Road pavement measurement data analysis method and system based on big data

Abstract

The invention discloses a road pavement measurement data analysis method and system based on big data, and belongs to the field of road disease detection and maintenance management.The road pavement measurement data analysis method comprises the steps that pavement roadbed disease data is obtained, the roadbed slope rate change condition is evaluated through settlement data, and a disease evolution model is constructed; the pavement crack width and the differential settlement are introduced into a disease evolution model, the influence of thermal stress, traffic load, material degradation and rainfall on disease evolution is synthesized, the pavement crack width change condition and the roadbed settlement condition in the future are evaluated, and a vehicle damage evaluation model is constructed; the road surface crack width and the roadbed slope rate change are imported into a vehicle damage assessment model, the vehicle speed influence and the tire condition are integrated, the damage influence of highway diseases on vehicle driving is assessed, an optimal maintenance time assessment model is established, the vehicle damage risk is imported into the optimal maintenance time assessment model, and the time with the minimum comprehensive maintenance cost is assessed. And the maintenance efficiency is improved.

Description

Technical Field

[0001] This application belongs to the field of road disease detection and maintenance management, specifically a road pavement measurement data analysis method and system based on big data. Background Art

[0002] With increasing traffic volume and the extension of road age, highway damage is becoming increasingly serious, impacting not only vehicle safety but also substantial repair costs and traffic congestion. Traditional methods for detecting and maintaining road damage, relying primarily on manual inspections and regular patrols, fail to promptly reflect damage trends. This results in some road damage being overlooked or delayed, severely impacting traffic safety. Existing maintenance decisions typically only consider the impact of the damage itself, without considering its impact on vehicle operation. These decisions often focus on repair costs, but lack a comprehensive assessment of factors such as traffic flow and road lifespan, making it difficult to optimize resource and cost allocation.

[0003] This application combines the predicted data of pavement damage, comprehensively considers multiple factors such as traffic flow, maintenance costs and vehicle damage risks, and selects the maintenance time that can minimize economic losses and traffic impacts while ensuring traffic safety, and takes into account the impact on vehicle driving. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application proposes a road pavement measurement data analysis method and system based on big data.

[0005] To achieve the above objectives, this application provides the following technical solutions: The road pavement measurement data analysis method and system based on big data includes the following specific steps: Obtain roadbed disease data and evaluate roadbed slope changes through settlement data; Construct a damage evolution model, import pavement crack width and differential settlement into the model, and comprehensively consider the effects of thermal stress, traffic load, material degradation, and rainfall on damage evolution to assess future changes in pavement crack width and subgrade settlement. Construct a vehicle damage assessment model, incorporate pavement crack width and roadbed slope changes into the vehicle damage assessment model, and comprehensively consider the impact of vehicle speed and tire conditions to assess the damage impact of highway diseases on vehicle driving; The optimal maintenance time assessment model imports the vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the lowest comprehensive maintenance cost.

[0006] Preferably, the step of obtaining road subgrade disease data and evaluating the road subgrade slope change using settlement data comprises the following specific steps: S11. Collect highway pavement images using a 3D laser scanner and a high-definition camera to obtain 3D pavement topography data and crack data. Use image binarization and skeleton extraction algorithms to identify pavement crack widths on the collected highway pavement images. The pavement crack width is the average of the local pixel widths. Obtain pavement subgrade settlement data using laser radar scanning and ground-penetrating radar. The differential settlement calculation formula is: , where, in the cross slope direction, is the settlement of the roadbed center, is the settlement of the roadbed edge. In the longitudinal slope direction, the maximum settlement at one end of the transition section is , the minimum settlement at the other end is , the transverse half width of the roadbed is The length of the longitudinal settlement transition section of the roadbed is G. Substitute the differential settlement into the slope change calculation formula to calculate the transverse slope change and longitudinal slope change of the roadbed. The transverse slope change calculation formula is: ,in, is the differential settlement of the transverse slope, where the calculation formula for the longitudinal slope change of the roadbed is: ,in, is the differential settlement of the longitudinal slope; S12. Obtain historical traffic flow data of different sections of the expressway, obtain vehicle driving data, including vehicle driving speed, vehicle tire data, etc., and obtain environmental data.

[0007] Preferably, the construction of the disease evolution model, importing the pavement crack width and differential settlement into the disease evolution model, comprehensively considering the effects of thermal stress, traffic load, material degradation and rainfall on disease evolution, and evaluating future changes in pavement crack width and roadbed settlement includes the following specific steps: S21. Substitute the crack width into the crack width evolution calculation formula to evaluate the future change in pavement crack width. The crack width calculation formula at time t is: ,in, is the initial crack width, for Thermal stress at the moment represents the expansion and contraction caused by temperature changes. For safety thermal stress, for Traffic load stress at time t represents the repeated wheel action of vehicles, For safe traffic load stress, for The amount of pavement material degradation at the time is determined by the pavement material, where the thermal stress calculation formula is: , where E is the elastic modulus of the pavement material, is the thermal expansion coefficient, determined according to the pavement material, is the daily temperature difference, where The calculation formula of traffic load stress at the moment is: , where n is the number of vehicle group types, For the i-th type of vehicle The number of passes per unit time at a given moment, is the standard axle weight of the i-th category vehicle, is the load propagation factor, which indicates the relative impact of this type of vehicle on road fatigue and is determined through experience or simulation fitting. The material degradation calculation formula is: ,in, is the pavement material degradation coefficient; S22. Settlement evolution is predicted using the Hoshino method. The Hoshino method is based on the on-site measured settlement data and the proportional relationship between total settlement and the square root of time. The differential settlement is substituted into the settlement evolution variable calculation formula to evaluate future roadbed settlement. The settlement evolution variable calculation formula is: ,in, is the initial settlement, is the initial time, is the sedimentation rate fitting parameter, which is used to control the maximum sedimentation growth rate. k is the time growth adjustment factor, which is used to control the curvature of the growth rate over time. is the load-settlement sensitivity, is the rainwater deposition sensitivity, is the effective rainfall per unit time, and the evolved settlement is substituted into the slope calculation formula to calculate the transverse slope change and the longitudinal slope change of the roadbed respectively.

[0008] Preferably, the construction of the vehicle damage assessment model, importing the pavement crack width and roadbed slope changes into the vehicle damage assessment model, and comprehensively considering the impact of vehicle speed and tire conditions to assess the damage impact of highway diseases on vehicle driving includes the following specific steps: S31. Substitute the width of the road crack into the tire stress calculation formula to evaluate the impact of the crack on the vehicle tire. The calculation formula for the tire stress induced by the crack is: ,in, is the crack width, is the standard material modulus of the tire, r is the standard radius of the tire, is the speed influence function, is the vehicle deflection angle, where the speed influence function is calculated as: ,in, is the average speed of the vehicle at time t, is the vehicle's reference speed, is the speed sensitivity factor; S32. Substitute the changes in the transverse slope and longitudinal slope of the roadbed into the vertical impact acceleration calculation formula to evaluate the vertical impact of the vehicle caused by the road surface settlement. The vertical impact acceleration calculation formula is: , where g is the acceleration due to gravity, is the slope change at time t, is the vehicle tire compression buffer constant, where the slope change calculation formula at time t is: ,in, and are the horizontal weight coefficient and the vertical weight coefficient; S33. Substitute the tire stress induced by cracks and the vehicle vertical impact acceleration caused by settlement into the vehicle damage risk calculation formula to evaluate the vehicle driving condition, where the vehicle damage risk calculation formula is: ,in, is the maximum safe value of tire stress, It is the maximum safe vertical impact acceleration.

[0009] Preferably, the construction of the optimal maintenance time evaluation model and the introduction of the vehicle damage risk into the optimal maintenance time evaluation model to evaluate the time with the minimum comprehensive maintenance cost include the following specific steps: S41. Define the maximum acceptable damage threshold for each vehicle category, compare the vehicle damage risk with the maximum tolerable damage value, and select the time with the lowest overall maintenance cost for maintenance within the time interval when the vehicle damage is still within the acceptable range; S42. Substitute the vehicle damage risk into the optimal maintenance time calculation formula to optimize the maintenance time, wherein the optimal maintenance time calculation formula is: ,in, The time interval during which the vehicle damage is within an acceptable range. The risk cost caused by maintenance delay is used to indirectly reflect the extent of damage. is the direct cost of performing maintenance during the maintenance window, is the traffic impact cost caused by traffic flow during the maintenance period, 、 and is the weight, where the risk cost calculation formula brought by maintenance delay is: ,in, is the expected cost per unit of damage, for Risk of vehicle damage at all times, for The vehicle damage risk at the time, where the direct cost calculation formula for maintenance during the maintenance period is: ,in, is the fixed maintenance cost, is the resource cost related to the maintenance time, among which the traffic impact cost caused by the traffic flow during the maintenance period is calculated as follows: ,in, For traffic flow, The economic conversion value of congestion per unit traffic flow is used, and the time period when the damage does not exceed the safety threshold and the cost is the lowest is selected for maintenance.

[0010] The road pavement measurement data analysis system based on big data is implemented based on the above-mentioned road pavement measurement data analysis method based on big data, and specifically includes: Data acquisition module, used to obtain road surface and subgrade disease data, vehicle driving data and environmental data; The disease evolution module is used to evaluate the future changes in pavement crack width and subgrade settlement through pavement crack width and differential settlement; The vehicle damage assessment module is used to evaluate the damage impact of highway diseases on vehicle driving by measuring the width of pavement cracks and changes in roadbed slope; The optimal maintenance time evaluation module is used to evaluate the time with the minimum comprehensive maintenance cost by importing vehicle damage risk into the optimal maintenance time evaluation model.

[0011] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned highway subgrade and pavement disease detection method based on big data analysis by calling the computer program stored in the memory.

[0012] A computer-readable storage medium is characterized in that it stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned road pavement measurement data analysis method based on big data.

[0013] Compared with the prior art, the present invention has the following advantages: This application obtains pavement and subgrade disease data, and evaluates the changes in subgrade slope through settlement data, constructs a disease evolution model, imports pavement crack width and differential settlement into the disease evolution model, comprehensively considers the effects of thermal stress, traffic load, material degradation and rainfall on disease evolution, evaluates future pavement crack width changes and subgrade settlement, constructs a vehicle damage assessment model, imports pavement crack width and subgrade slope changes into the vehicle damage assessment model, comprehensively considers the impact of vehicle speed and tire conditions, evaluates the damage effect of highway diseases on vehicle driving, and constructs an optimal maintenance time assessment model, imports vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the lowest comprehensive maintenance cost, thereby improving maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of the overall process of the road pavement measurement data analysis method based on big data in this application; Figure 2 This is a schematic diagram of the disease evolution in this application; Figure 3 Flowchart for calculating vehicle damage risk for this application; Figure 4 This is a schematic diagram of the overall framework of the road pavement measurement data analysis system based on big data in this application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0016] Example 1

[0017] See also Figure 1-3 The present application provides an embodiment of a road surface measurement data analysis method based on big data, which includes the following specific steps: Obtain roadbed disease data and evaluate roadbed slope changes through settlement data; Construct a damage evolution model, import pavement crack width and differential settlement into the model, and comprehensively consider the effects of thermal stress, traffic load, material degradation, and rainfall on damage evolution to assess future changes in pavement crack width and subgrade settlement. Construct a vehicle damage assessment model, incorporate pavement crack width and roadbed slope changes into the vehicle damage assessment model, and comprehensively consider the impact of vehicle speed and tire conditions to assess the damage impact of highway diseases on vehicle driving; The optimal maintenance time assessment model imports the vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the lowest comprehensive maintenance cost.

[0018] In this embodiment, it should be specifically explained that obtaining road subgrade disease data and evaluating the subgrade slope change using settlement data includes the following specific steps: S11. Collect highway pavement images using a 3D laser scanner and a high-definition camera to obtain 3D pavement topography data and crack data. Use image binarization and skeleton extraction algorithms to identify pavement crack widths on the collected highway pavement images. The pavement crack width is the average of the local pixel widths. Obtain pavement subgrade settlement data using laser radar scanning and ground-penetrating radar. The differential settlement calculation formula is: , where, in the cross slope direction, is the settlement of the roadbed center, is the settlement of the roadbed edge. In the longitudinal slope direction, the maximum settlement at one end of the transition section is , the minimum settlement at the other end is , the transverse half width of the roadbed is The length of the longitudinal settlement transition section of the roadbed is G. Substitute the differential settlement into the slope change calculation formula to calculate the transverse slope change and longitudinal slope change of the roadbed. The transverse slope change calculation formula is: ,in, is the differential settlement of the transverse slope, where the calculation formula for the longitudinal slope change of the roadbed is: ,in, is the differential settlement of the longitudinal slope; It should be specifically explained here that one end and the other end of the longitudinal slope direction correspond to two characteristic points or sections in the longitudinal direction of the roadbed (the direction in which the road extends); For example, the transition section where the roadbed connects to the structure has an uphill section or a thicker fill section at one end, and a downhill section or a thinner fill section at the other end. S12. Obtain historical traffic flow data of different sections of the expressway, obtain vehicle driving data, including vehicle driving speed, vehicle tire data, etc., and obtain environmental data.

[0019] In this embodiment, it should be specifically explained that constructing a disease evolution model, importing pavement crack width and differential settlement into the disease evolution model, and comprehensively considering the effects of thermal stress, traffic load, material degradation, and rainfall on disease evolution to evaluate future changes in pavement crack width and roadbed settlement include the following specific steps: S21. Substitute the crack width into the crack width evolution calculation formula to evaluate the future change in pavement crack width. The crack width calculation formula at time t is: ,in, is the initial crack width, for Thermal stress at the moment represents the expansion and contraction caused by temperature changes. For safety thermal stress, for Traffic load stress at time t represents the repeated wheel action of vehicles, For safe traffic load stress, for The amount of pavement material degradation at the time is determined by the pavement material, where the thermal stress calculation formula is: , where E is the elastic modulus of the pavement material, is the thermal expansion coefficient, determined according to the pavement material, is the daily temperature difference, where The calculation formula of traffic load stress at the moment is: , where n is the number of vehicle group types, For the i-th type of vehicle The number of passes per unit time at a given moment, is the standard axle weight of the i-th category vehicle, is the load propagation factor, which indicates the relative impact of this type of vehicle on road fatigue and is determined through experience or simulation fitting. The material degradation calculation formula is: ,in, is the pavement material degradation coefficient; For example, in this embodiment, vehicles are grouped into: passenger cars, light trucks, and heavy trucks, indicating that when different vehicles encounter road hazards, their weight and speed have an impact on the degree of damage to the vehicle; S22. Settlement evolution is predicted using the Hoshino method. The Hoshino method is based on the on-site measured settlement data and the proportional relationship between total settlement and the square root of time. The differential settlement is substituted into the settlement evolution variable calculation formula to evaluate future roadbed settlement. The settlement evolution variable calculation formula is: ,in, is the initial settlement, is the initial time, is the sedimentation rate fitting parameter, which is used to control the maximum sedimentation growth rate. k is the time growth adjustment factor, which is used to control the curvature of the growth rate over time. is the load-settlement sensitivity, is the rainwater deposition sensitivity, is the effective rainfall per unit time, and the evolved settlement is substituted into the slope calculation formula to calculate the transverse slope change and the longitudinal slope change of the roadbed respectively; It is necessary to explain specifically here that The value of k is obtained by plotting the relationship curve between time and differential settlement through the measured settlement amount, performing curve fitting calculation through the settlement evolution calculation formula, obtaining multiple straight lines that conform to the linear relationship, and taking the straight line that best conforms to the linear relationship to obtain the corresponding coefficient and k.

[0020] In this embodiment, it should be specifically explained that constructing a vehicle damage assessment model, importing the pavement crack width and roadbed slope changes into the vehicle damage assessment model, and comprehensively considering the impact of vehicle speed and tire conditions to assess the damage impact of highway diseases on vehicle driving includes the following specific steps: S31. Substitute the width of the road crack into the tire stress calculation formula to evaluate the impact of the crack on the vehicle tire. The calculation formula for the tire stress induced by the crack is: ,in, is the crack width, is the standard material modulus of the tire, r is the standard radius of the tire, is the speed influence function, is the vehicle deflection angle, where the speed influence function is calculated as: ,in, is the average speed of the vehicle at time t, is the vehicle's reference speed, is the speed sensitivity factor; Exemplarily, in this embodiment, the speed sensitivity factor defaults to 1.3; S32. Substitute the changes in the transverse slope and longitudinal slope of the roadbed into the vertical impact acceleration calculation formula to evaluate the vertical impact of the vehicle caused by the road surface settlement. The vertical impact acceleration calculation formula is: , where g is the acceleration due to gravity, is the slope change at time t, is the vehicle tire compression buffer constant, which is obtained according to the tire material standard. The slope change at time t is calculated as follows: ,in, and are the horizontal weight coefficient and the vertical weight coefficient; For example, in this embodiment, the transverse weight coefficient and the longitudinal weight coefficient are 0.4 and 0.6 respectively; S33. Substitute the tire stress induced by cracks and the vehicle vertical impact acceleration caused by settlement into the vehicle damage risk calculation formula to evaluate the vehicle driving condition, where the vehicle damage risk calculation formula is: ,in, is the maximum safe value of tire stress, It is the maximum safe vertical impact acceleration.

[0021] In this embodiment, it should be specifically explained that constructing an optimal maintenance time evaluation model and introducing vehicle damage risk into the optimal maintenance time evaluation model to evaluate the time with the minimum comprehensive maintenance cost includes the following specific steps: S41. Define the maximum acceptable damage threshold for each vehicle category, compare the vehicle damage risk with the maximum tolerable damage value, and select the time with the lowest overall maintenance cost for maintenance within the time interval when the vehicle damage is still within the acceptable range; S42. Substitute the vehicle damage risk into the optimal maintenance time calculation formula to optimize the maintenance time, wherein the optimal maintenance time calculation formula is: ,in, The time interval during which the vehicle damage is within an acceptable range. The risk cost caused by maintenance delay is used to indirectly reflect the extent of damage. is the direct cost of performing maintenance during the maintenance window, is the traffic impact cost caused by traffic flow during the maintenance period, 、 and is the weight, where the risk cost calculation formula brought by maintenance delay is: ,in, is the expected cost per unit of damage, for Risk of vehicle damage at all times, for The vehicle damage risk at the time, where the direct cost calculation formula for maintenance during the maintenance period is: ,in, is the fixed maintenance cost, is the resource cost associated with maintenance time, including nighttime construction and construction on holidays. The cost calculation formula for the traffic impact caused by traffic flow during the maintenance period is: ,in, For traffic flow, The economic conversion value of congestion per unit traffic flow is used, and the time period when the damage does not exceed the safety threshold and the cost is the lowest is selected for maintenance.

[0022] It should be noted here that the method for determining the maximum acceptable damage threshold for a vehicle is: obtain representative historical vehicle damage data, substitute it into the vehicle damage risk calculation formula to evaluate the vehicle damage risk, obtain experts' safety assessment of the vehicle damage risk, import the vehicle damage risk value and safety assessment value into the fitting software, and output the vehicle risk value that meets the maximum safety range.

[0023] The advantages of this embodiment over the prior art are: This application obtains pavement and subgrade disease data, and evaluates the changes in subgrade slope through settlement data, constructs a disease evolution model, imports pavement crack width and differential settlement into the disease evolution model, comprehensively considers the effects of thermal stress, traffic load, material degradation and rainfall on disease evolution, evaluates future pavement crack width changes and subgrade settlement, constructs a vehicle damage assessment model, imports pavement crack width and subgrade slope changes into the vehicle damage assessment model, comprehensively considers the impact of vehicle speed and tire conditions, evaluates the damage effect of highway diseases on vehicle driving, and constructs an optimal maintenance time assessment model, imports vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the lowest comprehensive maintenance cost, thereby improving maintenance efficiency.

[0024] Example 2

[0025] like Figure 4As shown, a road pavement measurement data analysis system based on big data is implemented based on the above-mentioned road pavement measurement data analysis method based on big data, and specifically includes a data acquisition module, a disease evolution module, a vehicle damage assessment module and an optimal maintenance time assessment module. The data acquisition module is used to obtain pavement and subgrade disease data, vehicle driving data and environmental data; the disease evolution module is used to assess future changes in pavement crack width and subgrade settlement through pavement crack width and differential settlement; the vehicle damage assessment module is used to assess the damage impact of highway diseases on vehicle driving through changes in pavement crack width and subgrade slope; the optimal maintenance time assessment module is used to assess the time with the minimum comprehensive maintenance cost by importing vehicle damage risk into the optimal maintenance time assessment model.

[0026] Example 3

[0027] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned road pavement measurement data analysis method based on big data by calling the computer program stored in the memory.

[0028] This electronic device may vary significantly depending on its configuration or performance. It can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the big data-based road pavement measurement data analysis method provided in the above-mentioned method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0029] Example 4

[0030] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program is executed on a computer device, the computer device is enabled to execute the above-mentioned road pavement measurement data analysis method based on big data.

[0031] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0032] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A road pavement measurement data analysis method based on big data, characterized in that: It includes the following specific steps: Obtain roadbed disease data and evaluate roadbed slope changes through settlement data; Construct a damage evolution model, import pavement crack width and differential settlement into the model, and comprehensively consider the effects of thermal stress, traffic load, material degradation, and rainfall on damage evolution to assess future changes in pavement crack width and subgrade settlement. Construct a vehicle damage assessment model, incorporate pavement crack width and roadbed slope changes into the vehicle damage assessment model, and comprehensively consider the impact of vehicle speed and tire conditions to assess the damage impact of highway diseases on vehicle driving; The optimal maintenance time assessment model imports the vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the lowest comprehensive maintenance cost.

2. The road surface measurement data analysis method based on big data according to claim 1, characterized in that: The construction of the disease evolution model, importing the pavement crack width and differential settlement into the disease evolution model, comprehensively considering the effects of thermal stress, traffic load, material degradation and rainfall on disease evolution, and evaluating the future changes in pavement crack width and roadbed settlement includes the following specific steps: Substitute the crack width into the crack width evolution calculation formula to evaluate the future change in pavement crack width. The crack width calculation formula at time t is: ,in, is the initial crack width, for Thermal stress at all times, For safety thermal stress, for Traffic load stress at each moment, For safe traffic load stress, for The amount of pavement material degradation at the moment, where the thermal stress calculation formula is: , where E is the elastic modulus of the pavement material, is the coefficient of thermal expansion, is the daily temperature difference, where The calculation formula of traffic load stress at the moment is: , where n is the number of vehicle group types, For the i-th type of vehicle The number of passes per unit time at a given moment, is the standard axle weight of the i-th category vehicle, is the load propagation factor, where the material degradation calculation formula is: ,in, is the pavement material degradation coefficient; The settlement evolution is predicted by the Hoshino method, and the differential settlement is substituted into the settlement evolution variable calculation formula to evaluate the future roadbed settlement. The settlement evolution variable calculation formula is: ,in, is the initial settlement, is the initial time, is the sedimentation rate fitting parameter, k is the time growth adjustment factor, is the load-settlement sensitivity, is the rainwater deposition sensitivity, is the effective rainfall per unit time, and the evolved settlement is substituted into the slope calculation formula to calculate the transverse slope change and the longitudinal slope change of the roadbed respectively.

3. The road surface measurement data analysis method based on big data according to claim 2, characterized in that: The vehicle damage assessment model is constructed, and the pavement crack width and roadbed slope change are introduced into the vehicle damage assessment model. The vehicle speed and tire conditions are comprehensively considered to assess the damage effect of highway diseases on vehicle driving. The specific steps include: Substitute the width of the road crack into the tire stress calculation formula to evaluate the impact of the crack on the vehicle tire. The calculation formula for the tire stress induced by the crack is: ,in, is the crack width, is the standard material modulus of the tire, r is the standard radius of the tire, is the speed influence function, is the vehicle deflection angle, where the speed influence function is calculated as: ,in, is the average speed of the vehicle at time t, is the vehicle's reference speed, is the speed sensitivity factor; Substitute the changes in the transverse slope and longitudinal slope of the roadbed into the vertical impact acceleration calculation formula to evaluate the vertical impact of the vehicle caused by road surface settlement. The vertical impact acceleration calculation formula is: , where g is the acceleration due to gravity, is the slope change at time t, is the vehicle tire compression buffer constant, where the slope change calculation formula at time t is: ,in, and are the horizontal weight coefficient and the vertical weight coefficient, is the change in the transverse slope of the roadbed at time t, is the change in longitudinal slope of the roadbed at time t; The tire stress induced by cracks and the vertical impact acceleration of the vehicle caused by settlement are substituted into the vehicle damage risk calculation formula to evaluate the vehicle driving condition. The vehicle damage risk calculation formula is: ,in, is the maximum safe value of tire stress, It is the maximum safe vertical impact acceleration.

4. The road surface measurement data analysis method based on big data according to claim 3, characterized in that: The construction of the optimal maintenance time evaluation model and the introduction of vehicle damage risk into the optimal maintenance time evaluation model to evaluate the time with the minimum comprehensive maintenance cost include the following specific steps: Define the maximum acceptable damage threshold for vehicles of different categories, compare the vehicle damage risk with the maximum tolerable damage value, and select the time with the lowest comprehensive maintenance cost to perform maintenance within the time period when the vehicle damage is still within the acceptable range; Substitute the vehicle damage risk into the optimal maintenance time calculation formula to optimize the maintenance time. The optimal maintenance time calculation formula is: ,in, The time interval during which the vehicle damage is within an acceptable range. The risk cost of maintenance delays, is the direct cost of performing maintenance during the maintenance window, is the traffic impact cost caused by traffic flow during the maintenance period, 、 and is the weight, where the risk cost calculation formula brought by maintenance delay is: ,in, is the expected cost per unit of damage, for The risk of vehicle damage at all times, for The vehicle damage risk at the time, where the direct cost calculation formula for maintenance during the maintenance period is: ,in, is the fixed maintenance cost, is the resource cost related to the maintenance time, among which the traffic impact cost caused by the traffic flow during the maintenance period is calculated as follows: ,in, For traffic flow, The economic conversion value of congestion per unit traffic flow is used, and the time period when the damage does not exceed the safety threshold and the cost is minimized is selected for maintenance.

5. The road surface measurement data analysis method based on big data according to claim 4, characterized in that: The method of obtaining roadbed disease data and evaluating the roadbed slope change using settlement data includes the following specific steps: Highway pavement images were collected using a 3D laser scanner and a high-definition camera to obtain 3D pavement topography data and crack data. The collected highway pavement images were then binarized and subjected to a skeleton extraction algorithm to identify crack widths. LiDAR scanning and ground-penetrating radar were used to obtain road subgrade settlement data. The differential settlement calculation formula is: , where, in the cross slope direction, is the settlement of the roadbed center, is the settlement of the roadbed edge. In the longitudinal slope direction, the maximum settlement at one end of the transition section is , the minimum settlement at the other end is , the transverse half width of the roadbed is The length of the longitudinal settlement transition section of the roadbed is G. Substitute the differential settlement into the slope change calculation formula to calculate the transverse slope change and longitudinal slope change of the roadbed. The transverse slope change calculation formula is: ,in, is the differential settlement of the transverse slope, where the calculation formula for the longitudinal slope change of the roadbed is: ,in, is the differential settlement of the longitudinal slope; Obtain historical traffic flow data for different sections of the highway, obtain vehicle travel data, and obtain environmental data.

6. A road pavement measurement data analysis system based on big data, which is implemented based on the road pavement measurement data analysis method based on big data according to any one of claims 1 to 5, characterized in that: Specifically include: Data acquisition module, used to obtain road surface and subgrade disease data, vehicle driving data and environmental data; The disease evolution module is used to evaluate the future changes in pavement crack width and subgrade settlement through pavement crack width and differential settlement; The vehicle damage assessment module is used to evaluate the damage impact of highway diseases on vehicle driving by measuring the width of pavement cracks and changes in roadbed slope; The optimal maintenance time evaluation module is used to evaluate the time with the minimum comprehensive maintenance cost by importing vehicle damage risk into the optimal maintenance time evaluation model.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the road pavement measurement data analysis method based on big data according to any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the road pavement measurement data analysis method based on big data according to any one of claims 1 to 5.

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