A durability optimization method for highway traffic protection facilities based on digital twin

Abstract

A digital twin-based durability optimization method for highway traffic protection facilities, involving the field of electronic digital data processing, involves: standardizing environmental parameters and performing a weighted calculation with the damage contribution coefficient to determine the environmental stress level; analyzing the temporal variation characteristics of the environmental stress level to determine the migration path and accumulation area; categorizing differentiated protection levels based on the stress intensity distribution within the accumulation area; and using numerical simulation methods to determine coating parameters. Implementing this method can extend the service life of highway traffic protection facilities.

Description

Technical Field

[0001] The present application relates to the field of electrical digital data processing, and in particular to a durability optimization method for highway traffic protection facilities based on digital twins. Background Art

[0002] With the rapid development of my country's transportation infrastructure, highway traffic protection facilities are playing an increasingly important role in ensuring driving safety. These facilities are exposed to complex and changing natural environments for long periods of time, facing the test of various environmental factors such as temperature, humidity, wind speed, and precipitation. Their durability is directly related to road operation safety and maintenance costs.

[0003] Currently, the anti-corrosion protection of highway traffic protection facilities mainly uses a unified standard anti-corrosion coating. This method is to apply a certain thickness of anti-corrosion coating to the surface of the facilities and apply it according to a fixed coating formula ratio in order to achieve the purpose of corrosion prevention and extend the service life.

[0004] However, due to the diversity of geographical environments and meteorological conditions, a standardized anti-corrosion treatment solution is difficult to adapt to the differentiated protection needs brought about by environmental factors in different regions. Especially in areas with drastic changes in environmental conditions, anti-corrosion coatings are prone to localized performance degradation, which significantly reduces the service life of protective equipment and increases maintenance and replacement costs. Summary of the Invention

[0005] This application provides a durability optimization method for highway traffic protection facilities based on digital twins, which is used to improve the service life of highway traffic protection facilities.

[0006] In a first aspect, the present application provides a road traffic protection facility durability optimization method based on digital twins, which is applied to a road traffic protection facility durability optimization system. The method includes:

[0007] Real-time data of environmental parameters are obtained through multiple monitoring points, and the real-time data of environmental parameters include temperature, humidity, wind speed and precipitation; the gradient values ​​of environmental parameters between the monitoring points are calculated, and the environmental stress degree is calculated based on the real-time data of environmental parameters. The environmental stress degree is obtained by weighted calculation of the damage contribution coefficient corresponding to the standardized environmental parameters, and the damage contribution coefficient is determined based on statistical analysis of historical damage data; the time-series variation characteristics of the environmental stress degree of each adjacent monitoring point are analyzed to determine the migration path and accumulation area of ​​the environmental stress, and the time-series variation characteristics of the environmental stress degree include the stress intensity change rate, migration speed and accumulation time; based on the stress intensity distribution in the accumulation area, the anti-corrosion coating on the highway traffic protection facilities is divided into differentiated protection levels; the anti-corrosion coating thickness parameters and composition ratio parameters of the anti-corrosion coating areas with different protection levels are determined by numerical simulation methods, and the anti-corrosion coating thickness parameters and the composition ratio parameters are sent to the target client.

[0008] In the above embodiment, the environmental parameters are standardized and then weightedly calculated with the damage contribution coefficient to obtain the environmental stress degree. The temporal variation characteristics of the environmental stress degree are analyzed to determine the migration path and the cumulative area. Different protection levels are divided based on the stress intensity distribution in the cumulative area. The coating parameters are determined by numerical simulation methods, forming a complete set of protection parameter optimization processes, achieving precise matching of protective coating performance and maximization of resource utilization, effectively extending the service life of the protection facilities, and reducing maintenance costs.

[0009] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the environmental parameter gradient value between the monitoring points and calculating the environmental stress degree based on the real-time data of the environmental parameters specifically includes: calculating the environmental parameter gradient value of the real-time data of the environmental parameters between adjacent monitoring points; calculating the standardized environmental parameters based on the historical maximum and minimum values ​​corresponding to the real-time data of the environmental parameters; extracting the correspondence between each of the environmental parameters and the damage records of the protective facilities from the historical damage database, and obtaining the damage contribution coefficient of each of the environmental parameters based on the damage frequency statistics; multiplying the standardized environmental parameter by the corresponding damage contribution coefficient and summing them to obtain the environmental stress degree.

[0010] In the above embodiment, the calculated environmental parameter gradient value reflects the spatial variation characteristics of the environmental factors, the historical maximum and minimum values ​​are used for standardization to eliminate the dimensional effect, the damage contribution coefficient obtained based on the damage frequency statistics reflects the actual impact of each environmental factor, the standardized parameters are combined with the contribution coefficient to calculate the environmental stress degree, and a quantitative assessment model for environmental impact is established, thereby improving the accuracy and reliability of the environmental stress degree calculation.

[0011] In combination with some embodiments of the first aspect, in some embodiments, the step of dividing the differentiated protection levels of the anti-corrosion coating on the highway traffic protection facilities based on the stress intensity distribution of the cumulative area specifically includes: calculating the average value and variation coefficient of the environmental stress degree of each point in the cumulative area; dividing the cumulative area into different stress intensity sub-areas according to the size relationship of the average value of the environmental stress degree; analyzing the environmental stress degree variation coefficient within each stress intensity sub-area, merging adjacent sub-areas with the same range of the environmental stress degree variation coefficient to obtain merged sub-areas; and dividing the anti-corrosion coating of the highway traffic protection facilities into different protection level areas according to the distribution of the merged sub-areas.

[0012] In the above embodiment, the average value and coefficient of variation of the environmental stress degree are calculated to characterize the overall level and fluctuation characteristics of the stress intensity. The stress intensity sub-regions are divided according to the size of the average value to achieve preliminary zoning. The coefficient of variation is analyzed and similar sub-regions are merged to optimize the zoning results. The anti-corrosion coating is divided into areas with different protection levels, and a scientific and reasonable differentiated protection zoning system is constructed, which improves the targetedness and economy of the protection plan.

[0013] In combination with some embodiments of the first aspect, in some embodiments, the step of using a numerical simulation method to determine the anti-corrosion coating thickness parameters and composition ratio parameters of areas with different protection levels specifically includes: obtaining the environmental stress degree of areas with different protection levels and the service life requirements of the highway traffic protection facilities; calculating the coating corrosion rate of each protection level area based on the environmental stress degree; calculating the anti-corrosion coating thickness parameters required for each protection level area based on the coating corrosion rate and the service life requirements; and determining the composition ratio parameters based on the anti-corrosion coating thickness parameters.

[0014] In the above embodiment, the calculation of the coating corrosion rate based on the environmental stress degree reveals the wear pattern of the coating caused by the environment. The determination of the anti-corrosion coating thickness parameters in combination with the service life requirements reflects the performance guarantee requirements during the service period. The optimization of the component ratio according to the coating thickness parameters ensures the matching of material properties, realizes the precise control of the protective performance and service life, and improves the protective effect of the coating.

[0015] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the anti-corrosion coating thickness parameter and the component ratio parameter to the target client, the method also includes: obtaining terrain data and water system data of the area where the highway traffic protection facility is located; calculating the surface runoff channel based on the terrain data, and calculating the pollutant migration path based on the water system data and the surface runoff channel; calculating the cumulative concentration of pollutants along the pollutant migration path, converting the cumulative concentration of pollutants into an environmental stress increment, and superimposing the environmental stress increment with the environmental stress to obtain a regional stress degree; dividing the protection level partition according to the regional stress degree, calculating the coating technical parameters of the protection level partition and sending them to the target client.

[0016] In the above embodiment, the surface runoff channels calculated using terrain data and water system data reflect the characteristics of water flow movement, the pollutant migration paths determined based on the runoff channels reflect the propagation laws of environmental factors, the cumulative concentrations of pollutants are converted into environmental stress increments and superimposed to obtain the regional stress, a complete environmental impact propagation evolution model is constructed, and the dynamic changes in the environmental pressure on protective facilities are accurately predicted.

[0017] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the anti-corrosion coating thickness parameter and the component ratio parameter to the target client, the method also includes: receiving weather forecast data, and calculating the predicted environmental stress degree based on the weather forecast data; comparing the predicted environmental stress degree with the environmental stress degree to obtain a stress degree trend graph, and determining the protection zone boundary based on the stress degree trend graph; calculating the coating technical parameters within the protection zone boundary, and sending the coating technical parameters to the target client.

[0018] In the above embodiment, the received meteorological forecast data is used to calculate and predict the environmental stress level, which reflects the future trend of environmental changes. The comparative analysis results in a stress level trend graph that shows the evolution of environmental pressure. The protection zone boundaries are determined based on the trend graph and the coating technical parameters are calculated, forming an active protection mechanism based on environmental prediction, thereby enhancing the adaptability and foresight of the protection plan to environmental changes.

[0019] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the anti-corrosion coating thickness parameter and the component ratio parameter to the target client, the method also includes: extracting the usage environment data of the highway traffic protection facility, and calculating the material fatigue index of the highway traffic protection facility based on the usage environment data; analyzing the acceleration effect of environmental stress on material fatigue, and predicting the remaining service life of the highway traffic protection facility based on the material fatigue index and the acceleration effect; determining the maintenance time window of the highway traffic protection facility based on the remaining service life, and dividing the maintenance priority of the highway traffic protection facility.

[0020] In the above embodiment, the material fatigue index is calculated based on the usage environment data to reflect the performance degradation state of the protective facilities, and the acceleration effect of environmental stress on material fatigue is analyzed to reveal the influence mechanism of environmental factors. The remaining service life is predicted based on the fatigue index and the acceleration effect, the maintenance time window is determined and the maintenance priority is divided, which optimizes the maintenance resource allocation and ensures the service performance of the protective facilities.

[0021] In the second aspect, an embodiment of the present application provides a highway traffic protection facility durability 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 highway traffic protection facility durability optimization system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0022] 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 highway traffic protection facility durability optimization system, the above-mentioned highway traffic protection facility durability optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] 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 highway traffic protection facility durability optimization system, the highway traffic protection facility durability optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0024] It is understood that the highway traffic protection facility durability 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 achievable by these methods can be referenced to the beneficial effects of the corresponding methods and will not be further elaborated here.

[0025] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0026] 1. This application obtains the environmental stress degree by weighted calculation after standardizing the environmental parameters and the damage contribution coefficient, analyzes the temporal variation characteristics of the environmental stress degree to determine the migration path and accumulation area, divides the differentiated protection levels based on the stress intensity distribution in the accumulation area, and uses numerical simulation methods to determine the coating parameters, forming a complete set of protection parameter optimization processes, achieving accurate matching of protective coating performance and maximization of resource utilization, effectively extending the service life of protective facilities and reducing maintenance costs.

[0027] 2. This application reflects the spatial variation characteristics of environmental factors by calculating the gradient values ​​of environmental parameters, eliminates the dimensional influence by standardization using historical maximum and minimum values, obtains the damage contribution coefficient based on damage frequency statistics to reflect the actual impact of each environmental factor, combines the standardized parameters with the contribution coefficient to calculate the environmental stress degree, establishes a quantitative assessment model for environmental impacts, and improves the accuracy and reliability of environmental stress calculations.

[0028] 3. This application reflects the characteristics of water flow movement by using terrain data and water system data to calculate surface runoff channels. The pollutant migration paths determined based on runoff channels reflect the propagation laws of environmental factors. The cumulative concentration of pollutants is converted into environmental stress increments and superimposed to obtain regional stress. A complete environmental impact propagation evolution model is constructed to accurately predict the dynamic changes in the environmental pressure on protective facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a method for optimizing the durability of highway traffic protection facilities based on digital twins in an embodiment of the present application;

[0030] Figure 2 This is another flow chart of the durability optimization method for highway traffic protection facilities based on digital twins in an embodiment of the present application;

[0031] Figure 3 It is a schematic diagram of the structure of a physical device of the highway traffic protection facility durability 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. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present 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] A 120-kilometer-long expressway spans mountainous, plain, and coastal terrain. The mountainous section experiences frequent rain and fog, with an average annual precipitation of 1,500 mm and a relative humidity of 85%. The plain section experiences higher temperatures, with an average annual temperature of 25°C and a diurnal temperature fluctuation of 15°C. The coastal section has a high concentration of salt ions and an average annual wind speed of 6 m / s. Over 3,000 sets of guardrails have been installed along the entire line, employing a hot-dip galvanizing and anti-corrosion coating solution. During operation, it was discovered that the coating in the mountainous section frequently cracked and peeled, the surface of the coating in the plain section showed severe powdering, and the bottom of the coastal section showed significant rust. On-site testing revealed that the base metal in the mountainous section showed pitting corrosion up to 1.2 mm deep at the cracks. The coating thickness in the plain section had lost over 45%, and the rusted area in the coastal section accounted for over 30% of the surface area. These localized degradations significantly shortened the service life of the protective equipment, which averaged only 65% ​​of its design life. Furthermore, due to the scattered locations of damage, repair and replacement costs were high, with annual maintenance costs exceeding 2 million yuan. This reflects that anti-corrosion solutions with unified standards are difficult to adapt to the environmental characteristics of different regions.

[0036] Existing technology employs an anti-corrosion coating with a fixed formulation and thickness applied to protective structures. For example, the guardrails on a certain highway are coated with a three-layer coating system consisting of an epoxy zinc-rich primer (80μm), an epoxy micaceous iron midcoat (120μm), and a polyurethane topcoat (50μm), for a total coating thickness of 250μm. The primer contains 75% zinc powder, the midcoat contains 35% iron mica, and the topcoat contains 60% polyurethane resin. Application is carried out according to standardized spraying procedures: surface rust removal grade Sa2.5, a single coat of primer, two coats of midcoat, and a single coat of topcoat, with a 24-hour interval between each coat. However, in actual use, the rainy environment in mountainous areas weakens the adhesion between the coating and the substrate, causing cracking; the high temperature in plains accelerates resin aging and causes coating powdering; and chloride ions in marine environments penetrate the coating and reach the substrate, causing corrosion. Because these environmental differences are not considered, the standardized coating system exhibits different failure modes in different regions and cannot be optimized based on environmental characteristics, resulting in suboptimal protection.

[0037] Following this proposed solution, environmental monitoring stations were first installed every 2 kilometers along the highway to collect real-time data on temperature, humidity, wind speed, and precipitation. Calculations revealed that environmental stress levels in mountainous areas increased by 50% during the rainy season, migrating at a rate of 3 km / day. Stress levels in plain areas fluctuated significantly during the day and night, peaking at 2 p.m. Coastal areas exhibited a decreasing stress gradient from sea to land, with the accumulation zone primarily located within 0.5 meters of the bottom of the guardrail. Based on these characteristics, the system divided the protective facilities into five protection levels, each receiving a different coating formulation: increasing the curing agent content in mountainous areas to improve crosslinking density; adding an anti-aging agent to improve thermal stability in plain areas; and increasing the zinc powder content in coastal areas to enhance cathodic protection. Coating thickness was also adjusted accordingly: increasing to 350μm in mountainous areas, maintaining 250μm in plain areas, and increasing to 400μm at the bottom of the coastal area. Follow-up testing one year after implementation showed that the coating integrity rate had increased to 95%, annual maintenance costs had decreased by 40%, and the projected service life was expected to exceed 90% of the design value, fully demonstrating the superiority of the differentiated protection solution.

[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 the durability optimization method of highway traffic protection facilities based on digital twins in an embodiment of the present application.

[0039] S101. Acquire real-time data of environmental parameters through multiple monitoring points. The real-time data of environmental parameters include temperature, humidity, wind speed, and precipitation.

[0040] Monitoring points represent the installation locations of environmental parameter collection equipment deployed around highway traffic protection facilities. Real-time environmental parameter data refers to the physical quantity values ​​collected by sensors that characterize environmental conditions. Temperature indicates the degree of hotness or coldness of the air, measured in degrees Celsius or Kelvin. Humidity indicates the water vapor content in the air, expressed as a relative humidity percentage. Wind speed indicates the air flow rate, measured in meters per second. Precipitation indicates the volume of precipitation per unit time, measured in millimeters per hour.

[0041] This step is performed when monitoring and evaluating the environmental conditions of highway traffic protection facilities is necessary. Specifically, the system collects environmental parameter data from multiple monitoring points distributed around the protection facilities. The monitoring points are positioned according to the principle of representativeness to ensure they reflect the characteristics of the regional environment. Each monitoring point is equipped with temperature sensors, humidity sensors, wind speed sensors, and rain gauges, which continuously collect data at a preset sampling frequency. After outlier processing and data completion, the collected data is stored in a time series format in the system database.

[0042] In some embodiments, the acquisition of real-time data of environmental parameters can be achieved in a variety of ways: Optionally, the system installs an integrated environmental monitoring station at each monitoring point, which includes a temperature and humidity composite sensor, an ultrasonic anemometer, and an automatic rain gauge, and sends the collected data to the data center in real time through a wireless transmission module. The data center performs quality control and format conversion on the received data to form a standardized environmental parameter data set; Optionally, the system uses mobile monitoring equipment to conduct patrol monitoring on a preset route. The mobile device is equipped with a GPS positioning module and an environmental sensor array. It continuously collects location information and environmental parameters during movement. The collected data is uploaded to the server through the mobile communication network. The server associates the data with a fixed monitoring point based on the location information. It is understandable that other methods can also be used to achieve the acquisition of real-time data of environmental parameters, such as through weather station data sharing, remote sensing monitoring, etc., which are not limited here.

[0043] S102. Calculate the gradient value of the environmental parameters between the monitoring points, and calculate the environmental stress degree based on the real-time data of the environmental parameters. The environmental stress degree is obtained by weighted calculation of the damage contribution coefficient corresponding to the standardized environmental parameters. The damage contribution coefficient is determined based on statistical analysis of historical damage data.

[0044] The environmental parameter gradient value represents the rate of change of environmental parameters between adjacent monitoring points. Environmental stress refers to the combined pressure exerted by environmental factors on protective facilities. Standardization is the process of converting environmental parameters of different dimensions to a unified scale. The damage contribution coefficient represents the weight of each environmental parameter's impact on protective facility damage. Historical damage data refers to recorded cases of protective facility damage and their associated environmental conditions.

[0045] After acquiring real-time environmental parameter data, this step is executed when the impact of the environment on protective facilities needs to be assessed. Specifically, the system first calculates the environmental parameter gradient between adjacent monitoring points. For each environmental parameter, the measured value is normalized using the maximum and minimum values ​​to obtain a normalized parameter value. The system extracts damage records of protective facilities from the historical database, statistically analyzes the correlation between each environmental parameter and the occurrence of damage, and determines the damage contribution coefficient. The standardized environmental parameter is multiplied by the corresponding damage contribution coefficient and the sum is calculated to obtain a stress value reflecting the comprehensive environmental impact.

[0046] In some embodiments, the calculation of environmental stress can be achieved in a variety of ways: Optionally, the system uses a linear superposition model, first standardizes the environmental parameters, and calculates the standardized value Z = (X-μ) / σ, where X is the measured value, μ is the historical average, and σ is the standard deviation; then, the principal component analysis method is used to determine the weight coefficient of each parameter; finally, the standardized value is multiplied and summed by the weight coefficient to obtain the environmental stress degree; Optionally, the system establishes a nonlinear neural network model, uses the standardized environmental parameters as the input layer, and obtains the environmental stress degree output through nonlinear transformation of the hidden layer, and the weight parameters of the model are obtained through historical data training. It is understandable that other methods can also be used to calculate the environmental stress degree, such as fuzzy comprehensive evaluation, gray correlation analysis, and other methods, which are not limited here.

[0047] This step specifically includes the following steps:

[0048] Calculate the environmental parameter gradient value of the real-time environmental parameter data between adjacent monitoring points.

[0049] In this step, real-time environmental parameter data refers to real-time measurements of environmental indicators such as temperature, humidity, wind speed, and precipitation collected via a sensor network. Adjacent monitoring points refer to two adjacent environmental parameter collection points spaced at a specific distance around the protective facility. The environmental parameter gradient represents the rate of change of the environmental parameter per unit distance and is used to characterize the spatial variation of the environmental parameter. The system calculates the environmental parameter gradient using the central difference method. For an environmental parameter x between adjacent monitoring points i and j, the gradient is calculated as: grad(x) = (xj - xi) / d, where d is the distance between the two points. In implementation, the three-dimensional coordinates (x, y, z) and environmental parameter measurements of all monitoring points are first obtained. Then, for each pair of adjacent points, the gradient of each environmental parameter is calculated. Taking the temperature gradient as an example, if the temperature at monitoring point A is 25°C and the temperature at adjacent point B is 23°C, and the distance between the two points is 100 meters, the temperature gradient is -0.02°C / meter. The system calculates the gradients of all environmental parameters to generate complete gradient field data.

[0050] The standardized environmental parameters are calculated based on the historical maximum and minimum values ​​corresponding to the real-time data of the environmental parameters.

[0051] In this step, the historical maximum and minimum values ​​refer to the extreme values ​​of the environmental parameters recorded at a specific monitoring point during the historical observation period. Standardizing environmental parameters refers to converting environmental parameters of different dimensions into dimensionless values ​​of a unified scale. The system uses the range standardization method to perform parameter standardization. For environmental parameter x, its standardized value calculation formula is: x'=(x-xmin) / (xmax-xmin), where xmax and xmin are the historical maximum and minimum values, respectively. The system extracts the historical environmental parameter records of each monitoring point from the database and determines the value range of each parameter. The environmental parameters collected in real time are then standardized so that the values ​​of all parameters are mapped to the interval [0, 1]. For example, if the temperature at a certain point is 25°C, the historical highest temperature is 35°C, and the lowest temperature is -5°C, then the standardized temperature value is 0.75.

[0052] The corresponding relationship between each environmental parameter and the damage record of protective facilities is extracted from the historical damage database, and the damage contribution coefficient of each environmental parameter is obtained based on the damage frequency statistics.

[0053] In this step, the historical damage database stores the damage event records of protective facilities and the corresponding environmental condition data. The damage contribution coefficient represents the weight of the impact of each environmental parameter on the damage of the facility. The system determines the damage contribution coefficient through statistical analysis. First, the historical damage records are grouped according to the environmental parameter value interval, and the frequency of damage events in each interval is calculated. The contribution coefficient is calculated using the relative frequency method: w=fi / Σfi, where fi is the frequency of damage caused by the environmental parameter. The system performs frequency statistics and coefficient calculations on each environmental parameter to obtain a complete set of contribution coefficients. For example, statistics show that the frequency of damage under high temperature (>30°C) conditions accounts for 30% of the total damage events, and the damage contribution coefficient of the temperature parameter is 0.3.

[0054] The standardized environmental parameter is multiplied by the corresponding damage contribution coefficient and the sum is calculated to obtain the environmental stress degree.

[0055] In this step, the standardized environmental parameters are dimensionless environmental indicators after normalization. The damage contribution coefficient represents the impact weight of each environmental parameter. The environmental stress level is a comprehensive indicator reflecting the degree of impact of environmental conditions on the facility.

[0056] The system uses a weighted summation method to calculate the degree of environmental stress. The calculation formula is: S = Σ(wi × xi'), where wi is the damage contribution coefficient of the i-th environmental parameter, and xi' is the corresponding standardized parameter value. In specific implementation, the system multiplies the standardized value of each environmental parameter by its contribution coefficient, and then sums the weighted values ​​of all parameters to obtain the environmental stress degree for the monitoring point. For example, if the standardized temperature at a point is 0.75 and the standardized humidity is 0.6, the corresponding contribution coefficients are 0.3 and 0.4, respectively. The environmental stress degree S = 0.75 × 0.3 + 0.6 × 0.4 = 0.465. The system performs this calculation process for all monitoring points to generate an environmental stress distribution map.

[0057] S103, analyzing the temporal variation characteristics of the environmental stress degree of each adjacent monitoring point to determine the migration path and accumulation area of ​​the environmental stress. The temporal variation characteristics of the environmental stress degree include the stress intensity change rate, migration speed and accumulation time.

[0058] The temporal variation characteristics of environmental stress intensity represent the regularity of its temporal changes. The migration path of environmental stress refers to the trajectory of its spatial spread. The accumulation area represents the spatial extent of the accumulation of environmental stress. The stress intensity change rate refers to the change in environmental stress intensity per unit time. The migration speed represents the rate of spatial propagation of environmental stress. The accumulation duration refers to the duration of environmental stress in a specific area. Monitoring points are fixed locations where environmental parameter collection equipment is installed.

[0059] This step is performed when understanding the dynamic evolution of environmental stress is necessary. Specifically, the system analyzes the time series data for environmental stress levels at each pair of adjacent monitoring points, calculates the first-order derivative of the stress level to obtain the rate of change, calculates the migration speed based on the time difference and spatial distance between the peak stress levels of adjacent points, and calculates the cumulative duration of stress levels exceeding the threshold. By analyzing the relationship between stress level changes at multiple monitoring points, the direction and path of stress propagation can be determined, and locations where stress is likely to accumulate can be identified.

[0060] In some embodiments, the analysis of environmental stress migration and accumulation characteristics can be achieved in a variety of ways: Optionally, the system uses a spatiotemporal correlation analysis method, first performing a wavelet transform on the stress degree time series of the monitoring point to extract periodic characteristics, then calculating the cross-correlation function between adjacent points to determine the time delay, and finally constructing a migration field based on the delay time and spatial distance, and identifying the accumulation area by the divergence of the field; Optionally, the system establishes a stress propagation model, treating the stress degree as a scalar field, using vector calculus methods to calculate the gradient and flux of the field, obtaining the migration path by solving the propagation equation, and combining boundary condition analysis to determine the accumulation area. It is understandable that other methods can also be used to analyze the migration and accumulation characteristics of environmental stress, such as geostatistical methods, cellular automaton models, etc., which are not limited here.

[0061] S104. Based on the stress intensity distribution in the cumulative area, differentiated protection levels of the anti-corrosion coating on the highway traffic protection facilities are divided.

[0062] The cumulative area represents the spatial extent of significant environmental stress concentration. The stress intensity distribution refers to the spatial distribution of environmental stress within the cumulative area. Anti-corrosion coating refers to the protective layer applied to the surface of protective facilities to prevent corrosion. Differentiated protection levels refer to different levels of protection requirements based on the intensity of environmental stress. Highway traffic protection facilities refer to facilities installed on roads to ensure traffic safety.

[0063] This step is executed when developing an anti-corrosion protection plan for protective facilities. Specifically, the system sets multiple stress intensity thresholds based on the spatial distribution of stress intensity within the cumulative area, dividing the protective facility surface into different protection zones. Statistical characteristics are calculated for each zone, including average stress intensity, maximum stress intensity, and fluctuation range. This comprehensive assessment of the protection needs of the region determines the corresponding protection level.

[0064] In some embodiments, the division of differentiated protection levels can be achieved in a variety of ways: Optionally, the system uses a cluster analysis method to first grid the cumulative area, extract the stress intensity feature vector of each grid, and then use the K-means clustering algorithm to group the grids. Finally, the protection level is determined based on the stress characteristics of each group, and the partition boundaries are smoothed. Optionally, the system establishes a multi-index evaluation system, performs a weighted combination of indicators such as stress intensity, duration, and frequency of change, calculates a comprehensive score, divides the protection level according to the score interval, and performs spatial continuity optimization. It is understandable that other methods can also be used to achieve the division of differentiated protection levels, such as fuzzy classification, support vector machine, etc., which are not limited here.

[0065] This step specifically includes the following steps:

[0066] Calculate the average value and coefficient of variation of environmental stress at each point in the cumulative area.

[0067] In this step, the cumulative region refers to the spatial range where environmental stress is significantly concentrated, a continuous area consisting of multiple discrete monitoring points. The mean environmental stress level represents the arithmetic average level of stress levels across all monitoring points within the region. The coefficient of variation, the ratio of the standard deviation to the mean, measures the degree of data dispersion. The system uses statistical analysis methods to process environmental stress data within the cumulative region. The mean is calculated as μ = (1 / n) × Σsi, where n is the number of monitoring points and si is the environmental stress level at point i. The standard deviation is calculated as σ = sqrt[(1 / n) × Σ(si - μ)²]. The coefficient of variation is calculated as CV = σ / μ. The system first determines the spatial location and stress level of all monitoring points within the cumulative region and calculates the overall mean. The sum of the squared deviations of the stress levels at each point from the mean is then calculated to obtain the standard deviation. Finally, the standard deviation is divided by the mean to obtain the coefficient of variation. For example, if a cumulative region contains 10 monitoring points, the mean stress level is 0.6, and the standard deviation is 0.12, the coefficient of variation is 0.2.

[0068] The cumulative area is divided into different stress intensity sub-areas according to the magnitude relationship of the average value of the environmental stress degree.

[0069] In this step, the average environmental stress intensity reflects the overall level of environmental pressure within the region. Stress intensity subregions are continuous spatial units with similar stress intensity levels. The system uses cluster analysis to perform regional division. First, a stress intensity threshold sequence {T1, T2, ..., Tn} is set, and monitoring points within the cumulative region are grouped according to their average stress intensity. The minimum distance method is used to determine the subregion to which each monitoring point belongs: when μi∈[Tk-1, Tk], the point is assigned to the kth level subregion. The system constructs a continuous stress intensity distribution field using spatial interpolation methods and then divides the continuous field into subregions of different levels based on the threshold sequence. For example, if the threshold sequence {0.3, 0.6, 0.9} is set, the region is divided into four subregions: low stress (≤0.3), medium stress (0.3-0.6), high stress (0.6-0.9), and extremely high stress (>0.9).

[0070] The coefficient of variation of the environmental stress degree in each stress intensity sub-region is analyzed, and adjacent sub-regions with the same range of the coefficient of variation of the environmental stress degree are merged to obtain a merged sub-region.

[0071] In this step, stress intensity subregions are initially divided into regional units with similar environmental pressures. The coefficient of variation of environmental stress intensity indicates the degree of stress fluctuation within a subregion. Adjacent subregions are defined as two subregions that share a common boundary.

[0072] The system optimizes the initial partitioning results using a region merging algorithm. First, the coefficient of variation of environmental stress within each subregion is calculated, and a similarity threshold ε is set for the coefficient of variation. For any two adjacent subregions i and j, if |CVi - CVj| < ε, the two subregions are merged. The system iterates the region merging until the difference in the coefficient of variation of all adjacent regions exceeds the threshold. For example, if the coefficients of variation of two adjacent subregions are 0.18 and 0.21, respectively, and the similarity threshold ε = 0.05, the system merges the two subregions into a new regional unit because 0.21 - 0.18 < 0.05.

[0073] According to the distribution of the merged sub-areas, the anti-corrosion coatings of highway traffic protection facilities are divided into different protection level areas.

[0074] In this step, the merged sub-regions represent the environmental stress zones optimized through coefficient of variation analysis. Anti-corrosion coatings are protective materials applied to the surface of protective equipment. Protection level zones are defined based on the intensity of environmental stress and require different levels of protection.

[0075] The system determines the protection level of the anti-corrosion coating based on the spatial distribution of the merged sub-areas. First, a mapping relationship between stress level and protection requirements is established: the required protection intensity under different stress levels is determined based on the material performance test data. Each merged sub-area is then mapped to the corresponding protection level to generate a protection zoning scheme. The system uses GIS tools for spatial analysis, identifies the boundary lines of the protection level areas, and smoothes the boundaries to ensure the continuity and feasibility of the protection zoning. For example, the protection level is divided into three levels. Areas with a stress level of <0.4 use level one protection, areas with a stress level of 0.4-0.7 use level two protection, and areas with a stress level of >0.7 use level three protection. The system generates a protection level zoning map based on this.

[0076] S105. Determine the anti-corrosion coating thickness parameters and component ratio parameters for areas with different protection levels using a numerical simulation method, and send the anti-corrosion coating thickness parameters and the component ratio parameters to a target client.

[0077] Numerical simulation methods refer to methods that simulate physical processes using mathematical models and computer simulation techniques. Protection level zones refer to areas with different protection requirements, divided according to the degree of environmental stress. The anti-corrosion coating thickness parameter represents the actual thickness of the coating, measured in microns. The composition ratio parameter refers to the weight or volume ratio of each component in the coating. The target client refers to the terminal device or system that receives the coating parameters. Anti-corrosion coatings are protective materials applied to the surface of a facility to prevent corrosion.

[0078] This step is performed when the protection level area division is completed and the specific protection parameters of each area need to be determined. Specifically, the system establishes a numerical model of the anti-corrosion coating-environment interaction, treating the coating as a multi-layer composite structure with each layer having different physical and chemical properties. According to the environmental stress conditions of different protection level areas, the finite element method is used to calculate the stress distribution, corrosion rate and other parameters of the coating. By solving the coating damage evolution equation and the corrosion depth calculation formula, the minimum thickness of the anti-corrosion coating required for each area is determined. According to the thickness requirements and the performance characteristics of the coating material, the ratio of each component is optimized to achieve the best protection effect.

[0079] In some embodiments, the determination of anti-corrosion coating parameters can be achieved in a variety of ways: Optionally, the system uses a finite element analysis method to first establish a three-dimensional geometric model of the coating, define material properties and boundary conditions, then apply environmental loads to perform stress-strain analysis, calculate stress concentration areas and deformations, and finally determine thickness parameters based on strength and stiffness requirements, and optimize the component ratio through the response surface method; Optionally, the system uses a corrosion kinetics model to establish a relationship equation between corrosion rate and environmental factors, calculate the corrosion depth through numerical integration, determine the coating thickness, and optimize the coating formulation in combination with electrochemical impedance spectroscopy data. It is understandable that other methods can also be used to determine the anti-corrosion coating parameters, such as molecular dynamics simulation, artificial neural network and other methods, which are not limited here.

[0080] This step specifically includes the following steps:

[0081] Obtain the environmental stress levels of areas with different protection levels and the service life requirements of the highway traffic protection facilities.

[0082] In this step, the protection level area is a zone with different protection requirements divided according to the intensity of environmental stress, which contains multiple levels of spatial partitions. The environmental stress degree is a comprehensive indicator that characterizes the degree of impact of environmental conditions on facilities, and is obtained by weighted calculation of environmental parameters. The service life requirement refers to the length of time that the protective facilities need to remain functional under normal conditions of use, usually in years. The system obtains protection level area information and related parameters through the data interface. First, the protection partition data is read to extract the spatial range and environmental stress value of each area. The environmental stress data includes the average value, maximum value and fluctuation range. At the same time, the service life requirements of the protective facilities are extracted from the engineering design specifications, including the minimum service life and the design service life. For example, a protective facility is divided into three protection level areas, and their environmental stress degrees are 0.3, 0.5 and 0.8 respectively, and the design service life requirement is 20 years. The system stores these data in the calculation parameter database for subsequent corrosion rate and coating parameter calculations.

[0083] The coating corrosion rate of each protection level area is calculated based on the environmental stress level.

[0084] In this step, the coating corrosion rate indicates the corrosion loss rate of the anti-corrosion coating material under specific environmental conditions, expressed as the thickness of the material loss per year. The environmental stress degree is a comprehensive representation of the effect of environmental conditions on the corrosion of materials. The system uses a corrosion kinetics model to calculate the coating corrosion rate. The corrosion rate calculation formula is: v=v0×exp(k×S), where v0 is the baseline corrosion rate, k is the environmental sensitivity coefficient, and S is the environmental stress degree. The system first determines the baseline corrosion rate and environmental sensitivity coefficient through material test data, and then substitutes the environmental stress degree of each protection level area into the formula to calculate the actual corrosion rate. For the coupling effect of environmental factors such as temperature and humidity, correction coefficients are used for correction: v'=v×Πfi, where fi is the correction coefficient of each environmental factor. For example, if the environmental stress degree of a certain area is 0.5, the baseline corrosion rate is 0.1mm / year, and the environmental sensitivity coefficient is 1.5, the calculated corrosion rate is 0.247mm / year.

[0085] Based on the coating corrosion rate and the service life requirement, the anti-corrosion coating thickness parameters required for each protection level area are calculated.

[0086] In this step, the anti-corrosion coating thickness parameter refers to the actual thickness value of the coating, in microns. The coating corrosion rate indicates the annual corrosion loss of the material. The service life requirement is the length of time the facility needs to remain functional. The system determines the coating thickness parameters through numerical calculations. The thickness calculation formula is: d=v×t×(1+α), where v is the corrosion rate, t is the design service life, and α is the safety factor. The system performs calculations for each protection level area, taking into account the corrosion loss and safety margin of the coating. At the same time, the coating structure factor is introduced for correction: d'=d×β, where β is the structural correction factor, which reflects the influence of the coating structure on the protective performance. For example, the corrosion rate in a certain area is 0.247mm / year, the design service life is 20 years, the safety factor is 0.2, and the structural correction factor is 1.1. The calculated coating thickness is 6.5mm.

[0087] The composition ratio parameter is determined according to the anti-corrosion coating thickness parameter.

[0088] In this step, the component ratio parameter refers to the weight or volume ratio of each component of the anti-corrosion coating. The anti-corrosion coating thickness parameter is the design thickness value of the coating. The system determines the coating formula through the material performance optimization method. First, establish a coating performance and component relationship model: P=f(x1, x2, ..., xn), where xi is the content of each component. Set the optimization objective function: min{c(x)}, where c(x) is the cost function. Constraints include: component content range xi_min≤xi≤xi_max, performance index requirement P≥P0. The system uses a nonlinear programming algorithm to solve the optimal ratio. For example, a coating contains three components: resin, curing agent, and filler. The optimal ratio obtained through optimization calculation is 50:30:20. At the same time, the system generates coating formula process parameters, including process requirements such as mixing order and mixing time.

[0089] 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 durability optimization method of highway traffic protection facilities based on digital twins in an embodiment of the present application.

[0090] S201. Determine the anti-corrosion coating thickness parameters and component ratio parameters for areas with different protection levels using a numerical simulation method, and send the anti-corrosion coating thickness parameters and the component ratio parameters to a target client.

[0091] Numerical simulation method refers to the method of simulating and calculating the system through mathematical models, including technical means such as finite element analysis and computational fluid dynamics. The protection level area is a zone of different protection requirements divided according to the degree of environmental stress, reflecting the corrosive intensity of the environment in which the protective facilities are located. The anti-corrosion coating thickness parameter refers to the actual thickness value of the coating, usually measured in microns (μm). The composition ratio parameter refers to the weight or volume ratio of each component in the coating, which directly determines the protective performance of the coating. The target client refers to the terminal device or system that receives the coating parameters, which is used to specifically guide the construction of the coating.

[0092] First, a numerical model of the anti-corrosion coating-environment interaction is established, and the coating is regarded as a multi-layer composite structure, each layer having different physical and chemical properties. According to the environmental stress conditions in different protection level areas, the finite element method is used to calculate the stress distribution, corrosion rate and other parameters of the coating. By solving the coating damage evolution equation dD / dt=A•σα•exp(-Q / RT) (where D is the damage amount, σ is the stress, and T is the temperature), the damage evolution law of the coating is obtained. Based on the corrosion depth calculation formula h=k•t^n (where k is the corrosion rate constant, t is the time, and n is the time exponent), the minimum thickness of the anti-corrosion coating required for each area is determined δ=h•SF (where SF is the safety factor). Then, according to the thickness requirements and combined with the performance characteristics of the coating material, the ratio of each component is optimized to achieve the best protection effect. Finally, the calculated specific coating thickness value and component ratio data are sent to the target client through the network interface.

[0093] S202: Obtain terrain data and water system data for the area where the highway traffic protection facility is located.

[0094] Topographic data is a dataset describing landform features such as surface elevation, slope, and aspect, containing detailed information on surface undulations. Water system data contains the spatial distribution and attributes of water bodies such as rivers and lakes, reflecting regional hydrological characteristics. Highway traffic protection facilities refer to road safety devices such as guardrails and anti-collision devices, whose performance directly impacts traffic safety.

[0095] This step is accomplished by determining the geographic coordinate range of the protective facility and extracting the digital elevation model (DEM) data for the area from a geographic information system (GIS) database, obtaining the latitude, longitude, and elevation values ​​for each grid point. Water system vector data, including attributes such as water type, flow direction, and width, is also extracted. Remote sensing image interpretation technology is used to extract terrain features, and a high-precision terrain model is constructed using survey data. Real-time hydrological data is obtained from hydrological monitoring stations. All acquired data is stored using a unified geographic coordinate system and data format to ensure data consistency and availability.

[0096] S203: Calculate a surface runoff channel based on the terrain data, and calculate a pollutant migration path based on the water system data and the surface runoff channel.

[0097] Surface runoff pathways are the paths of water flow formed by the confluence of precipitation, reflecting the movement of surface water. Pollutant migration pathways are the trajectories of pollutants as they migrate and diffuse with surface water flow, reflecting the diffusion process of pollutants in the environment.

[0098] This step is specifically implemented as follows: using the D8 algorithm to perform hydrological analysis on the DEM data and calculate the flow direction of each grid. Based on the flow direction information, a flow path network is constructed to determine the main runoff channels. The water system data is superimposed and analyzed, taking into account the confluence of rivers, to obtain a complete surface water flow network. Based on this network, the convection-diffusion equation is used. (where C is the pollutant concentration, v is the velocity vector, and D is the diffusion coefficient) to simulate pollutant transport. By numerically solving this equation, the trajectory of pollutants along the water path from the source is tracked. The calculation process comprehensively considers the impact of factors such as terrain slope and surface roughness on transport velocity, achieving accurate prediction of pollutant migration paths.

[0099] S204: Calculate the cumulative concentration of pollutants along the pollutant migration path, convert the cumulative concentration of pollutants into an environmental stress increment, and superimpose the environmental stress increment with the environmental stress to obtain a regional stress degree.

[0100] The cumulative concentration of pollutants refers to the concentration of pollutants at each point along their transport path, expressed in mg / L. The incremental environmental stress level refers to the additional environmental pressure caused by pollutant concentration and is dimensionless. Regional stress is an indicator of overall environmental pressure that takes into account both baseline environmental stress and the impact of pollutants.

[0101] The specific implementation process of this step is as follows: the system first calculates the cumulative concentration along the path based on the pollutant transport equation, specifically using the mass conservation equation Calculations are performed, where M is the pollutant mass, v is the migration velocity, and S is the source-sink term. At each calculation node, the cumulative concentration C = M / V (V is the control volume). The concentration value is then converted to a stress increment using the formula ΔS = k•(C / C0)^α, where k is the conversion coefficient, C0 is the baseline concentration, and α is the nonlinear exponent. For n environmental factors, the total regional stress is calculated as St = S0 + Σ(wi•ΔSi), where S0 is the baseline environmental stress and wi is the weighting coefficient. The system performs calculations for each grid point, generating a regional stress distribution map.

[0102] S205: Divide the protection level into zones according to the stress level of the region, calculate the coating technical parameters of the protection level zones, and send them to the target client.

[0103] Regional stress is a comprehensive indicator that characterizes the intensity of environmental pressure. Protection level zoning refers to the division of areas with different protection requirements based on stress levels. Coating technical parameters include process indicators such as coating thickness and material ratio. The target client is the terminal device that receives these parameters.

[0104] The implementation process of this step is as follows: The system uses the clustering analysis method to classify the regional stress degree, and sets a threshold sequence {T1, T2,..., Tn} to divide the stress degree into n levels. For the stress degree value S, when Ti-1 < S ≤ Ti, it belongs to the i-th protection area. After determining the partition, the system calculates the coating parameters for each protection level. The coating thickness δ is determined by the corrosion life equation δ = V • t + δ0, where V is the corrosion rate, t is the design life, and δ0 is the process margin. The material ratio is solved by an optimization algorithm, and the objective function is min{f(x1, x2,..., xm)}, where xi is the content of each component, satisfying the constraint condition of Σxi = 1. After the calculation is completed, the system transmits the coating parameter data packets of each partition to the target client through the network.

[0105] S206. Receive meteorological forecast data and calculate the predicted environmental stress degree based on this meteorological forecast data.

[0106] The meteorological forecast data includes the predicted values of meteorological elements such as temperature, humidity, wind speed, and precipitation. The predicted environmental stress degree is a future environmental pressure index calculated based on the forecast data.

[0107] The specific implementation of this step is as follows: The system obtains the meteorological forecast data through the data interface, including the hourly forecast values for the next 7 days. For each forecast time t, calculate the standardized value of each meteorological element zi(t) = (xi(t) - μi) / σi, where xi(t) is the forecast value, and μi and σi are the historical mean and standard deviation respectively. The predicted environmental stress degree is obtained by weighted summation: S(t) = Σ(wi • zi(t)), where wi is the weight coefficient of each element, determined by the regression analysis of historical data. The system calculates all times within the forecast period to generate a stress degree time series for subsequent analysis. The coupling effect between meteorological elements is considered in the calculation, and cross-term correction is used: S'(t) = S(t) + Σ(kij • zi(t) • zj(t)), where kij is the coupling coefficient.

[0108] S207. Compare the predicted environmental stress degree with the environmental stress degree to obtain a stress degree trend graph, and determine the boundary of the protection partition according to this stress degree trend graph.

[0109] The stress degree trend graph is a two-dimensional curve graph representing the time variation law of the environmental stress degree, with the horizontal axis being time and the vertical axis being the stress degree value. The boundary of the protection partition refers to the dividing line between different protection level areas, determined by the gradient change of the stress degree. The predicted environmental stress degree and the environmental stress degree represent the future and current environmental pressure levels respectively.

[0110] The specific implementation process of this step is as follows: the system first plots the predicted environmental stress degree sequence Sp(t) and the current environmental stress degree Sa(t) in the same coordinate system, and calculates the difference sequence ΔS(t)=Sp(t)-Sa(t). The periodic characteristics of the difference sequence are analyzed by Fourier transform: F(ω)=∫ΔS(t)•e^(-iωt)dt. Based on the spectrum analysis results, the main cycles and trends of stress degree changes are identified. In space, the stress degree gradient vector is calculated. When the gradient value exceeds the threshold γ, it is determined as a partition boundary point. All boundary points are fitted with a B-spline curve to form a continuous partition boundary line B(u)=ΣPi•Ni,k(u), where Pi is the coordinate of the control point and Ni,k(u) is the B-spline basis function.

[0111] S208: Calculate the coating technical parameters within the protection zone boundary, and send the coating technical parameters to the target client.

[0112] Coating technical parameters include specific indicators such as coating thickness, material composition ratio, and construction process requirements. Protection zone boundaries define the areas requiring differentiated protection. The target client is the terminal device used to receive and display technical parameters.

[0113] The implementation process of this step is as follows: the system calculates the coating parameter combination for each protection zone. The coating thickness is calculated using the corrosion life equation: d=v•t•f(S)+d0, where v is the baseline corrosion rate, t is the design life, f(S) is the stress correction function, and d0 is the safety margin. The material component optimization adopts a nonlinear programming method, and the objective function is min{c(x)+λ•p(x)}, where c(x) is the cost function, p(x) is the performance function, x is the component vector, and λ is the trade-off factor. The constraints include: the component content range xi_min≤xi≤xi_max, and the performance index requirement gj(x)≥bj. The optimal parameter combination obtained by solving is sent to the target client through the data interface. The data includes the coating structure parameter matrix D and the process parameter vector P.

[0114] S209: Extracting the use environment data of the highway traffic protection facility, and calculating the material fatigue index of the highway traffic protection facility based on the use environment data.

[0115] Environmental data refers to the measured values ​​of physical quantities such as temperature, humidity, and stress in the environment where protective equipment is located. Material fatigue indicators are quantitative indicators that characterize the degree of material degradation, including fatigue damage and residual strength. Highway traffic protection facilities refer to safety devices such as guardrails and anti-collision devices on roads.

[0116] This step is specifically implemented as follows: the system collects the operating environment data of the protective facilities through the sensor network, including the environmental load sequence σ(t) and the environmental factor sequence E(t). The material damage evolution is calculated based on the Paris fatigue crack growth formula: da / dN=C•(ΔK)^m, where a is the crack length, N is the number of cycles, and ΔK is the stress intensity factor range. Considering the influence of environmental factors, the correction coefficient C=C0•exp(βE), where C0 is the baseline value and β is the environmental sensitivity coefficient. The cumulative damage degree is calculated using the Miner criterion: D=Σ(ni / Ni), where ni is the actual number of cycles and Ni is the critical number of cycles. The system stores the calculated fatigue index in the database for subsequent life prediction.

[0117] S210. Analyze the accelerating effect of environmental stress on material fatigue, and predict the remaining service life of the highway traffic protection facility based on the material fatigue index and the accelerating effect.

[0118] The acceleration effect of environmental stress refers to the effect of environmental factors on the material fatigue process, quantified by the acceleration factor. Material fatigue indicators include parameters such as damage degree and crack size that reflect material performance degradation. Remaining useful life refers to the length of time it takes for a protective device to reach failure criteria from its current state. Highway traffic protective devices refer to protective equipment used to ensure road safety.

[0119] The specific implementation process of this step is as follows: the system first establishes an environmental-fatigue coupling model and calculates the environmental acceleration factor AF = exp[Ea(1 / T0-1 / T)], where Ea is the apparent activation energy, T0 is the reference temperature, and T is the actual temperature. For the action of multiple environmental factors, the total acceleration factor AFt = ΠAFi. The fatigue life is calculated based on the modified Manson-Coffin equation: Nf = [εf'•exp(AF•kt)]^c, where εf' is the fatigue ductility coefficient, k is the time coefficient, and c is the material constant. Combined with the current damage state D and the failure threshold Dc, the remaining life is solved using the integral equation: ∫[dD / (1-D)] = ∫[A•σα•exp(-Q / RT)]dt, where A is the material constant, σ is the stress, Q is the activation energy, and R is the gas constant. The specific value of the remaining life tr is obtained through numerical integration.

[0120] S211. Determine a maintenance time window for the highway traffic protection facility based on the remaining service life, and prioritize the maintenance of the highway traffic protection facility.

[0121] The maintenance window is the optimal time period for protective equipment maintenance, determined by the remaining service life and maintenance duration. Maintenance priority is a ranking of the urgency of protective equipment maintenance. Remaining service life is the remaining time before a protective equipment fails.

[0122] The implementation process of this step is as follows: The system sets the maintenance time interval [t1, t2] according to the remaining life tr, where t1 = tr - Δt1, t2 = tr - Δt2, and Δt1 and Δt2 are the safety margin and construction period respectively. For n protective facilities, a priority scoring function is constructed: P = w1•(1 / tr) + w2•D + w3•L, where w1, w2, and w3 are weight coefficients, D is the current damage degree, and L is the facility importance coefficient. The facilities are sorted according to the scoring value, and the priority intervals are divided: P > P1 is the first-level priority, P2 < P ≤ P1 is the second-level priority, and P ≤ P2 is the third-level priority. The system generates a maintenance schedule including the maintenance time window [t1, t2] and the priority P, and stores it in the database for maintenance management. At the same time, the maintenance resource requirement R = Σ(ri•ni) for each priority interval is calculated, where ri is the resource requirement of a single facility and ni is the number of facilities in this priority.

[0123] The durability optimization system of highway traffic protection facilities in the embodiment of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the durability optimization system of highway traffic protection facilities in the embodiment of the present application.

[0124] It should be noted that Figure 3 The structure of the durability optimization system of highway traffic protection facilities shown is only an example, and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0125] As Figure 3 shown, the durability optimization system of highway traffic protection facilities includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 into the random access memory (RAM) 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0126] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0127] 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 the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0128] 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.

[0129] 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.

[0130] Specifically, the highway traffic protection facility durability 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 highway traffic protection facility durability optimization method based on digital twins provided in the above embodiment is implemented.

[0131] As another aspect, the present invention further provides a computer-readable storage medium. This storage medium may be included in the highway traffic protection facility durability optimization system described in the above embodiments, or may exist independently and not be incorporated into the highway traffic protection facility durability optimization system. The storage medium carries one or more computer programs. When executed by a processor of the highway traffic protection facility durability optimization system, the highway traffic protection facility durability optimization system implements the highway traffic protection facility durability optimization method based on digital twins provided in the above embodiments.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail 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.

[0133] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0134] 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 durability optimization method for highway traffic protection facilities based on digital twins, characterized in that: Applied to a durability optimization system for highway traffic protection facilities, the method comprises: Acquiring real-time data of environmental parameters through multiple monitoring points, the real-time data of environmental parameters including temperature, humidity, wind speed and precipitation; Calculating the environmental parameter gradient between the monitoring points and calculating the environmental stress level based on the real-time environmental parameter data. The environmental stress level is obtained by weighted calculation of the damage contribution coefficient corresponding to the standardized environmental parameter. The damage contribution coefficient is determined based on statistical analysis of historical damage data. The environmental parameter gradient represents the rate of change of the environmental parameter per unit distance and is used to characterize the spatial variation characteristics of the environmental parameter. Analyze the temporal variation characteristics of the environmental stress degree of each adjacent monitoring point to determine the migration path and accumulation area of ​​the environmental stress. The temporal variation characteristics of the environmental stress degree include the rate of change of the stress intensity, the migration speed, and the accumulation time. The temporal variation characteristics of the environmental stress degree represent the regularity of the change of the environmental stress degree over time. The migration path refers to the trajectory of the spread and diffusion of the environmental stress in space. The accumulation area represents the spatial range of the aggregation and accumulation of the environmental stress. Based on the stress intensity distribution of the cumulative area, differentiated protection levels of the anti-corrosion coating on the highway traffic protection facilities are divided; A numerical simulation method is used to determine the thickness parameters and component ratio parameters of the anti-corrosion coating in areas with different protection levels, and the anti-corrosion coating thickness parameters and the component ratio parameters are sent to the target client.

2. The method according to claim 1, characterized in that The step of calculating the environmental parameter gradient value between the monitoring points and calculating the environmental stress degree according to the real-time data of the environmental parameters specifically includes: Calculate the environmental parameter gradient value of the real-time environmental parameter data between adjacent monitoring points; Calculating standardized environmental parameters based on historical maximum and minimum values ​​corresponding to the real-time data of the environmental parameters; Extracting the corresponding relationship between each environmental parameter and the damage record of protective facilities from the historical damage database, and obtaining the damage contribution coefficient of each environmental parameter based on damage frequency statistics; The standardized environmental parameter is multiplied by the corresponding damage contribution coefficient and the sum is calculated to obtain the environmental stress degree.

3. The method according to claim 1, characterized in that The step of dividing the differentiated protection levels of the anti-corrosion coating on the highway traffic protection facilities based on the stress intensity distribution of the cumulative area specifically includes: Calculate the average value and coefficient of variation of environmental stress at each point in the cumulative area; Dividing the cumulative area into different stress intensity sub-areas according to the magnitude relationship of the average values ​​of the environmental stress degree; Analyze the environmental stress degree variation coefficient in each of the stress intensity sub-regions, and merge adjacent sub-regions whose environmental stress degree variation coefficients are in the same range to obtain a merged sub-region; The anti-corrosion coating of the highway traffic protection facility is divided into different protection level areas according to the distribution of the merged sub-areas.

4. The method according to claim 1, wherein The step of using a numerical simulation method to determine the thickness parameters and component ratio parameters of the anti-corrosion coating in areas of different protection levels specifically includes: Obtain the environmental stress levels of areas with different protection levels and the service life requirements of the highway traffic protection facilities; Calculating the coating corrosion rate of each protection level area according to the environmental stress degree; Calculating the required anti-corrosion coating thickness parameters for each protection level area based on the coating corrosion rate and the service life requirement; The composition ratio parameter is determined according to the anti-corrosion coating thickness parameter.

5. The method according to claim 4, characterized in that After the step of sending the anti-corrosion coating thickness parameter and the component ratio parameter to the target client, the method further includes: Obtaining topographic data and water system data of the area where the highway traffic protection facilities are located; Calculating a surface runoff channel based on the terrain data, and calculating a pollutant migration path based on the water system data and the surface runoff channel; Calculating the cumulative concentration of pollutants along the pollutant migration path, converting the cumulative concentration of pollutants into an environmental stress increment, and superimposing the environmental stress increment with the environmental stress to obtain a regional stress degree; Protection level partitions are divided according to the regional stress levels, and coating technical parameters of the protection level partitions are calculated and sent to the target client.

6. The method according to claim 4, characterized in that After the step of sending the anti-corrosion coating thickness parameter and the component ratio parameter to the target client, the method further includes: receiving weather forecast data, and calculating a predicted environmental stress degree based on the weather forecast data; Comparing the predicted environmental stress degree with the environmental stress degree to obtain a stress degree trend graph, and determining a protection zone boundary based on the stress degree trend graph; Calculate the coating technical parameters within the protection zone boundary and send the coating technical parameters to the target client.

7. The method according to claim 1, characterized in that After the step of sending the anti-corrosion coating thickness parameter and the component ratio parameter to the target client, the method further includes: extracting usage environment data of the highway traffic protection facility, and calculating a material fatigue index of the highway traffic protection facility based on the usage environment data; Analyzing the accelerated effect of environmental stress on material fatigue, and predicting the remaining service life of the highway traffic protection facility based on the material fatigue index and the accelerated effect; A maintenance time window for the highway traffic protection facility is determined based on the remaining service life, and maintenance priorities of the highway traffic protection facility are divided.

8. A road traffic protection facility durability optimization system, characterized in that: The highway traffic protection facility durability 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 highway traffic protection facility durability optimization system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a highway traffic protection device durability optimization system, the highway traffic protection device durability optimization system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a highway traffic protection facility durability optimization system, the highway traffic protection facility durability optimization system is enabled to execute the method according to any one of claims 1 to 7.

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