Road protection multi-scene coupling simulation method and system based on digital twinning

Abstract

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing, and in particular, to a multi-scenario coupling simulation method and system for highway protection based on digital twin. Background Art

[0002] With the rapid development of highway construction and the continuous growth of traffic flow in China, highway protection facilities, as important infrastructure for ensuring driving safety, have received increasing attention for their safety performance. In the actual use environment, highway protection facilities not only have to withstand vehicle collision impacts, but also have to cope with the effects brought by natural factors such as wind loads and earthquakes, which puts higher requirements on the mechanical properties of the protection facilities.

[0003] In the related art, the mechanical properties of protection facilities can be evaluated through on-site detection and single-scenario simulation. In terms of on-site detection, sensors are arranged to obtain parameters such as strain and acceleration of the structure, and a finite element model is established for stress analysis. In terms of simulation, usually independent simulation calculations are carried out for single scenarios such as vehicle collision, wind load or seismic waves, and the mechanical responses of the protection facilities in various scenarios are evaluated respectively.

[0004] However, this single-scenario detection and simulation method has limitations. Since various loads in the natural environment often act simultaneously, single-scenario simulation cannot reflect the mutual influence between loads. At the same time, the existing simulation methods do not adequately consider the prestress state and stiffness attenuation characteristics of the structure under multiple loads, resulting in a deviation between the simulation results and the actual working conditions. Summary of the Invention

[0005] This application provides a multi-scenario coupling simulation method and system for highway protection based on digital twin, which is used to reduce the deviation between the simulation results of the mechanical properties of protection facilities and the actual working conditions.

[0006] In a first aspect, the present application provides a method for coupling simulation and optimization of mechanical properties in multiple scenarios of highway traffic protection based on digital twins, which is applied to a system for coupling simulation and optimization of mechanical properties in multiple scenarios. The method includes: collecting dynamic response data of traffic protection facilities using multiple types of sensors. The dynamic response data includes strain data, acceleration data, and displacement data, and storing the dynamic response data to form a scenario feature library. The scenario feature library includes vehicle collision scenarios, wind load scenarios, and seismic wave scenarios; synchronously projecting the dynamic response data onto a unified time axis, identifying the multi-scenario overlapping interval, and determining the scenario action sequence and duration in the multi-scenario overlapping interval. The multi-scenario overlapping interval is the time intersection area of the response data of each scenario; determining the dominant scenario and the secondary scenario in the multi-scenario overlapping interval according to the scenario action sequence, duration, and scenario response amplitude to obtain a scenario determination result; extracting mechanical parameter correction values and standard response parameters from the scenario feature library according to the scenario determination result. The standard response parameters include prestress state values and stiffness attenuation coefficients; establishing a digital twin model of the protection facility, and according to the digital twin model, standard response parameters, and scenario action sequence, simulating and calculating the initial response of the structure under the dominant scenario, using the initial response as the initial condition for simulating the secondary scenario, and performing coupled simulation calculations to obtain a structural stress distribution cloud map and the coordinates of the stress concentration area; evaluating the structural damage state according to the coordinates of the stress concentration area to obtain the structural damage level, and generating a safety performance evaluation report for the protection facility.

[0007] In the above embodiment, multiple types of sensors are used to collect dynamic response data and establish a scenario feature library. The multi-scenario overlapping interval is identified based on a unified time axis, and the dominant and secondary scenarios are determined by combining scenario influence factors. Through the digital twin model, the initial response of the dominant scenario is used as the initial condition for the secondary scenario for coupled simulation calculations, and at the same time, the prestress state and stiffness attenuation characteristics are considered, improving the simulation accuracy of the structural response under the simultaneous action of multiple loads and making the simulation results closer to the actual working conditions.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the step of synchronously projecting the dynamic response data onto a unified time axis, identifying the multi-scenario overlapping interval, and determining the scenario action sequence and duration in the multi-scenario overlapping interval specifically includes: synchronously mapping the strain data, acceleration data, and displacement data according to the acquisition timestamp to generate a unified time axis; marking the start and end time points of the vehicle collision scenario, wind load scenario, and seismic wave scenario on the unified time axis; calculating the intersection of the time intervals of each scenario to obtain the multi-scenario overlapping interval; determining the scenario action sequence according to the start time point of each scenario in the multi-scenario overlapping interval; calculating the duration of each scenario in the multi-scenario overlapping interval to obtain the duration.

[0009] In the above embodiments, strain, acceleration, and displacement data are synchronously mapped to a unified time axis according to the acquisition timestamps, the start and end time points of each scenario are marked, and the intersection of time intervals is calculated. By accurately identifying the boundaries and durations of the overlapping intervals of scenarios, the accurate positioning of the temporal relationships of multiple scenarios is achieved, providing a reliable time reference for subsequent determination of the dominant and secondary scenarios, and effectively solving the problem of mixed temporal coupling of multiple scenarios.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the steps of determining the dominant and secondary scenarios in the overlapping interval of multiple scenarios according to the scenario action order, duration, and scenario response amplitude, and obtaining the scenario determination result specifically include: extracting the peak values of the strain data, acceleration data, and displacement data of each scenario in the overlapping interval of multiple scenarios to obtain the scenario response amplitude; calculating the product of the scenario response amplitude of each scenario and its corresponding duration to obtain the scenario influence factor; comparing the scenario influence factors of each scenario, and determining the scenario with the largest scenario influence factor as the dominant scenario; determining the scenarios with scenario influence factors lower than the preset influence threshold as secondary scenarios to obtain the scenario determination result.

[0011] In the above embodiments, the peak values of various types of data in the overlapping interval of scenarios are extracted to obtain the scenario response amplitude, and it is multiplied by the duration to obtain the scenario influence factor. Based on the magnitude of the scenario influence factor, the dominant scenario is determined, and a preset influence threshold is set to screen the secondary scenarios, establishing an objective scenario determination mechanism, providing a scientific scenario action relationship for subsequent coupled simulations, and effectively guiding the calculation order of multi-scenario coupling analysis.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the steps of evaluating the structural damage state according to the coordinates of the stress concentration area, obtaining the structural damage level, and generating a safety performance evaluation report of the protection facility, the method further includes: performing mesh division on the digital twin model according to the dynamic response data to obtain a finite element analysis mesh; setting the initial stress state of the structure according to the prestress state value, and substituting the dynamic response data of the dominant scenario into the finite element analysis mesh to calculate the stress values of each mesh node of the finite element analysis mesh; correcting the stress values of each mesh node based on the stiffness attenuation coefficient to obtain the corrected stress values; performing zonal rendering on the corrected stress values according to the isoline principle to generate a structural stress distribution nephogram; setting a stress warning threshold based on the allowable stress value of the structural material, and when it is detected that the corrected stress value in the target area in the structural stress distribution nephogram exceeds the stress warning threshold, marking a warning sign in the target area.

[0013] In the above embodiments, the digital twin model is meshed, and the initial stress state is set based on the prestress state value. The dynamic response data of the dominant scenario are substituted to calculate the stress values of the grid nodes. After correcting the stress values through the stiffness attenuation coefficient, zonal rendering is performed, and the warning threshold is set for real-time monitoring, realizing the accurate visual expression of the structural stress distribution and enhancing the accuracy and timeliness of stress warning.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after the step of setting the stress warning threshold based on the allowable stress value of the structural material and marking a warning sign in the target area when it is detected that the corrected stress value in the target area in the structural stress distribution nephogram exceeds the stress warning threshold, the method further includes: stratifying the structural stress distribution nephogram according to the dominant scenario and the secondary scenario to obtain a stratified stress distribution diagram; calculating the stress contribution value of each scenario according to the stratified stress distribution diagram and determining the stress superposition coefficient; decomposing the stress in the stress concentration area based on the stress superposition coefficient to identify the contribution degree of each scenario to the structural damage; establishing a stress reduction strategy library according to the stress contribution value, the contribution degree, and the scenario action sequence, and generating a structural reinforcement plan for different scenario combinations.

[0015] In the above embodiments, the structural stress distribution nephogram is stratified by scenario, the stress contribution value and the stress superposition coefficient of each scenario are calculated, and the stress concentration area is decomposed to identify the damage contribution degree of each scenario. Establishing a stress reduction strategy library according to the stress contribution value, the contribution degree, and the scenario action sequence improves the pertinence and scientificity of the structural reinforcement plan.

[0016] Combined with some embodiments of the first aspect, in some embodiments, after the step of evaluating the structural damage state according to the coordinates of the stress concentration area to obtain the structural damage level and generating a safety performance evaluation report of the protection facilities, the method further includes: comparing the stress distribution nephogram with the actual monitoring data in real time to establish a stress trend prediction model; calculating the stress evolution rate based on the stress trend prediction model, and determining the warning time window according to the stress evolution rate; formulating a hierarchical response strategy according to the warning time window, and the hierarchical response strategy includes emergency disposal instructions, personnel evacuation plans, and equipment risk avoidance measures.

[0017] In the above embodiments, the stress distribution nephogram is compared with the actual monitoring data in real time to establish a stress trend prediction model, and the warning time window is determined based on the stress evolution rate. By formulating a hierarchical response strategy including emergency disposal instructions, personnel evacuation plans, and equipment risk avoidance measures, the foresight and systematicness of the safety warning of the protection facilities are improved.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the steps of evaluating the structural damage state according to the coordinates of the stress concentration area, obtaining the structural damage level, and generating a safety performance evaluation report for the protective facilities, the method further includes: generating a stress warning report according to the warning identification area in the stress distribution contour map, where the stress warning report includes the over-limit stress value, the warning area range, and the stress growth rate; recording the stress evolution data during the warning process and storing it in the stress warning event database.

[0019] In the above embodiments, generating a stress warning report including the over-limit stress value, the warning area range, and the stress growth rate according to the warning identification area in the stress distribution contour map, and storing the stress evolution data during the warning process in the warning event database completely records the development process and evolution law of the structural stress over-limit, improving the traceability of the stress warning and the integrity of the warning report.

[0020] In a second aspect, an embodiment of the present application provides a multi-scenario mechanical property coupling simulation optimization system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the multi-scenario mechanical property coupling simulation optimization system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, and when the computer program product runs on the multi-scenario mechanical property coupling simulation optimization system, it enables the multi-scenario mechanical property coupling simulation optimization system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, and when the instructions run on the multi-scenario mechanical property coupling simulation optimization system, it enables the multi-scenario mechanical property coupling simulation optimization system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the multi-scenario mechanical property coupling simulation optimization system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application collects dynamic response data using multiple types of sensors and establishes a scene feature library. It identifies the overlapping intervals of multiple scenes based on a unified time axis, and determines the dominant and secondary scenes by combining scene influence factors. Through the digital twin model, the initial response of the dominant scene is used as the initial condition for the secondary scene for coupled simulation calculations. Considering the prestress state and stiffness decay characteristics at the same time, the simulation accuracy of the structural response under the simultaneous action of multiple loads is improved, making the simulation results closer to the actual working conditions.

[0025] 2. This application synchronously maps strain, acceleration, and displacement data to a unified time axis according to the acquisition timestamps, marks the start and end time points of each scene, and calculates the intersection of time intervals. By accurately identifying the boundaries and durations of the scene overlapping intervals, the accurate positioning of the temporal relationship of multiple scenes is achieved, providing a reliable time reference for subsequent determination of the dominant and secondary scenes, and effectively solving the problem of mixed temporal sequences in multi-scene coupling.

[0026] 3. This application obtains the scene response amplitude by extracting the peak values of various types of data within the scene overlapping interval, multiplies it by the duration to obtain the scene influence factor. The dominant scene is determined based on the magnitude of the scene influence factor, and a preset influence threshold is set to screen the secondary scenes, establishing an objective scene determination mechanism, providing a scientific scene action relationship for subsequent coupled simulations, and effectively guiding the calculation sequence of multi-scene coupling analysis. Description of the Drawings

[0027] Figure 1 is a flowchart of a digital twin-based multi-scene coupling simulation method for highway protection in an embodiment of this application; Figure 2 is another flowchart of a digital twin-based multi-scene coupling simulation method for highway protection in an embodiment of this application; Figure 3 is another flowchart of a digital twin-based multi-scene coupling simulation method for highway protection in an embodiment of this application; Figure 4 is a schematic structural diagram of an entity device of a multi-scene mechanical property coupling simulation optimization system in an embodiment of this application. Detailed Embodiments

[0028] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] On a mountain highway, a large number of guardrails and guardrail systems are set up to ensure driving safety. These protective facilities need to cope with multiple complex working conditions simultaneously: vehicle collisions, strong wind weather, and seismic activities. In a certain section of the road, during an accident where a heavy truck collided with the guardrail, it was windy at that time, resulting in severe deformation of the guardrail under the combined load. Post-accident analysis found that a single anti-collision performance design could not accurately evaluate the structural response when multiple loads acted simultaneously. Similar situations have occurred many times in other sections, such as when the protective facilities were affected by continuous strong winds during the typhoon season and an earthquake occurred at the same time, or vehicle collisions occurred during aftershocks. These incidents have exposed the complex situation of the combined action of multiple loads faced by highway protective facilities in the actual service environment, and it is necessary to systematically consider the mutual influence of various loads and their comprehensive effects on the structural performance.

[0032] When a design unit conducts performance evaluation of highway protective facilities, it uses the traditional single-scenario simulation method. Vehicle collision models, wind load models, and seismic response models are respectively established, and the structural responses under various working conditions are obtained through independent calculations. For example, for the vehicle collision working condition, a rigid body collision model is used to calculate the impact force; for the wind load working condition, the wind load is calculated based on the wind pressure coefficient; for the seismic working condition, the response spectrum method is used to calculate the seismic response. However, this method cannot reflect the coupling effect of loads in the actual working condition. In an evaluation, the independent calculation results showed that the guardrail met the design requirements under each single working condition, but in actual accidents, due to the wind load weakening the anti-impact ability of the structure, the guardrail suffered damage beyond expectations during vehicle collisions. This has exposed that single-scenario simulation cannot accurately predict the structural behavior when multiple loads act simultaneously.

[0033] The digital twin coupled simulation system adopting this solution has achieved good results in the design of a certain highway protection facility. The system arranges strain sensors, acceleration sensors and displacement sensors at key parts of the guardrail to collect structural response data in real time. Through the time synchronization algorithm, a complex working condition of the superposition of a strong wind (wind speed of 32 m / s), a truck collision (impact speed of 60 km / h) and an earthquake (intensity of 6 degrees) is identified. The system determines that the vehicle collision is the dominant scenario, uses the prestress state generated by it as the initial condition for calculating the wind load and seismic wave, and conducts a coupled analysis considering the stiffness attenuation effect. The simulation results show that the structural deformation caused by the collision reduces the wind resistance stiffness of the guardrail by 40%, and at the same time, the vibration caused by the seismic wave exacerbates the structural stress concentration. Based on the analysis results, the design team timely adjusted the guardrail structure, added stiffeners and optimized the connection nodes, so that the protection facility withstood the test of multiple loads in subsequent actual service.

[0034] For the convenience of understanding, the method provided in this embodiment will be described in terms of its process in combination with the above scenarios. Please refer to Figure 1 , which is a schematic flow chart of a method for multi-scenario coupling simulation of highway protection based on digital twin in an embodiment of the present application.

[0035] S101. Collect dynamic response data of traffic protection facilities by using multiple types of sensors. The dynamic response data includes strain data, acceleration data and displacement data, and store the dynamic response data to form a scenario feature library. The scenario feature library includes vehicle collision scenarios, wind load scenarios and seismic wave scenarios.

[0036] Among them, the dynamic response data represents the mechanical response information generated by the traffic protection facilities collected in real time when they are subjected to external actions. The strain data is a quantitative index indicating the degree of structural deformation and is used to represent the deformation state of a local area of the structure. The acceleration data represents the change characteristics of the structural vibration acceleration over time. The displacement data is used to represent the offset of the structural spatial position. The scenario feature library is a data set storing the dynamic response characteristics of the structure under different working conditions. The vehicle collision scenario represents the working condition when a vehicle collides with the protection facility. The wind load scenario refers to the working condition when the structure is subjected to wind force. The seismic wave scenario represents the working condition under seismic wave action.

[0037] Before the protection facilities are put into use, it is necessary to collect dynamic response data. Specifically, first arrange strain sensors, acceleration sensors and displacement sensors at key parts of the protection facilities, and use the real-time sampling method to obtain the dynamic response data of the structure. Perform preprocessing on the collected data, including operations such as data cleaning, outlier removal and data standardization. Then classify and store the processed data according to the scenario type to form a scenario feature library.

[0038] In some embodiments, the acquisition and storage of dynamic response data can be achieved in various ways: Optionally, a wired sensor network is adopted, and various sensors are connected to the data acquisition system through signal lines to collect data in real time and upload it to the server. After data preprocessing, it is classified and stored according to the scenario type; Optionally, a wireless sensor network is adopted, and each sensor node forms a network through wireless communication, transmits the collected data to the gateway node, and then the gateway node uploads the data to the cloud server for storage and processing. It can be understood that other methods can also be used to achieve the acquisition of dynamic response data and the construction of the scenario feature library, which is not limited here.

[0039] S102. Synchronously project the dynamic response data onto a unified time axis, identify the multi-scenario overlapping interval, and determine the scenario action sequence and duration of the multi-scenario overlapping interval. The multi-scenario overlapping interval is the time intersection area of the response data of each scenario.

[0040] Among them, the unified time axis represents the reference coordinate axis that uniformly maps different types of data in chronological order. The multi-scenario overlapping interval refers to the area where the response data of different scenarios intersect in the time dimension. The scenario action sequence is used to represent the sequence of occurrence of multiple scenarios. The duration represents the time length of each scenario's continuous action. The time intersection area refers to the interval where multiple scenarios overlap on the time axis.

[0041] After obtaining the dynamic response data, time synchronization processing is required. Specifically, first align the strain, acceleration, and displacement data according to the acquisition timestamp to generate a unified time reference axis. Then mark the start and end time points of each scenario on the time axis, and calculate the time overlapping interval between scenarios. For the overlapping interval, determine the action sequence according to the chronological order of the scenario start times, and calculate the duration of each scenario within the overlapping interval.

[0042] In some embodiments, data time synchronization can be achieved in various ways: Optionally, the data interpolation method is adopted to interpolate the data with different sampling frequencies so that they have the same time interval, and then align them according to the timestamp; Optionally, the sliding time window method is adopted to set a time window with a fixed length, synchronize the data within the window, and the window slides forward over time to achieve continuous synchronization. It can be understood that other methods can also be used to achieve the time synchronization processing of dynamic response data, which is not limited here.

[0043] S103. Determine the dominant scenario and the secondary scenario in the multi-scenario overlapping interval according to the scenario action sequence, duration, and scenario response amplitude to obtain the scenario determination result.

[0044] Among them, the dominant scenario refers to the scenario that has the most significant impact on the structural response within the multi-scenario overlapping interval. The secondary scenario refers to the scenario that has a relatively small impact on the structural response. The scenario action sequence is used to represent the sequence of occurrence of each scenario. The duration represents the duration of the scenario action. The scenario response amplitude refers to the maximum response value of the structure under the action of a certain scenario. The scenario determination result is used to represent the recognition conclusion of the dominant scenario and the secondary scenario. The scenario influence factor represents the comprehensive influence degree of a certain scenario on the structural response.

[0045] After the identification of the scenario overlapping interval is completed, the determination of the primary and secondary scenarios needs to be carried out. Specifically, first, the peak values of the strain, acceleration, and displacement data corresponding to each scenario within the overlapping interval are extracted as the scenario response amplitude. Then, the scenario response amplitude is multiplied by the corresponding duration to obtain the scenario influence factor. By comparing the magnitudes of the influence factors of different scenarios, the scenario with the largest influence factor is determined as the dominant scenario. For the scenarios with influence factors less than the preset threshold, they are determined as secondary scenarios. Finally, a scenario determination result containing information on the dominant scenario and the secondary scenario is generated.

[0046] In some embodiments, the determination of the dominant scenario and the secondary scenario can be achieved in various ways: Optionally, using the data statistical analysis method, first, the statistical features of the response data within the overlapping interval are extracted, including calculating the mean, standard deviation, peak value, etc., then the scenario influence index is calculated based on the statistical features, and finally, the scenario classification is achieved by setting the discrimination threshold; Optionally, using the machine learning method, a scenario classification model is trained using historical data. The model input includes the scenario response features and the time series features, and the output is the scenario category. The automatic discrimination of the primary and secondary scenarios is achieved through model prediction. It can be understood that other methods can also be used to implement the determination process of the dominant scenario and the secondary scenario, which is not limited here.

[0047] S104. Extract the mechanical parameter correction value and the standard response parameter in the scenario feature library according to the scenario determination result. The standard response parameter includes the prestress state value and the stiffness attenuation coefficient.

[0048] Among them, the mechanical parameter correction value represents the correction coefficient used to adjust the structural mechanical parameters. The standard response parameter is the reference parameter that describes the mechanical characteristics of the structure. The prestress state value is used to represent the initial stress state of the structure. The stiffness attenuation coefficient represents the attenuation characteristic of the structural stiffness over time. The scenario feature library is a database that stores the structural response features and mechanical parameters under different scenarios. The response feature represents the dynamic response characteristics of the structure under external actions.

[0049] After obtaining the scene determination result, corresponding mechanical parameters need to be extracted. Specifically, first, according to the types of the dominant scene and the secondary scene, the corresponding mechanical parameter records are retrieved from the scene feature library. For the dominant scene, its standard response parameters are extracted as the reference values. Then, according to the influence degree of the secondary scene, the correction values of the mechanical parameters are determined. The prestress state value and the stiffness attenuation coefficient are adjusted by the correction values to reflect the actual mechanical characteristics of the structure under the multi-scene coupling effect.

[0050] In some embodiments, the extraction and correction of mechanical parameters can be achieved in various ways: Optionally, the parameter mapping method is adopted to establish a mapping relationship table between the scene type and the mechanical parameters, and the corresponding parameters are directly obtained by looking up the table according to the scene determination result, and the parameters are adjusted by the correction coefficient; Optionally, the parameter optimization method is adopted, with the measured response data as the target, and the mechanical parameters are inversely calculated by the optimization algorithm. The multi-scene coupling effect is considered in the optimization process to obtain the optimal parameter combination. It can be understood that other methods can also be used to achieve the extraction and correction process of mechanical parameters, which are not limited here.

[0051] S105. Establish a digital twin model of the protection facility. According to the digital twin model, the standard response parameters, and the scene action sequence, simulate and calculate the initial response of the structure under the dominant scene, and use the initial response as the initial condition for the simulation of the secondary scene to perform the coupled simulation calculation to obtain the structural stress distribution nephogram and the coordinates of the stress concentration area.

[0052] Among them, the digital twin model represents the digital virtual mapping of the actual protection facility. The standard response parameters refer to the set of reference parameters used to describe the mechanical characteristics of the structure. The initial response of the structure represents the initial mechanical state of the structure under the action of the dominant scene. The coupled simulation calculation is used to represent the calculation process considering the multi-scene interaction. The stress distribution nephogram is a visual graph describing the stress magnitude distribution of each part of the structure. The coordinates of the stress concentration area are used to represent the position information of the key area with a relatively large stress value. The initial condition represents the initial state parameters of the structure when the simulation calculation starts.

[0053] After the extraction of the mechanical parameters is completed, the coupled simulation calculation needs to be carried out. Specifically, first, an accurate digital twin model is established based on the geometric dimensions, material properties, and boundary conditions of the actual protection facility. The extracted standard response parameters are substituted into the model, and according to the scene action sequence, the dominant scene is first simulated and calculated to obtain the initial deformation and stress state of the structure. Then, this initial state is used as the starting condition for the simulation of the secondary scene, and the coupled calculation is carried out considering the mutual influence between the scenes. Finally, a structural stress distribution nephogram reflecting the multi-scene coupling effect is generated, and the spatial coordinate information of the stress concentration area is extracted.

[0054] In some embodiments, coupled simulation calculations can be achieved in various ways: Optionally, the sequential coupling method can be adopted. First, an independent simulation calculation is performed on the dominant scenario to obtain the structural response, and the response result is imported into the secondary scenario model as the initial condition to achieve one-way coupling between scenarios. Finally, the calculation results of each scenario are superimposed to obtain the final stress distribution. Optionally, the two-way coupling method can be adopted. A unified model including multiple scenarios is established, and the interaction between scenarios is considered in each calculation step. Through iterative solution, real-time coupling of scenario responses is achieved to obtain a more accurate stress distribution result. It can be understood that other methods can also be used to achieve the coupled simulation calculation of the protection facilities, which is not limited here.

[0055] S106. Evaluate the structural damage state according to the coordinates of the stress concentration area, obtain the structural damage level, and generate a safety performance evaluation report for the protection facilities.

[0056] Among them, the structural damage state represents the damaged degree and failure form of the protection facilities. The structural damage level refers to the risk level divided according to the damage degree. The safety performance evaluation report is used to represent the comprehensive evaluation result of the safety condition of the protection facilities. The damage evaluation index represents the parameter used to quantify the damage degree. The remaining structural life refers to the time that the protection facilities are expected to continue to be used. The risk level is used to represent the dangerous degree of structural failure.

[0057] After obtaining the stress distribution result, safety performance evaluation is required. Specifically, first determine the key stressed parts according to the coordinates of the stress concentration area, and judge whether there is overstress in the structure by combining the material strength limit. Then evaluate the structural damage degree based on the stress state and deformation characteristics, including various forms such as fatigue damage, plastic deformation, and cracking. Grade the damage degree and predict the remaining service life of the structure. Finally, generate a safety performance evaluation report including the damage state, risk level, and maintenance information.

[0058] In some embodiments, structural damage assessment can be achieved in various ways: Optionally, the traditional assessment method can be adopted. Based on the static analysis theory, by calculating indexes such as stress level, deformation amount, and fatigue damage, and combining the specification standards to determine the structural damage level, an assessment report including damage description, dangerous degree, and disposal information is formed. Optionally, the intelligent assessment method can be adopted. Using machine learning algorithms to establish a damage assessment model, inputting multi-dimensional data such as stress distribution and deformation characteristics, automatically identifying the damage type and degree, and generating an intelligent assessment report. It can be understood that other methods can also be used to achieve the assessment process of the structural damage state, which is not limited here.

[0059] The following further describes the method provided in this embodiment in a more specific process. Please refer to Figure 2 , which is another process schematic diagram of the multi-scenario coupled simulation method for highway protection based on digital twins in the embodiments of the present application.

[0060] S201. Adopt multi - type sensors to collect dynamic response data of traffic protection facilities. The dynamic response data includes strain data, acceleration data, and displacement data, and store the dynamic response data to form a scene feature library. The scene feature library includes vehicle collision scenes, wind load scenes, and seismic wave scenes.

[0061] Among them, the dynamic response data represents real - time monitoring data of the structural stress state change. The strain data refers to the time - history curve of the structural deformation amount. The acceleration data represents the time - history record of the structural vibration acceleration. The displacement data refers to the time - history data of the structural spatial position change. The scene feature library is a data set of the response characteristics of the structure under different working conditions.

[0062] In practical applications, the system collects dynamic response data through a sensor network deployed at key parts of the protection facilities. Specifically, strain sensors are deployed at key stress parts such as the columns, cross - beams, and connection nodes of the protection facilities, and the sampling frequency is set to 200 Hz to record the strain change of components in real time; tri - axial acceleration sensors are installed at the top and middle, with a sampling frequency of 100 Hz to monitor the vibration characteristics of the structure; laser displacement sensors are deployed at the reference points, with a sampling frequency of 50 Hz to track the structural displacement. The collected data is subjected to analog - to - digital conversion and signal conditioning through a data collector. After anti - noise processing and outlier rejection, it is classified and stored in the database according to the scene type. For vehicle collision scenes, the transient response during the collision is recorded; for wind load scenes, the structural response under wind load is continuously collected; for seismic wave scenes, the structural vibration response during the earthquake is recorded. The system integrates the processed data into the scene feature library and establishes a data indexing mechanism for subsequent analysis and retrieval.

[0063] S202. Synchronously map the strain data, acceleration data, and displacement data according to the acquisition timestamps to generate a unified time axis.

[0064] Among them, the timestamp represents the precise moment of data acquisition. The unified time axis refers to a reference axis that unifies different types of data to the same time reference. Synchronous mapping refers to the process of aligning data by time.

[0065] The system performs time synchronization processing on the collected multi-source heterogeneous data. First, the timestamp information of each data record is extracted, and the timestamps are uniformly converted into the standard time format (year-month-day-hour-minute-second-millisecond). Then, based on the highest sampling frequency (200Hz), interpolation processing is performed on the low-frequency sampled data: for the 100Hz acceleration data, a cubic spline interpolation method is used to insert a data point between adjacent sampling points; for the 50Hz displacement data, three data points are inserted between adjacent sampling points. Through interpolation, the three types of data have the same time interval (5ms). Finally, the interpolated data is mapped to the unified time axis one by one according to the timestamps to form an equally spaced data sequence.

[0066] S203. Mark the start and end time points of the vehicle collision scenario, wind load scenario, and seismic wave scenario on the unified time axis.

[0067] Among them, the start and end time points represent the moments when the scenario starts and ends. The scenario marking refers to the identifier for differentiating different scenarios on the time axis.

[0068] The system identifies the time ranges of different scenarios according to the data characteristics. For the vehicle collision scenario, the start moment of the collision is determined by detecting the mutation points of the strain and acceleration data, and it is marked as the end moment when the response decays to less than 5% of the initial value; for the wind load scenario, the start and end times of the wind load action are determined based on the measurement data of the anemometer; for the seismic wave scenario, the arrival and end moments of the seismic wave are determined through the seismic monitoring data. The system marks the time intervals of each scenario on the unified time axis with different colors: red represents the vehicle collision scenario, blue represents the wind load scenario, and green represents the seismic wave scenario. For the determination of the scenario occurrence moment, the system adopts a data characteristic recognition method: when the instantaneous change rate of the sensing data exceeds the preset threshold, it is determined as the start point of the scenario, and when the data returns to the normal fluctuation range, it is determined as the end point of the scenario.

[0069] S204. Calculate the intersection of the time intervals of each scenario to obtain the multi-scenario overlapping interval.

[0070] Among them, the scenario time interval represents the start and end time ranges of a single scenario. The intersection refers to the overlapping part of the time intervals of multiple scenarios. The multi-scenario overlapping interval is the time period when the responses of each scenario coexist.

[0071] The system performs an intersection operation on the time intervals of the marked completed scenarios. First, represent the time interval of each scenario as a sequence of timestamps. For example, the vehicle collision scenario is [t1, t2], the wind load scenario is [t3, t4], and the seismic wave scenario is [t5, t6]. The system uses an interval intersection algorithm to calculate the overlapping time: for any two intervals, take the maximum of the start times and the minimum of the end times. If the maximum start time is less than the minimum end time, there is an overlapping interval. The specific calculation process is as follows: First, calculate the intersection of two adjacent scenarios, such as [max(t1, t3), min(t2, t4)] to obtain the overlapping interval [ta, tb] between the vehicle collision and the wind load; then intersect this result with the third scenario [max(ta, t5), min(tb, t6)] to finally obtain the overlapping interval [tc, td] of the three scenarios. If the calculation result of a certain time is an empty set, it means that there is no overlapping interval for the three scenarios.

[0072] S205. Determine the action order of the scenarios according to the start time points of each scenario in the multi-scenario overlapping interval.

[0073] Among them, the start time point refers to the moment when the scenario starts to act. The action order of the scenarios represents the sequence of occurrence of multiple scenarios. The time priority refers to the sorting of scenarios determined according to the start time.

[0074] The system determines the action order based on the start time of the scenarios within the overlapping interval. The specific implementation process is as follows: Extract the earliest valid data time points of each scenario within the overlapping interval, sort these time points from smallest to largest to obtain the time priority of the scenarios. The system stores the sorted scenario sequence as an ordered list, and each element in the list contains the scenario type identifier and the corresponding start timestamp. For example, when t1 < t3 < t5, the obtained action order is: vehicle collision scenario (t1) -> wind load scenario (t3) -> seismic wave scenario (t5). This sorting method based on time sequence ensures that the subsequent coupling analysis is carried out in the order of actual scenario occurrence.

[0075] S206. Calculate the duration of each scenario within the multi-scenario overlapping interval to obtain the duration. The multi-scenario overlapping interval is the time intersection area of the response data of each scenario.

[0076] Among them, the duration represents the action time of the scenario within the overlapping interval. The duration is a quantitative description of the duration. The time intersection area refers to the common action interval of the response data of multiple scenarios in the time dimension.

[0077] The system accurately calculates the duration of each scenario within the overlapping interval. The calculation method is as follows: for each scenario, take the difference between the timestamp of the last valid data point and the timestamp of the first valid data point within the overlapping interval to obtain the duration of the scenario. The specific process is as follows: Let the overlapping interval be [tc, td]. For any scenario, its duration T = td - tc. The system calculates the duration of each scenario separately and stores the calculation results in seconds as scenario attributes. For example, the duration of the vehicle collision scenario T1 = t2 - t1, the duration of the wind load scenario T2 = t4 - t3, and the duration of the seismic wave scenario T3 = t6 - t5. These duration data are used for subsequent calculation of the influence factor of the scenario.

[0078] S207. Extract the peak values of the strain data, acceleration data, and displacement data of each scenario within the multi-scenario overlapping interval to obtain the scenario response amplitude.

[0079] The peak value refers to the maximum absolute value in the data sequence and is used to characterize the extreme value feature of data fluctuations. The scenario response amplitude refers to the maximum response value of the structure under the action of a specific scenario and is used to quantify the stress level of the structure. The strain data represents the degree of structural deformation and is collected by strain gauges. The acceleration data represents the vibration acceleration of the structure and is collected by acceleration sensors. The displacement data represents the displacement of the structure and is collected by displacement sensors.

[0080] For the strain data within the overlapping interval, first calculate the principal strain ε1 and the secondary strain ε2 using the formula ε1,2 = (εx + εy) / 2 ± √[(εx - εy)² / 4 + γxy²], where εx and εy are the strains in the orthogonal directions and γxy is the shear strain. Take the maximum value of |ε1| and |ε2| as the strain peak value εmax. For the acceleration data, obtain the acceleration components ax, ay, and az in the x, y, and z directions respectively, and calculate the maximum value of the combined acceleration through the formula amax = √(ax² + ay² + az²). For the displacement data, measure the horizontal displacement δh and the vertical displacement δv respectively, and calculate the maximum value of the displacement resultant through the formula δmax = √(δh² + δv²). Combine these three types of peak values to form the scenario response amplitude vector R = [εmax, amax, δmax], which is used to characterize the maximum mechanical response of the structure under this scenario.

[0081] S208. Calculate the product of the scenario response amplitude of each scenario and its corresponding duration to obtain the scenario influence factor.

[0082] The scenario influence factor represents the comprehensive influence degree of the scenario on the structure, considering both the response intensity and the action time. The response amplitude vector contains three types of peak value data: strain, acceleration, and displacement. The duration refers to the action duration of the scenario within the overlapping interval. The product operation comprehensively considers the response intensity and time factors.

[0083] First, normalize the response amplitude vector \(R = [\varepsilon_{max}, a_{max}, \delta_{max}]\) to obtain the dimensionless response index \(R' = [\varepsilon', a', \delta']\). The normalization formula is: \(\varepsilon'=\frac{\varepsilon_{max}}{[\varepsilon]}\), \(a'=\frac{a_{max}}{[\ a]}\), \(\delta'=\frac{\delta_{max}}{[\delta]}\), where \([\varepsilon]\), \([a]\), and \([\delta]\) are the corresponding reference values. Then introduce the weight coefficient vector \(w = [w_1, w_2, w_3]\), where \(w_1 + w_2+w_3 = 1\), and calculate the weighted response amplitude \(V = w_1\varepsilon'+w_2a'+w_3\delta'\). Determine the weight coefficients according to the structure type: for flexible structures, \(w_1 = 0.5\), \(w_2 = 0.3\), \(w_3 = 0.2\); for rigid structures, \(w_1 = 0.4\), \(w_2 = 0.4\), \(w_3 = 0.2\). Finally, multiply the weighted response amplitude \(V\) by the duration \(T\) to obtain the scenario impact factor \(F = V\times T\). Through this method, a quantitative evaluation of the scenario impact degree is achieved.

[0084] S209. Compare the scenario impact factors of each scenario, and determine the scenario with the largest scenario impact factor as the dominant scenario.

[0085] Among them, the dominant scenario refers to the scenario that has the most significant impact on the structural response. Scenario determination refers to the process of determining the primary and secondary relationships between scenarios. Impact factor comparison refers to the method of determining the importance of scenarios by the numerical magnitude.

[0086] The system determines the dominant scenario by comparing the numerical magnitudes of the scenario impact factors. First, sort the calculated scenario impact factors of each scenario in descending order from large to small to generate a descending sequence \([F_1, F_2, F_3]\). The system marks the scenario with the largest impact factor (\(\max\{F_1, F_2, F_3\}\)) as the dominant scenario and updates the scenario attribute label in the database. The specific determination process uses the direct comparison method: compare the magnitudes of \(F_1\) and \(F_2\), and \(F_2\) and \(F_3\) in turn to determine the scenario corresponding to the maximum value. For example, when \(F_1>F_2>F_3\), scenario 1 is the dominant scenario; when \(F_2>F_1>F_3\), scenario 2 is the dominant scenario; when \(F_3>F_2>F_1\), scenario 3 is the dominant scenario. The system records the determination result in the scenario feature library as the basis for subsequent coupling analysis.

[0087] S210. Determine the scenarios with scenario impact factors lower than the preset impact threshold as secondary scenarios to obtain the scenario determination result.

[0088] Among them, the preset impact threshold represents the critical value for determining the importance of scenarios. The secondary scenario refers to the scenario that has a relatively small impact on the structural response. The scenario determination result is a definite conclusion about the primary and secondary relationships between scenarios. Impact degree classification refers to the classification of scenarios according to the impact factors.

[0089] The system completes the determination of the secondary nature of the scenario based on a preset threshold. First, set the impact threshold according to the structural type: for rigid protection facilities, set the threshold to 30% of the impact factor of the dominant scenario; for flexible protection facilities, set the threshold to 40% of the impact factor of the dominant scenario. The system compares the impact factor of each scenario with the threshold: when the scenario impact factor is less than the threshold, mark the scenario as a secondary scenario. For example, let the impact factor of the dominant scenario be F1 and the threshold coefficient be 0.3, then when Fi < 0.3F1, scenario i is determined as a secondary scenario. The system organizes the determination results of the dominant scenario and the secondary scenario into structured data, including information such as scenario type, impact factor value, and scenario level.

[0090] S211. Extract the mechanical parameter correction value and standard response parameters from the scenario feature library according to the scenario determination result. The standard response parameters include the prestress state value and the stiffness attenuation coefficient.

[0091] The mechanical parameter correction value is a correction coefficient used to adjust the standard parameters. The standard response parameters refer to a set of reference parameters that describe the mechanical characteristics of the structure. The prestress state value represents the initial stress level of the structure. The stiffness attenuation coefficient refers to the attenuation characteristics of the structure stiffness with damage evolution. The scenario feature library stores the mechanical parameter data of the structure under different scenarios.

[0092] Extract the standard response parameters corresponding to the dominant scenario from the scenario feature library as the reference values, including the initial prestress state P0 and the initial stiffness coefficient K0. Then calculate the correction coefficient according to the influence degree of the secondary scenario: the prestress correction coefficient is calculated using the formula αp = 1 + ΣFi / F1, where Fi is the impact factor of the secondary scenario i and F1 is the impact factor of the dominant scenario; the stiffness correction coefficient is calculated using the formula αk = 1 - ΣFi / F1. Apply the correction coefficient to the standard parameters: the corrected prestress state value P = αp × P0, and the corrected stiffness attenuation coefficient K = αk × K0. This correction method takes into account the influence of the secondary scenario on the mechanical characteristics of the structure, making the parameters more in line with the actual situation under the multi-scenario coupling effect. When there are n secondary scenarios, the total correction coefficient is obtained by accumulating the influences of each scenario: α = 1 ± Σ(Fi / F1), where "+" is used for prestress correction and "-" is used for stiffness correction.

[0093] S212. Establish a digital twin model of the protection facility. According to the digital twin model, the standard response parameters, and the scenario action sequence, simulate and calculate the initial response of the structure under the dominant scenario, use the initial response as the initial condition for the simulation of the secondary scenario, and perform coupled simulation calculations to obtain the structural stress distribution cloud map and the coordinates of the stress concentration area.

[0094] Among them, the digital twin model refers to the digital virtual representation of the protection facilities. The initial response represents the structural response state under the action of the dominant scenario. The coupled simulation calculation refers to the numerical analysis considering the interaction of scenarios. The stress distribution contour map represents the visual expression of the structural stress field. The coordinates of the stress concentration area refer to the position information of the area with a relatively high stress level.

[0095] The system performs a coupled analysis calculation based on the digital twin. First, an accurate digital model including geometric features, material properties, and boundary conditions is established. The corrected prestress state value and stiffness attenuation coefficient are substituted into the model, and the analysis is carried out according to the order of scenario actions: first, calculate the structural response under the action of the dominant scenario to obtain the deformation field u(x, y, z) and stress field σ(x, y, z); then, use these response results as the initial conditions and superimpose the action of the secondary scenario for coupled calculation. The system uses an explicit dynamic analysis method to solve the coupled problem, with a time step of 0.001 s, and the final stress distribution is obtained through iterative calculation. The system generates a stress contour map and extracts the coordinates (xi, yi, zi) of the area where the stress exceeds the allowable value to form the spatial distribution data of the stress concentration area.

[0096] S213. Evaluate the structural damage state according to the coordinates of the stress concentration area, obtain the structural damage grade, and generate a safety performance evaluation report for the protection facilities.

[0097] Among them, the structural damage state represents the damaged degree and failure form of the protection facilities. The damage grade refers to the risk level divided according to the severity of the damage. The safety performance evaluation report is a systematic evaluation document of the structural safety status. The stress damage index represents the degree of structural damage caused by the stress level. The fatigue damage index represents the cumulative damage of the structure under cyclic loading. The remaining bearing capacity index represents the remaining bearing capacity of the structure.

[0098] The system performs damage assessment based on the stress distribution results. First, calculate the stress damage index: The ratio of the maximum principal stress σmax in the stress concentration area to the ultimate strength of the material σu is used as the stress damage index Ds = σmax / σu; calculate the fatigue damage index: Using the Miner linear cumulative damage theory, convert the stress time history into the equivalent stress cycle number n, combine with the material S-N curve to obtain the allowable cycle number N, and calculate the fatigue damage index Df = Σ(n / N); calculate the remaining bearing capacity index: Determine the ratio Dr = Pc / Pd of the current bearing capacity Pc of the structure to the design bearing capacity Pd through the ultimate bearing capacity analysis. The system determines the damage level according to the comprehensive scores of these three indicators: When max(Ds, Df) < 0.3 and Dr > 0.9, it is determined as minor damage; when 0.3 ≤ max(Ds, Df) < 0.7 and 0.7 < Dr ≤ 0.9, it is determined as moderate damage; when max(Ds, Df) ≥ 0.7 or Dr ≤ 0.7, it is determined as severe damage. The system generates an assessment report including the following content: basic information of the structure, detection data analysis, damage status assessment, bearing capacity analysis, service safety rating, and repair and reinforcement information. The assessment report adopts a hierarchical display method, uses different warning signs for different damage levels, and gives corresponding disposal information.

[0099] The following is a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of the digital twin-based multi-scenario coupling simulation method for highway protection in the embodiments of the present application.

[0100] S301. Perform mesh division on the digital twin model according to the dynamic response data to obtain a finite element analysis mesh.

[0101] The dynamic response data refers to the response quantities such as displacement, velocity, and acceleration that change with time generated by the protection facilities under the action of external forces. The digital twin model refers to a digital virtual model with the same geometric dimensions, material properties, and boundary conditions as the actual protection facilities. Mesh division is the process of discretizing a continuous structure into a finite number of elements. The finite element analysis mesh refers to a discretized calculation mesh composed of nodes and elements.

[0102] The specific process of the system performing mesh division is as follows: First, determine the deformation characteristic regions of the structure based on the dynamic response data. Use a denser mesh division in the regions with larger deformations and a sparser mesh division in the regions with smaller deformations. For the columns of the protection facilities, hexahedral elements are used, for the cross beams, shell elements are used, and for the connection parts, transition elements are used. The mesh size is determined according to the structural characteristic size: for the case where the cross-sectional size of the column is 200 mm, the mesh size is set to 20 mm; for the cross beam plate with a thickness of 5 mm, the mesh size is set to 10 mm. The system uses the mapped mesh division method to generate regular meshes and the free mesh division method to process irregular regions, and finally generates the finite element analysis mesh data including node coordinates and element connection relationships.

[0103] S302. Set the initial stress state of the structure according to the prestress state value, and substitute the dynamic response data of the dominant scenario into the finite element analysis mesh to calculate the stress values of each mesh node in the finite element analysis mesh.

[0104] The prestress state value represents the initial stress level of the structure before the action of external loads. The initial stress state refers to the stress distribution of the structure before deformation. Substituting the dynamic response data means applying the measured dynamic response quantities to the finite element model. Stress value calculation means solving the stress components of each mesh node.

[0105] The system first sets the initial stress field according to the prestress state value: Apply the prestress value to the corresponding nodes according to the assembly sequence of the structure, and use the explicit integration method for stress balance calculation. Then substitute the dynamic response data of the dominant scenario into the mesh nodes: For the displacement response, directly assign the node displacement constraints; for the acceleration response, convert it into the node inertial force; for the strain response, convert it into the node deformation. The system uses the explicit dynamic analysis method to solve the node stresses: For any node i, its stress component σij is calculated through the element stress-strain relationship: σij = Dijkl·εkl, where Dijkl is the elastic constant tensor and εkl is the strain component. The stress time history of each node during the entire response process is obtained through time domain integration.

[0106] S303. Modify the stress values of each mesh node based on the stiffness decay coefficient to obtain the modified stress values.

[0107] The stiffness decay coefficient represents the decay characteristic of the structure stiffness with damage evolution. Node stress value modification refers to the stress recalculation process considering the influence of stiffness degradation. The modified stress value refers to the node stress after stiffness modification.

[0108] The specific steps for the system to perform stress correction are as follows: First, extract the initial stress value σ0 of each node and substitute the stiffness attenuation coefficient α into the correction formula for calculation. For isotropic materials, the corrected stress value σ = α·σ0; for anisotropic materials, the corrected stress value σij = αij·σ0ij, where αij is the direction-dependent stiffness attenuation coefficient. The system performs stress correction calculations on each node in the grid one by one to generate a corrected stress field considering the stiffness degradation effect. The correction results are stored in tensor form, including stress components in all directions. Through this method, the influence of structural damage evolution on stress distribution is realized.

[0109] S304. Partition and render the corrected stress values according to the contour principle to generate a structural stress distribution cloud map.

[0110] The corrected stress value refers to the nodal stress tensor after stiffness correction. The contour principle refers to the method of connecting points with the same value to form contour lines. Partition rendering refers to filling different regions with colors according to the magnitude of stress values. The structural stress distribution cloud map is a color graph representing the stress distribution of each part of the structure.

[0111] The specific process for the system to generate a stress cloud map is as follows: First, determine the stress display component. For isotropic materials, use the von Mises equivalent stress σe = √[(σ1 - σ2)²+(σ2 - σ3)²+(σ3 - σ1)²] / 2, and for anisotropic materials, use the principal stress σp. Then set the stress level division, and evenly divide the stress range [σmin, σmax] into 10 intervals. Assign different color codes to each interval: the minimum stress interval uses blue (RGB: 0, 0, 255), the maximum stress interval uses red (RGB: 255, 0, 0), and the intermediate intervals obtain transitional colors through linear interpolation according to the magnitude of stress values. The system determines the interval to which each node belongs according to its stress value, assigns the corresponding color code to the node, and fills the interior of the element with a gradient color through an interpolation algorithm to finally generate a continuous and smooth stress distribution cloud map.

[0112] S305. Set a stress warning threshold based on the allowable stress value of the structural material. When it is detected that the corrected stress value in the target area of the structural stress distribution cloud map exceeds the stress warning threshold, mark a warning sign in the target area.

[0113] The allowable stress value refers to the maximum safe stress that the structural material can withstand. The stress warning threshold is the stress critical value that triggers a warning. The target area refers to the structural part that needs to be monitored key points. The warning sign is a warning symbol used to mark the over-limit area.

[0114] The specific steps for the system to perform warning marking are as follows: first, determine the warning level according to the allowable stress [σ] of the structural material, and set the three-level warning threshold: the first-level warning threshold σw1=0.7[σ], the second-level warning threshold σw2=0.85[σ], and the third-level warning threshold σw3=[σ]. The system scans the corrected stress value σm of each node in the stress cloud map. When σw1≤σm<σw2 is detected, a yellow semi-transparent layer is superimposed in the area and the number "1" is marked; when σw2≤σm<σw3, an orange semi-transparent layer is superimposed and the number "2" is marked; when σm≥σw3, a red semi-transparent layer is superimposed and the number "3" is marked. For the warning area, the system automatically extracts its boundary contour coordinates and generates warning information data including warning level, position range, and stress value. The warning mark uses a striking font style to ensure that it is clearly visible on the stress cloud map.

[0115] S306. Layering the structural stress distribution cloud map according to the dominant scenario and the secondary scenario to obtain a layered stress distribution map.

[0116] Layering refers to the separate display of stress distributions generated by different scenarios. The layered stress distribution diagram is a collection of layers that represent the independent stress distribution of each scenario. The dominant scenario refers to the condition that has the most significant impact on the structure. The secondary scenario refers to the condition with a relatively small impact.

[0117] The specific process of the system performing stress stratification is as follows: first, a multi-layer layer structure is established, with the bottom layer being the structural geometric model, and the upper layers being the dominant scene stress layer and the secondary scene stress layer. For the dominant scene, the node stress value σp under its independent action is extracted to generate the main scene stress cloud map; for each secondary scene i, the node stress value σsi under its independent action is extracted to generate the corresponding stress cloud map. Each layer uses the same stress interval division and color mapping scheme, and the inter-layer superposition effect is achieved by adjusting the layer transparency (the main scene layer is set to 0.8, and the secondary scene layer is set to 0.6). The system configures independent display control for each layer, and realizes flexible switching display of stress distribution in different scenes through layer switch combination.

[0118] S307. Calculate the stress contribution value of each scene according to the layered stress distribution diagram, and determine the stress superposition coefficient.

[0119] The stress contribution value refers to the stress generated by the independent action of each scenario. The stress superposition coefficient represents the stress enhancement effect under the coupling of multiple scenarios. The scenario-independent stress refers to the stress response under the action of a single scenario. The layered stress distribution diagram is a collection of layers representing the independent stress distribution of each scenario. The stress tensor is a second-order tensor that describes the stress state and contains stress components in all directions.

[0120] For each node j, extract the stress tensors in each scenario layer: the dominant scenario stress tensor σp = [σxx σxy σxz; σyx σyy σyz; σzx σzy σzz]p, and the stress tensor σsi of the secondary scenario i. Calculate the stress tensor norm using the Frobenius norm: ||σ|| = √(Σi,jσij²). The stress contribution value of node j is calculated by normalization: the dominant scenario contribution value Cpj = ||σpj|| / Σ||σij||, and the contribution value Csij of the secondary scenario i is Csij = ||σsij|| / Σ||σij||. The overall stress contribution value of the structure is calculated using stress weighted averaging: Cp = ΣwjCpj, Csi = ΣwjCsij, where the weight coefficient wj = ||σj|| / Σ||σk||. The stress superposition coefficient is determined by the ratio of the sum of the coupled stress and the independent stress: α = ||σc|| / Σ||σi||, where σc is the total stress tensor obtained by coupled calculation. For anisotropic materials, the stress components in the principal direction of the material need to be considered.

[0121] S308. Decompose the stress in the stress concentration region based on the stress superposition coefficient to identify the contribution degree of each scenario to the structural damage.

[0122] Stress decomposition refers to the process of distributing the total stress to each scenario. The stress concentration region refers to a local area with a relatively high stress level. The contribution degree represents the influence ratio of each scenario on the structural damage. Structural damage refers to the degree of loss of the bearing capacity of the component. The allowable stress is the maximum safe stress that the material can withstand.

[0123] Extract the total stress σt and the superposition coefficient α of the nodes in the stress concentration region, and decompose the stress of node i: the stress component σpi of the dominant scenario is σpi = α·Cpi·σti, and the stress component σsji of the secondary scenario j is σsji = α·Csji·σti. Calculate the damage contribution based on the von Mises failure criterion: the damage contribution Dp of the dominant scenario is Dp = Σ[(σpi,eq / [σ])^m], and the damage contribution Dsj of the secondary scenario j is Dsj = Σ[(σsji,eq / [σ])^m], where σeq = √[(σ1 - σ2)² + (σ2 - σ3)² + (σ3 - σ1)²] / √2 is the equivalent stress, [σ] is the allowable stress, and m is the material-related damage index. Normalize the damage contribution: dp = Dp / ΣDk × 100%, dsj = Dsj / ΣDk × 100%. For fatigue damage, use the Miner linear cumulative damage theory: D = Σ(ni / Ni), where ni is the actual number of cycles and Ni is the allowable number of cycles at the stress level σi.

[0124] S309. Establish a stress reduction strategy library based on the stress contribution value, contribution degree, and scenario action sequence, and generate a structural reinforcement plan for different scenario combinations.

[0125] The stress reduction strategy library is a collection of methods for reducing the structural stress level. The structural reinforcement plan refers to specific measures to improve the load-bearing capacity of the structure. The scenario combination represents the working conditions where multiple scenarios act simultaneously. The scenario action sequence refers to the sequence of occurrence of each scenario.

[0126] The specific process for the system to execute the generation of the reinforcement plan is as follows: First, grade the scenarios according to the stress contribution value. The reduction priority of the dominant scenario is 1, and the reduction priority of the secondary scenario i is Csi / Cp. Then, determine the reinforcement measures according to the contribution degree: For the dominant scenario with dp>60%, adopt local reinforcement measures such as strengthening the cross-section of the component and adding stiffeners; for the secondary scenario with 30%<dsj≤60%, adopt overall reinforcement measures such as adding supports and adjusting constraints; for the secondary scenario with dsj≤30%, adopt auxiliary reinforcement measures such as optimizing construction details and improving connections. The system integrates information such as scenario combination, stress characteristics, and reinforcement measures into the reinforcement plan database to support the structural reinforcement design under different working conditions. The reinforcement plan includes technical parameters such as specific construction practices, material selection, and construction technology.

[0127] S310. Compare the stress distribution contour map with the actual monitoring data in real time and establish a stress trend prediction model.

[0128] The stress distribution contour map refers to a color graph representing the structural stress distribution. The actual monitoring data is the stress measurement values collected by on-site sensors. The stress trend prediction model is a mathematical model describing the time evolution law of stress. The root mean square error is used to evaluate the prediction accuracy. Time series analysis is a statistical method for studying the time variation characteristics of data.

[0129] Pair the node stress value σc in the stress contour map with the monitored stress value σm of the corresponding measuring point, and calculate the relative error δ = |σc - σm| / σm. Collect continuous monitoring data to construct an ARIMA(p, d, q) model: yt=(1 - L)dXt = c + Σφiyt - i + Σθjεt - j + εt, where L is the lag operator, d is the order of differencing, p is the order of autoregression, q is the order of moving average, φi is the autoregressive coefficient, θj is the moving average coefficient, and εt is a white noise sequence. Determine the model order through the autocorrelation function ACF(k)=γk / γ0 and the partial autocorrelation function PACF(k), where γk is the kth autocovariance. Use the maximum likelihood estimation method to solve the model parameters. The model prediction accuracy is evaluated through the root mean square error: RMSE = √[Σ(σp - σm)² / n], where σp is the predicted value and n is the number of samples.

[0130] S311. Calculate the stress evolution rate based on the stress trend prediction model and determine the early warning time window according to the stress evolution rate.

[0131] The stress evolution rate refers to the rate at which stress changes over time. The warning time window is the remaining time until the warning threshold is reached. The stress prediction value is the future stress level calculated by the model. The rate threshold is the critical value of the stress change rate that triggers a warning. The evolution equation describes the relationship between stress and time.

[0132] The stress evolution rate is calculated using the central difference scheme: v(t) = [σ(t + Δt) - σ(t - Δt)] / (2Δt), where the time step Δt = 1h. The rate warning levels are set as: v1 = 0.1 MPa / h (low speed), v2 = 0.5 MPa / h (medium speed), v3 = 1.0 MPa / h (high speed). The warning time window T is determined by solving the stress evolution equation: dσ / dt = f(σ, t), σ(0) = σ0, σ(T) = σw, where f(σ, t) is the stress rate function, σ0 is the current stress value, and σw is the warning threshold. For linear evolution, T = (σw - σ0) / v(t). When considering the stress acceleration effect, a second-order evolution equation is used: d²σ / dt² = g(σ, dσ / dt, t), where g is the acceleration function. The 95% confidence interval is calculated according to the stress prediction model: [σ(t) ± 1.96se(t)], where se(t) is the prediction standard error.

[0133] S312. Develop a hierarchical response strategy based on the warning time window. The hierarchical response strategy includes emergency disposal instructions, personnel evacuation plans, and equipment risk avoidance measures.

[0134] The hierarchical response strategy is a system of disposal plans for different warning levels. The emergency disposal instructions guide the specific requirements for on-site emergency actions. The personnel evacuation plan stipulates the routes and procedures for the emergency evacuation of personnel. The equipment risk avoidance measures refer to the specific methods for protecting important equipment.

[0135] The system executes the response strategy formulation based on the warning time window T: when T > 6h, initiate a level-three response, issue a warning message, increase the monitoring frequency to 5 minutes per time, and prepare emergency supplies; when 2h < T ≤ 6h, initiate a level-two response, issue a temporary control order, evacuate non-essential personnel, and transfer valuable equipment; when T ≤ 2h, initiate a level-one response, implement a full control, organize the emergency evacuation of personnel, and take equipment protection measures. The emergency disposal instructions clarify the personnel division of labor, communication methods, and disposal processes. The personnel evacuation plan stipulates the evacuation routes, assembly points, and methods for counting the number of people. The equipment risk avoidance measures include power-off requirements, protection methods, and transfer procedures. The system stores the response strategy as structured data to support on-site emergency disposal.

[0136] S313. Generate a stress warning report based on the warning identification area in the stress distribution cloud map. The stress warning report includes the over-limit stress value, the warning area range, and the stress growth rate.

[0137] The early warning identification area refers to the structural part where the early warning symbol is marked in the stress nephogram. The over-limit stress value refers to the stress value that exceeds the early warning threshold. The scope of the early warning area represents the spatial position and geometric size of the early warning area. The stress growth rate refers to the increase in stress per unit time. The stress early warning report is a standardized document recording early warning information.

[0138] The specific steps for the system to generate an early warning report are as follows: First, extract the node data of the early warning identification area, including node numbers, spatial coordinates, and stress values. For each early warning area i, the system records the following information: the over-limit stress value σi = max{σj, j ∈ Ni}, where Ni is the set of nodes within the area; the spatial range of the early warning area [xmin, xmax] × [ymin, ymax] × [zmin, zmax]; the stress growth rate vi = (σi,t - σi,t-Δt) / Δt, where Δt is the sampling time interval. The system organizes this data into an early warning report in a unified format. The report structure includes: basic information (time, location, early warning level), stress data (over-limit value, growth rate), spatial information (area range, key nodes), and early warning information. The report is stored in the form of a data table for subsequent analysis and query.

[0139] S314. Record the stress evolution data during the early warning process and store it in the stress early warning event database.

[0140] The stress evolution data refers to the complete record of the stress value changing with time during the early warning process. The stress early warning event refers to the set of all information in a single early warning process. The early warning event database is a structured database storing historical early warning information. Data storage is the process of saving data in a specific format.

[0141] The specific process for the system to record early warning data is as follows: Establish an early warning event data table, including fields such as event number, occurrence time, early warning level, and structural part. For the stress evolution during the early warning process, the system records the following data: the stress time history curve {σ(t), t ∈ [t0, te]}, where t0 is the start time of the early warning and te is the end time of the early warning; the stress derivative curve {dσ / dt, t ∈ [t0, te]}; the cumulative damage value D(t) = ∫[σ(t) / [σ]]²dt. The system organizes this data into a relational database structure: event table (EventID, Time, Level, Location), stress table (EventID, NodeID, Time, Stress), rate table (EventID, NodeID, Time, Rate). The database supports retrieval according to conditions such as time, location, and early warning level, for statistical analysis and historical backtracking. Each early warning record contains the complete stress evolution process, providing data support for subsequent optimization of the early warning strategy.

[0142] The multi-scenario mechanical property coupling simulation optimization system in the embodiments of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 4 , which is a schematic structural diagram of an entity device of the multi-scenario mechanical property coupling simulation optimization system in the embodiments of the present application.

[0143] It should be noted that Figure 4 The structure of the multi-scenario mechanical property coupling simulation optimization system shown is only an example, and should not bring any limitations to the functions and usage scopes of the embodiments of the present invention.

[0144] As Figure 4 shown, the multi-scenario mechanical property coupling simulation optimization system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 into the random access memory (RAM) 403, such as executing the methods described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0145] The following components are connected to the I / O interface 405: an input part 406 including an audio input device, a button switch, etc.; an output part 407 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage part 408 including a hard disk, etc.; and a communication part 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part 409 performs communication processing via a network such as the Internet. The drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that the computer program read from it can be installed into the storage part 408 as needed.

[0146] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are executed.

[0147] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0148] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings.

[0149] Specifically, the multi-scenario mechanical property coupling simulation optimization system of this embodiment includes a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the multi-scenario coupling simulation method for highway protection based on digital twin provided in the above embodiment is implemented.

[0150] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the multi-scenario mechanical property coupling simulation optimization system described in the above embodiments; or may exist alone without being assembled into the multi-scenario mechanical property coupling simulation optimization system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the multi-scenario mechanical property coupling simulation optimization system, the multi-scenario mechanical property coupling simulation optimization system implements the digital twin-based multi-scenario coupling simulation method for highway protection provided in the above embodiments.

[0151] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the foregoing embodiments have been described in detail, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0152] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...".

[0153] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage media include: various media such as ROM or random access memory RAM, magnetic disks, or optical discs that can store program codes.

Claims

1. A digital-twin-based multi-scenario coupling simulation method for highway protection, characterized in that Applied to a multi-scenario mechanical property coupling simulation optimization system, the method includes: Collect dynamic response data of traffic protection facilities using multiple types of sensors. The dynamic response data includes strain data, acceleration data, and displacement data, and store the dynamic response data to form a scenario feature library, which includes vehicle collision scenarios, wind load scenarios, and seismic wave scenarios; Synchronously project the dynamic response data onto a unified time axis, identify the multi-scenario overlapping interval, and determine the scenario action sequence and duration of the multi-scenario overlapping interval. The multi-scenario overlapping interval is the time intersection area of the response data of each scenario; Judge the dominant scenario and the secondary scenario in the multi-scenario overlapping interval according to the scenario action sequence, duration, and scenario response amplitude to obtain a scenario judgment result; Extract the mechanical parameter correction value and standard response parameter in the scenario feature library according to the scenario judgment result. The standard response parameter includes the prestress state value and the stiffness attenuation coefficient; Establish a digital twin model of the protection facility, and according to the digital twin model, the standard response parameter, and the scenario action sequence, simulate and calculate the initial response of the structure under the dominant scenario, and use the initial response as the initial condition for the simulation of the secondary scenario to perform coupled simulation calculations to obtain the structural stress distribution cloud map and the coordinates of the stress concentration area; Evaluate the structural damage state according to the coordinates of the stress concentration area to obtain the structural damage level, and generate a safety performance evaluation report of the protection facility.

2. The method according to claim 1, wherein The step of synchronously projecting the dynamic response data onto a unified time axis, identifying the multi-scenario overlapping interval, and determining the scenario action sequence and duration of the multi-scenario overlapping interval specifically includes: Synchronously map the strain data, the acceleration data, and the displacement data according to the acquisition timestamp to generate a unified time axis; Mark the start and end time points of the vehicle collision scenario, the wind load scenario, and the seismic wave scenario on the unified time axis; Calculate the intersection of the time intervals of each scenario to obtain the multi-scenario overlapping interval; Determine the scenario action sequence according to the start time point of each scenario in the multi-scenario overlapping interval; Calculate the duration of each scenario in the multi-scenario overlapping interval to obtain the duration.

3. The method according to claim 1, wherein The step of judging the dominant scenario and the secondary scenario in the multi-scenario overlapping interval according to the scenario action sequence, duration, and scenario response amplitude to obtain a scenario judgment result specifically includes: Extract the peak values of the strain data, acceleration data, and displacement data of each scenario in the multi-scenario overlapping interval to obtain the scenario response amplitude; Calculate the product of the scenario response amplitude of each scenario and its corresponding duration to obtain the scenario influence factor; Compare the scenario influence factors of each scenario, and judge the scenario with the largest scenario influence factor as the dominant scenario; Judge the scenario with a scenario influence factor lower than the preset influence threshold as the secondary scenario to obtain a scenario judgment result.

4. The method according to claim 1, characterized in that, After the step of evaluating the structural damage state according to the coordinates of the stress concentration area to obtain the structural damage level and generating a safety performance evaluation report of the protection facility, the method further includes: Mesh the digital twin model according to the dynamic response data to obtain a finite element analysis mesh; Set the initial stress state of the structure according to the prestress state value, and substitute the dynamic response data of the dominant scenario into the finite element analysis mesh to calculate the stress values of each mesh node in the finite element analysis mesh; Modify the stress values of each mesh node based on the stiffness decay coefficient to obtain modified stress values; Partition and render the modified stress values according to the isoline principle to generate a structural stress distribution nephogram; Set a stress warning threshold based on the allowable stress value of the structural material. When it is detected that the modified stress value in the target area in the structural stress distribution nephogram exceeds the stress warning threshold, mark a warning sign in the target area.

5. The method according to claim 4, wherein After the step of setting a stress warning threshold based on the allowable stress value of the structural material and marking a warning sign in the target area when it is detected that the modified stress value in the target area in the structural stress distribution nephogram exceeds the stress warning threshold, the method further includes: Layer the structural stress distribution nephogram according to the dominant scenario and the secondary scenario to obtain a layered stress distribution diagram; Calculate the stress contribution value of each scenario according to the layered stress distribution diagram and determine the stress superposition coefficient; Decompose the stress in the stress concentration area based on the stress superposition coefficient to identify the contribution degree of each scenario to structural damage; Establish a stress reduction strategy library according to the stress contribution value, the contribution degree and the scenario action sequence, and generate a structural reinforcement plan for different scenario combinations.

6. The method according to claim 4, characterized in that After the step of evaluating the structural damage state according to the coordinates of the stress concentration area to obtain the structural damage level and generating a safety performance evaluation report of the protection facilities, the method further includes: Compare the stress distribution nephogram with the actual monitoring data in real time to establish a stress trend prediction model; Calculate the stress evolution rate based on the stress trend prediction model, and determine the warning time window according to the stress evolution rate; Formulate a hierarchical response strategy according to the warning time window, and the hierarchical response strategy includes emergency disposal instructions, personnel evacuation plans and equipment hazard avoidance measures.

7. The method according to claim 4, wherein After the step of evaluating the structural damage state according to the coordinates of the stress concentration area to obtain the structural damage level and generating a safety performance evaluation report of the protection facilities, the method further includes: Generate a stress warning report according to the warning sign area in the stress distribution nephogram, and the stress warning report includes the over-limit stress value, the warning area range and the stress growth rate; Record the stress evolution data during the warning process and store it in the stress warning event database.

8. A multi-scenario mechanical property coupling simulation optimization system, characterized in that, The multi-scenario mechanical property coupling simulation optimization system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the multi-scenario mechanical property coupling simulation optimization system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the multi-scenario mechanical property coupling simulation optimization system, the multi-scenario mechanical property coupling simulation optimization system is caused to execute the method described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the multi-scenario mechanical property coupling simulation optimization system, the multi-scenario mechanical property coupling simulation optimization system is caused to execute the method described in any one of claims 1-7.

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