A multi-scenario coupling simulation method and system for highway protection based on digital twins

Abstract

A multi-scenario coupling simulation method and system for highway protection based on digital twins relates to the field of electrical digital data processing. The method includes: using multiple sensors to collect dynamic response data (strain, acceleration, displacement) of traffic protection facilities, and establishing a scenario feature library containing vehicle collisions, wind loads, and seismic waves. Projecting the response data onto a unified time axis, identifying overlapping intervals of multiple scenarios, and determining the order and duration of action. Determining the primary and secondary scenarios based on scenario action parameters, extracting mechanical correction values ​​and standard response parameters. Establishing a digital twin model for coupled simulation, obtaining stress distribution, evaluating the structural damage level, and generating a safety performance report. Implementing this method can reduce the deviation between the mechanical performance simulation results of protective facilities and the actual working conditions.

Description

Technical Field

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

[0002] With the rapid development of highway construction and the continued growth of traffic volume in my country, the safety performance of highway protective facilities, as important infrastructure for ensuring driving safety, has received increasing attention. In actual use, highway protective facilities must not only withstand the impact of vehicle collisions, but also cope with the impact of natural factors such as wind loads and earthquakes, which places higher demands on the mechanical performance of protective facilities.

[0003] In related technologies, the mechanical performance of protective equipment can be evaluated through field testing and single-scenario simulation. In field testing, sensors are deployed to obtain parameters such as structural strain and acceleration, and a finite element model is constructed for force analysis. Simulations typically involve independent simulations of single scenarios, such as vehicle collisions, wind loads, or seismic waves, to evaluate the mechanical response of protective equipment under each scenario.

[0004] However, this single-scenario testing and simulation approach has limitations. Because various loads often act simultaneously in natural environments, single-scenario simulations cannot reflect the interactions between these loads. Furthermore, existing simulation methods fail to adequately consider the prestressed state and stiffness decay characteristics of structures under multiple loads, leading to discrepancies between simulation results and actual operating conditions. Summary of the Invention

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

[0006] In the first aspect, the present application provides a multi-scenario mechanical performance coupling simulation optimization method for highway traffic protection based on digital twins, which is applied to a multi-scenario mechanical performance coupling simulation optimization system. The method includes: using multiple types of sensors to collect dynamic response data of traffic protection facilities, the dynamic response data includes strain data, acceleration data and displacement data, and storing 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; synchronously projecting the dynamic response data onto a unified time axis, identifying the multi-scenario overlapping interval, and determining the scene 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 scene; according to the scene action sequence The dominant and secondary scenarios in the overlapping interval of multiple scenarios are determined based on the sequence, duration and scenario response amplitude to obtain the scenario determination results; the mechanical parameter correction values ​​and standard response parameters are extracted from the scenario feature library according to the scenario determination results, and the standard response parameters include the prestress state value and the stiffness attenuation coefficient; a digital twin model of the protective facility is established, and the initial response of the structure under the dominant scenario is simulated and calculated according to the digital twin model, standard response parameters and scenario action sequence. The initial response is used as the initial condition for the simulation of the secondary scenario, and coupled simulation calculations are performed to obtain the structural stress distribution cloud map and the coordinates of the stress concentration area; the structural damage state is evaluated according to the coordinates of the stress concentration area, the structural damage level is obtained, and a safety performance assessment report for the protective facility is generated.

[0007] In the above-mentioned embodiment, dynamic response data was collected using multiple sensor types and a scenario feature library was established. The overlapping intervals of multiple scenarios were identified based on a unified timeline, and the dominant and secondary scenarios were determined by combining scenario influencing factors. A coupled simulation calculation was performed using the initial response of the dominant scenario as the initial condition for the secondary scenario through a digital twin model. This coupled simulation, which also considered the prestressed state and stiffness decay characteristics, improved the accuracy of the structural response simulation under multiple simultaneous loads, making the simulation results more accurate and closer to actual working conditions.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the dynamic response data is synchronously projected onto a unified time axis, multiple scene overlapping intervals are identified, and the scene action sequence and duration of the multiple scene overlapping intervals are determined, specifically including: 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 scene, wind load scene, and seismic wave scene on the unified time axis; calculating the intersection of each scene time interval to obtain the multiple scene overlapping interval; determining the scene action sequence based on the starting time point of each scene in the multiple scene overlapping interval; calculating the duration of each scene in the multiple scene overlapping interval to obtain the duration.

[0009] In the above example, strain, acceleration, and displacement data are synchronously mapped onto a unified timeline based on acquisition timestamps. The start and end times of each scene are marked, and the intersection of time intervals is calculated. By precisely identifying the boundaries and duration of overlapping scene intervals, the temporal relationships between multiple scenes are accurately located, providing a reliable time reference for subsequent determination of dominant and secondary scenes, effectively resolving the issue of mixed temporal sequences across multiple scenes.

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

[0011] In the above example, the peak values ​​of various data types within the overlapping intervals of the scenarios were extracted to obtain the scenario response amplitude, which was then multiplied by the duration to obtain the scenario impact factor. The dominant scenario was determined based on the magnitude of the scenario impact factor, and a preset impact threshold was set to filter out secondary scenarios. This established an objective scenario determination mechanism, providing a scientific relationship between scenario interactions for subsequent coupled simulations and effectively guiding the computational sequence of multi-scenario coupled 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 assessment report for protective facilities, the method further includes: meshing the digital twin model according to the dynamic response data to obtain a finite element analysis grid; setting the initial stress state of the structure according to the prestressed state value, and substituting the dynamic response data of the dominant scenario into the finite element analysis grid to calculate the stress value of each grid node of the finite element analysis grid; correcting the stress value of each grid node based on the stiffness attenuation coefficient to obtain a corrected stress value; partitioning and rendering the corrected stress value according to the contour line principle to generate a structural stress distribution cloud map; 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 of the target area in the structural stress distribution cloud map exceeds the stress warning threshold, marking a warning mark in the target area.

[0013] In the above example, the digital twin model is meshed, and the initial stress state is set based on the prestressed state value. The dynamic response data of the dominant scenario is substituted into the calculated mesh node stress values. Stress values ​​are corrected using the stiffness attenuation coefficient, and then rendered in sections. Warning thresholds are set for real-time monitoring, achieving precise visualization of the structural stress distribution and enhancing the accuracy and timeliness of stress warnings.

[0014] In combination with some embodiments of the first aspect, in some embodiments, a stress warning threshold is set based on the allowable stress value of the structural material. When it is detected that the corrected stress value of the target area in the structural stress distribution cloud map exceeds the stress warning threshold, after the step of marking a warning mark on the target area, the method also includes: layering the structural stress distribution cloud map according to the dominant scenario and the secondary scenario to obtain a layered stress distribution map; calculating the stress contribution value of each scenario according to the layered stress distribution map, and determining the stress superposition coefficient; performing stress decomposition on the stress concentration area based on the stress superposition coefficient, and identifying the contribution degree of each scenario to the structural damage; establishing a stress reduction strategy library according to the stress contribution value, contribution degree and scenario action order, and generating structural reinforcement plans for different scenario combinations.

[0015] In the above example, the structural stress distribution cloud map is layered by scenario, the stress contribution value and stress superposition coefficient for each scenario are calculated, and the stress concentration areas are decomposed to identify the damage contribution of each scenario. A stress reduction strategy library is established based on the stress contribution value, contribution degree, and scenario action sequence, improving the targeted and scientific nature of the structural reinforcement plan.

[0016] 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 assessment report for protective facilities, the method further includes: comparing the stress distribution cloud map 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 early warning time window according to the stress evolution rate; formulating a graded response strategy according to the early warning time window, the graded response strategy including emergency response instructions, personnel evacuation plans, and equipment risk avoidance measures.

[0017] In the above-mentioned embodiment, stress distribution cloud maps are compared with actual monitoring data in real time to establish a stress trend prediction model, and a warning time window is determined based on the stress evolution rate. By developing a hierarchical response strategy that includes emergency response instructions, personnel evacuation plans, and equipment risk avoidance measures, the foresight and systematic nature of safety warnings for protective facilities are enhanced.

[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 assessment report for the protective facilities, the method further includes: generating a stress warning report according to the warning identification area in the stress distribution cloud map, the stress warning report including 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 a stress warning event database.

[0019] In the above embodiment, a stress warning report including the excessive stress value, the warning area range, and the stress growth rate is generated based on the warning identification area in the stress distribution cloud map, and the stress evolution data during the warning process is stored in the warning event database, which fully records the development process and evolution law of the structural stress excess, thereby improving the traceability of the stress warning and the integrity of the warning report.

[0020] In the second aspect, an embodiment of the present application provides a multi-scenario mechanical performance 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 performance coupling simulation optimization system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on a multi-scenario mechanical performance coupling simulation optimization system, the above-mentioned multi-scenario mechanical performance coupling simulation optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a multi-scenario mechanical performance coupling simulation optimization system, the multi-scenario mechanical performance coupling simulation optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It is understood that the multi-scenario mechanical performance coupled simulation and 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 methods provided in the embodiments of this application. Therefore, the beneficial effects achieved by these methods can be referenced to the beneficial effects of the corresponding methods and will not be further elaborated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. This application uses multiple types of sensors to collect dynamic response data and establish a scenario feature library. It then identifies overlapping intervals of multiple scenarios based on a unified timeline and determines dominant and secondary scenarios based on scenario influencing factors. Through a digital twin model, the initial response of the dominant scenario is used as the initial condition for the secondary scenario for coupled simulation calculations. This, combined with consideration of the prestressed state and stiffness attenuation characteristics, improves the accuracy of structural response simulations under multiple simultaneous loads, making the simulation results more accurate and closer to actual working conditions.

[0026] 2. This application synchronously maps strain, acceleration, and displacement data onto a unified timeline according to acquisition timestamps, marking the start and end time points of each scene and calculating the intersection of time intervals. By precisely identifying the boundaries and duration of overlapping scene intervals, it accurately locates the temporal relationship between multiple scenes, providing a reliable time reference for subsequent determination of dominant and secondary scenes, and effectively solving the problem of mixed temporal coupling among multiple scenes.

[0027] 3. This application extracts the peak values ​​of various data within the overlapping intervals of the scenarios to obtain the scenario response amplitude, and multiplies this value by the duration to obtain the scenario impact factor. The dominant scenario is determined based on the magnitude of the scenario impact factor, and a preset impact threshold is set to filter out secondary scenarios. This establishes an objective scenario determination mechanism, provides a scientific scenario interaction relationship for subsequent coupled simulations, and effectively guides the calculation sequence of multi-scenario coupling analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a multi-scenario coupled simulation method for highway protection based on digital twins in an embodiment of the present application;

[0029] Figure 2 This is another flowchart of the multi-scenario coupling simulation method for highway protection based on digital twins in an embodiment of the present application;

[0030] Figure 3 This is another flowchart of the multi-scenario coupling simulation method for highway protection based on digital twins in an embodiment of the present application;

[0031] Figure 4 It is a schematic diagram of the structure of a physical device of the multi-scenario mechanical performance coupling simulation optimization system in the embodiment of the present application. DETAILED DESCRIPTION

[0032] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations of one or more of the listed items.

[0033] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0034] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0035] On a mountain highway, a large number of guardrails and guardrail systems are installed to ensure driving safety. These protective facilities need to cope with multiple complex working conditions simultaneously: vehicle collisions, strong winds, and seismic activity. In one accident, a heavy truck collided with a guardrail on a certain section of road during strong winds, causing the guardrail to severely deform under the action of combined loads. Subsequent analysis found that a single anti-collision performance design cannot accurately evaluate the structural response when multiple loads act simultaneously. Similar situations have occurred many times on other sections of road, such as when protective facilities were subjected to continuous strong winds during typhoon season and an earthquake occurred, or when a vehicle collision occurred during an earthquake aftershock. These incidents expose the complex situation that highway protective facilities face in actual service environments due to the coupling of multiple loads, requiring systematic consideration of the mutual influence of various loads and their combined effects on structural performance.

[0036] When a design unit was conducting a performance evaluation of highway protective facilities, it used the traditional single-scenario simulation method to establish a vehicle collision model, a wind load model, and a seismic response model, and obtained the structural responses under various working conditions through independent calculations. For example, for vehicle collision conditions, the rigid body collision model was used to calculate the impact force; for wind load conditions, the wind load was calculated based on the wind pressure coefficient; and for seismic conditions, the response spectrum method was used to calculate the seismic response. However, this method cannot reflect the coupling effect of loads in actual working conditions. In one evaluation, the independent calculation results showed that the guardrail met the design requirements under each single working condition, but when an actual accident occurred, the wind load weakened the structure's impact resistance, resulting in unexpected damage to the guardrail during a vehicle collision. This exposes the fact that single-scenario simulation cannot accurately predict structural behavior when multiple loads act simultaneously.

[0037] The digital twin coupled simulation system employed in this solution has achieved promising results in the design of a highway guardrail. The system deployed strain sensors, accelerometers, and displacement sensors at key locations on the guardrail to collect structural response data in real time. A time-synchronized algorithm identified a complex operating condition involving the combined effects of strong winds (32 m / s), a truck collision (60 km / h impact velocity), and an earthquake (magnitude 6). The system identified the vehicle collision as the dominant scenario and used the resulting prestressed state as the initial condition for calculating wind loads and seismic waves, conducting a coupled analysis considering the stiffness attenuation effect. Simulation results showed that the structural deformation caused by the collision reduced the guardrail's wind stiffness by 40%, while the vibrations induced by the seismic waves exacerbated structural stress concentrations. Based on these analysis results, the design team promptly adjusted the guardrail structure, adding stiffeners and optimizing connection nodes, ensuring that the guardrail withstood the multiple loads in subsequent service.

[0038] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a multi-scenario coupling simulation method for highway protection based on digital twins in an embodiment of the present application.

[0039] S101. Use multiple types of sensors to collect dynamic response data of traffic protection facilities. The dynamic response data includes strain data, acceleration data, and displacement data. The dynamic response data is stored to form a scene feature library. The scene feature library includes vehicle collision scenarios, wind load scenarios, and seismic wave scenarios.

[0040] Among them, dynamic response data refers to the real-time collection of mechanical response information generated by traffic protection facilities when subjected to external forces. Strain data refers to a quantitative indicator of the degree of structural deformation, which is used to represent the deformation state of a local area of ​​the structure. Acceleration data represents the characteristics of the change in structural vibration acceleration over time. Displacement data is used to represent the offset of the spatial position of the structure. The scenario feature library refers to a data set that stores the dynamic response characteristics of the structure under different working conditions. The vehicle collision scenario refers to the working condition when a vehicle collides with a protective facility. The wind load scenario refers to the working condition when the structure is subjected to wind force. The seismic wave scenario refers to the working condition under the action of seismic waves.

[0041] Before protective equipment is put into use, dynamic response data collection is required. Specifically, strain sensors, accelerometers, and displacement sensors are deployed at key locations within the protective equipment, and real-time sampling is used to acquire dynamic response data. The collected data is then preprocessed, including data cleaning, outlier removal, and data standardization. The processed data is then categorized and stored by scenario type to form a scenario feature library.

[0042] In some embodiments, the collection and storage of dynamic response data can be achieved through a variety of methods: optionally, a wired sensor network method is used, where various sensors are connected to the data collection system via signal lines, and data is collected in real time and uploaded to the server. After data preprocessing, the data is classified and stored according to scene type; optionally, a wireless sensor network method is used, where each sensor node is networked through wireless communication, and the collected data is transmitted to the gateway node, which then uploads the data to the cloud server for storage and processing. It is understandable that other methods can also be used to achieve the collection of dynamic response data and the construction of the scene feature library, which are not limited here.

[0043] S102: Synchronously project the dynamic response data onto a unified time axis, identify a multi-scene overlapping interval, and determine the scene action sequence and duration of the multi-scene overlapping interval. The multi-scene overlapping interval is the time intersection area of ​​the response data of each scene.

[0044] The unified time axis represents the reference coordinate axis that maps different types of data in a unified chronological order. The multi-scenario overlap interval refers to the region where the response data from different scenarios intersects in the temporal dimension. The scenario action sequence represents the order in which multiple scenarios take effect. Duration represents the duration of each scenario. The temporal intersection region refers to the region where multiple scenarios overlap on the time axis.

[0045] After acquiring dynamic response data, time synchronization is required. Specifically, strain, acceleration, and displacement data are first aligned according to the acquisition timestamp to generate a unified time axis. The start and end points of each scenario are then marked on the time axis, and the time overlap between scenarios is calculated. For overlapping intervals, the order of action is determined based on the order of scenario start times, and the duration of each scenario within the overlapping interval is calculated.

[0046] In some embodiments, data time synchronization can be achieved through a variety of methods: optionally, data interpolation can be used to interpolate data with different sampling frequencies to have the same time interval, and then align them according to the timestamp; optionally, a sliding time window method can be used to set a fixed-length time window, synchronize the data within the window, and slide the window forward over time to achieve continuous synchronization. It is understood that other methods can also be used to achieve time synchronization of dynamic response data, which are not limited here.

[0047] S103 , determining the dominant scene and the secondary scene in the multi-scene overlapping interval according to the scene action sequence, duration, and scene response amplitude, and obtaining a scene determination result.

[0048] The dominant scenario represents the scenario with the most significant impact on the structural response within the overlapping interval of multiple scenarios. The secondary scenario refers to the scenario with a relatively small impact on the structural response. The scenario action sequence indicates the order in which the scenarios occur. The duration indicates how long the scenario action lasts. The scenario response amplitude refers to the maximum response value of the structure under the action of a certain scenario. The scenario determination result indicates the identification conclusion of the dominant and secondary scenarios. The scenario impact factor indicates the comprehensive impact of a scenario on the structural response.

[0049] After completing the identification of the scene overlap interval, it is necessary to determine the primary and secondary scenes. Specifically, first extract the peak values ​​of the strain, acceleration, and displacement data corresponding to each scene in the overlapping interval as the scene response amplitude. Then multiply the scene response amplitude by the corresponding duration to obtain the scene impact factor. By comparing the impact factors of different scenes, the scene with the largest impact factor is determined as the dominant scene. For scenes with an impact factor less than a preset threshold, they are determined to be secondary scenes. Finally, a scene determination result containing information on the dominant and secondary scenes is generated.

[0050] In some embodiments, the determination of dominant and secondary scenarios can be achieved in a variety of ways: optionally, using a data statistical analysis method, first extracting statistical features of the response data in the overlapping interval, including calculating the mean, standard deviation, and peak value, and then calculating the scene impact index based on the statistical features, and finally achieving scene classification by setting a discrimination threshold; optionally, using a machine learning method, using historical data to train a scene classification model, the model input includes scene response features and time series features, the output is the scene category, and the automatic discrimination of primary and secondary scenarios is achieved through model prediction. It is understandable that other methods can also be used to achieve the determination process of dominant and secondary scenarios, which are not limited here.

[0051] S104 , extracting mechanical parameter correction values ​​and standard response parameters from the scene feature library according to the scene determination result, where the standard response parameters include prestress state values ​​and stiffness attenuation coefficients.

[0052] The mechanical parameter correction value represents the correction factor used to adjust the mechanical parameters of the structure. The standard response parameter refers to the benchmark parameter that describes the mechanical properties of the structure. The prestressed state value represents the initial stress state of the structure. The stiffness attenuation coefficient represents the attenuation of the structural stiffness over time. The scenario feature library is a database that stores structural response characteristics and mechanical parameters for different scenarios. The response characteristics represent the dynamic response characteristics of the structure under external loads.

[0053] After obtaining the scenario determination results, the corresponding mechanical parameters need to be extracted. Specifically, the corresponding mechanical parameter records are first retrieved from the scenario feature library based on the types of dominant and secondary scenarios. For the dominant scenario, its standard response parameters are extracted as baseline values. Then, based on the degree of influence of the secondary scenario, the corrected values ​​of the mechanical parameters are determined. These corrected values ​​are used to adjust the prestress state value and stiffness attenuation coefficient to reflect the actual mechanical properties of the structure under the coupled multi-scenario interaction.

[0054] In some embodiments, mechanical parameter extraction and correction can be achieved through a variety of methods: optionally, a parameter mapping method is used to establish a mapping relationship table between scenario types and mechanical parameters, and the corresponding parameters are directly retrieved from the table based on the scenario determination results, and the parameters are adjusted using correction coefficients; optionally, a parameter optimization method is used to invert the mechanical parameters using an optimization algorithm based on the measured response data. The optimization process takes into account the multi-scenario coupling effect to obtain the optimal parameter combination. It is understood that other methods can also be used to achieve the mechanical parameter extraction and correction process, which are not limited here.

[0055] S105. Establish a digital twin model of the protective facility. Based on the digital twin model, standard response parameters, and 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 secondary scenario simulation, perform coupled simulation calculations, and obtain the structural stress distribution cloud map and stress concentration area coordinates.

[0056] The digital twin model represents a digital virtual mapping of actual protective facilities. Standard response parameters refer to a benchmark parameter set used to describe the mechanical properties of the structure. The initial structural response represents the initial mechanical state of the structure under the dominant scenario. Coupled simulation calculations are used to represent the calculation process that considers the interaction of multiple scenarios. The stress distribution cloud map is a visual graphic that describes the stress distribution in various parts of the structure. The stress concentration area coordinates are used to indicate the location information of critical areas with high stress values. The initial conditions represent the initial state parameters of the structure at the beginning of the simulation calculation.

[0057] After the mechanical parameters are extracted, coupled simulation calculations need to be performed. Specifically, first, an accurate digital twin model is established based on the geometric dimensions, material properties, and boundary conditions of the actual protective facilities. The extracted standard response parameters are substituted into the model, and the dominant scenario is simulated and calculated first in the order of scenario action to obtain the initial deformation and stress state of the structure. This initial state is then used as the starting condition for the simulation of the secondary scenario, and coupled calculations are performed considering the mutual influence between the scenarios. Finally, a structural stress distribution cloud map reflecting the multi-scenario coupling effect is generated, and the spatial coordinate information of the stress concentration area is extracted.

[0058] In some embodiments, coupled simulation calculations can be implemented in a variety of ways: Optionally, a sequential coupling approach can be used to first independently simulate the dominant scenario to obtain the structural response, then import the response results as initial conditions into the secondary scenario model to achieve unidirectional coupling between scenarios, and finally superimpose the calculation results of each scenario to obtain the final stress distribution. Optionally, a bidirectional coupling approach can be used to establish a unified model containing multiple scenarios, considering the interaction between scenarios at each calculation step, and achieving real-time coupling of scenario responses through iterative solution to obtain more accurate stress distribution results. It is understood that other methods can also be used to implement coupled simulation calculations of protective facilities, which are not limited here.

[0059] S106. Evaluate the structural damage state based on the coordinates of the stress concentration area, obtain the structural damage level, and generate a safety performance evaluation report for the protective facilities.

[0060] The structural damage state indicates the extent of damage and the type of failure to protective equipment. The structural damage level is a hazard level based on the degree of damage. The safety performance assessment report presents a comprehensive assessment of the safety status of protective equipment. The damage assessment index represents the parameter used to quantify the extent of damage. The structural remaining life refers to the expected duration of continued use of protective equipment. The risk level indicates the degree of risk of structural failure.

[0061] After obtaining stress distribution results, a safety performance assessment is required. Specifically, the coordinates of the stress concentration areas are used to identify key stress points. The material's ultimate strength is then used to determine whether the structure is overstressed. The stress state and deformation characteristics are then used to assess the extent of structural damage, including fatigue damage, plastic deformation, and cracking. The damage level is then graded, and the remaining service life of the structure is predicted. Finally, a safety performance assessment report is generated, including the damage state, risk level, and maintenance information.

[0062] In some embodiments, structural damage assessment can be achieved through a variety of methods: Optionally, a traditional assessment method based on static analysis theory can be used to calculate indicators such as stress level, deformation, and fatigue damage, and then determine the structural damage level in accordance with standards and regulations, generating an assessment report containing damage descriptions, risk levels, and treatment information. Alternatively, an intelligent assessment method can be used to establish a damage assessment model using machine learning algorithms. By inputting multidimensional data such as stress distribution and deformation characteristics, the model automatically identifies the damage type and extent, and generates an intelligent assessment report. It is understood that other methods can also be used to achieve the structural damage status assessment process, which is not limited here.

[0063] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , which is another flow chart of the multi-scenario coupling simulation method for highway protection based on digital twins in an embodiment of the present application.

[0064] S201. Use multiple types of sensors to collect dynamic response data of traffic protection facilities. The dynamic response data includes strain data, acceleration data, and displacement data. The dynamic response data is stored to form a scene feature library. The scene feature library includes vehicle collision scenes, wind load scenes, and seismic wave scenes.

[0065] Dynamic response data represents real-time monitoring of changes in the structural stress state. Strain data represents the time history of structural deformation. Acceleration data represents the time history of structural vibration acceleration. Displacement data represents the time history of changes in the structural spatial position. The scenario feature library is a collection of data on the structural response characteristics under different operating conditions.

[0066] In practice, the system collects dynamic response data through a network of sensors deployed at key locations within the protective structure. Specifically, strain sensors, with a sampling frequency set to 200Hz, are installed at key load-bearing locations such as columns, beams, and connection nodes to record component strain changes in real time. Triaxial accelerometers, with a sampling frequency of 100Hz, are installed at the top and center of the protective structure to monitor structural vibration characteristics. Laser displacement sensors, with a sampling frequency of 50Hz, are deployed at reference points to track structural displacement. The collected data undergoes analog-to-digital conversion and signal conditioning via a data acquisition device. After noise reduction and outlier removal, it is categorized and stored in a database according to scenario type. For vehicle collision scenarios, transient responses during the collision are recorded; for wind load scenarios, the structural response under wind load is continuously collected; and for seismic wave scenarios, the structural vibration response during earthquakes is recorded. The system integrates the processed data into a scenario feature library and establishes a data indexing mechanism to facilitate subsequent analysis and retrieval.

[0067] S202 : synchronously mapping the strain data, acceleration data, and displacement data according to the acquisition timestamps to generate a unified time axis.

[0068] The timestamp represents the precise moment of data collection. A unified time axis unifies different types of data onto a common time reference. Synchronous mapping is the process of aligning data in time.

[0069] The system performs time synchronization on the collected multi-source heterogeneous data. First, the timestamp information for each data record is extracted and uniformly converted to a standard time format (year-month-day-hour-minute-second-millisecond). Then, based on the highest sampling frequency (200Hz), the low-frequency sampling data is interpolated: for 100Hz acceleration data, one data point is inserted between adjacent sampling points using cubic spline interpolation; for 50Hz displacement data, three data points are inserted between adjacent sampling points. Interpolation ensures that the three types of data have the same time interval (5ms). Finally, the interpolated data are mapped one-to-one to a unified time axis according to the timestamp correspondence, forming an equally spaced data sequence.

[0070] S203: Mark the start and end time points of the vehicle collision scenario, wind load scenario, and earthquake wave scenario on a unified time axis.

[0071] The start and end time points represent the start and end times of a scene, and scene markers are used to distinguish different scenes on the timeline.

[0072] The system identifies the time ranges of different scenarios based on data features. For vehicle collision scenarios, the collision start time is determined by detecting the mutation point of the strain and acceleration data, and the end time is marked when the response decays to less than 5% of the initial value; for wind load scenarios, the start and end time of the wind load action is determined based on the measurement data of the anemometer; for seismic wave scenarios, the arrival and end times of the seismic waves are determined through seismic monitoring data. The system marks the time intervals of each scenario with different colors on a unified time axis: red for vehicle collision scenarios, blue for wind load scenarios, and green for seismic wave scenarios. To determine the moment when a scenario occurs, the system uses a data feature recognition method: when the instantaneous change rate of the sensor data exceeds the preset threshold, it is determined as the starting point of the scenario, and when the data returns to the normal fluctuation range, it is determined as the end point of the scenario.

[0073] S204: Calculate the intersection of the time intervals of each scene to obtain the overlapping interval of multiple scenes.

[0074] The scenario time interval represents the start and end time range of a single scenario. The intersection refers to the overlapping portion of multiple scenario time intervals. The multi-scenario overlap interval is the time period when responses from each scenario coexist.

[0075] The system performs an intersection operation on the marked completed scenario time intervals. 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 intersection interval. The specific calculation process is as follows: First, calculate the intersection of adjacent two scenarios, such as [max(t1, t3), min(t2, t4)] to obtain the overlapping interval [ta, tb] of the vehicle collision and the wind load; then intersect this result with the third scenario [max(ta, t5), min(tb, t6)], and 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.

[0076] S205. Determine the scenario action order according to the start time point of each scenario in the multi-scenario overlapping interval.

[0077] Among them, the start time point refers to the moment when the scenario starts to act. The scenario action order represents the sequence relationship of multiple scenarios occurring. The time priority refers to the scenario sorting determined according to the start time.

[0078] 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, and 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 the actual scenarios occurring.

[0079] 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.

[0080] 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.

[0081] The system accurately calculates the duration of each scene in the overlapping interval. The calculation method is: for each scene, take the difference between the timestamp of the last valid data point and the timestamp of the first valid data point in the overlapping interval to obtain the duration of the scene. The specific process is as follows: let the overlapping interval be [tc, td]. For any scene, its duration T=td-tc. The system calculates the duration of each scene separately, and expresses the calculation results in time units (seconds) and stores them as scene attributes. For example, the duration of the vehicle collision scene is T1=t2-t1, the duration of the wind load scene is T2=t4-t3, and the duration of the seismic wave scene is T3=t6-t5. These duration data are used for subsequent calculations of the impact factors of the scenes.

[0082] S207 , extracting the peak values ​​of strain data, acceleration data, and displacement data of each scene within the overlapping interval of multiple scenes to obtain a scene response amplitude.

[0083] Peak refers to the maximum absolute value in a data sequence and is used to characterize extreme fluctuations in the data. Scenario response amplitude refers to the maximum response of a structure under a specific scenario and is used to quantify the degree of stress on the structure. Strain data represents the degree of structural deformation and is collected using strain gauges. Acceleration data represents the vibration acceleration of the structure and is collected using accelerometers. Displacement data represents the displacement of the structure and is collected using displacement sensors.

[0084] For strain data within the overlapping interval, the principal strain ε1 and secondary strain ε2 are first calculated using the formula ε1,2=(εx+εy) / 2±√[(εx-εy)² / 4+γxy²], where εx and εy are strains in the orthogonal directions and γxy is the shear strain. The maximum of |ε1| and |ε2| is taken as the strain peak εmax. For acceleration data, the acceleration components ax, ay, and az in the x, y, and z directions are obtained respectively, and the maximum value of the composite acceleration is calculated using the formula amax=√(ax²+ay²+az²). For displacement data, the horizontal displacement δh and vertical displacement δv are measured respectively, and the maximum value of the resultant displacement is calculated using the formula δmax=√(δh²+δv²). These three types of peaks are combined into the scenario response amplitude vector R=[εmax, amax, δmax], which is used to characterize the maximum mechanical response of the structure under this scenario.

[0085] S208 : Calculate the product of the scene response amplitude and the corresponding duration of each scene to obtain a scene impact factor.

[0086] The scenario impact factor represents the comprehensive impact of a scenario on a structure, taking into account both response magnitude and duration. The response magnitude vector includes peak values ​​for strain, acceleration, and displacement. Duration refers to the duration of the scenario's effect within the overlapping interval. The product operation takes both response magnitude and duration into account.

[0087] First, the response amplitude vector R = [εmax, amax, δmax] is normalized to obtain the dimensionless response index R' = [ε', a', δ']. The normalization formula is: ε' = εmax / [ε], a' = amax / [a], δ' = δmax / [δ], where [ε], [a], and [δ] are the corresponding baseline values. Then, a weight coefficient vector w = [w1, w2, w3] is introduced, where w1 + w2 + w3 = 1, and the weighted response amplitude V = w1ε' + w2a' + w3δ' is calculated. The weight coefficients are determined based on the structural type: for flexible structures, w1 = 0.5, w2 = 0.3, w3 = 0.2; for rigid structures, w1 = 0.4, w2 = 0.4, w3 = 0.2. Finally, the weighted response amplitude V is multiplied by the duration T to obtain the scenario impact factor F = V × T. This method achieves a quantitative assessment of the impact of the scenario.

[0088] S209: Compare the scene influence factors of each scene, and determine the scene with the largest scene influence factor as the dominant scene.

[0089] The dominant scenario is the one with the most significant impact on the structural response. Scenario determination is the process of determining the primary and secondary relationships between scenarios. Impact factor comparison is a method of determining the importance of scenarios by numerical value.

[0090] The system determines the dominant scenario by comparing the numerical values ​​of the scenario impact factors. First, the calculated scenario impact factors are sorted from largest to smallest, generating a descending sequence [F1, F2, F3]. The system marks the scenario with the largest impact factor (max{F1, F2, F3}) as the dominant scenario and updates the scenario attribute label in the database. The specific determination process uses a direct comparison method: F1 is compared with F2, and F2 with F3, in turn, to determine the scenario corresponding to the maximum value. For example, when F1>F2>F3, scenario 1 is the dominant scenario; when F2>F1>F3, scenario 2 is the dominant scenario; and when F3>F2>F1, scenario 3 is the dominant scenario. The system records the determination results in the scenario feature library as a basis for subsequent coupling analysis.

[0091] S210: Determine a scene whose scene impact factor is lower than a preset impact threshold as a secondary scene, and obtain a scene determination result.

[0092] The preset impact threshold represents the critical value for determining scenario importance. A secondary scenario is one with a relatively small impact on the structural response. The scenario determination result is a definitive conclusion about the primary and secondary relationships of the scenarios. Impact grading refers to the classification of scenarios based on their impact factors.

[0093] The system completes the secondary judgment of the scene based on the preset threshold. First, the impact threshold is set according to the structure type: for rigid protective facilities, the threshold is set to 30% of the dominant scene impact factor; for flexible protective facilities, the threshold is set to 40% of the dominant scene impact factor. The system compares the impact factor of each scene with the threshold: when the scene impact factor is less than the threshold, the scene is marked as a secondary scene. For example, suppose the impact factor of the dominant scene is F1 and the threshold coefficient is 0.3. When Fi<0.3F1, scene i is judged to be a secondary scene. The system organizes the judgment results of the dominant and secondary scenes into structured data, which contains information such as scene type, impact factor value and scene level.

[0094] S211. Extracting mechanical parameter correction values ​​and standard response parameters from the scene feature library according to the scene determination result. The standard response parameters include prestress state values ​​and stiffness attenuation coefficients.

[0095] Mechanical parameter correction values ​​are correction factors used to adjust standard parameters. Standard response parameters refer to the baseline parameter set that describes the mechanical properties of a structure. The prestressed state value represents the initial stress level of the structure. The stiffness attenuation coefficient describes the attenuation of structural stiffness as damage evolves. The scenario feature library stores mechanical parameter data for structures under different scenarios.

[0096] The standard response parameters corresponding to the dominant scenario are extracted from the scenario feature library as baseline values, including the initial prestress state P0 and the initial stiffness coefficient K0. Correction factors are then calculated based on the influence of the secondary scenarios: the prestress correction factor is calculated using the formula αp=1+ΣFi / F1, where Fi is the influence factor of secondary scenario i and F1 is the influence factor of the dominant scenario; the stiffness correction factor is calculated using the formula αk=1-ΣFi / F1. The correction factors are applied to the standard parameters: the corrected prestress state P=αp×P0, and the corrected stiffness attenuation coefficient K=αk×K0. This correction method accounts for the influence of secondary scenarios on the structural mechanical properties, making the parameters more consistent with the actual situation under the coupling of multiple scenarios. When there are n secondary scenarios, the total correction factor is obtained by summing the influence of each scenario: α=1±Σ(Fi / F1), where "+" is for prestress correction and "-" is for stiffness correction.

[0097] S212. Establish a digital twin model of the protective facility. Based on the digital twin model, standard response parameters, and 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 secondary scenario simulation, perform coupled simulation calculations, and obtain the structural stress distribution cloud map and stress concentration area coordinates.

[0098] The digital twin model refers to a digital, virtual representation of the protective equipment. The initial response represents the structural response under the dominant scenario. Coupled simulation refers to a numerical analysis that considers scenario interactions. The stress distribution cloud map visualizes the structural stress field. The coordinates of the stress concentration area indicate locations with high stress levels.

[0099] The system performs coupled analysis calculations based on digital twins. First, an accurate digital model containing geometric features, material properties and boundary conditions is established. The corrected prestressed state value and stiffness attenuation coefficient are substituted into the model, and the analysis is performed in the order of the scenarios: 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 initial conditions, and superimpose the effects of the secondary scenarios for coupled calculations. The system uses an explicit dynamic analysis method to solve the coupling problem, with a time step of 0.001s, and obtains the final stress distribution through iterative calculations. The system generates a stress cloud 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.

[0100] S213. Evaluate the structural damage state based on the coordinates of the stress concentration area, obtain the structural damage level, and generate a safety performance evaluation report for the protective facilities.

[0101] The structural damage state indicates the extent of damage and the type of failure to protective equipment. The damage level refers to the hazard level based on the severity of the damage. The safety performance assessment report is a systematic assessment document of the structural safety status. The stress damage index indicates the degree of structural damage caused by stress levels. The fatigue damage index indicates the cumulative damage to the structure under cyclic loading. The residual bearing capacity index indicates the residual bearing capacity of the structure.

[0102] 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 material strength limit σu is used as the stress damage index Ds = σmax / σu; calculate the fatigue damage index: Using Miner's 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 ultimate bearing capacity analysis. The system determines the damage level according to the comprehensive score of these three indicators: When max(Ds, Df) < 0.3 and Dr > 0.9, it is judged as minor damage; when 0.3 ≤ max(Ds, Df) < 0.7 and 0.7 < Dr ≤ 0.9, it is judged as moderate damage; when max(Ds, Df) ≥ 0.7 or Dr ≤ 0.7, it is judged as severe damage. The system generates an assessment report containing the following content: basic information of the structure, analysis of test data, assessment of damage status, analysis of bearing capacity, safety rating for use, and information on repair and reinforcement. The assessment report adopts a hierarchical display method, uses different warning signs for different damage levels, and gives corresponding disposal information.

[0103] 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.

[0104] S301. Perform mesh division on the digital twin model according to the dynamic response data to obtain a finite element analysis mesh.

[0105] 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.

[0106] The specific process of the system's meshing is as follows: first, the deformation characteristic area of ​​the structure is determined based on the dynamic response data, and a denser mesh is used in areas with larger deformation, and a sparser mesh is used in areas with smaller deformation. Hexahedral elements are used for the columns of the protective facilities, shell elements are used for the beams, and transition elements are used for the connections. The mesh size is determined based on the characteristic dimensions of the structure: for a column with a cross-sectional size of 200mm, the mesh size is set to 20mm; for a beam plate with a thickness of 5mm, the mesh size is set to 10mm. The system uses a mapped meshing method to generate a regular mesh and a free meshing method to handle irregular areas, ultimately generating finite element analysis mesh data containing node coordinates and unit connection relationships.

[0107] S302 , setting the initial stress state of the structure according to the prestressed state value, substituting the dynamic response data of the dominant scenario into the finite element analysis grid, and calculating the stress value of each grid node of the finite element analysis grid.

[0108] The prestressed state value represents the initial stress level of a structure before external loads are applied. The initial stress state refers to the stress distribution before the structure deforms. Dynamic response data substitution involves applying measured dynamic response values ​​to the finite element model. Stress calculation involves solving for the stress components at each mesh node.

[0109] The system first sets the initial stress field based on the prestressed state value: the prestressed values ​​are applied to the corresponding nodes according to the structure's assembly sequence, and the stress equilibrium calculation is performed using an explicit integration method. The dynamic response data of the dominant scenario is then substituted into the mesh nodes: for displacement responses, node displacement constraints are directly assigned; for acceleration responses, these are converted into node inertia forces; and for strain responses, these are converted into node deformations. The system uses an explicit dynamic analysis method to solve for node stresses: for any node i, its stress component σij is calculated using the element stress-strain relationship: σij = Dijkl·εkl, where Dijkl is the elastic constant tensor and εkl is the strain component. The stress history of each node throughout the response process is obtained through time-domain integration.

[0110] S303 , correcting the stress value of each grid node based on the stiffness attenuation coefficient to obtain a corrected stress value.

[0111] The stiffness degradation coefficient describes the attenuation of structural stiffness as damage evolves. Nodal stress correction refers to the stress recalculation process that accounts for the effects of stiffness degradation. The corrected stress value refers to the nodal stress after the stiffness correction.

[0112] The system performs stress correction as follows: First, the initial stress value σ0 at each node is extracted and the stiffness attenuation coefficient α is substituted 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 then performs stress correction calculations on all nodes in the mesh, generating a corrected stress field that accounts for stiffness degradation. The correction results are stored as tensors containing stress components in all directions. This method accounts for the effects of structural damage evolution on stress distribution.

[0113] S304: Render the corrected stress values ​​in partitions according to the contour line principle to generate a structural stress distribution cloud map.

[0114] Corrected stress values ​​refer to the nodal stress tensor after stiffness correction. The contour principle connects points with the same value to form contour lines. Zonal rendering is the process of color-coding different regions based on stress values. A structural stress distribution cloud is a color-coded graphic that displays the stress distribution across various parts of a structure.

[0115] The system generates stress contours as follows: First, the stress display components are determined. For isotropic materials, the von Mises equivalent stress σe = √[(σ1-σ2)² + (σ2-σ3)² + (σ3-σ1)²] / 2 is used, while for anisotropic materials, the principal stress σp is used. Next, the stress level is set, evenly dividing the stress range [σmin, σmax] into 10 intervals. Each interval is assigned a different color code: the minimum stress interval is blue (RGB: 0, 0, 255), the maximum stress interval is red (RGB: 255, 0, 0), and the intermediate intervals are linearly interpolated to generate transition colors based on the stress values. The system determines the interval to which each node belongs based on its stress value, assigns the corresponding color code to the node, and uses an interpolation algorithm to fill the internal elements with gradient colors, ultimately generating a continuous and smooth stress distribution contour.

[0116] 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 of a target area in the structural stress distribution cloud map exceeds the stress warning threshold, mark the target area with a warning mark.

[0117] The allowable stress value refers to the maximum safe stress that a structural material can withstand. The stress warning threshold is the critical stress value that triggers a warning. The target area refers to the structural area that requires key monitoring. The warning mark is a warning symbol used to mark areas that exceed the limit.

[0118] The system performs warning marking as follows: First, the warning level is determined based on the allowable stress [σ] of the structural material, and three warning thresholds are set: 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 then scans the corrected stress value σm of each node in the stress cloud map. When σw1 ≤ σm < σw2, a yellow semi-transparent layer is superimposed on the area and labeled with the number "1"; when σw2 ≤ σm < σw3, an orange semi-transparent layer is superimposed and labeled with the number "2"; and when σm ≥ σw3, a red semi-transparent layer is superimposed and labeled with the number "3". For the warning area, the system automatically extracts its boundary contour coordinates and generates warning information data including the warning level, location range, and stress value. The warning label uses a striking font style to ensure clear visibility on the stress cloud map.

[0119] S306. Layer the structural stress distribution cloud map according to the dominant scenario and the secondary scenario to obtain a layered stress distribution map.

[0120] Layered stress distribution is the process of displaying stress distributions generated by different scenarios separately. A layered stress distribution diagram is a collection of layers representing the independent stress distributions for each scenario. The dominant scenario is the one with the most significant impact on the structure. The secondary scenario is the one with a relatively minor impact.

[0121] The specific process of the system's stress layering is as follows: first, a multi-layer layer structure is established, with the bottom layer being the structural geometry model, and the upper layers being the dominant scenario stress layer and the secondary scenario stress layer, respectively. For the dominant scenario, the node stress values ​​σp under its independent action are extracted to generate the primary scenario stress cloud map; for each secondary scenario i, the node stress values ​​σsi under its independent action are 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 (set to 0.8 for the primary scene layer and 0.6 for the secondary scene layer). The system configures independent display control for each layer, and flexible switching and display of stress distributions in different scenarios is achieved through layer switch combinations.

[0122] S307. Calculate the stress contribution value of each scene according to the layered stress distribution diagram, and determine the stress superposition coefficient.

[0123] The stress contribution value refers to the stress generated by each scenario independently. The stress superposition coefficient represents the stress enhancement effect of multiple scenarios coupled together. Scenario-independent stress refers to the stress response under a single scenario. A 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.

[0124] For each node j, the stress tensors for each scenario layer are extracted: the dominant scenario stress tensor σp = [σxx σxy σxz; σyx σyy σyz; σzx σzy σzz]p, and the stress tensor σsi for the secondary scenario i is σsi. The norm of the stress tensor is calculated using the Frobenius norm: ||σ|| = √(Σi, jσij²). The stress contribution of node j is calculated by normalization: the dominant scenario contribution Cpj = ||σpj|| / Σ||σij||, and the contribution of the secondary scenario i, Csij = ||σsij|| / Σ||σij||. The stress contribution of the entire structure is calculated using the stress-weighted average: Cp = ΣwjCpj, Csi = ΣwjCsij, with the weight coefficient wj = ||σj|| / Σ||σk||. The stress superposition factor is determined by the ratio of the coupled stress to the sum of the independent stresses: α = ||σc|| / Σ||σi||, where σc is the total stress tensor obtained from the coupled calculation. For anisotropic materials, the stress components along the principal axes of the material must also be considered.

[0125] S308. Perform stress decomposition on the stress concentration area based on the stress superposition coefficient to identify the contribution of each scenario to the structural damage.

[0126] Stress decomposition is the process of distributing total stress among various scenarios. Stress concentration areas are localized regions of high stress levels. The contribution level indicates the proportional impact of each scenario on structural damage. Structural damage refers to the degree of loss in a component's load-bearing capacity. The allowable stress is the maximum stress a material can safely withstand.

[0127] In the stress concentration region, the total node stress σt and the superposition coefficient α are extracted. Stress decomposition is performed for node i: the dominant scenario stress component σpi = α·Cpi·σti, and the secondary scenario j stress component σsji = α·Csji·σti. Damage contributions are calculated based on the von Mises failure criterion: the dominant scenario damage contribution Dp = Σ[(σpi, eq / [σ])^m], and the secondary scenario j damage contribution 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. Damage contributions are normalized: dp = Dp / ΣDk × 100%, and dsj = Dsj / ΣDk × 100%. For fatigue damage, Miner linear cumulative damage theory is adopted: D=Σ(ni / Ni), where ni is the actual number of cycles and Ni is the allowable number of cycles under stress level σi.

[0128] S309. Establish a stress reduction strategy library based on stress contribution value, contribution degree and scenario action sequence, and generate structural reinforcement solutions for different scenario combinations.

[0129] The stress reduction strategy library is a collection of methods for reducing the structural stress level. The structural reinforcement plan refers to the specific measures to improve the structural bearing capacity. 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.

[0130] The specific process for the system to execute the generation of the reinforcement plan is as follows: First, the scenarios are classified 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, the reinforcement measures are determined according to the contribution degree: For the dominant scenario with dp>60%, local reinforcement measures such as strengthening the cross-section of the member and adding stiffeners are adopted; for the secondary scenario with 30%<dsj≤60%, overall reinforcement measures such as adding supports and adjusting constraints are adopted; for the secondary scenario with dsj≤30%, auxiliary reinforcement measures such as optimizing the construction details and improving the connection are adopted. 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.

[0131] S310. Compare the stress distribution nephogram with the actual monitoring data in real time and establish a stress trend prediction model.

[0132] The stress distribution nephogram refers to the color graph representing the structural stress distribution. The actual monitoring data is the stress measurement value 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.

[0133] Pair the node stress value σc in the stress nephogram with the monitoring 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 the 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 k - order autocovariance. Solve the model parameters using the maximum likelihood estimation method. 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.

[0134] S311. Calculate the stress evolution rate based on the stress trend prediction model and determine the warning time window according to the stress evolution rate.

[0135] 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.

[0136] 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 follows: 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.

[0137] 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.

[0138] 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 specifies the routes and procedures for the emergency evacuation of personnel. The equipment risk avoidance measures refer to the specific methods for protecting important equipment.

[0139] 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 once every 5 minutes, 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 procedures. The personnel evacuation plan specifies 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.

[0140] 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.

[0141] Warning identification areas refer to structural areas marked with warning symbols in stress cloud maps. Excessive stress values ​​refer to stress values ​​exceeding the warning threshold. The warning area range represents the spatial location and geometric dimensions of the warning area. The stress growth rate refers to the amount of stress increase per unit time. The stress warning report is a standardized document that records warning information.

[0142] The system generates early warning reports as follows: First, it extracts node data for the warning area, including node numbers, spatial coordinates, and stress values. For each 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 extent of the warning area [xmin, xmax] × [ymin, ymax] × [zmin, zmax]; and the stress growth rate vi = (σi, t - σi, t - Δt) / Δt, where Δt is the sampling interval. The system organizes this data into a warning report in a unified format. The report structure includes basic information (time, location, warning level), stress data (over-limit value, growth rate), spatial information (area extent, key nodes), and warning information. The report is stored in a data table format to facilitate subsequent analysis and query.

[0143] S314. Record the stress evolution data during the warning process and store it in the stress warning event database.

[0144] Stress evolution data refers to a complete record of stress changes over time during the warning process. A stress warning event refers to the entire process of a single warning. The warning event database is a structured database that stores historical warning information. Data storage is the process of saving data in a specific format.

[0145] The system's specific process for recording early warning data involves establishing an early warning event data table containing fields such as event number, occurrence time, warning level, and structural location. The system records the following data regarding stress evolution during the early warning process: stress-time curves {σ(t), t∈[t0, te]}, where t0 is the warning start time and te is the warning end time; stress derivative curves {dσ / dt, t∈[t0, te]}; and cumulative damage value D(t) = ∫[σ(t) / [σ]]²dt. The system organizes this data into a relational database structure: an event table (EventID, Time, Level, Location); a stress table (EventID, NodeID, Time, Stress); and a rate table (EventID, NodeID, Time, Rate). The database supports retrieval based on time, location, and warning level, enabling statistical analysis and historical review. Each early warning record contains the complete stress evolution process, providing data support for subsequent optimization of early warning strategies.

[0146] The following describes the multi-scenario mechanical performance coupling simulation optimization system in the embodiment of the present invention from the perspective of hardware processing. Figure 4 , which is a schematic diagram of the physical device structure of the multi-scenario mechanical performance coupling simulation optimization system in an embodiment of the present application.

[0147] It should be noted that Figure 4 The structure of the multi-scenario mechanical performance coupling simulation optimization system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0148] like Figure 4 As shown, the multi-scenario mechanical performance coupling simulation and optimization system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403, such as executing the methods described in the above embodiments. RAM 403 also stores various programs and data required for system operation. CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0149] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, push button switches, and the like; an output section 407 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read from the removable media can be installed in the storage section 408 as needed.

[0150] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409 and / or installed from removable media 411. When executed by central processing unit (CPU) 401, the computer program performs the various functions defined in the present invention.

[0151] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0153] Specifically, the multi-scenario mechanical performance coupling simulation optimization system of this embodiment includes a processor and a memory. A computer program is stored in the memory. When the computer program is executed by the processor, the multi-scenario coupling simulation method for highway protection based on digital twins provided in the above embodiment is implemented.

[0154] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the multi-scenario mechanical performance coupling simulation and optimization system described in the above embodiments, or may exist independently and not be incorporated into the multi-scenario mechanical performance coupling simulation and optimization system. The storage medium carries one or more computer programs, which, when executed by a processor of the multi-scenario mechanical performance coupling simulation and optimization system, enable the multi-scenario mechanical performance coupling simulation and optimization system to implement the multi-scenario coupling simulation method for highway protection based on digital twins provided in the above embodiments.

[0155] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although detailed descriptions have been made with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, 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 embodiments of the present application.

[0156] As used in the above embodiments, the term “when” may be interpreted as “if” or “after” or “in response to determining” or “in response to detecting”, depending on the context.

[0157] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A multi-scenario coupling simulation method for highway protection based on digital twins, characterized in that: Applied to a multi-scenario mechanical performance coupling simulation optimization system, the method includes: Using multiple types of sensors to collect dynamic response data of traffic protection facilities, the dynamic response data includes strain data, acceleration data, and displacement data, and storing the dynamic response data to form a scene feature library, the scene feature library including vehicle collision scenarios, wind load scenarios, and seismic wave scenarios; Synchronously projecting the dynamic response data onto a unified time axis, identifying a multi-scenario overlapping interval, and determining the scene action sequence and duration of the multi-scenario overlapping interval, where the multi-scenario overlapping interval is a temporal intersection area of ​​the response data of each scenario; Determine the dominant scene and the secondary scene in the overlapping interval of the multiple scenes according to the scene action sequence, duration, and scene response amplitude, and obtain a scene determination result, wherein the scene response amplitude refers to the maximum response value of the structure under the action of a certain scene; Extracting mechanical parameter correction values ​​and standard response parameters from the scene feature library according to the scene determination result, wherein the standard response parameters include a prestress state value and a stiffness attenuation coefficient; Establish a digital twin model of the protective facility, simulate and calculate the initial response of the structure under the dominant scenario based on the digital twin model, the standard response parameters, and the scenario action sequence, use the initial response as the initial condition for the secondary scenario simulation, perform coupled simulation calculations, and obtain a structural stress distribution cloud map and stress concentration area coordinates; The structural damage state is evaluated according to the coordinates of the stress concentration area, the structural damage level is obtained, and a safety performance evaluation report of the protective facilities is generated.

2. The method according to claim 1, characterized in that The step of synchronously projecting the dynamic response data onto a unified time axis, identifying a multi-scene overlapping interval, and determining the scene action sequence and duration of the multi-scene overlapping interval specifically includes: Synchronously mapping the strain data, the acceleration data, and the displacement data according to acquisition timestamps to generate a unified time axis; Marking the start and end time points of the vehicle collision scenario, the wind load scenario, and the earthquake wave scenario on the unified time axis; Calculating the intersection of the time intervals of each scene to obtain the overlapping interval of the multiple scenes; Determining the scene action order according to the starting time point of each scene in the overlapping interval of the multiple scenes; The duration of each scene in the overlapping interval of the multiple scenes is calculated to obtain the duration.

3. The method according to claim 1, characterized in that The step of determining the dominant scene and the secondary scene in the overlapping interval of the multiple scenes according to the scene action sequence, duration, and scene response amplitude to obtain the scene determination result specifically includes: Extracting the peak values ​​of strain data, acceleration data, and displacement data of each scene within the overlapping interval of the multiple scenes to obtain a scene response amplitude; Calculating the product of the scene response amplitude and the corresponding duration of each scene to obtain a scene impact factor; Comparing the scene influence factors of each of the scenes, and determining the scene with the largest scene influence factor as the dominant scene; The scenes whose scene impact factors are lower than the preset impact threshold are determined as secondary scenes to obtain a scene determination result.

4. The method according to claim 1, wherein 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 protective facilities, the method further includes: Meshing the digital twin model according to the dynamic response data to obtain a finite element analysis mesh; Setting an initial stress state of the structure according to the prestressed state value, substituting the dynamic response data of the dominant scenario into the finite element analysis grid, and calculating the stress value of each grid node of the finite element analysis grid; Correcting the stress value of each grid node based on the stiffness attenuation coefficient to obtain a corrected stress value; The modified stress values ​​are partitioned and rendered according to the isoline principle to generate a structural stress distribution cloud map; A stress warning threshold is set based on the allowable stress value of the structural material. When it is detected that the corrected stress value of a target area in the structural stress distribution cloud map exceeds the stress warning threshold, a warning mark is marked on the target area.

5. The method according to claim 4, characterized in that In the step of setting a stress warning threshold based on the allowable stress value of the structural material, when it is detected that a corrected stress value of a target area in the structural stress distribution cloud map exceeds the stress warning threshold, after marking a warning mark on the target area, the method further includes: Layering the structural stress distribution cloud map according to the dominant scenario and the secondary scenario to obtain a layered stress distribution map; Calculating the stress contribution value of each of the scenarios according to the layered stress distribution diagram, and determining a stress superposition coefficient; Performing stress decomposition on the stress concentration area based on the stress superposition coefficient to identify the contribution of each scenario to structural damage; A stress reduction strategy library is established according to the stress contribution value, the contribution degree and the scenario action sequence, and structural reinforcement solutions for different scenario combinations are generated.

6. The method according to claim 4, characterized in that 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 protective facilities, the method further includes: Comparing the stress distribution cloud map with actual monitoring data in real time to establish a stress trend prediction model; Calculating a stress evolution rate based on the stress trend prediction model, and determining a warning time window according to the stress evolution rate; A hierarchical response strategy is formulated according to the warning time window, and the hierarchical response strategy includes emergency disposal instructions, personnel evacuation plans and equipment risk avoidance measures.

7. The method according to claim 4, characterized in that 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 protective facilities, the method further includes: generating a stress warning report according to the warning identification area in the stress distribution cloud map, wherein the stress warning report includes an excessive stress value, a warning area range, and a stress growth rate; The stress evolution data during the warning process is recorded and stored in the stress warning event database.

8. A multi-scenario mechanical performance coupling simulation optimization system, characterized in that: The multi-scenario mechanical performance 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 performance coupling simulation optimization system to execute the method described in any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a multi-scenario mechanical performance coupling simulation optimization system, the multi-scenario mechanical performance coupling simulation optimization system executes the method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a multi-scenario mechanical performance coupling simulation optimization system, the multi-scenario mechanical performance coupling simulation optimization system executes the method according to any one of claims 1 to 7.

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