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

Abstract

The present application discloses a road pavement measurement data analysis method and system based on big data, which belongs to the field of road disease detection and maintenance management. The present application obtains pavement and subgrade disease data, and evaluates the subgrade slope change through settlement data, constructs a disease evolution model, introduces the 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, introduces the pavement crack width and subgrade slope change 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, introduces vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the minimum comprehensive maintenance cost, thereby improving maintenance efficiency.

Description

Technical Field

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

[0002] With increasing traffic volume and the aging of roads, highway defects are becoming increasingly severe, affecting not only driving safety but also leading to substantial maintenance costs and traffic congestion. Traditional methods of road defect detection and maintenance rely primarily on manual inspections and periodic patrols, which cannot reflect the development trends of defects in a timely manner. This results in some road defects being overlooked or delayed in treatment, seriously impacting traffic safety. Existing maintenance decisions typically only consider the impact of the defects themselves, neglecting their effects on vehicle operation, and often focus on repair costs without a comprehensive assessment of factors such as traffic flow and road lifespan, making it difficult to achieve optimal resource and cost allocation.

[0003] This application combines road surface distress prediction data with a comprehensive consideration of factors such as traffic flow, maintenance costs, and vehicle damage risks. Under the premise of ensuring traffic safety, it selects the maintenance time that can minimize economic losses and traffic impact, and also takes into account the impact on vehicle driving. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application proposes a road surface measurement data analysis method and system based on big data.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A road surface measurement data analysis method and system based on big data includes the following specific steps:

[0007] Acquire data on pavement and subgrade defects, and assess changes in subgrade slope using settlement data;

[0008] A disease evolution model was constructed, and pavement crack width and differential settlement were imported into the model. The effects of thermal stress, traffic load, material degradation and rainfall on disease evolution were comprehensively considered to assess the future changes in pavement crack width and subgrade settlement.

[0009] A vehicle damage assessment model was constructed, and the changes in pavement crack width and roadbed slope were incorporated into the model. The impact of vehicle speed and tire condition were considered to assess the damage to vehicle operation caused by highway defects.

[0010] The optimal maintenance time assessment model incorporates vehicle damage risk into the assessment of the time that minimizes overall maintenance costs.

[0011] Preferably, the process of acquiring pavement and subgrade distress data and assessing subgrade slope changes through settlement data includes the following specific steps:

[0012] S11. Highway pavement images are acquired using a 3D laser scanner and a high-definition camera to obtain 3D pavement topography and crack data. The acquired highway pavement images are then processed using image binarization and skeleton extraction algorithms to identify pavement crack widths. The crack width is determined as the average width of local pixels. Pavement and subgrade settlement data are obtained using LiDAR scanning and ground-penetrating radar. The formula for calculating differential settlement is as follows: In the transverse slope direction, This is the settlement at the center of the roadbed. The settlement at the edge of the roadbed is [value]. In the longitudinal slope direction, the maximum settlement at one end of the transition section is [value]. 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. The differential settlement is substituted into the slope change calculation formula to calculate the transverse slope change and the longitudinal slope change of the roadbed. The formula for calculating the transverse slope change is as follows: ,in, The differential settlement is calculated based on the cross slope, where the formula for calculating the longitudinal slope change of the roadbed is: ,in, This represents the differential settlement along the longitudinal slope.

[0013] S12. Obtain historical traffic flow data for different sections of the highway, obtain vehicle driving data, including vehicle speed, vehicle tire data, etc., and obtain environmental data.

[0014] Preferably, the construction of the disease evolution model, which incorporates pavement crack width and differential settlement into the model, and comprehensively considers the impacts of thermal stress, traffic load, material degradation, and rainfall on disease evolution to assess future changes in pavement crack width and subgrade settlement, includes the following specific steps:

[0015] S21. Substitute the crack width into the crack width evolution calculation formula to assess the future changes in pavement crack width, where the crack width calculation formula at time t is: ,in, The initial crack width, for Thermal stress at any given moment represents the expansion and contraction caused by temperature changes. For safety thermal stress, for Traffic load stress at any given time represents the repeated wheel action on a vehicle. To ensure safe traffic load stress, for The amount of pavement material degradation at any given time is determined by the pavement material itself, and the formula for calculating thermal stress is as follows: Where E is the elastic modulus of the road surface material. The coefficient of thermal expansion is determined based on the road surface material. This refers to the daily temperature difference, among which, The formula for calculating traffic load stress at time t is: Where n is the number of vehicle group types, For vehicle of class i in The number of passages per unit time at any given moment. The standard axle load for vehicle class i, The load propagation factor represents the relative impact of this type of vehicle on road fatigue, determined through empirical or simulation fitting. The formula for calculating material degradation is: ,in, The road surface material deterioration coefficient;

[0016] S22. Settlement evolution prediction using the Hoshino method. The Hoshino method is based on field-measured settlement data and the direct proportionality between total settlement and the square root of time. The differential settlement is substituted into the settlement evolution variable calculation formula to assess future subgrade settlement. The settlement evolution variable calculation formula is as follows: ,in, This is the initial settlement. The initial time, is the settling rate fitting parameter, used to control the maximum settlement growth rate; k is the time growth adjustment factor, used to control the curvature of the growth rate as a function of time. For load settlement sensitivity, Sensitivity to rainwater settling. The effective rainfall per unit time is used to calculate the changes in the transverse and longitudinal slopes of the roadbed, respectively, by substituting the evolved settlement into the slope calculation formula.

[0017] Preferably, the construction of the vehicle damage assessment model, which incorporates changes in pavement crack width and roadbed slope into the model, and comprehensively considers vehicle speed and tire conditions to assess the impact of highway defects on vehicle operation, includes the following specific steps:

[0018] 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 formula for calculating the tire stress induced by the crack is as follows: ,in, The width of the crack. Here, r is the standard material modulus of the tire, and r is the standard tire radius. Let velocity be the influence function. Let be the vehicle deflection angle, where the formula for calculating the speed influence function is: ,in, Let be the average speed of the vehicle at time t. The vehicle's base speed. It is a speed-sensitive factor;

[0019] S32. Substitute the changes in the transverse and longitudinal slopes of the roadbed into the formula for calculating the vertical impact acceleration to assess the vertical impact of vehicles caused by road surface settlement. The formula for calculating the vertical impact acceleration is as follows: Where g is the acceleration due to gravity. Let be the slope change at time t. Let be the vehicle tire compression buffer constant, where the slope change at time t is calculated using the following formula: ,in, and These are the horizontal weighting coefficients and the vertical weighting coefficients;

[0020] S33. Substitute the tire stress induced by the crack and the vehicle's vertical impact acceleration caused by settlement into the vehicle damage risk calculation formula to assess the vehicle's driving condition. The vehicle damage risk calculation formula is as follows: ,in, This represents the maximum safe value of tire stress. This is the maximum safe vertical impact acceleration.

[0021] Preferably, the construction of the optimal maintenance time assessment model, which incorporates vehicle damage risk into the optimal maintenance time assessment model to evaluate the time that minimizes overall maintenance costs, includes the following specific steps:

[0022] S41. 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 overall maintenance cost within the time interval when the vehicle damage is still within the acceptable range.

[0023] S42. Optimize maintenance time by incorporating vehicle damage risk into the optimal maintenance time calculation formula. The optimal maintenance time calculation formula is as follows: ,in, The timeframe during which vehicle damage remains within an acceptable range. To account for the risk costs associated with maintenance delays, and to indirectly reflect the extent of damage. The direct cost of performing maintenance during the maintenance period, Costs related to traffic disruptions caused by traffic flow during the maintenance period. , and The weights are given by the formula for calculating the risk cost of maintenance delay: ,in, The expected cost per unit of damage. for The risk of vehicle damage at any time for The risk of vehicle damage at any given time, where the direct cost of maintenance during the maintenance period is calculated using the following formula: ,in, To fix maintenance costs, The resource costs associated with maintenance time, including the cost of traffic impact caused by traffic flow during the maintenance period, are calculated using the following formula: ,in, For traffic flow, The economic value of congestion per unit of traffic flow is used to select the time period with the least cost and where the damage does not exceed the safety threshold for maintenance.

[0024] The road surface measurement data analysis system based on big data is implemented based on the aforementioned road surface measurement data analysis method based on big data, and specifically includes:

[0025] The data acquisition module is used to acquire data on road surface and subgrade defects, vehicle driving data, and environmental data.

[0026] The disease evolution module is used to assess future changes in pavement crack width and subgrade settlement by using pavement crack width and differential settlement.

[0027] The vehicle damage assessment module is used to assess the impact of highway distress on vehicle operation by measuring changes in pavement crack width and roadbed slope.

[0028] The optimal maintenance time assessment module is used to evaluate the time with the lowest overall maintenance cost by importing vehicle damage risk into the optimal maintenance time assessment model.

[0029] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0030] The processor executes the above-mentioned method for detecting highway subgrade and pavement defects based on big data analysis by calling the computer program stored in the memory.

[0031] A computer-readable storage medium is characterized by storing instructions that, when executed on a computer, cause the computer to perform the aforementioned road surface measurement data analysis method based on big data.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] This application acquires pavement and subgrade distress data, assesses subgrade slope changes through settlement data, constructs a distress evolution model, and incorporates pavement crack width and differential settlement into the distress evolution model. It comprehensively considers the impact of thermal stress, traffic load, material degradation, and rainfall on distress evolution to assess future pavement crack width changes and subgrade settlement. A vehicle damage assessment model is also constructed, incorporating pavement crack width and subgrade slope changes into the model. It comprehensively considers vehicle speed and tire conditions to assess the damage impact of highway distress on vehicle operation. Finally, an optimal maintenance time assessment model is developed, incorporating vehicle damage risk into the optimal maintenance time assessment to determine the time with the lowest overall maintenance cost, thereby improving maintenance efficiency. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the overall process of the road surface measurement data analysis method based on big data in this application;

[0035] Figure 2 This is a schematic diagram illustrating the evolution of the disease in this application;

[0036] Figure 3 This is a flowchart illustrating the vehicle damage risk calculation process for this application.

[0037] Figure 4 This is a schematic diagram of the overall framework of the road surface measurement data analysis system based on big data in this application. Detailed Implementation

[0038] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0039] Example 1

[0040] See also Figure 1-3 This application provides an embodiment of a road surface measurement data analysis method based on big data, which includes the following specific steps:

[0041] Acquire data on pavement and subgrade defects, and assess changes in subgrade slope using settlement data;

[0042] A disease evolution model was constructed, and pavement crack width and differential settlement were imported into the model. The effects of thermal stress, traffic load, material degradation and rainfall on disease evolution were comprehensively considered to assess the future changes in pavement crack width and subgrade settlement.

[0043] A vehicle damage assessment model was constructed, and the changes in pavement crack width and roadbed slope were incorporated into the model. The impact of vehicle speed and tire condition were considered to assess the damage to vehicle operation caused by highway defects.

[0044] The optimal maintenance time assessment model incorporates vehicle damage risk into the assessment of the time that minimizes overall maintenance costs.

[0045] In this embodiment, it is necessary to specifically explain that obtaining pavement and subgrade distress data and assessing subgrade slope changes through settlement data includes the following specific steps:

[0046] S11. Highway pavement images are acquired using a 3D laser scanner and a high-definition camera to obtain 3D pavement topography and crack data. The acquired highway pavement images are then processed using image binarization and skeleton extraction algorithms to identify pavement crack widths. The crack width is determined as the average width of local pixels. Pavement and subgrade settlement data are obtained using LiDAR scanning and ground-penetrating radar. The formula for calculating differential settlement is as follows: In the transverse slope direction, This is the settlement at the center of the roadbed. The settlement at the edge of the roadbed is [value]. In the longitudinal slope direction, the maximum settlement at one end of the transition section is [value]. 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. The differential settlement is substituted into the slope change calculation formula to calculate the transverse slope change and the longitudinal slope change of the roadbed. The formula for calculating the transverse slope change is as follows: ,in, The differential settlement is calculated based on the cross slope, where the formula for calculating the longitudinal slope change of the roadbed is: ,in, This represents the differential settlement along the longitudinal slope.

[0047] It should be specifically noted here that one end and the other end of the longitudinal slope correspond to two feature points or sections in the longitudinal direction of the roadbed (road extension direction);

[0048] For example, the transition section connecting the roadbed and the structure has one end as an uphill section of longitudinal slope or a section with a large fill thickness, and the other end as a downhill section of longitudinal slope or a section with a small fill thickness.

[0049] S12. Obtain historical traffic flow data for different sections of the highway, obtain vehicle driving data, including vehicle speed, vehicle tire data, etc., and obtain environmental data.

[0050] In this embodiment, it is necessary to specifically explain that the construction of the disease evolution model, which incorporates pavement crack width and differential settlement, and comprehensively considers the impact of thermal stress, traffic load, material degradation, and rainfall on disease evolution, and assesses future changes in pavement crack width and subgrade settlement, includes the following specific steps:

[0051] S21. Substitute the crack width into the crack width evolution calculation formula to assess the future changes in pavement crack width, where the crack width calculation formula at time t is: ,in, The initial crack width, for Thermal stress at any given moment represents the expansion and contraction caused by temperature changes. For safety thermal stress, for Traffic load stress at any given time represents the repeated wheel action on a vehicle. To ensure safe traffic load stress, for The amount of pavement material degradation at any given time is determined by the pavement material itself, and the formula for calculating thermal stress is as follows: Where E is the elastic modulus of the road surface material. The coefficient of thermal expansion is determined based on the road surface material. This refers to the daily temperature difference, among which, The formula for calculating traffic load stress at time t is: Where n is the number of vehicle group types, For vehicle of class i in The number of passages per unit time at any given moment. The standard axle load for vehicle class i, The load propagation factor represents the relative impact of this type of vehicle on road fatigue, determined through empirical or simulation fitting. The formula for calculating material degradation is: ,in, The road surface material deterioration coefficient;

[0052] For example, in this embodiment, the vehicles are grouped as: passenger cars, light trucks and heavy trucks, indicating that when different vehicles encounter road damage, their weight and speed have an impact on the degree of damage to the vehicles.

[0053] S22. Settlement evolution prediction using the Hoshino method. The Hoshino method is based on field-measured settlement data and the direct proportionality between total settlement and the square root of time. The differential settlement is substituted into the settlement evolution variable calculation formula to assess future subgrade settlement. The settlement evolution variable calculation formula is as follows: ,in, This is the initial settlement. The initial time, is the settling rate fitting parameter, used to control the maximum settlement growth rate; k is the time growth adjustment factor, used to control the curvature of the growth rate as a function of time. For load settlement sensitivity, Sensitivity to rainwater settling. The effective rainfall per unit time is used to calculate the changes in the transverse and longitudinal slopes of the roadbed, respectively, by substituting the evolved settlement into the slope calculation formula.

[0054] It is necessary to explain in detail here that The method for determining the value of k is as follows: A curve showing the relationship between time and differential settlement is plotted using measured settlement data. Curve fitting is then performed using the settlement evolution calculation formula to obtain multiple straight lines that conform to a linear relationship. The line that best fits the linear relationship is then selected to obtain the corresponding coefficient. and k.

[0055] In this embodiment, it is necessary to specifically explain that the construction of a vehicle damage assessment model, which incorporates changes in pavement crack width and roadbed slope, and integrates the effects of vehicle speed and tire condition, assesses the impact of highway defects on vehicle operation through the following specific steps:

[0056] 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 formula for calculating the tire stress induced by the crack is as follows: ,in, The width of the crack. Here, r is the standard material modulus of the tire, and r is the standard tire radius. Let velocity be the influence function. Let be the vehicle deflection angle, where the formula for calculating the speed influence function is: ,in, Let be the average speed of the vehicle at time t. The vehicle's base speed. It is a speed-sensitive factor;

[0057] For example, in this embodiment, the speed sensitivity factor is set to 1.3 by default;

[0058] S32. Substitute the changes in the transverse and longitudinal slopes of the roadbed into the formula for calculating the vertical impact acceleration to assess the vertical impact of vehicles caused by road surface settlement. The formula for calculating the vertical impact acceleration is as follows: Where g is the acceleration due to gravity. Let be the slope change at time t. The compression buffer constant of the vehicle tire is obtained according to the tire material standard. The formula for calculating the slope change at time t is: ,in, and These are the horizontal weighting coefficients and the vertical weighting coefficients;

[0059] For example, in this embodiment, the horizontal weighting coefficient and the vertical weighting coefficient are 0.4 and 0.6, respectively;

[0060] S33. Substitute the tire stress induced by the crack and the vehicle's vertical impact acceleration caused by settlement into the vehicle damage risk calculation formula to assess the vehicle's driving condition. The vehicle damage risk calculation formula is as follows: ,in, This represents the maximum safe value of tire stress. This is the maximum safe vertical impact acceleration.

[0061] In this embodiment, it is necessary to specifically explain that constructing the optimal maintenance time assessment model and incorporating vehicle damage risk into the optimal maintenance time assessment model to evaluate the time that minimizes the overall maintenance cost includes the following specific steps:

[0062] S41. 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 overall maintenance cost within the time interval when the vehicle damage is still within the acceptable range.

[0063] S42. Optimize maintenance time by incorporating vehicle damage risk into the optimal maintenance time calculation formula. The optimal maintenance time calculation formula is as follows: ,in, The timeframe during which vehicle damage remains within an acceptable range. To account for the risk costs associated with maintenance delays, and to indirectly reflect the extent of damage. The direct cost of performing maintenance during the maintenance period, Costs related to traffic disruptions caused by traffic flow during the maintenance period. , and The weights are given by the formula for calculating the risk cost of maintenance delay: ,in, The expected cost per unit of damage. for The risk of vehicle damage at any time for The risk of vehicle damage at any given time, where the direct cost of maintenance during the maintenance period is calculated using the following formula: ,in, To fix maintenance costs, The resource costs associated with maintenance time, including nighttime and holiday construction, are calculated using the following formula: ,in, For traffic flow, The economic value of congestion per unit of traffic flow is used to select the time period with the least cost and where the damage does not exceed the safety threshold for maintenance.

[0064] It should be noted that the maximum acceptable damage threshold for a vehicle is determined as follows: representative historical vehicle damage data is obtained, substituted into the vehicle damage risk calculation formula to assess the vehicle damage risk, expert safety assessments of the vehicle damage risk are obtained, the vehicle damage risk value and safety assessment value are imported into the fitting software, and the vehicle risk value that meets the maximum safety range is output.

[0065] The advantages of this embodiment compared to the prior art are:

[0066] This application acquires pavement and subgrade distress data, assesses subgrade slope changes through settlement data, constructs a distress evolution model, and incorporates pavement crack width and differential settlement into the distress evolution model. It comprehensively considers the impact of thermal stress, traffic load, material degradation, and rainfall on distress evolution to assess future pavement crack width changes and subgrade settlement. A vehicle damage assessment model is also constructed, incorporating pavement crack width and subgrade slope changes into the model. It comprehensively considers vehicle speed and tire conditions to assess the damage impact of highway distress on vehicle operation. Finally, an optimal maintenance time assessment model is developed, incorporating vehicle damage risk into the optimal maintenance time assessment to determine the time with the lowest overall maintenance cost, thereby improving maintenance efficiency.

[0067] Example 2

[0068] like Figure 4 As shown, the road pavement measurement data analysis system based on big data is implemented based on the aforementioned road pavement measurement data analysis method based on big data. Specifically, it includes a data acquisition module, a damage evolution module, a vehicle damage assessment module, and an optimal maintenance time assessment module. The data acquisition module is used to acquire pavement and subgrade damage data, vehicle driving data, and environmental data. The damage 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 impact of highway damage on vehicle driving through changes in pavement crack width and subgrade slope. The optimal maintenance time assessment module is used to import vehicle damage risk into the optimal maintenance time assessment model to evaluate the time with the lowest overall maintenance cost.

[0069] Example 3

[0070] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0071] The processor executes the aforementioned road surface measurement data analysis method based on big data by calling computer programs stored in memory.

[0072] The electronic device can vary considerably depending on its configuration and performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the road surface measurement data analysis method based on big data provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted here.

[0073] Example 4

[0074] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0075] When a computer program runs on a computer device, it causes the computer device to perform the aforementioned road surface measurement data analysis method based on big data.

[0076] 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 devices.

[0077] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are 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 transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of 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 surface measurement data analysis method based on big data, characterized in that, It includes the following specific steps: Acquire data on pavement and subgrade defects, and assess changes in subgrade slope using settlement data; A disease evolution model was constructed, and pavement crack width and differential settlement were imported into the model. The effects of thermal stress, traffic load, material degradation and rainfall on disease evolution were comprehensively considered to assess the future changes in pavement crack width and subgrade settlement. A vehicle damage assessment model was constructed, and the changes in pavement crack width and roadbed slope were incorporated into the model. The impact of vehicle speed and tire condition were considered to assess the damage to vehicle operation caused by highway defects. The specific steps include the following: The impact of road cracks on vehicle tires is assessed by substituting the width of the cracks into the tire stress calculation formula. The formula for calculating the tire stress induced by the cracks is as follows: ,in, The width of the crack. Here, r is the standard material modulus of the tire, and r is the standard tire radius. Let velocity be the influence function. Let be the vehicle deflection angle, where the formula for calculating the speed influence function is: ,in, Let be the average speed of the vehicle at time t. The vehicle's base speed. It is a speed-sensitive factor; The changes in the transverse and longitudinal slopes of the roadbed are substituted into the formula for calculating the vertical impact acceleration to assess the vertical impact of vehicles caused by road surface settlement. The formula for calculating the vertical impact acceleration is as follows: Where g is the acceleration due to gravity. Let be the slope change at time t. Let be the vehicle tire compression buffer constant, where the slope change at time t is calculated using the following formula: ,in, and These are the horizontal weighting coefficients and the vertical weighting coefficients. Let be the change in the lateral slope of the roadbed at time t. Let be the change in longitudinal slope of the roadbed at time t; The vehicle's driving condition is assessed by substituting the tire stress induced by the crack and the vertical impact acceleration of the vehicle due to settlement into the vehicle damage risk calculation formula. The vehicle damage risk calculation formula is as follows: ,in, This represents the maximum safe value of tire stress. The maximum safe vertical impact acceleration; The optimal maintenance time assessment model incorporates vehicle damage risk into the assessment of the time that minimizes overall maintenance costs.

2. The road surface measurement data analysis method based on big data as described in claim 1, characterized in that, The construction of the pavement disease evolution model, which incorporates pavement crack width and differential settlement, integrates the effects of thermal stress, traffic load, material degradation, and rainfall on disease evolution to assess future changes in pavement crack width and subgrade settlement, includes the following specific steps: The crack width is substituted into the crack width evolution calculation formula to assess the future change in pavement crack width, where the crack width calculation formula at time t is: ,in, The initial crack width, for Thermal stress at any time, For safety thermal stress, for Traffic load stress at any given time To ensure safe traffic load stress, for The amount of road surface material degradation at any given time, where the thermal stress is calculated using the following formula: Where E is the elastic modulus of the road surface material. The coefficient of thermal expansion is... This refers to the daily temperature difference, among which, The formula for calculating traffic load stress at time t is: Where n is the number of vehicle group types, For vehicle of class i in The number of passages per unit time at any given moment. The standard axle load for vehicle class i, Let be the load propagation factor, where the formula for calculating material degradation is: ,in, The road surface material deterioration coefficient; Settlement evolution prediction was performed using the Hoshino method. The differential settlement was substituted into the settlement evolution variable calculation formula to assess future subgrade settlement. The settlement evolution variable calculation formula is as follows: ,in, This is the initial settlement. The initial time, Here, k represents the settling rate fitting parameter, and k is the time growth adjustment factor. For load settlement sensitivity, Sensitivity to rainwater settling. The effective rainfall per unit time is used to calculate the changes in the transverse and longitudinal slopes of the roadbed, respectively, by substituting the evolved settlement into the slope calculation formula.

3. The road surface measurement data analysis method based on big data as described in claim 2, characterized in that, The construction of the optimal maintenance time assessment model, which incorporates vehicle damage risk into the model to evaluate the time that minimizes overall maintenance costs, includes 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 overall maintenance cost within the time interval when the vehicle damage is still within the acceptable range. The maintenance time is optimized by incorporating vehicle damage risk into the optimal maintenance time calculation formula, where the optimal maintenance time calculation formula is: ,in, The timeframe during which vehicle damage remains within an acceptable range. To mitigate the risks and costs associated with maintenance delays, The direct cost of performing maintenance during the maintenance period, Costs related to traffic disruptions caused by traffic flow during the maintenance period. , and The weights are given by the formula for calculating the risk cost of maintenance delay: ,in, The expected cost per unit of damage. for The risk of vehicle damage at any time for The risk of vehicle damage at any given time, where the direct cost of maintenance during the maintenance period is calculated using the following formula: ,in, To fix maintenance costs, The resource costs associated with maintenance time, including the cost of traffic impact caused by traffic flow during the maintenance period, are calculated using the following formula: ,in, For traffic flow, The economic value of congestion per unit of traffic flow is used to select the time period with the least cost and where the damage does not exceed the safety threshold for maintenance.

4. The road surface measurement data analysis method based on big data as described in claim 3, characterized in that, The process of acquiring pavement and subgrade distress data and assessing subgrade slope changes through settlement data includes the following specific steps: Highway pavement images were acquired using a 3D laser scanner and high-definition camera to obtain 3D topographic and crack data. The acquired highway pavement images were then processed using image binarization and skeleton extraction algorithms to identify crack widths. Pavement and subgrade settlement data were obtained using LiDAR scanning and ground-penetrating radar. The differential settlement was calculated using the following formula: In the transverse slope direction, This is the settlement at the center of the roadbed. The settlement at the edge of the roadbed is [value]. In the longitudinal slope direction, the maximum settlement at one end of the transition section is [value]. 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. The differential settlement is substituted into the slope change calculation formula to calculate the transverse slope change and the longitudinal slope change of the roadbed. The formula for calculating the transverse slope change is as follows: ,in, The differential settlement is calculated based on the cross slope, where the formula for calculating the longitudinal slope change of the roadbed is: ,in, This represents the differential settlement along the longitudinal slope. Historical traffic flow data, vehicle driving data, and environmental data are obtained for different sections of the highway.

5. A road surface measurement data analysis system based on big data, implemented based on the road surface measurement data analysis method based on big data as described in any one of claims 1-4, characterized in that, Specifically, it includes: The data acquisition module is used to acquire data on road surface and subgrade defects, vehicle driving data, and environmental data. The disease evolution module is used to assess future changes in pavement crack width and subgrade settlement by using pavement crack width and differential settlement. The vehicle damage assessment module is used to assess the impact of highway distress on vehicle operation by measuring changes in pavement crack width and roadbed slope. The optimal maintenance time assessment module is used to evaluate the time with the lowest overall maintenance cost by importing vehicle damage risk into the optimal maintenance time assessment model.

6. 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 is characterized in that it executes the road surface measurement data analysis method based on big data as described in any one of claims 1-4 by calling a computer program stored in the memory.

7. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the road surface measurement data analysis method based on big data as described in any one of claims 1-4.

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