-physical field graph, normalize the data to the interval [-3σ, 3σ], and perform time series trend correction based on the sliding window length L = 50 to generate standardized multi-physical field data;

[0108] In this embodiment, data standardization is performed on the spectral smooth multi-physical field graph. To ensure that the data of different physical fields have the same scale and dimension, the min-max normalization method is applied to normalize the data into the interval [-3σ, 3σ] (σ refers to the standard deviation of the dataset) to prevent the physical fields with different dimensions from having an unbalanced impact on the model. Then, based on the sliding window length of 50, time series trend correction is performed on the data, and the window step size is set to 5 to ensure that the change trend within each time window is smoothly adjusted. During this process, the data is denoised to ensure the stability and consistency of the multi-physical field data.

[0109] Step S125: Perform Bayesian optimal interpolation prediction based on the standardized multi-physical field data, set the anomaly threshold δ = 3σ to remove anomaly points, and perform interpolation and completion under the confidence interval α = 95% to generate the standardized multi-physical field data.

[0110] In this embodiment, by analyzing the missing points in the data, a Bayesian regression model is used to predict the missing data. The anomaly threshold is set to 3 times the standard deviation, and the outliers outside this range are removed to ensure the reliability of the data during the interpolation process. To optimize the interpolation effect, interpolation and completion are performed within the confidence interval α = 95%, and the Monte Carlo method is used for multiple samplings to generate multiple interpolation results, thereby ensuring the accuracy and stability of the interpolation. Finally, the generated standardized multi-physical field data set has high precision and strong fault tolerance for missing values and anomaly points, providing a reliable data basis for further simulation analysis.

[0111] Optionally, step S2 is specifically as follows:

[0112] Step S21: Perform finite element mesh division on the multi-physical field topology snapshot and adjust the element size to generate an initial finite element mesh data set;

[0113] In this embodiment, finite element mesh division is performed on the basis of the multi-physical field topology snapshot to decompose the complex physical field into multiple small and computable units. The standard tetrahedral elements are used in the mesh division process, and the selection of the element size is crucial as it directly affects the calculation accuracy and efficiency. In this embodiment, the maximum size of the element is set to 5 mm to ensure that details can be captured sufficiently without causing excessive computational burden. After the division, an initial finite element mesh data set is obtained. To improve the accuracy, the boundary layer region is locally refined after the division to ensure that the physical behavior in the key region can be accurately described. Finally, the mesh division result provides the preliminary data structure required for engineering simulation.

[0114] Step S22: Identify the non-linear contact region based on the initial finite element mesh data set, construct the contact stiffness distribution matrix, and generate the non-linear contact region feature matrix;

[0115] In this embodiment, the contact region is determined by analyzing the points where the physical fields interact with each other and the geometric contact situation between the elements. The materials or surfaces within the contact region exhibit non-linear behavior, so it is crucial to accurately model them. Then, the contact stiffness distribution matrix is constructed, and the element values of the matrix represent the stiffness relationship between the contact points. Here, the initial estimated value of the contact stiffness is 10 4 N / m 2, through the analysis of physical fields and material properties, a preliminary estimation of the contact stiffness is carried out. The generated characteristic matrix of the nonlinear contact region provides the basic data for subsequent optimization steps.

[0116] Step S23: Implement contact constraint optimization on the characteristic matrix of the nonlinear contact region, calculate the contact mechanical response, and obtain the stiffness matrix of the nonlinear contact pair; based on the stiffness matrix of the nonlinear contact pair, dynamically adjust the stiffness distribution to generate a contact-optimized finite element mesh;

[0117] In this embodiment, contact constraint optimization is implemented on the characteristic matrix of the nonlinear contact region. In this step, the optimization process is divided into two stages: the first stage is the calculation of the contact mechanical response, and the second stage is the dynamic adjustment of the stiffness matrix. In the first stage, by solving the initial finite element mesh model, the displacement and stress responses between contact points are calculated using the finite element analysis method. During this process, different physical field interactions within the contact region are considered, such as nonlinear phenomena like friction, slip, and adhesion. Specifically, the incremental method is used for iterative solution of displacement and stress. In each iteration, based on the initial contact stiffness matrix, the displacement and stress values of each contact point are calculated, and the contact stiffness matrix is updated according to the interaction forces between adjacent contact points. To accurately capture the nonlinear behavior of the contact region, a nonlinear constitutive model based on contact mechanics theory is adopted. This model considers the microscopic topography of the contact interface, adhesion force, and the plastic characteristics of the material. In the second stage, based on the calculated contact mechanical response, the mechanical properties of the contact region are optimized by dynamically adjusting the contact stiffness matrix. For this purpose, an iterative optimization algorithm is introduced. Each optimization corrects the contact stiffness matrix based on the previous round of calculation results, and the objective function of minimizing the contact stress is used for optimization. Specifically, the "Adaptive Gradient Descent" (AGD) is adopted. In each round of iteration, the optimization algorithm dynamically adjusts the stiffness distribution according to the stress distribution in the contact region. By dynamically adjusting the contact stiffness, the phenomenon of contact stress concentration can be reduced while maintaining contact stability. The initial value of the contact stiffness matrix can be set to 104 N / m 2 , and the maximum allowable stress concentration is set to 50 MPa to prevent plastic deformation of the contact points. After each round of optimization, the contact stiffness matrix will be updated and provide a new stiffness value for the next calculation. After multiple optimization iterations, the contact stiffness matrix converges to the optimal solution, generating the stiffness distribution after contact optimization, thus ensuring that the mechanical response within the contact region meets the design requirements.

[0118] Step S24: Conduct multi-agent reinforcement learning strategy training based on the contact-optimized finite element mesh, optimize the strategy convergence, and generate a nonlinear contact strategy model;

[0119] In this embodiment, based on the optimized finite element mesh for contact, the training of the multi-agent reinforcement learning strategy is carried out. In this step, the reinforcement learning method is used to dynamically adjust the contact optimization. Each agent represents a contact point and learns how to minimize the contact stress and maximize the contact stiffness under the non-linear contact constraints. During the training process, the deep Q-learning algorithm is adopted, and the goal is to make each agent adjust its own strategy through training to optimize the mechanical response of the entire contact area. The training step size of each agent is set to 0.01, and the total number of training episodes is set to 5000 to ensure that the final strategy has good convergence. After 5000 episodes of training, a converged non-linear contact strategy model is obtained, which can effectively handle complex contact problems and optimize the contact mechanical response.

[0120] Step S25: Perform policy tensor encoding on the non-linear contact strategy model to generate a non-linear contact policy tensor.

[0121] In this embodiment, the trained non-linear contact strategy model is subjected to policy tensor encoding. The process of tensor encoding converts the output of the policy model into a high-dimensional tensor, which can more efficiently store and calculate the complex relationships of contact constraints. In this step, tensor decomposition technology is used, and the tensor dimension is set to 4×4×4 to adapt to the multi-dimensional characteristics of multi-physical-field contact behavior. Each dimension of the tensor encoding represents the interaction relationships between physics, mechanics, materials, and the environment among contact points. Specifically, the dimensions of the tensor correspond to the contact point position, contact force, contact stiffness, and contact state respectively, and each dimension contains 100 discrete points. After the policy tensor encoding, the non-linear contact policy is compressed into an efficient tensor format, which greatly improves the calculation efficiency of subsequent simulation software and also lays a foundation for the rapid deployment of the model in different scenarios.

[0122] Optionally, the encoded structural dynamics smart contract described in step S3 is specifically:

[0123] Perform tensor low-rank decomposition on the non-linear contact policy tensor, set the tensor rank threshold r∈[5,15], and obtain the structural dynamics feature tensor;

[0124] In this embodiment, tensor low-rank decomposition is performed according to the non-linear contact strategy tensor. First, the non-linear contact strategy tensor is obtained, which contains the interactions of different physical fields within the contact region. Then, tensor low-rank decomposition is carried out to reduce the computational complexity while retaining the key information in the tensor. The main objective of low-rank decomposition is to extract the main modes of the tensor. By setting a tensor rank threshold (for example, setting the rank threshold to 10), the tensor is decomposed into multiple sub-tensors with small ranks. Using the Singular Value Decomposition (SVD) or Higher-Order Singular Value Decomposition (HOSVD) method, the dimensionality of the non-linear contact strategy tensor is reduced while retaining most of the information. The finally obtained structural dynamics characteristic tensor is a refined representation, which can be used for subsequent analysis more efficiently.

[0125] Based on the structural dynamics characteristic tensor, a Lagrangian constraint optimization model is constructed, and the optimization iteration step size η = 0.01 and the convergence error threshold ε ≤ 10 are set -5 , and the generalized coordinate transformation matrix is calculated to obtain the set of structural mechanics optimization parameters;

[0126] In this embodiment, a Lagrangian constraint optimization model is constructed based on the structural dynamics characteristic tensor. Based on the obtained structural dynamics characteristic tensor, a Lagrangian constraint optimization model is constructed, which handles the constraint conditions by introducing Lagrange multipliers. The constraints include mechanical constraints, geometric constraints, etc., to ensure that the physical and geometric limitations of the system are satisfied during the optimization process. For example, the constraint conditions may include the contact mechanics limitations between objects, displacement limitations, and the equilibrium requirements of the system. During the optimization process, the optimization iteration step size (such as setting the step size to 0.01) and the convergence error threshold (such as setting the convergence threshold to 10 -6 ) are set to ensure the accuracy and computational efficiency of the optimization results. In each iteration, the model parameters are updated and the generalized coordinate transformation matrix is calculated (using the least squares method), which is used to describe the transformation relationship of the dynamic system. Finally, the obtained set of structural mechanics optimization parameters will be used in the subsequent steps.

[0127] According to the set of structural dynamics optimization parameters, a dynamic state transition equation is established, and a state-control coupling model is constructed in combination with the preset multi-body system constraint equations, and the constraint weight matrix W is set c ∈[0.1, 1.0] to generate the basic framework of the intelligent contract;

[0128] In this embodiment, a dynamic state transition equation is established according to the structural dynamics optimization parameter set. Based on the obtained optimization parameter set, a dynamic state transition equation is established to describe the dynamic behavior of the system changing over time. On this basis, combined with the preset multi-body system constraint equations (such as the momentum conservation equation, the mechanical equilibrium equation, etc.), a state-control coupling model is constructed. To ensure the accuracy of the model, a constraint weight matrix is set (for example, the weight matrix W is set as a diagonal matrix, and the weight range is set to [0.1, 1.0]) to adjust the relative importance of each constraint condition in the optimization process. By jointly solving the state transition equation and the control equation, the basic framework of the smart contract is finally generated.

[0129] Perform a verifiable computation transformation on the basic framework of the smart contract, and set the state consistency verification threshold δ s ≤0.05, construct a state consistency verification sub-module, and generate a dynamically credible smart contract;

[0130] In this embodiment, a verifiable computation transformation is performed on the basic framework of the smart contract. Based on the obtained basic framework of the smart contract, the contract is processed through verifiable computation transformation (VCT) to ensure that it meets the security and performance requirements of the system. Set the state consistency verification threshold (for example, set the verification threshold to 0.01). At this threshold, check the state consistency of the system to ensure that the state information during the contract execution always meets the expectations. During this process, by constructing a state consistency verification sub-module, verify each state of the system to ensure that during the execution of the smart contract, the change of the state is always within an acceptable range. If the state of the system deviates from the predetermined standard, it can be corrected by adjusting parameters or the model correction scheme. After the verification is completed, a dynamically credible smart contract that has passed the credible verification is generated.

[0131] Perform a consensus mechanism test on the dynamically credible smart contract, and set the number of consensus nodes N c = 10 and the number of consensus verification rounds R ∈ [50, 100], and optimize the execution time of the smart contract based on the consensus mechanism test results to obtain a structural dynamics smart contract.

[0132] In this embodiment, a consensus mechanism test is performed on the kinetic trusted smart contract. For the obtained kinetic trusted smart contract, a consensus mechanism test is carried out to ensure that the smart contract can operate normally in a multi-node environment. Set the number of consensus nodes (for example, set the number of consensus nodes to 20) and the number of consensus verification rounds (set the number of verification rounds to 60) to simulate the consensus process in a multi-node environment. In the test, a hierarchical consensus mechanism (such as Raft or Tendermint) and the Practical Byzantine Fault Tolerance (PBFT) algorithm are adopted to ensure that the contract can work properly in an environment with low latency and high throughput. Through the test, ensure that the execution efficiency and accuracy of the contract meet the expectations, and optimize the execution time of the smart contract during this process. Finally, set the maximum execution time delay to 100 ms, and optimize the execution time of the smart contract through the consensus mechanism test results to ensure that the delay during contract execution is minimized in a complex multi-physical field simulation system, thereby obtaining the final structural dynamics smart contract. Specifically, use a distributed computing framework (such as Apache Spark or Apache Flink) to disperse the computing tasks of the smart contract, divide the processing logic of the contract into multiple subtasks, and allocate them to different computing nodes for parallel execution. This method effectively reduces the delay caused by insufficient computing resources of a single node during contract execution and improves the processing speed. Combine a distributed storage system (such as HDFS) to optimize data storage and access, reduce the data transmission delay between nodes, and ensure data consistency and reliability. Through the load balancing mechanism of distributed computing, automatically adjust the computing tasks of each node to ensure load balancing of each node, thereby avoiding an increase in delay caused by overloading of some nodes. Through this optimization method, the execution time of the smart contract is effectively shortened, and finally, it is ensured in the test that the maximum execution time delay does not exceed 100 ms, thereby obtaining the final structural dynamics smart contract.

[0133] Optionally, the kinetic trust verification of the simulation software in step S3 is specifically:

[0134] Deploy the smart contract environment for the structural dynamics smart contract, and set the calculation accuracy threshold ∈ = 10 -5 and the maximum number of iterations I max = 500 to generate the initial environment for the kinetic verification of the simulation software;

[0135] In this embodiment, set the smart contract environment in the engineering simulation software to ensure that it can efficiently process multi-physical field simulation tasks. Set the calculation accuracy threshold to ∈ = 10 -5, the maximum number of iterations is 500 times to ensure that the tasks can be completed within a reasonable computational accuracy during the contract execution and to prevent excessive iterations during the calculation process. To this end, the initial environment includes an efficient solver (e.g., a solver based on the Gauss-Seidel method) and high-performance computing resources to support the dynamic calculations and simulations in the contract. When the simulation software is deployed, it will also automatically adjust the solution process according to the set maximum number of iterations to avoid errors caused by non-convergent calculations. This environment will generate the initial environment for the dynamic verification of the simulation software, providing a basis for contract verification.

[0136] Based on the initial environment for the dynamic verification of the simulation software, extract the dynamic state variables of the contract execution, and thus construct a state observation matrix;

[0137] In this embodiment, based on the initial environment for the dynamic verification of the simulation software, extract the key dynamic state variables during the contract execution. These variables include, but are not limited to, displacement, acceleration, stress, strain, and temperature, etc., which reflect the changes of the structure under the loading conditions. Set the minimum monitoring period for each variable to 0.01 s to ensure that the state updates in the simulation can reflect the real dynamic behavior in real time. When using the simulation system to extract these data, organize all physical quantities into a state observation matrix (e.g., a 6×6 matrix, which contains six main physical quantities such as displacement, velocity, and acceleration) for subsequent analysis and processing. This matrix will be updated in real time and capture the interactions between different physical fields through multiple calculations during the simulation process.

[0138] Conduct a numerical stability analysis on the state observation matrix to generate a state stability assessment report;

[0139] In this embodiment, conduct a numerical stability analysis on the state observation matrix. Use the eigenvalue analysis method to calculate the eigenvalues of the state matrix, and obtain the maximum eigenvalue through the Jacobi method. Assume that the modulus of the maximum eigenvalue |λ_max| needs to be less than 1 to maintain numerical stability. To ensure the stability of the matrix calculation, we set the stability threshold as |λ_max| < 0.99. If it is found that the maximum eigenvalue of the state matrix exceeds this threshold, adjust it by adjusting the initial conditions, modifying the step size, or introducing damping, etc., and recalculate the state matrix. Generate a state stability assessment report based on this analysis result, which includes the stability assessment of each physical quantity, the stability optimization plan, and provides corresponding adjustment suggestions.

[0140] According to the state stability assessment report, adaptively adjust the time step of the dynamic state variables. Set the initial time step Δt0 = 0.001 s, the maximum step size Δt max = 0.1 s, and combine the preset error control factor γ ∈ [0.8, 1.2] to perform dynamic step size update to generate the optimized dynamic data of the time step

[0141] In this embodiment, according to the state stability evaluation report, the time step of the kinetic state variables is adaptively adjusted. The initial time step is set to 0.001 s, the maximum step is 0.1 s, and the time step is automatically adjusted based on an error control factor (set to 0.1). When the error exceeds 1e-5 during the calculation, the time step is automatically reduced to ensure the calculation accuracy; if the error is lower than the threshold, the system will increase the time step to improve the calculation efficiency. In this way, the goal of the step is to optimize the balance between accuracy and efficiency in the calculation process by dynamically adjusting the time step. According to these optimizations, a kinetic data set with optimized time step is generated, and this data is used as the basis for subsequent simulations and analyses.

[0142] Calculate the credibility of the kinetic data with optimized time step, and set the lower limit of credibility P min = 95%, calculate the consistency error of the execution of the smart contract, and set the consistency error control criterion E cons ≤ 10 -4 Conduct credibility optimization on the calculation results of the consistency error to generate a kinetic credibility verification report;

[0143] In this embodiment, the credibility of the kinetic data with optimized time step is calculated. First, the lower limit of credibility is set to 0.95, and the consistency error during the execution of the smart contract is calculated. For this purpose, a model based on Gaussian process regression (GPR) is used to correct potential abnormal measurement points. The abnormal threshold is set to 10 -4 , specifically, the consistency error control criterion is set as: when it is detected that the consistency error is greater than 10 -4 , a weighted correction method is used to reduce the impact brought by the error, and the error after correction should be restored to 10 -4 or less. At this time, a weighted linear interpolation method is used to perform weighted correction on the abnormal point and its surrounding data points during the calculation of the smart contract, and the weight coefficient is determined by the relative difference between the abnormal point and the surrounding data points. In this way, the corrected data points can maintain consistency while minimizing the impact of the error on the overall calculation process to the greatest extent. During this process, the interpolation coefficient and weight are also dynamically adjusted according to historical data and current measurement results to ensure the consistency between the corrected data points and the contract state. After these steps, kinetic data optimized by the consistency error control criterion is generated, thereby improving the credibility of the contract execution and generating a final kinetic credibility verification report, which details the data stability, optimization effect, and the accuracy of the smart contract execution after error correction.

[0144] Verify the consensus mechanism for the kinetic credibility verification report, and set the number of consensus nodes N c= 10, the consensus verification round number R = 100, generating a kinetic credible chain.

[0145] In this embodiment, the kinetic credible verification report is verified by the consensus mechanism. The number of consensus nodes is set to 10, and the consensus verification round number is set to 100 rounds. A multi-node environment is simulated to verify the consensus process of the smart contract among the nodes. Each node will synchronously check the contract execution status during operation and jointly reach a decision based on the latest status information to ensure the synchronous execution of the entire contract in different node environments. During the consensus process, the verification nodes will calculate according to the state consistency error and the preset consensus rules, and finally obtain the consistency result of the contract execution. Based on the results of these consensus verifications, the execution time of the smart contract is further optimized, the maximum delay is set not to exceed 100 ms, and the stability of the contract execution is verified through the consensus mechanism test to generate the final kinetic credible chain, ensuring that the contract can be executed efficiently and accurately during the engineering simulation process.

[0146] Optionally, step S4 is specifically as follows:

[0147] Step S41: Extract the structural dynamics state variables based on the kinetic credible chain, and combine the contact stress distribution data in the non-linear contact strategy tensor to construct an initial dynamics-contact characteristic model;

[0148] In this embodiment, the key dynamics state variables of the structure, such as displacement, velocity, acceleration, etc., are extracted through the kinetic credible chain. Then, the stress distribution information of the contact area is obtained from the non-linear contact strategy tensor. To construct the initial dynamics-contact characteristic model, first align the time points of the dynamics state variables and the contact stress distribution data, and jointly map these two sets of data through the weighted summation method based on the adjacency matrix to obtain a joint characteristic matrix containing structural dynamics and contact stress information. This matrix is used for subsequent contact dynamics analysis. For the accuracy requirement of time alignment, the time step is set to 10 -4 s to ensure data synchronization.

[0149] Step S42: Conduct an energy conservation analysis on the initial dynamics-contact characteristic model, and calculate the stress gradient and deformation response of the contact area, thereby establishing a dynamic adjustment function for contact stiffness;

[0150] In this embodiment, by means of the method based on the law of conservation of energy, the energy flow in the dynamic-contact characteristic model is analyzed. In the model, the energy transfer at each contact point is determined by the contact stress and the structural deformation. By analyzing the stress gradient and the local deformation response in the contact area, the variation law of the contact stiffness is calculated. The finite element method (such as setting the mesh size to 0.5 mm) is used to accurately solve the stress and deformation fields in the contact area, and further derive the dynamic adjustment function of the contact stiffness to ensure the balance of energy among contact points. This function can automatically adjust the contact stiffness according to the real-time simulation data to optimize the structural response.

[0151] Step S43: Optimize the contact dynamics parameters according to the dynamic adjustment function of the contact stiffness to obtain the optimized contact dynamics parameter set;

[0152] In this embodiment, based on the established dynamic adjustment function of the contact stiffness, the contact dynamics parameters in the initial dynamic-contact characteristic model are optimized and adjusted. Through an optimization algorithm (such as the particle swarm optimization PSO), within the given range of the contact stiffness, the dynamic parameters such as the friction coefficient of the contact surface and the viscoelastic model parameters are adjusted. To improve the optimization accuracy, the optimization step size is set to 0.01, and the maximum number of iterations is set to 500 times. Through multiple iterations, the optimal contact dynamics parameter set is found to make the stress and deformation in the contact area more in line with the physical reality, and parallel processing is used during the calculation to improve the efficiency.

[0153] Step S44: Obtain the historical fluid simulation data, and extract the fluid-structure coupling relationship from the historical fluid simulation data to obtain the fluid coupling parameter set;

[0154] In this embodiment, the fluid mechanics data related to the structure are extracted from the simulation database in the simulation software to obtain the key parameters of the interaction between the structure and the fluid. The data such as the velocity field, pressure field, and vorticity distribution in the CFD simulation results are adopted, and the coupling relationship between the fluid and the structure is extracted in combination with the structural deformation data. The fluid-structure coupling parameters are determined through data fitting and regression analysis (such as polynomial regression), such as the influence coefficient of the fluid flow velocity on the structural stress, to obtain the fluid coupling parameter set. The data processing accuracy is set to 5×10-3 to ensure the accuracy of the coupling relationship.

[0155] Step S45: Construct a fluid mechanics vortex model according to the optimized contact dynamics parameter set and the fluid coupling parameter set;

[0156] In this embodiment, a hydrodynamic vortex model is constructed by using the optimized contact dynamics parameter set and fluid coupling parameter set and combining with the Navier-Stokes equation. This model can simulate the flow and vortex phenomena of fluid on the surface of complex structures. A hydrodynamic simulation software (such as OpenFOAM) is used for numerical calculation, and the time step is set to Δt = 10 -6 s during the calculation process, and the optimized mesh division accuracy (for example, the mesh size is 1 mm) is used to ensure that the simulated fluid vortex behavior accurately reflects the interaction between the structure and the fluid.

[0157] Step S46: Verify the implementation credibility of the hydrodynamic vortex model, optimize the dynamic-fluid interaction characteristic parameters based on the credibility verification results, and finally generate a vortex flow field atlas.

[0158] In this embodiment, by performing large-scale simulations on the hydrodynamic vortex model, its credibility under different physical conditions is evaluated. A reliability analysis based on the Monte Carlo method is used to evaluate the credibility of the simulation results to ensure that the vortex model can accurately reflect the interaction behavior between the structure and the fluid in practical applications. On this basis, adjust the dynamic-fluid interaction characteristic parameters (such as the fluid-structure coupling coefficient, the vortex generation position, etc.), optimize the hydrodynamic vortex model through the backpropagation algorithm, generate the final hydrodynamic vortex model, and then use the atlas technology to present the hydrodynamic parameters (such as vortex intensity, vortex position, etc.) output by the model in the form of an atlas, so as to obtain the vortex flow field atlas. This atlas can be used in practical engineering to predict the complex interaction behavior between the fluid and the structure and provide a basis for structural optimization.

[0159] Optionally, the phase-field damage evolution described in step S5 is specifically as follows:

[0160] Extract the initial damage field based on the convergent optimization field and perform time evolution analysis of the damage field to generate a preliminary damage evolution model;

[0161] In this embodiment, by using the convergent optimization field data, the potential damage areas in the structure are initially identified. Using the convergent optimization field obtained from the previous multi-physics field coupling simulation (for example, the optimization result that converges to an error less than 1×10 -4 ), combined with the historical simulation data of temperature, stress and vibration, apply the phase-field damage model in the LS-DYNA environment to extract the initial damage field. In the specific operation, the area where the local stress of the material exceeds 150 MPa is defined as the initial damage area, and the time step is set to 0.01 seconds, and the damage evolution within each time step is iteratively simulated. Through dynamic time evolution analysis, a preliminary damage evolution model reflecting the generation and expansion of local damage in the structure is formed, and this model can be used to predict the initial failure trend of the material under actual working conditions.

[0162] The preliminary damage evolution model is discretized by finite element to obtain the local damage evolution feature map;

[0163] In this embodiment, the preliminary damage evolution model is discretized by finite element to achieve more detailed damage prediction. The eight-node cubic element is used for mesh generation, and local refinement is performed in the damage area. The discretization of the damage model takes into account the nonlinear characteristics of different material properties and damage evolution stages. By performing more accurate finite element discretization of the local area, the local damage evolution feature map is obtained, which shows the damage changes at different positions and different time points.

[0164] Based on the local damage evolution feature map, a time evolution analysis is performed to obtain the damage propagation path;

[0165] In this embodiment, based on the local damage evolution feature map, a time evolution analysis is performed. The step-by-step loading and load-unloading cycles are used to simulate the damage propagation path. According to the damage propagation path, a directional and regional model of damage propagation is established, with emphasis on the crack propagation speed and direction, and the maximum damage propagation rate is set to 0.01 mm / s. Using this path information, the critical damage areas can be identified, and the model can be optimized through a feedback mechanism to determine the future damage evolution trend.

[0166] Based on the damage propagation path, the damage field parameters of the preliminary damage evolution model are optimized, and the damage evolution process is updated to obtain an updated damage evolution model;

[0167] In this embodiment, based on the damage propagation path, the damage field parameters of the preliminary damage evolution model are optimized. The optimization algorithm is set as the particle swarm optimization (PSO), with the maximum number of particles set to 50 and the number of iterations set to 100. This process optimizes the material constitutive relationship, damage threshold, and fatigue parameters of the damage model, thereby improving the accuracy of the model, updating the damage evolution process, and generating an updated damage evolution model.

[0168] Based on the multi-physical field topology snapshots and the updated damage evolution model, the multi-physical field interaction integration is performed to obtain the multi-physical field evolution twin.

[0169] In this embodiment, combining the multi-physical field topology snapshots and the updated damage evolution model, the multi-physical field interaction integration is performed. This process integrates multi-physical field data such as temperature, pressure, and stress, and uses a multi-field coupling method to fuse the influencing factors of different fields. During the calculation process, a three-dimensional scenario and a multi-scale method are used to ensure the coupling accuracy between different scales and different physical fields. Finally, the multi-physical field evolution twin is obtained, which provides a powerful tool for dynamic monitoring, prediction, and optimization in practical applications.

[0170] Optionally, a software operation and maintenance system in this specification is used to execute the software operation and maintenance method described above. The software operation and maintenance system includes:

[0171] A coupled tensor compression module, configured to obtain engineering simulation heterogeneous hardware data and perform multi-physical field coupled tensor compression on the engineering simulation heterogeneous hardware data to obtain a multi-physical field topology snapshot;

[0172] A non-linear constraint module, configured to perform finite element mesh non-linear constraints based on the multi-physical field topology snapshot to obtain a non-linear contact strategy tensor;

[0173] A dynamics trust verification module, configured to encode a structural dynamics smart contract according to the non-linear contact strategy tensor and perform simulation software dynamics trust verification on the structural dynamics smart contract to obtain a dynamics trust chain;

[0174] A vortex flow field construction module, configured to construct a hydrodynamics vortex model based on the dynamics trust chain and perform boundary layer separation simulation flow optimization on the hydrodynamics vortex model to obtain a vortex flow field atlas;

[0175] A multi-physical field interaction and evolution module, configured to perform explicit dynamics explicit integration on the vortex flow field atlas to obtain a convergence optimization field; perform phase field damage evolution according to the convergence optimization field to obtain a multi-physical field evolution twin, and deploy the multi-physical field evolution twin through a preset engineering simulation cloud architecture.

[0176] Optionally, this specification also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed, the software operation and maintenance method described above is implemented.

[0177] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0178] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A software operation and maintenance method, characterized in that: The following steps are involved: Step S1: Acquire engineering simulation heterogeneous hardware data, and perform multi-physics coupling tensor compression on the engineering simulation heterogeneous hardware data to obtain a multi-physics topology snapshot; Step S2: Execute nonlinear constraints on the finite element mesh based on the multi-physics field topology snapshot to obtain a nonlinear contact strategy tensor; Step S3: Encode the structural dynamics smart contract according to the nonlinear contact strategy tensor, and perform dynamics trustworthiness verification of the simulation software on the structural dynamics smart contract to obtain a dynamics trustworthiness chain; Step S4: constructing a fluid mechanics vortex model based on the dynamics trust chain, and optimizing the boundary layer separation simulation flow of the fluid mechanics vortex model to obtain a vortex flow field map; Step S5: Perform explicit dynamics explicit integration on the vortex flow field map to obtain a converged optimization field; perform phase field damage evolution based on the converged optimization field to obtain a multi-physics field evolution twin, and deploy the multi-physics field evolution twin through a preset engineering simulation cloud architecture.

2. The software operation and maintenance method according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring multi-physics field data of heterogeneous hardware of engineering simulation, and performing time-sensitive synchronization processing on the multi-physics field data to generate an initial multi-physics field data set; Step S12: performing non-Euclidean topological mapping on the initial multi-physics field data set, and performing data standardization and time consistency correction to generate standardized multi-physics field data; Step S13: performing a tensor principal component analysis based on the standardized multi-physics field data, extracting material nonlinear characteristics including strain-stress coupling parameters, non-uniform thermal expansion coefficients, and contact stiffness evolution parameters, and generating a nonlinear material characteristic matrix; Step S14: constructing a multi-physics field topology structure across time and space scales according to the nonlinear material characteristic matrix, and generating a multi-physics field topology initial model; Step S15: Perform nonlinear tensor low-rank approximation dimensionality reduction on the multi-physics field topology initial model, compress and retain the physical interaction relationship, and generate a multi-physics field topology snapshot.

3. The software operation and maintenance method according to claim 2, characterized in that: Step S12 is specifically as follows: Step S121: Align the initial multi-physics field data set at discrete time points, at a time step of Δt=10 -3 s to interpolate missing data, and set the maximum offset correction threshold to ±5×10 -3 s adjusts the time offset of adjacent measurement points to generate a time-aligned multi-physics data set; Step S122: performing non-Euclidean graph topology mapping on the time-aligned multi-physics field data set, and constructing a local physical interaction graph to generate a non-Euclidean multi-physics field initial graph; Step S123: Perform hierarchical spectral clustering analysis on the initial graph of non-Euclidean multi-physics fields, select the first N = 10 eigenvectors for Laplace eigendecomposition, and use a threshold of ε = 10 -4 Perform feature truncation to generate spectrally smoothed multi-physics field maps; Step S124: normalizing the spectral smoothed multi-physics field graph, normalizing the data to the interval [-3σ, 3σ], and performing time series trend correction based on a sliding window length L=50 to generate standardized multi-physics field data; Step S125: Perform Bayesian optimal interpolation prediction based on the standardized multi-physics field data, set the abnormal threshold δ=3σ to eliminate abnormal points, and perform interpolation completion under the confidence interval α=95% to generate standardized multi-physics field data.

4. The software operation and maintenance method according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: performing finite element meshing on the multi-physics field topology snapshot, and adjusting the unit size to generate an initial finite element mesh data set; Step S22: identifying the nonlinear contact region based on the initial finite element mesh data set, constructing a contact stiffness distribution matrix, and generating a nonlinear contact region characteristic matrix; Step S23: performing contact constraint optimization on the characteristic matrix of the nonlinear contact region, calculating the contact mechanical response, and obtaining the nonlinear contact pair stiffness matrix; Dynamically adjust the stiffness distribution of the stiffness matrix based on nonlinear contact and generate contact optimized finite element mesh; Step S24: performing multi-agent reinforcement learning strategy training according to the contact optimized finite element mesh, optimizing strategy convergence, and generating a nonlinear contact strategy model; Step S25: Perform strategy tensor encoding on the nonlinear contact strategy model to generate a nonlinear contact strategy tensor.

5. The software operation and maintenance method according to claim 1, characterized in that: The encoding structure dynamics smart contract described in step S3 is specifically: According to the nonlinear contact strategy tensor, the tensor low-rank decomposition is performed, and the tensor rank threshold r∈[5,15] is set to obtain the structural dynamic characteristic tensor; The Lagrangian constrained optimization model is constructed based on the structural dynamics characteristic tensor, and the optimization iteration step η is set to 0.01 and the convergence error threshold ε ≤ 10 -5 , and calculate the generalized coordinate transformation matrix to obtain the structural mechanics optimization parameter set; The dynamic state transfer equation is established according to the structural dynamics optimization parameter set, and the state-control coupling model is constructed by combining the preset multi-body system constraint equation, and the constraint weight matrix W is set. c ∈[0.1,1.0], generate the basic framework of smart contract; Perform verifiable computation conversion on the smart contract basic framework and set the state consistency verification threshold δ s ≤0.05, construct state consistency verifier and generate dynamics trusted smart contract; Test the consensus mechanism of the Kinetics Trusted Smart Contract and set the number of consensus nodes N c =10 and the number of consensus verification rounds R∈[50,100], and based on the consensus mechanism test results, the smart contract execution time is optimized to obtain the structural dynamics smart contract.

6. The software operation and maintenance method according to claim 1, characterized in that: The simulation software dynamics credibility verification described in step S3 is specifically as follows: Deploy the smart contract environment for the structural dynamics smart contract and set the calculation accuracy threshold c = 10 -5 and the maximum number of iterations I max =500, generate the initial environment for dynamic verification of simulation software; Based on the dynamic verification of the simulation software, the initial environment is extracted to extract the dynamic state variables of the contract execution, thereby constructing the state observation matrix; Perform numerical stability analysis on the state observation matrix and generate a state stability assessment report; According to the state stability assessment report, the time step of the dynamic state variables is adaptively adjusted, and the initial time step Δt0 = 0.001s and the maximum step Δt max = 0.1s, and combined with the preset error control factor γ∈[0.8,1.2] to dynamically update the step size and generate time step optimized dynamic data Perform credibility calculation on the time step optimized kinetic data and set the credibility lower limit P min = 95%, calculate the consistency error of smart contract execution, and set the consistency error control criterion E cons ≤10 -4 Optimize the credibility of the consistency error calculation results and generate a dynamic credibility verification report; Verify the consensus mechanism for the dynamics credible verification report and set the number of consensus nodes N c =10, the number of consensus verification rounds R = 100, and a dynamic trusted chain is generated.

7. The software operation and maintenance method according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: extracting structural dynamic state variables based on the dynamics trust chain, and building an initial dynamics-contact characteristic model in combination with contact stress distribution data in the nonlinear contact strategy tensor; Step S42: performing energy conservation analysis on the initial dynamics-contact characteristic model, and calculating the stress gradient and deformation response of the contact area, thereby establishing a contact stiffness dynamic adjustment function; Step S43: optimizing the contact dynamics parameters according to the contact stiffness dynamic adjustment function to obtain an optimized contact dynamics parameter set; Step S44: acquiring historical fluid simulation data, and extracting fluid-structure coupling relationship from the historical fluid simulation data to obtain a fluid coupling parameter set; Step S45: constructing a fluid mechanics vortex model according to the optimized contact dynamics parameter set and the fluid coupling parameter set; Step S46: Perform simulation credibility verification on the fluid mechanics vortex model, and optimize the dynamics-fluid interaction characteristic parameters based on the credibility verification results, and finally generate a vortex flow field map.

8. The software operation and maintenance method according to claim 1, characterized in that: The phase field damage evolution described in step S5 is specifically: Extract the initial damage field based on the converged optimization field, analyze the time evolution of the damage field, and generate a preliminary damage evolution model; The preliminary damage evolution model is discretized by finite element method to obtain the local damage evolution characteristic diagram; Perform time evolution analysis based on the local damage evolution characteristic diagram to obtain the damage extension path; Optimize the damage field parameters of the preliminary damage evolution model according to the damage propagation path, update the damage evolution process, and obtain an updated damage evolution model; Based on the multi-physics topology snapshot and the updated damage evolution model, the multi-physics interaction is integrated to obtain the multi-physics evolution twin.

9. A software operation and maintenance system, characterized in that: Used to execute the software operation and maintenance method according to claim 1, the software operation and maintenance system comprises: A coupled tensor compression module is used to obtain heterogeneous hardware data of engineering simulation and perform multi-physics coupled tensor compression on the heterogeneous hardware data of engineering simulation to obtain a multi-physics topology snapshot; Nonlinear constraint module, used to perform nonlinear constraints on finite element meshes based on multiphysics topology snapshots to obtain nonlinear contact strategy tensors; The dynamics trusted verification module is used to encode the structural dynamics smart contract according to the nonlinear contact strategy tensor, and to perform dynamics trusted verification of the simulation software on the structural dynamics smart contract to obtain a dynamics trusted chain; The vortex flow field construction module is used to construct a fluid mechanics vortex model based on the dynamics trust chain, and to optimize the boundary layer separation simulation flow of the fluid mechanics vortex model to obtain the vortex flow field map; The multi-physics interactive evolution module is used to implement explicit dynamics and explicit integration on the vortex flow field spectrum to obtain a converged optimization field; phase field damage evolution is performed based on the converged optimization field to obtain a multi-physics evolution twin, and the multi-physics evolution twin is deployed through a preset engineering simulation cloud architecture.

10. A computer-readable storage medium, characterized in that: A computer program is stored therein, and when the computer program is executed, the software operation and maintenance method according to any one of claims 1 to 8 is implemented.

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