Road guardrail collision data evaluation and analysis system

Abstract

The invention relates to the technical field of road guardrail detection, in particular to a road guardrail collision data evaluation and analysis system which comprises a test data acquisition module, a guardrail information input module and a data analysis and evaluation module. The test data acquisition module comprises a high-speed camera acquisition sub-module, a manual data entry sub-module and a data preprocessing sub-module, and the high-speed camera acquisition sub-module adopts a high frame rate. In the scheme, through cooperative work of the guardrail information input module and the data analysis and evaluation module, deep analysis and scientific evaluation of guardrail materials and structural performance are realized, and the guardrail information input module records the material type, physical attribute parameters, structural design information and the like of the guardrail in detail; a data potential rule is mined through a visual means, then factors having key influences on a collision result are accurately positioned through correlation analysis, and then a model is constructed through regression analysis to quantify a variable relationship for prediction analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of road guardrail detection, and in particular to a road guardrail collision data evaluation and analysis system. Background Art

[0002] Road guardrails play a vital role in ensuring road traffic safety. Their performance is directly related to the safety of vehicles and passengers in collision accidents. With the continuous development of road construction and the increasing traffic volume, the accurate evaluation of road guardrail performance is becoming more and more important. The road guardrail collision data evaluation and analysis system came into being. Traditional road guardrail collision data evaluation and analysis systems often have many shortcomings. Most traditional systems simply list and perform basic calculations on the data, but fail to deeply explore the potential laws behind the data. The analysis of guardrail materials and structural performance is not comprehensive and scientific enough. There is a lack of systematic correlation analysis and regression analysis, and it is impossible to accurately evaluate the rationality, stability and adaptability of materials and structures in collisions. For example, it is impossible to accurately judge the relationship between the physical properties of materials such as elastic modulus and yield strength and collision results such as the maximum intrusion of vehicles and the energy absorption efficiency of guardrails. It is difficult to determine the influence of structural factors such as reasonable vertical pole spacing and crossbar cross-section shape on collision performance, and thus it is impossible to provide comprehensive and scientific guidance for the optimal design of guardrails. To this end, the present invention proposes a road guardrail collision data evaluation and analysis system, which realizes comprehensive and accurate acquisition of road guardrail collision data by constructing a complete test data acquisition module, and ensures the integrity and accuracy of the data by combining a high-speed camera acquisition submodule and a manual data entry submodule, and uses scientific algorithms to process data and calculate key indicators. At the same time, a guardrail information entry module is set to work together with a data analysis and evaluation module to deeply analyze the guardrail material and structural performance, and comprehensively evaluate the performance of the guardrail in a collision through multi-factor correlation analysis, regression analysis and other means, and generate visual results and detailed reports, thereby providing a reliable basis for the optimal design of the guardrail, safety improvement, and reasonable selection of materials and structures, effectively promoting the development and progress of road guardrail technology, and ensuring road traffic safety. Summary of the invention

[0003] Technical problems solved: Traditional analysis systems lack systematic correlation analysis and regression analysis, and are unable to accurately evaluate the rationality, stability and adaptability of materials and structures in collisions.

[0004] In view of the deficiencies in the prior art, the present invention provides a road guardrail collision data evaluation and analysis system, thereby solving the technical problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A road guardrail collision data evaluation and analysis system, the system comprising a test data acquisition module, a guardrail information input module, and a data analysis and evaluation module; The test data acquisition module includes a high-speed video acquisition submodule, a manual data entry submodule and a data preprocessing submodule. The high-speed video acquisition submodule uses a high-frame rate and high-resolution camera to shoot the collision process. The manual data entry submodule records information that cannot be captured by the camera. The data preprocessing submodule performs preliminary processing on the collected data. The guardrail information input module includes a guardrail material information input submodule and a guardrail structure information input submodule, and the guardrail information input module is used to input guardrail material and structure related information; The data analysis and evaluation module includes data classification and organization, single-factor correlation analysis, multi-factor interaction analysis, optimization scheme screening and evaluation, and result visualization and report generation. The data classification and organization is used to classify and integrate the collected data according to materials, structures, and collision results. The single-factor correlation analysis uses statistical methods to calculate the Pearson correlation coefficient to analyze the correlation between material characteristic parameters and collision result data, and between structural elements and collision results. The multi-factor interaction analysis uses multivariate regression analysis to construct a model with material characteristics and structural parameters as independent variables and collision result indicators as dependent variables to determine factor weights and interaction relationships. The optimization scheme screening and evaluation is used to screen potential optimization schemes based on the analysis results and use regression models to predict performance and compare and evaluate. The result visualization and report generation uses visualization technology to draw relevant charts to generate a comprehensive evaluation report covering data processing, analysis, optimization schemes and conclusion recommendations.

[0006] In a possible implementation, the specific formula for calculating the Pearson correlation coefficient using the statistical method is as follows: ; in is the sample value of the material elastic modulus, is the sample mean of the elastic modulus of the material, is the sample value of the maximum vehicle intrusion, is the sample mean of the maximum intrusion of vehicles, is the sample size.

[0007] In a possible implementation, the specific formula for constructing the multivariate regression analysis model is as follows: ; ; ; in, ..... , ..... , ..... is the regression coefficient, , , is the error term, , , They are energy absorption efficiency, maximum vehicle intrusion, and maximum guardrail deformation. is the guardrail material density, is the guardrail crossbar diameter, is the yield strength, is the elastic modulus, Encode the connection mode. The distance between the poles is calculated by the least squares algorithm to solve the regression coefficient and determine the weight and interaction relationship of each factor in the model.

[0008] Beneficial effects compared with the prior art: 1. In this scheme, by constructing a complete test data acquisition module, comprehensive and accurate acquisition of road guardrail collision data is achieved. This module not only uses the high-speed camera acquisition submodule to record the collision moment and subsequent dynamic changes with high frame rate and high resolution to ensure that key details are captured, but also sets up a manual data entry submodule to supplement the information that is difficult to obtain by camera, and optimizes various types of data through data preprocessing submodule to provide a high-quality data basis for subsequent analysis. Based on this, when calculating key indicators such as crossbar deformation, pole displacement, collision force and energy absorption efficiency, a variety of scientific algorithms (such as based on image analysis, mechanical theory, etc.) can be used to obtain accurate results, which makes the evaluation of guardrail performance highly accurate and can truly reflect the actual performance of guardrails under different collision conditions. It provides a reliable basis for the optimization design of guardrails, safety improvement, and reasonable selection of materials and structures, effectively promoting the development and progress of road guardrail technology and ensuring road traffic safety; 2. In this scheme, by setting up the collaborative work of the guardrail information entry module and the data analysis and evaluation module, an in-depth analysis and scientific evaluation of the guardrail material and structural performance are achieved. The guardrail information entry module records the material type, physical property parameters and structural design information of the guardrail in detail, providing comprehensive basic data for subsequent analysis. The data analysis and evaluation module first integrates and analyzes the data, and mines the potential patterns of the data through visualization. Then, the correlation analysis is used to accurately locate the factors that have a key impact on the collision results. Then, a model is constructed through regression analysis to quantify the variable relationship for predictive analysis. Finally, a comprehensive evaluation index system is formulated and graded, and a report feedback result is generated. Such a process enables a comprehensive evaluation of the rationality, stability and adaptability of guardrail materials and structures in collisions, provides scientific guidance for the design improvement, production and manufacturing of guardrails, and the formulation of industry standards, helps to improve the overall quality of guardrails, meet the safety requirements of different road scenarios, and reduce the risk of injury to vehicles and occupants in traffic accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0010] Figure 1 A schematic diagram of a road guardrail collision simulation of the present invention; Figure 2 It is a schematic diagram of high-speed camera acquisition of the present invention; Figure 3 This is a system framework diagram of a road guardrail collision data evaluation and analysis system. DETAILED DESCRIPTION

[0011] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can also be implemented in various forms, so the present invention is not limited to the embodiments described below. The technical solution in the embodiment of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows:

[0012] Example: Please refer to Figure 1-3 As shown, this embodiment introduces a road guardrail collision data evaluation and analysis system, including a test data acquisition module, a guardrail information input module, a data comparison module, and a data analysis and evaluation module; 1. Test data acquisition module 1.1. High-speed camera acquisition submodule: Use a high-speed camera to take all-round shots of the collision process between the vehicle and the guardrail to ensure that the collision moment and subsequent dynamic changes can be clearly recorded; The camera has the characteristics of high frame rate (e.g., above 1000fps) and high resolution (e.g., 1920×1080 pixels and above), which can capture the details of the contact between the vehicle and the guardrail, the initiation and development of the deformation of the guardrail bar, etc. 1.2. Manual data entry submodule: Design a manual data entry interface for information that cannot be accurately captured or quantified by high-speed camera acquisition equipment; After the collision test is completed, the data entry personnel can enter data based on on-site observations and data obtained by measuring tools, such as the exact model of the vehicle (which can be obtained from the vehicle nameplate or related information), the estimated speed before the collision (estimated by speed measuring instruments or experience), the depth of the dent in a specific part of the vehicle body (measured using a depth gauge), etc. The entered data must be verified for validity to ensure the accuracy and completeness of the data; 1.3. Data preprocessing submodule: responsible for the preliminary processing of video data collected by high-speed cameras and manually entered pictures and text data; For video data, key frame extraction is performed to convert the continuous video stream into a discrete image sequence for subsequent analysis. At the same time, the image is processed by noise reduction, contrast enhancement, etc. to improve image quality and facilitate feature extraction. The text data is formatted and cleaned to remove redundant information and erroneous data.

[0013] In summary, the analysis algorithm formula is as follows: Calculation of crossbar deformation The deformation of the guardrail crossbar can be calculated by analyzing the key frame images extracted by high-speed camera, determining the original position and the position after collision in the image, and measuring the displacement difference between the two in the direction perpendicular to the axis of the crossbar. (Unit: m), which is the deformation of the crossbar. To improve the measurement accuracy, the image edge detection and feature matching algorithm can be used to accurately identify the contour and feature points of the crossbar, so as to accurately calculate the deformation; Calculation of pole displacement The displacement of the guardrail pole can also be obtained from the high-speed camera image. The connection point between the bottom of the pole and the ground is used as the reference point to measure the horizontal displacement of the top of the pole or other key parts relative to the reference point before and after the collision. (Unit: m) and vertical displacement (Unit: m), the total displacement of the pole is calculated by the Pythagorean theorem ; If the pole rotates during the collision, its rotation angle must also be measured. (Unit: rad). The rotation angle can be determined by marking a specific angle reference line on the pole and measuring the angle change of the reference line before and after the collision in the image. Combining the rotation angle with the geometric dimensions of the pole, the torsional deformation and bending deformation of the pole can be further analyzed.

[0014] Collision force calculation based on vehicle acceleration According to Newton's second law , install an acceleration sensor on the vehicle to obtain the acceleration time history of the vehicle during the collision (Unit: m / s²). Given the vehicle mass (Unit: kg), the time history of the collision force on the vehicle during the collision can be calculated ,Since the mass of the vehicle may change due to the detachment or deformation of parts during a collision, the mass change factor must be considered in the accurate calculation. Multiple sensors can be installed in different parts of the vehicle to monitor the changes in the vehicle's mass distribution and make corresponding corrections to the collision force calculation; Collision force estimation based on guardrail deformation When the material mechanical properties of the guardrail are known (such as elastic modulus , Section moment of inertia ) and deformation, the collision force can be estimated based on the beam bending theory in material mechanics. For the guardrail crossbar, it is assumed that it is simplified to a simply supported beam model subjected to a lateral concentrated force. According to the beam deflection curve equation (in For the beam at position The deflection at For concentration, is the distance from the point of concentrated force application to one end of the beam, is the length of the beam), the maximum deformation of the crossbar (i.e. the maximum deflection ) and its corresponding position, the collision force can be inferred For the guardrail pole, if its deformation under the combined action of axial force and bending moment is considered, a more complex beam-column theory can be used to estimate the collision force. It is necessary to comprehensively consider the geometric dimensions, material properties, boundary conditions and connection method of the pole with the crossbar, and establish a corresponding mechanical model to solve the collision force. Energy absorption efficiency calculation First calculate the initial kinetic energy of the vehicle before the collision (in is the vehicle mass, is the vehicle speed before the collision), and then calculates the energy absorbed by the guardrail during the collision. , can be calculated by integrating the strain energy during the guardrail deformation process, or by calculating the area enclosed by the collision force and displacement curve. For example, if the collision force is known Deformation of guardrail The relationship curve, then (in is the maximum deformation of the guardrail), and finally calculate the energy absorption efficiency of the guardrail This indicator reflects the ability of the guardrail to absorb the vehicle's kinetic energy during a collision. The higher the energy absorption efficiency, the better the guardrail's buffering and absorption of the vehicle's kinetic energy during a collision, and the more effectively it can protect the safety of the vehicle and its occupants.

[0015] 2. Guardrail information entry module 2.1. Guardrail material information input submodule: allows users to input the material type of the guardrail, such as steel (specific models such as Q235, Q345, etc.), aluminum alloy, composite material, etc., and further input the material's physical property parameters, such as elastic modulus, yield strength, density and other information. These parameters will provide basic data for subsequent analysis of the mechanical response of the guardrail during a collision; 2.2. Guardrail structure information input submodule: Enter the structural design information of the guardrail in detail, including the dimensions (diameter, wall thickness, length, etc.), spacing, connection methods (welding, bolt connection, etc.), overall height and width of the crossbars and vertical bars, and other geometric parameters. In addition, the installation method of the guardrail (embedded, anchor bolt type, etc.) and foundation structure information can also be entered to comprehensively evaluate the stability and deformation characteristics of the guardrail structure under the action of collision force.

[0016] 3. Data analysis and evaluation module 3.1 Data classification and organization First, the collected guardrail information data is carefully classified. For guardrail material information, different types of materials (such as carbon steel, alloy steel, aluminum alloy, composite materials, etc.) are clearly distinguished, and the key physical property parameters of each material are recorded in detail, including but not limited to elastic modulus, yield strength, tensile strength, density, Poisson's ratio, etc. For guardrail structure information, its geometric structure characteristics are comprehensively sorted out, such as the cross-sectional shape (circular, square, etc.), size specifications (diameter, side length, wall thickness, etc.), length and spacing layout of the crossbar and vertical pole, as well as the connection method (welding, bolt connection, riveting, etc.) and the overall installation form (embedded, anchor bolt type, etc.) and installation foundation parameters; Integrate the collision result data, classify and summarize the energy absorption data (such as the energy value absorbed by the guardrail, the calculation results of energy absorption efficiency, etc.), displacement data (the maximum intrusion of the vehicle, the displacement of each guardrail component, etc.) and deformation data (the bending deformation degree of the crossbar, the bending or twisting deformation of the vertical pole, etc.) to ensure the accuracy and completeness of the data, laying a solid foundation for subsequent in-depth analysis; 3.2 Univariate Correlation Analysis For each physical property parameter of the guardrail material, a correlation analysis with the collision result data was carried out respectively, and the Pearson correlation coefficient was calculated using statistical methods to measure the degree of linear correlation between the two. For example, the correlation between the elastic modulus of the material and the maximum vehicle intrusion was analyzed. If the correlation coefficient is close to -1, it means that the elastic modulus and the maximum vehicle intrusion are strongly negatively correlated, that is, the larger the elastic modulus, the smaller the maximum vehicle intrusion, which means that the material performs better in resisting vehicle intrusion. Similarly, the yield strength and the guardrail energy absorption efficiency are analyzed for correlation. If there is a positive correlation and the correlation coefficient is large, it means that the material with a higher yield strength can absorb energy more effectively during a collision. The above statistical method is used to calculate the Pearson correlation coefficient, and the formula is: ,in is the sample value of the material elastic modulus, is the sample mean of the elastic modulus of the material, is the sample value of the maximum vehicle intrusion, is the sample mean of the maximum intrusion of vehicles, is the sample size; For example, in a road guardrail collision test, 10 sets of guardrail and vehicle collision data with different elastic modulus materials were collected, and the correlation coefficient between the material elastic modulus and the maximum vehicle intrusion was calculated. ,near , indicating that the elastic modulus is strongly negatively correlated with the maximum vehicle intrusion, that is, the larger the elastic modulus, the smaller the maximum vehicle intrusion, which means that the material performs better in resisting vehicle intrusion; Carry out similar analysis on various elements of the guardrail structure, explore the relationship between the cross-section shape of the crossbar and the overall energy absorption performance of the guardrail, and determine which shape is more conducive to energy absorption and dispersion by comparing the energy absorption data of different cross-section shapes (such as round and square) under the same collision conditions. Analyze the effect of the vertical pole spacing on the displacement trajectory of the vehicle after the collision. If it is found that a smaller vertical pole spacing can significantly reduce the deviation angle of the vehicle, it means that a reasonable vertical pole spacing design can help better guide the vehicle's driving direction and improve the guidance of the guardrail. Correlation analysis between the cross-section shape of the crossbar and the overall energy absorption performance of the guardrail. By comparing the energy absorption data of crossbars with different cross-section shapes (such as round and square) under the same collision conditions, the correlation coefficient (such as Pearson correlation coefficient) is calculated using statistical methods to measure the degree of correlation between the two. For example, in a series of collision tests, circular crossbar guardrails and square crossbar guardrails were tested separately. For the circular crossbar guardrail, the average value of its energy absorption data in multiple tests was , the standard deviation is ; For the square crossbar guardrail, the average energy absorption data is , the standard deviation is . Calculate the Pearson correlation coefficient between the two ,like If it is close to 1, it means that the cross-sectional shape of the crossbar is strongly positively correlated with the energy absorption performance of the guardrail, that is, a certain shape (such as square) may be more conducive to energy absorption and dispersion; if If it is close to -1, it is strongly negatively correlated; If it is close to 0, the correlation is weak; for example, , indicating that the square crossbar has a certain positive effect on the energy absorption performance of the guardrail under this test condition compared with the round crossbar, but it is not absolutely dominant and may need to be judged comprehensively in combination with other factors; Analysis of the effect of pole spacing on the displacement trajectory of vehicles after collision. In the guardrail collision test with different pole spacings, the displacement trajectory parameters such as the deviation angle of the vehicle after collision are measured, and the correlation between the pole spacing and the vehicle displacement trajectory parameters is analyzed using statistical methods. Specific application example: Set up multiple groups of guardrails with different pole spacings for collision tests, such as , , For each spacing, multiple collision tests are carried out and the deviation angle of the vehicle after collision is recorded. , , Calculate the correlation coefficient between the vertical pole spacing and the deviation angle If a smaller distance between the poles is found (such as ) corresponds to the deviation angle It is significantly smaller than the deviation angle at other intervals, and the correlation coefficient If it is close to -1, it indicates a reasonable pole spacing design (such as ) helps to better guide the direction of vehicle travel and improve the guidance of the guardrail. On the contrary, if the correlation coefficient is not significant or close to 0, it means that the distance between the vertical poles has little effect on the vehicle displacement trajectory under this test condition, and it may be necessary to further adjust the test parameters or consider the influence of other structural factors; 3.3 Analysis of multi-factor interaction effects Considering the complex interaction between guardrail materials and structures, a multivariate regression analysis method was used to construct a model to comprehensively evaluate the comprehensive impact of multiple factors on the collision results. , yield strength ,density etc.) and structural parameters (such as crossbar diameter , Pole spacing , Connection mode coding etc.) as independent variables, and collision result indicators (such as energy absorption efficiency , Maximum vehicle intrusion , Maximum deformation of guardrail etc.) as dependent variables and establish a multiple linear regression model: ; ; ; in, ..... , ..... , ..... is the regression coefficient, , , is the error term, , , They are energy absorption efficiency, maximum vehicle intrusion, and maximum guardrail deformation. is the guardrail material density, is the guardrail crossbar diameter, is the yield strength, is the elastic modulus, Encode the connection mode. The regression coefficient is solved by the least squares algorithm for the pole spacing to determine the weight and interaction relationship of each factor in the model. For example, the regression results may show that there is an interaction term between the elastic modulus and the crossbar diameter, and the interaction term has a significant effect on the energy absorption efficiency. This shows that under certain conditions, the combination of the elastic modulus and the crossbar diameter plays a key role in the energy absorption efficiency, rather than the independent effect of a single factor. 3.4. Optimization scheme screening and evaluation Based on the results of single-factor correlation analysis and multi-factor interaction analysis, the guardrail material and structure combination schemes with potential optimization possibilities are screened out. For example, based on the strong negative correlation between the elastic modulus of the material and the maximum vehicle intrusion, materials with higher elastic modulus are given priority. Combined with the optimization parameter combination of energy absorption efficiency in the multi-factor regression model, the appropriate crossbar and vertical bar structure dimensions, connection methods, etc. are determined; For the selected optimization schemes, the established regression model is used to perform performance prediction and evaluation. The material and structural parameters in the optimization scheme are substituted into the model to calculate the predicted energy absorption efficiency, maximum vehicle intrusion, maximum guardrail deformation and other indicators, and compare them with the original design scheme or industry standard requirements. If the prediction results show that the optimization scheme has significant improvements in all indicators (such as energy absorption efficiency increased by more than 20%, maximum vehicle intrusion reduced by 30%, etc.), the scheme will be listed as a key recommended scheme. If some indicators have improved but still have not reached the ideal state, the optimization parameters will be further adjusted and re-evaluated until a satisfactory optimization effect is obtained; 3.5. Result visualization and report generation Use data visualization technology to present the analysis process and results in an intuitive and easy-to-understand chart format, draw a correlation matrix diagram of material properties and collision result indicators, clearly show the degree of correlation between various material parameters and different collision results, draw a residual diagram of the multi-factor regression model, evaluate the goodness of fit and reliability of the model, and make a bar chart or line chart comparing the optimized solution with the original solution to highlight the significance of the optimization effect; 3.5.1. Example of correlation matrix between material properties and collision result indicators Assume that we consider the elastic modulus of the guardrail material ( )、Yield Strength( ) and density ( ) these three characteristic parameters, and the maximum vehicle intrusion ( )、Energy absorption efficiency( ) and the maximum deformation of the guardrail ( ) These three collision result indicators.

[0017] Table 1: Correlation matrix between material properties and collision result indicators

[0018] As shown in Table 1, we can intuitively see the degree of correlation between different material properties and collision result indicators. For example, the elastic modulus is strongly negatively correlated with the maximum vehicle intrusion, which means that the larger the elastic modulus, the smaller the maximum vehicle intrusion. This is consistent with the influence of the elastic modulus on the ability to resist deformation in material mechanics. Through the matrix diagram shown in Table 1, road guardrail design and development personnel can quickly understand the direction and degree of influence of various material properties on collision results, so as to make more reasonable decisions when selecting materials; 3.5.2. Residual plot of multi-factor regression model Assume that we have established a relationship between energy absorption efficiency ( ) of the multivariate regression model, the independent variables included elastic modulus ( )、Rod diameter( ) and pole spacing ( ), we conducted multiple tests (assuming 20 times) and obtained the actual energy absorption efficiency value ( ) and the energy absorption efficiency value predicted by the regression model ( ), calculate the residual ( ), in the residual graph, the horizontal axis is the predicted value ( ), the ordinate is the residual ( ), if the model fits well, the residuals should be randomly distributed around zero with no obvious trend or pattern; For example, most of the residuals fluctuate between -0.1 and 0.1, and do not show obvious systematic deviations, such as curvilinear trends or clustering in a certain area. This indicates that the multi-factor regression model we established can fit the data well to a certain extent, and the model has a certain degree of reliability. However, if there is an obvious trend in the residual graph, such as the residual increases or decreases with the increase of the predicted value, or the residual shows a certain regular distribution, such as a U-shaped or inverted U-shaped distribution, this may mean that there is a problem with the model and further adjustment or improvement is needed, such as considering adding more independent variables, checking whether there are outliers in the data, or improving the assumptions of the model. 3.5.3. Line chart comparing the optimized solution and the original solution Suppose we compare the energy absorption efficiency of the front and rear guardrails ( )、Maximum vehicle intrusion( ) and the maximum deformation of the guardrail ( ) These three indicators; The average energy absorption efficiency of the original solution in multiple tests is 0.3, and the average energy absorption efficiency of the optimized solution is increased to 0.45. We draw a line graph with the number of tests as the horizontal axis and the energy absorption efficiency as the vertical axis. For the maximum vehicle intrusion, the average value of the original solution is 0.5m, and the optimized solution is reduced to 0.3m. Similarly, the line graph is drawn with the number of tests as the horizontal axis and the maximum vehicle intrusion as the vertical axis. For the maximum deformation of the guardrail, the average value of the original solution is 0.2m, and the optimized solution is reduced to 0.12m. The number of tests is also used as the horizontal axis and the maximum deformation of the guardrail is used as the vertical axis. The line graph is drawn by using the coordinates. In the comparison line graph of energy absorption efficiency, the line of the optimized solution is above the line of the original solution, and the gap between the two is obvious, which clearly shows the significant improvement of the energy absorption efficiency of the optimized solution. In the comparison line graph of the maximum vehicle intrusion, the line of the optimized solution is always lower than the line of the original solution, indicating that the optimized solution can effectively reduce the maximum vehicle intrusion and improve the protection performance of the guardrail to the vehicle. In the comparison line graph of the maximum deformation of the guardrail, the line of the optimized solution is lower than the line of the original solution, indicating that the optimized guardrail structure performs better in resisting deformation. Generate a detailed comprehensive evaluation report, which covers an overview of data sources and processing, single-factor correlation analysis results, multi-factor interaction analysis model construction and solution process, optimization scheme screening and evaluation details, visualization results display and final conclusions and recommendations. The report explains the engineering significance and application value of the analysis results in concise and clear language, and provides comprehensive, accurate and guiding reference materials for road guardrail design and development personnel, engineering and technical personnel and relevant decision-making departments, which helps to continuously optimize and upgrade road guardrails and improve the level of road traffic safety.

[0019] In summary, the specific implementation of the road guardrail collision data evaluation and analysis system is as follows: 1. High-speed camera acquisition submodule A high-speed camera with a high frame rate (above 1000fps) and high resolution (1920×1080 pixels and above) is used to shoot all-round footage of the collision between the vehicle and the guardrail, so as to clearly record the moment of collision and subsequent dynamic changes, including the contact details between the vehicle and the guardrail, the initiation and development of deformation of the guardrail rods, etc.

[0020] 2. Manual data entry submodule A manual data entry interface is designed to input information that cannot be accurately captured or quantified by high-speed camera acquisition equipment. After the collision test is completed, the data entry personnel enter the data based on on-site observations and measurement tools, such as the exact model of the vehicle (obtained from the vehicle nameplate or related information), the estimated speed before the collision (estimated by speed measuring instruments or experience), the depth of the dent in a specific part of the vehicle body (measured using a depth gauge), etc. The input data must be verified for validity to ensure accuracy and completeness.

[0021] 3. Data preprocessing submodule Key frames are extracted from video data collected by high-speed cameras, and continuous video streams are converted into discrete image sequences. At the same time, the images are processed by noise reduction, contrast enhancement, etc. to improve image quality and facilitate feature extraction. The format of manually entered text data is unified and cleaned to remove redundant information and erroneous data.

[0022] 4. Guardrail material information input submodule It allows users to input the material type of the guardrail, such as steel (specific models such as Q235, Q345, etc.), aluminum alloy, composite material, etc., and further enter the physical property parameters of the material, such as elastic modulus, yield strength, density and other information, to provide basic data for subsequent analysis of the mechanical response of the guardrail during a collision.

[0023] 5. Guardrail structure information input submodule Enter the guardrail's structural design information in detail, including the dimensions of the crossbar and vertical bars (diameter, wall thickness, length, etc.), spacing, connection methods (welding, bolt connection, etc.), overall height and width and other geometric parameters. You can also enter the guardrail's installation method (embedded, anchor bolt type, etc.) and foundation structure information to comprehensively evaluate the stability and deformation characteristics of the guardrail structure under collision force.

[0024] 6. Data classification and organization Classify the collected guardrail information data, clearly distinguish the types of guardrail materials, and record the key physical property parameters of the materials; sort out the geometric characteristics of the guardrail structure; integrate the collision result data, classify the energy absorption, displacement, deformation and other data, and lay the foundation for subsequent analysis.

[0025] 7. Univariate correlation analysis Based on the physical property parameters of the guardrail material and the collision result data, the Pearson correlation coefficient is calculated using statistical methods to measure the degree of linear correlation. For example, the correlation between the elastic modulus of the material and the maximum intrusion of the vehicle, the yield strength and the energy absorption efficiency of the guardrail are analyzed; similar analysis is also performed on the structural elements of the guardrail to explore the influence of the cross-section shape of the crossbar and the energy absorption performance of the guardrail, and the influence of the spacing between the vertical poles on the displacement trajectory of the vehicle after the collision.

[0026] 8. Multi-factor interaction analysis The model was constructed using the multivariate regression analysis method. The material characteristic parameters (such as elastic modulus, yield strength, density, etc.) and structural parameters (such as crossbar diameter, vertical pole spacing, connection method coding, etc.) of the guardrail were taken as independent variables, and the collision result indicators (such as energy absorption efficiency, maximum vehicle intrusion, maximum guardrail deformation, etc.) were taken as dependent variables. A multivariate linear regression model was established, and the regression coefficients were solved using algorithms such as the least squares method to determine the weights of each factor and their interaction relationships.

[0027] 9. Optimization plan screening and evaluation Based on the results of single-factor and multi-factor analysis, screen potential optimized guardrail materials and structural combination schemes, such as giving priority to materials with high elastic modulus, and determining the structural dimensions and connection methods of crossbars and vertical poles based on energy absorption efficiency optimization parameters; use regression models to predict the performance of the screened schemes and compare them with the original schemes or industry standards. If the indicators are significantly improved, they will be listed as key recommended schemes, otherwise adjust the parameters and re-evaluate.

[0028] 10. Result visualization and report generation Use data visualization technology to draw correlation matrices between material properties and collision result indicators, multi-factor regression model residual diagrams, comparison diagrams between optimized solutions and original solutions, etc.; generate detailed comprehensive evaluation reports, covering data sources and processing, analysis results, optimization solution details, visualization displays and conclusions and recommendations, provide reference materials for relevant personnel, and help optimize and upgrade road guardrails.

[0029] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the scope of protection of the present invention.

Claims

1. A road guardrail collision data evaluation and analysis system, characterized in that: The system includes a test data acquisition module, a guardrail information input module, and a data analysis and evaluation module; The test data acquisition module includes a high-speed video acquisition submodule, a manual data entry submodule and a data preprocessing submodule. The high-speed video acquisition submodule uses a high-frame rate and high-resolution camera to shoot the collision process. The manual data entry submodule records information that cannot be captured by the camera. The data preprocessing submodule performs preliminary processing on the collected data. The guardrail information input module includes a guardrail material information input submodule and a guardrail structure information input submodule, and the guardrail information input module is used to input guardrail material and structure related information; The data analysis and evaluation module includes data classification and organization, single-factor correlation analysis, multi-factor interaction analysis, optimization scheme screening and evaluation, and result visualization and report generation. The data classification and organization is used to classify and integrate the collected data according to materials, structures, and collision results. The single-factor correlation analysis uses statistical methods to calculate the Pearson correlation coefficient to analyze the correlation between material characteristic parameters and collision result data, and between structural elements and collision results. The multi-factor interaction analysis uses multivariate regression analysis to construct a model with material characteristics and structural parameters as independent variables and collision result indicators as dependent variables to determine factor weights and interaction relationships. The optimization scheme screening and evaluation is used to screen potential optimization schemes based on the analysis results and use regression models to predict performance and compare and evaluate. The result visualization and report generation uses visualization technology to draw relevant charts to generate a comprehensive evaluation report covering data processing, analysis, optimization schemes and conclusion recommendations.

2. A road guardrail collision data evaluation and analysis system as claimed in claim 1, characterized in that: The specific formula for calculating the Pearson correlation coefficient using the statistical method is as follows: ； in is the sample value of the material elastic modulus, is the sample mean of the elastic modulus of the material, is the sample value of the maximum vehicle intrusion, is the sample mean of the maximum intrusion of vehicles, is the sample size.

3. A road guardrail collision data evaluation and analysis system as claimed in claim 1, characterized in that: The specific formula for constructing the multivariate regression analysis model is as follows: ； ； ； in, ..... , ..... , ..... is the regression coefficient, , , is the error term, , , They are energy absorption efficiency, maximum vehicle intrusion, and maximum guardrail deformation. is the guardrail material density, is the guardrail crossbar diameter, is the yield strength, is the elastic modulus, Encode the connection mode. The distance between the poles is calculated by the least squares algorithm to solve the regression coefficient and determine the weight and interaction relationship of each factor in the model.

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