Road maintenance deployment system and method based on data analysis

Abstract

The invention relates to the technical field of resource scheduling, in particular to a road maintenance allocation system and method based on data analysis, and the system comprises a material aging evaluation module, a road wear evaluation module, a traffic load influence analysis module, a budget allocation module and a maintenance adjustment module. According to the method, by collecting and analyzing the initial components of the highway pavement material and monitoring changes in different time periods, the attenuation rate and the mass loss of the material can be accurately calculated, the recognition precision of the highway maintenance requirement is improved through data driving, it is ensured that resources can be more effectively distributed to the area where maintenance is most needed, and the maintenance efficiency is improved. Not only can the actual loss degree be responded, but also the future wear trend and damage expansion can be predicted, the utilization and budget distribution of maintenance resources can be optimized, the specific influence on the pavement wear can be analyzed in combination with traffic flow and vehicle load distribution data, and the fund and maintenance plan can be further accurately adjusted. And the maintenance work is more prospective and targeted.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling, and particularly to a highway maintenance allocation system and method based on data analysis. Background Art

[0002] The technical field of resource scheduling encompasses a wide range from simple task allocation to complex resource management and optimization. The core content focuses on how to effectively allocate and use limited resources to achieve predetermined goals, including the scheduling of physical resources such as machines, equipment, and human resources. The technical field of resource scheduling involves requirements analysis, resource allocation algorithms, formulation and execution of scheduling strategies, as well as monitoring and adjustment of results. The technology is widely applied in multiple industries such as manufacturing, transportation, information technology infrastructure, and the service industry, especially in scenarios with high demands for efficiency and cost control.

[0003] Among them, a highway maintenance allocation system based on data analysis refers to using data-driven methods to optimize the allocation and scheduling of highway maintenance resources. The technical matters targeted by this patent theme cover data collection, analysis, and decision-making. The system collects data on highway conditions, analyzes the data to identify the priorities and locations of maintenance needs, and conducts resource allocation based on the analysis results. This approach mainly utilizes data processing and analysis technologies, and determines the optimal resource allocation plan through algorithms to improve the efficiency and effectiveness of highway maintenance work. This system does not rely on complex model adjustments or advanced algorithms, but supports the decision-making process through direct data analysis.

[0004] Although the existing technology is widely applied in multiple industries in the field of resource scheduling, it lacks sufficient data support and analysis depth in the field of highway maintenance. Conventional methods rely more on empirical judgment rather than detailed data analysis, resulting in inaccurate or untimely resource allocation. Without detailed damage data and traffic load analysis, the maintenance plan cannot effectively respond to the emergency repair needs during peak hours, or ignores sections with low-frequency use but high damage. This situation causes the highway to enter the deterioration cycle faster, increases the long-term maintenance cost, and at the same time affects the safety and comfort of road users. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, such as the lack of sufficient data support and analysis depth in the field of highway maintenance, conventional methods relying more on empirical judgment rather than detailed data analysis, resulting in inaccurate or untimely resource allocation. Without detailed damage data and traffic load analysis, the maintenance plan cannot effectively respond to the emergency repair needs during peak hours, or ignores sections with low-frequency use but high damage. This situation causes the highway to enter the deterioration cycle faster, increases the long-term maintenance cost, and at the same time affects the safety and comfort of road users, the embodiments of the present invention provide a highway maintenance allocation system and method based on data analysis. The technical solutions are as follows:

[0006] On the one hand, a highway maintenance deployment system based on data analysis is provided. The system includes:

[0007] The material aging assessment module collects the initial composition data of highway pavement materials, sets a time-segmented monitoring window, compares the changes in the core components of the materials in different time periods, calculates the mass loss ratio per unit time, and analyzes the attenuation trend by combining the antioxidant capacity, volatilization characteristics, and surface structure stability of the materials to obtain the material composition attenuation characteristics;

[0008] Based on the material composition attenuation characteristics, the road wear assessment module analyzes the wear trend range per unit area of the road with reference to highway traffic flow, vehicle load distribution, and driving speed, evaluates the expansion of highway damage per unit time, and obtains the distribution trend of highway surface damage;

[0009] The traffic load impact analysis module uses the distribution trend of highway surface damage, records the heavy-duty freight vehicle flow and load peak periods of the highway, analyzes the load distribution state, evaluates the impact degree of different loads on road wear, and obtains the load impact assessment result;

[0010] The budget allocation module uses the load impact assessment result, compares the budget allocation for highway maintenance, changes in maintenance requirements, and road wear trends, adjusts the priority of fund allocation, sets the budget adjustment cycle, and obtains the maintenance budget adjustment result.

[0011] As a further solution of the present invention, the material composition attenuation characteristics include the oxidation rate of chemical components and the change in temperature stability. The distribution trend of highway surface damage includes surface crack distribution, pothole occurrence area, and material shedding. The load impact assessment result includes the degree of damage aggravation in the load concentration area, the wear condition in the load area, and the impact of periodic peak loads. The maintenance budget adjustment result includes the newly added budget amount, the items with priority adjustment, and the highway areas with budget adjustment.

[0012] As a further solution of the present invention, the material aging assessment module includes:

[0013] The composition monitoring sub-module collects the initial composition data of highway pavement materials, sets a time-segmented monitoring window, detects the concentration values of the core components of the materials in multiple time periods, and analyzes the change range of the core components in different time periods to obtain the change situation of the key components;

[0014] Based on the change situation of the key components, the concentration change analysis sub-module analyzes the concentration change of the core components of highway pavement materials, calculates the overall situation of material mass loss per unit time, screens the material categories with mass loss, and obtains the material mass loss situation;

[0015] The attenuation trend analysis sub-module uses the material mass loss situation, and with reference to the antioxidant capacity, volatility characteristics, and surface structure stability of the material, analyzes the attenuation trend of the material components in different time periods to obtain the attenuation characteristics of the material components.

[0016] As a further solution of the present invention, the road wear assessment module includes:

[0017] The wear trend analysis sub-module uses the attenuation characteristics of the material components, records the highway traffic flow, vehicle load distribution, and driving speed, analyzes the force condition per unit area of the highway under different traffic conditions, and obtains the wear recognition result per unit area of the highway;

[0018] The damage expansion assessment sub-module calculates the damage expansion rate of the highway surface layer based on the wear recognition result per unit area of the highway and combines with the change of the wear trend per unit time to obtain the highway damage expansion situation;

[0019] The highway damage distribution sub-module extracts the traffic flow and vehicle load characteristics of different regions through the highway damage expansion situation, analyzes the change of the damage distribution in multiple regions, and obtains the damage distribution trend of the highway surface layer.

[0020] As a further solution of the present invention, the traffic load impact analysis module includes:

[0021] The load flow monitoring sub-module records the number of freight vehicles passing through and the corresponding axle load information in multiple time periods through the damage distribution trend of the highway surface layer, identifies the total number of vehicles in different time periods, calculates the load peak value, and arranges the data according to the time sequence to obtain the load flow sequence data;

[0022] The formula for calculating the load peak value is as follows:

[0023]

[0024] Wherein, Nt is the load peak value, n represents the number of freight vehicles passing through, ai represents the axle load when the i-th vehicle passes through, wi represents the axle load value when the i-th vehicle passes through, represents the average axle load value, represents the absolute difference between the axle load value when the i-th vehicle passes through and the average axle load value;

[0025] The load distribution calculation sub-module uses the load flow sequence data to calculate the load cumulative value in multiple time periods, counts the occurrence frequency of different load levels, judges the peak load time period, and obtains the load distribution interval;

[0026] The load wear assessment sub-module analyzes the highway wear degree under multiple load intervals according to the load distribution interval, evaluates the contribution degree of the load concentration area to the pavement damage degree, combines the time series trend to analyze the cumulative impact of load differences on pavement damage, and obtains the load impact assessment result.

[0027] As a further solution of the present invention, the budget allocation module includes:

[0028] The budget demand calculation sub-module obtains the maintenance budget demand quantity by means of the load impact assessment result, referring to the maintenance requirements of different road sections, counting the pavement wear amount of multiple sections and comparing with the real-time maintenance budget configuration;

[0029] The fund priority adjustment sub-module, based on the maintenance budget demand quantity, compares the road wear trend, screens the sections with highway maintenance budget gaps, calculates the fund demand ratio, adjusts the fund allocation order, determines the fund priority of the demand sections, and obtains the fund priority allocation situation;

[0030] The budget cycle setting sub-module uses the fund priority allocation situation, analyzes the periodic fluctuation of the fund demand, evaluates the rationality of fund allocation under different budget cycles, screens the optimal budget adjustment cycle, sets the fund adjustment interval of multiple sections, and obtains the maintenance budget adjustment result.

[0031] As a further solution of the present invention, the formula for calculating the fund demand ratio is:

[0032]

[0033] Among them, Ro represents the fund demand ratio of the o-th section, Dom represents the budget demand quantity of the o-th section for the m-th maintenance project, Wm represents the weight coefficient of the m-th maintenance project, M represents the total number of maintenance projects, Po represents the road wear trend index of the o-th section, represents the average value of the road wear trend index.

[0034] As a further solution of the present invention, the system further includes a maintenance adjustment module:

[0035] The maintenance adjustment module, based on the maintenance budget adjustment result, combines the distribution trend of highway surface damage, evaluates the maintenance priority, matches the construction process, material replacement method and road traffic demand, adjusts the construction order and divides the construction area, and obtains the highway maintenance scheduling result;

[0036] The highway maintenance scheduling result includes the emergency repair area, the schedule of highway maintenance, and the phased traffic adjustment measures.

[0037] As a further solution of the present invention, the maintenance adjustment module includes:

[0038] The maintenance priority evaluation sub-module uses the maintenance budget adjustment result, combines it with the distribution trend of highway surface damage, evaluates the damage degree of different road sections, and obtains the maintenance priority ranking.

[0039] The damage type analysis sub-module analyzes the damage types of multiple sections based on the maintenance priority ranking, calculates the required material replacement quantity, matches the construction and material replacement methods, and obtains the section construction matching result.

[0040] The construction scheduling optimization sub-module uses the section construction matching result, combines it with the road traffic demand, evaluates the impact degree of construction on traffic flow, adjusts the construction sequence, divides the construction area, optimizes the construction schedule, and obtains the highway maintenance scheduling result.

[0041] On the other hand, the highway maintenance allocation method based on data analysis is executed based on the above-mentioned highway maintenance allocation system based on data analysis, and includes the following steps:

[0042] S1: Obtain the initial composition data of highway pavement materials, set the monitoring period, collect the changes in asphalt mixture components, mineral aggregate particle composition and adhesion in different time periods, calculate the mass loss ratio per unit time in each period, analyze the antioxidant rate, volatilization loss rate and surface stripping trend, and compare the composition decay curves to obtain the material composition decay characteristics.

[0043] S2: Based on the material composition decay characteristics, record the traffic flow, vehicle load distribution and average driving speed of road sections, calculate the tire contact pressure per unit area, analyze the material wear rate under different axle load levels, and calculate the damage expansion rate of the road surface to obtain the distribution trend of highway surface damage.

[0044] S3: Use the distribution trend of highway surface damage to record the heavy-duty freight vehicle flow and load peak periods, and analyze the contact stress concentration degree of the load distribution state on different damaged areas to obtain the load impact evaluation result.

[0045] S4: According to the load impact evaluation result, combine the highway maintenance fund allocation and road damage expansion data, calculate the deviation ratio of the damage rate and fund demand in different sections, adjust the fund allocation priority, set the budget adjustment period, and obtain the maintenance budget adjustment result.

[0046] S5: Through the maintenance budget adjustment result, refer to the road surface damage trend and construction resource requirements, combine the construction technology and material replacement methods, adjust the construction sequence, and obtain the highway maintenance scheduling result.

[0047] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0048] By collecting and analyzing the initial composition of highway pavement materials and monitoring the changes over different time periods, the attenuation rate and mass loss of the materials can be accurately calculated. This data-driven approach improves the accuracy of identifying highway maintenance needs, ensuring that resources can be more effectively allocated to the areas most in need of maintenance. It can not only respond to the actual degree of wear, but also predict future wear trends and damage propagation, optimizing the utilization of maintenance resources and budget allocation. By combining the analysis of traffic flow and vehicle load distribution data on the specific impact of pavement wear, the funds and maintenance plans can be further precisely adjusted, making the maintenance work more forward-looking and targeted. Brief Description of the Drawings

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 is a schematic diagram of a highway maintenance allocation system based on data analysis provided by an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of the system framework of the present invention;

[0052] Figure 3 is a flowchart of the material aging evaluation module of the present invention;

[0053] Figure 4 is a flowchart of the road wear evaluation module of the present invention;

[0054] Figure 5 is a flowchart of the traffic load impact analysis module of the present invention;

[0055] Figure 6 is a flowchart of the budget allocation module of the present invention;

[0056] Figure 7 is a flowchart of the maintenance adjustment module of the present invention;

[0057] Figure 8 is a flowchart of the highway maintenance allocation method based on data analysis provided by an embodiment of the present invention. Detailed Embodiments

[0058] The following will describe the technical solutions in the present invention in conjunction with the drawings.

[0059] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0060] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0061] In the embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0062] To make the technical problems to be solved, technical solutions and advantages of the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0063] The embodiments of the present invention provide a highway maintenance deployment system based on data analysis, as Figure 1-2 shown in the schematic diagram of the highway maintenance deployment system based on data analysis. The system includes:

[0064] The material aging assessment module collects the initial composition data of highway pavement materials, sets a time-segmented monitoring window, compares the changes in the core components of materials in different time periods, calculates the mass loss ratio per unit time, and analyzes the attenuation trend in combination with antioxidant capacity, volatility characteristics, and the stability of the material surface layer structure to obtain the material composition attenuation characteristics;

[0065] The road wear assessment module analyzes the wear trend range per unit area of the road based on the material composition attenuation characteristics, referring to highway traffic flow, vehicle load distribution, and driving speed, evaluates the expansion of highway damage per unit time, and obtains the distribution trend of highway surface damage;

[0066] The traffic load impact analysis module uses the distribution trend of highway surface damage, records the heavy-duty freight vehicle flow and load peak periods of the highway, analyzes the load distribution state, evaluates the impact degree of different loads on road surface wear, and obtains the load impact assessment result;

[0067] The budget allocation module uses the load impact assessment results to compare the budget allocation for highway maintenance, the changes in maintenance requirements, and the road wear trend, adjusts the priority of fund allocation, sets the budget adjustment cycle, and obtains the maintenance budget adjustment results;

[0068] The maintenance adjustment module, based on the maintenance budget adjustment results and combined with the distribution trend of highway surface damage, evaluates the maintenance priority, matches the construction technology, material replacement method, and road traffic demand, adjusts the construction sequence and divides the construction area, and obtains the highway maintenance scheduling results;

[0069] The material composition attenuation characteristics include the oxidation rate of chemical components and the change in temperature stability. The distribution trend of highway surface damage includes the surface crack distribution, pothole occurrence area, and material shedding. The load impact assessment results include the degree of damage aggravation in the load concentration area, the wear condition in the load area, and the impact of periodic peak loads. The maintenance budget adjustment results include the newly added budget amount, the items with priority adjustment, and the highway areas with budget adjustment. The highway maintenance scheduling results include the emergency repair area, the schedule of highway maintenance, and the phased traffic adjustment measures.

[0070] Specifically, as Figure 2 、 3 shown, the material aging assessment module includes:

[0071] The component monitoring sub-module collects the initial component data of highway pavement materials, sets a time-segmented monitoring window, detects the concentration values of the core components of the materials in multiple time periods, analyzes the change range of the core components in different time periods, and obtains the change situation of the key components;

[0072] Collect the initial component data of highway pavement materials. By setting a time-segmented monitoring window, continuously record the concentration values of the core components of the materials in different time periods, and calculate the change range of the concentration values in each time period. It is necessary to measure the initial components of highway pavement materials. Set a certain section of the road to be paved with asphalt concrete, which includes asphalt (accounting for 5.5%), mineral powder (accounting for 4%), fine aggregate (accounting for 40%), coarse aggregate (accounting for 50.5%), etc. Use a spectral analyzer (such as XRF, ICP-MS) to detect the initial concentration, record the reference concentration values of the core components (such as SiO2, CaO, Fe2O3), set a segmented monitoring window, and set a detection every 3 months. During this period, through the fixed-point sampling method, measure the material components at multiple measurement points (such as setting a sampling point every 5 kilometers along the highway) to obtain the core component concentration data in multiple time periods. Use mathematical modeling to compare the measurement results in each time period, set the calculation of the core component concentration change, and use the formula:

[0073]

[0074] Where, C0 is the initial concentration and Ct is the measured concentration at the current time;

[0075] If the initial concentration of SiO2 is 40% and the measured concentration after 3 months is 39.5%, then the change rate:

[0076]

[0077] Identify the change ranges of each core component, summarize the concentration change rate data for multiple time periods, plot the concentration change trend curve, and determine the fluctuation range of the core components through statistical analysis (such as calculating the coefficient of variation) to obtain the change situation of the key components.

[0078] Based on the change situation of the key components, the concentration change analysis sub-module analyzes the concentration change of the core components of the highway pavement materials, calculates the overall situation of the material mass loss per unit time, screens the material categories with mass loss, and obtains the material mass loss situation;

[0079] Identify the concentration change of the core components of the highway pavement materials, evaluate the material mass loss situation per unit time, and conduct concentration change analysis for different material categories (such as asphalt, mineral powder, aggregate) according to the core component change rate data. Use the formula:

[0080]

[0081] Where, M0 is the original material mass and ΔC is the component concentration change rate;

[0082] If the total mass of asphalt in a certain section is 1000 kg and ΔC = -1.25%, then the mass loss of asphalt is:

[0083]

[0084] Calculate the mass loss of various materials, screen the loss situations of different material categories, use the threshold judgment method to set the loss screening standard, and set that if the material loss per unit time exceeds 2% of the initial mass, then it is determined that the material category has significant loss. Compare the calculation results. For example, the initial mass of mineral powder is 200 kg, the loss mass is 5 kg, and the loss rate is:

[0085]

[0086] Then this material category is screened as a category with significant mass loss. Combine the loss data of all material categories, summarize the loss situations of each material, and sort them according to the loss rate to determine the mass loss situations of different material categories.

[0087] The attenuation trend analysis sub-module uses the material mass loss situation, and refers to the antioxidant capacity, volatility characteristics and surface structure stability of the material to analyze the attenuation trend of the material components in different time periods, and obtain the attenuation characteristics of the material components;

[0088] Combined with the antioxidant capacity, volatility characteristics and surface structure stability of the material, analyze the attenuation trend of the material components in different time periods. In terms of antioxidant capacity analysis, it is necessary to conduct hierarchical evaluation according to the chemical composition of the material and the aging environment. The antioxidant capacity of asphalt materials can be measured by the aging index, which is obtained from oxidation experiments such as the Rolling Thin Film Oven Test (RTFO) or the UV light aging test (UV). By measuring the mass change rate or viscosity change rate before and after aging, the antioxidant performance of the material is evaluated. After obtaining the antioxidant capacity data in multiple time periods, plot the change trend over time, and then judge the attenuation rate of the material's antioxidant capacity. In terms of volatility characteristics analysis, long-term tracking is carried out for volatile components (such as light components in asphalt). Thermogravimetric analysis (TGA) is used to record the mass loss of the material at different time nodes, and the stability of the material is determined by the change in the volatility rate at different temperature stages. It is set that asphalt materials are more volatile in high-temperature environments, and data under different environmental conditions need to be integrated to evaluate the volatility trend of the material. The volatility characteristic data collected in different time periods can reflect the loss degree of light components inside the material. Combined with on-site environmental conditions, such as external factors like temperature, humidity, and UV intensity, correction analysis is carried out to further improve the evaluation accuracy. In terms of the analysis of the surface structure stability of the material, it is necessary to monitor the cracks, spalling, and porosity changes on the surface of the material, use high-resolution microscopic imaging technology or laser scanning to measure the microscopic structure changes on the surface, and compare the surface deterioration conditions in different time periods with real-time data. Set that obvious micro-cracks appear on the asphalt surface of a certain highway section after two years of use, and the crack growth rate is higher than that of other sections, then it can be inferred that the surface stability of the material on this section is low. Macro pavement performance data, such as rut depth and skid resistance performance indicators, can also be combined to establish a material attenuation trend model, comprehensively judge the deterioration trend of the material in different time periods, and form a complete material attenuation trend curve based on multi-period data to obtain the attenuation characteristics of the material components.

[0089] Specifically, as Figure 2 、 4 shown, the road wear assessment module includes:

[0090] The wear trend analysis sub-module uses the attenuation characteristics of the material components, records the highway traffic flow, vehicle load distribution, and driving speed, analyzes the force condition of the highway per unit area under different traffic conditions, and obtains the wear identification result of the highway per unit area;

[0091] Analyze the stress condition of the road per unit area by combining traffic flow, vehicle load distribution, and driving speed data. Use sensors to monitor the long-term wear data of different types of road surfaces, set the attenuation parameters of the reference material, record the wear conditions of the road surface under different loads and speeds, set it on a highway, select a typical section of 100 m² for testing, record the number of passing vehicles and load conditions every day, and use a high-precision thickness gauge to measure the wear depth of the road surface. Establish a correlation formula between traffic flow T, vehicle load L, and driving speed V, and calculate the stress F on the road per unit area:

[0092]

[0093] Among them, A is the unit area. Set the average daily traffic flow T = 8000 vehicles, the average vehicle load L = 10 tons, and the average driving speed V = 80 km / h for a certain section of the road. Take the detection area A = 100 m², then the stress calculation per unit area is as follows:

[0094]

[0095] Substitute this value into the wear trend model and combine the material attenuation data to calculate the wear amount per unit area of the road. Set that if the wear rate is 0.00005×F, then the wear amount is:

[0096] 0.00005×64000 = 3.2;

[0097] By classifying and comparing the wear rates per unit area of different road sections, set a threshold. If the wear rate in a certain area is higher than 3, it is marked as a high-wear area, and the wear identification result per unit area of the road is output.

[0098] Based on the wear identification result per unit area of the road, combined with the change of the wear trend within a unit time, calculate the damage expansion rate of the road surface layer to obtain the road damage expansion situation;

[0099] Combined with the change of the wear trend within a unit time, calculate the damage expansion rate of the road surface layer, obtain the wear data at different time points, and use time difference to calculate the expansion rate. Set that the wear depth of a certain section of the road is 2.5 mm on the first day, 3.0 mm on the second day, and 3.8 mm on the third day, then calculate its expansion rate R:

[0100]

[0101] Substitute the values for calculation:

[0102]

[0103] Combined with traffic flow and load data, use regression analysis to establish an expansion rate model, set:

[0104] R = γT + δL;

[0105] Perform fitting calculations, set the regression parameters γ = 0.00005 and δ = 0.002. If the traffic flow T = 7000 vehicles and the average vehicle load L = 12 tons on a certain day, then calculate the expansion rate:

[0106] R = 0.00005 × 7000 + 0.002 × 12 = 0.35 + 0.024 = 0.374;

[0107] Set the damage classification threshold according to the expansion rate value. If R < 0.2 mm / day, it is recorded as mild expansion; if 0.2 ≤ R < 0.6 mm / day, it is recorded as moderate expansion; if R ≥ 0.6 mm / day, it is recorded as severe expansion, and output the highway damage expansion situation.

[0108] The highway damage distribution sub-module extracts the traffic flow and vehicle load characteristics of the differentiated areas through the highway damage expansion situation, analyzes the changes in the damage distribution of multiple areas, and obtains the damage distribution trend of the highway surface layer;

[0109] Extract the traffic flow and vehicle load characteristics of different areas, analyze the highway damage distribution trend, collect the expansion rate R values of each section and the corresponding traffic flow T and vehicle load L data, construct the traffic-load distribution matrix of different areas, and calculate the damage degree of different areas using weighted average. Let the weights wT and wL represent the influence weights of traffic flow and load respectively, then the calculation formula for the damage index D is as follows:

[0110] D = wTT + wLL;

[0111] Set the weights wT = 0.0001 and wL = 0.005. If the traffic flow T = 9000 vehicles and the average vehicle load L = 11 tons in a certain area, then calculate the damage index:

[0112] D = 0.0001 × 9000 + 0.005 × 11 = 0.9 + 0.055 = 0.955;

[0113] Set the damage classification threshold, set D < 0.5 as the low-damage area, 0.5 ≤ D < 1.0 as the medium-damage area, D ≥ 1.0 as the high-damage area, draw the highway damage distribution map, and output the damage distribution trend of the highway surface layer.

[0114] Specifically, as Figure 2 、 5 shown, the traffic load impact analysis module includes:

[0115] The load flow monitoring sub-module records the number of freight vehicles passing through and the corresponding axle load information within multiple time periods through the distribution trend of surface damage on the road, identifies the total number of vehicles in different time periods, calculates the load peak, and arranges the data in chronological order to obtain the load flow sequence data;

[0116] The formula for calculating the load peak is as follows:

[0117]

[0118] Among them, Nt is the load peak, n represents the number of freight vehicles passing through, ai represents the axle load when the i-th vehicle passes through, wi represents the axle load value when the i-th vehicle passes through, represents the average axle load value, represents the absolute difference between the axle load value when the i-th vehicle passes through and the average axle load value;

[0119] Suppose there are the following data points within a specific observation time period, and the data points are actually measured at the road monitoring point:

[0120] The number of freight vehicles passing through n = 4;

[0121] The axle load a for each vehicle passing through = [15, 12, 18, 10] tons;

[0122] The axle load value w for each vehicle passing through = [14, 16, 12, 18] tons;

[0123] Average axle load value tons;

[0124] Calculate the absolute difference between the axle load value for each vehicle passing through and the average axle load value:

[0125] |14 - 15| = 1;

[0126] |16 - 15| = 1;

[0127] |12 - 15| = 3;

[0128] |18 - 15| = 3;

[0129] Multiply the difference by the corresponding axle load weight and then sum:

[0130] 15×1 = 15;

[0131] 12×1 = 12;

[0132] 18×3 = 54;

[0133] 10×3 = 30;

[0134] Nt = 15 + 12 + 54 + 30 = 111;

[0135] The results show that within this time period, considering the fluctuations of axle loads, the calculated peak load is 111 tons, which helps to understand the impact of axle load fluctuations on highway structures during a specific time period. By comparing the Nt values of different time periods, the load change trend can be evaluated, and thus data support can be provided for highway maintenance and freight vehicle scheduling.

[0136] The load distribution calculation sub-module uses load flow sequence data to calculate the cumulative load values within multiple time periods, count the occurrence frequencies of different load levels, determine the peak load time periods, and obtain the load distribution intervals.

[0137] Calculate the cumulative load values within multiple time periods, and count the occurrence frequencies of different load levels. Cumulatively calculate the average axle load and the total number of vehicles for multiple time periods to obtain the total load. During the time period from 08:00 to 09:00, 50 trucks passed, and the average axle load was 10.2t. During the time period from 09:00 to 10:00, 65 trucks passed, and the average axle load was 9.8t. Then the total load is 50×10.2 + 65×9.8 = 1147t. According to the set intervals of load levels, such as [0 - 5t], [5 - 10t], [10 - 15t], [above 15t], count the number of vehicles in each interval and calculate the occurrence frequencies of each interval. The statistical results of one day show that there are 200 vehicles in the [5 - 10t] interval, 150 vehicles in the [10 - 15t] interval, and 50 vehicles in the [above 15t] interval. Then the statistical results of the load distribution for that day are: [5 - 10t, 200 vehicles], [10 - 15t, 150 vehicles], [above 15t, 50 vehicles]. By analyzing the change rate of the cumulative load values at different time periods, determine the peak load time period. If the cumulative load value from 09:00 to 10:00 increases by 25% compared to that from 08:00 to 09:00, then it can be determined that 09:00 - 10:00 is the peak load time period, and obtain the load distribution interval data.

[0138] The load wear assessment sub-module analyzes the highway wear degree under multiple load intervals according to the load distribution intervals, evaluates the contribution degree of the load concentration area to the pavement damage degree, and combines the time series trend to analyze the cumulative impact of load differences on pavement damage, and obtains the load impact assessment results.

[0139] Analyze the highway wear degree under different load intervals, and evaluate the impact of the load concentration area on pavement damage. Calculate the wear degree of the pavement under different axle loads, and use an empirical formula to represent the pavement loss per unit axle load. Set the wear impact value DA to be related to the axle load P, according to the formula:

[0140] DA = k×P4;

[0141] Calculation, where k is an empirical coefficient. For an axle load of 10t, if k = 0.02, the wear influence value is 0.02×10^4 = 20. For an axle load of 15t, the wear influence value is:

[0142] 0.02×15^4 = 101.25;

[0143] It can be seen that the wear influence of the 15t axle load is much greater than that of the 10t. Count the number of vehicles in different load intervals and calculate the cumulative wear value in each interval. During a certain period, there are 50 vehicles in the [5 - 10t] interval, 30 vehicles in the [10 - 15t] interval, and 20 vehicles in the [above 15t] interval. Then the cumulative calculation of the wear influence value is:

[0144] DA = 50×20 + 30×60 + 20×101.25 = 4825;

[0145] Combined with time series analysis of the load change trend, calculate the cumulative wear value to obtain the load influence evaluation result.

[0146] Specifically, as Figure 2 、 6 shown, the budget allocation module includes:

[0147] The budget demand calculation sub-module obtains the maintenance budget demand by referring to the maintenance needs of different road sections based on the load influence evaluation result, counting the pavement wear of multiple sections and comparing the real-time maintenance budget allocation.

[0148] Combined with the maintenance needs of different road sections, count the pavement wear of each section and compare the current maintenance budget allocation to obtain the maintenance budget demand. Set the pavement wear calculation method. According to the load influence evaluation result, the cumulative wear value of each section can be expressed as Dtotal,i, where i represents different road sections. For example, for sections A, B, and C, Dtotal,A = 5000, Dtotal,B = 7000, and Dtotal,C = 6000 are calculated respectively. Comparing the real-time maintenance budget allocation, assuming the existing maintenance funds are 4500, 6000, and 5000 respectively, then calculate the budget gap for each section:

[0149] Bgap,i = Dtotal,i - Bcurrent,i;

[0150] It is calculated that the budget gap for section A is 500, the budget gap for section B is 1000, and the budget gap for section C is 1000. Combining the budget gap data of all sections, determine the overall maintenance budget demand, and the total demand is 500 + 1000 + 1000 = 2500.

[0151] The capital priority adjustment sub-module, based on the maintenance budget demand, compares the road wear trend, screens the sections with a highway maintenance budget gap, calculates the capital demand ratio, adjusts the capital allocation order, determines the capital priority of the demand sections, and obtains the capital priority allocation situation;

[0152] The formula for calculating the capital demand ratio is:

[0153]

[0154] Among them, Ro represents the capital demand ratio of the o-th section, Dom represents the budget demand of the o-th section for the m-th maintenance project, Wm represents the weight coefficient of the m-th maintenance project, M represents the total number of maintenance projects, Po represents the road wear trend index of the o-th section, represents the average value of the road wear trend index;

[0155] Parameter meaning and calculation process:

[0156] Calculate the parameter Dom (budget demand):

[0157] Dom represents the budget demand of the o-th section for the m-th maintenance project, and this value is obtained through road inspections, maintenance plan evaluations, and maintenance cost data analysis. The calculation method is as follows:

[0158] Dom = Lo × Cm × Iom;

[0159] Among them, Lo is the road length of the o-th section, Cm is the unit length cost of the m-th maintenance project, and Iom is the damage index of the o-th section for the m-th maintenance project;

[0160] Specific value setting:

[0161] For the 3rd section (o = 3), the length L3 = 5.2 km;

[0162] For the 1st maintenance project (m = 1), the unit length cost C1 = 45000 yuan / km, and the damage index I31 = 0.75;

[0163] Calculate the budget demand:

[0164] D31 = 5.2 × 45000 × 0.75 = 175500;

[0165] Calculate the parameter Wm (maintenance project weight coefficient):

[0166] Wm represents the importance weight of different maintenance projects, which is calculated from the project impact degree, historical investment ratio, and road damage impact degree, and is normalized:

[0167]

[0168] Among them, Im is the average damage index of the m-th maintenance project, and Pm is the proportion of maintenance investment in the past five years for the m-th maintenance project;

[0169] Specific numerical settings:

[0170] For the 1st maintenance project, I1 = 0.72, P1 = 0.35;

[0171] For the 2nd maintenance project, I2 = 0.65, P2 = 0.40;

[0172] For the 3rd maintenance project, I3 = 0.80, P3 = 0.25,

[0173] Calculate the denominator of weight normalization:

[0174]

[0175] Calculate the weights:

[0176]

[0177] Calculate the total sum of capital requirements:

[0178]

[0179] Calculate the total sum of weights:

[0180]

[0181] Calculation of the first part of the capital requirement ratio:

[0182]

[0183] Calculate the correction factor

[0184] Po is the road wear trend index of the o-th section, is the average value of the road wear trend indices of all sections;

[0185] Specific numerical settings:

[0186] The wear index P3 of the 3rd section = 0.78, the average value of all sections

[0187] Calculate the correction factor:

[0188]

[0189] 1 + 0.114 = 1.114;

[0190] Calculate the capital requirement ratio:

[0191] R3 = 174012 × 1.114 = 193861.37;

[0192] The results show that the capital demand ratio is 193861.37, indicating the proportion of capital demand in this section under the influence of road length, unit maintenance cost, damage index, project weight, and road wear trend. A high capital demand ratio means that this section should be given a high priority in resource allocation.

[0193] The budget cycle setting sub-module uses the capital priority allocation situation to analyze the periodic fluctuations of capital demand, evaluate the rationality of capital allocation under different budget cycles, screen the optimal budget adjustment cycle, set the capital adjustment intervals for multiple sections, and obtain the maintenance budget adjustment results;

[0194] Analyze the periodic fluctuations of capital demand, evaluate the rationality of capital allocation under different budget cycles, screen the optimal budget adjustment cycle, calculate the changes in capital demand within different cycles, set three budget cycles of 6 months, 12 months, and 18 months, count the changes in capital demand for each cycle, set the capital demands within 6 months to be 800, 1200, and 1500 respectively, with a total demand of 3500, 2000, 2500, and 2800 for 12 months respectively, with a total demand of 7300, and 3000, 3600, and 4200 for 18 months respectively, with a total demand of 10800. Then calculate the capital demand volatility:

[0195] Rvar = max(Bcycle) - min(Bcycle);

[0196] The capital volatility for 6 months is 1500 - 800 = 700, for 12 months is 2800 - 2000 = 800, and for 18 months is 4200 - 3000 = 1200;

[0197] Screen the cycle with low capital volatility and high capital demand coverage rate as the optimal budget cycle. If the capital demand coverage rate for 12 months is good and the fluctuation is relatively moderate, then set 12 months as the maintenance budget adjustment cycle and obtain the maintenance budget adjustment results.

[0198] Specifically, as Figure 2 、 7 shown, the maintenance adjustment module includes:

[0199] The maintenance priority evaluation sub-module uses the maintenance budget adjustment results and combines with the distribution trend of highway surface damage to evaluate the damage degree of different road sections and obtain the maintenance priority ranking;

[0200] Combined with the distribution trend of the surface damage of the road, evaluate the damage degree of different road sections to obtain the maintenance priority ranking, set the quantitative indicators of the damage degree, such as the crack rate (Rf), pothole rate (Rp) and surface smoothness (Ir). In three sections A, B, and C, the measured Rf values are 8%, 12%, and 10% respectively, the Rp values are 5%, 7%, and 6% respectively, and the Ir values are 3.2, 4.5, and 4.0 respectively. Calculate the comprehensive damage index Sd:

[0201] Sd = w1×Rf + w2×Rp + w3×Ir;

[0202] Among them, w1, w2, and w3 are the weights of the damage indicators. For example, set w1 = 0.4, w2 = 0.3, w3 = 0.3, and calculate:

[0203] Sd,A = 0.4×8 + 0.3×5 + 0.3×3.2 = 5.46;

[0204] Sd,B = 0.4×12 + 0.3×7 + 0.3×4.5 = 8.15;

[0205] Sd,C = 0.4×10 + 0.3×6 + 0.3×4.0 = 7.2;

[0206] According to the ranking of the damage index, determine the maintenance priority B > C > A.

[0207] Based on the maintenance priority ranking, the damage type analysis sub-module analyzes the damage types of multiple sections, calculates the required material replacement quantity, matches the construction with the material replacement method, and obtains the section construction matching result;

[0208] Analyze the damage types of different sections, calculate the required material replacement quantity, match the construction with the material replacement method, and count the damage type ratio of each section. The damage types in section B include 60% crack repair (Rc), 30% pothole filling (Rp), and 10% surface milling and resurfacing (Rm). According to the unit material consumption of each type of damage, calculate the total material demand. Mckg filler is used per square meter for crack repair, Mpkg asphalt is used per square meter for pothole filling, and Mmkg asphalt is used per square meter for surface milling. Set the repair area of section B At = 10000 square meters, where the crack repair area Ac = 6000 square meters, the pothole filling area Ap = 3000 square meters, and the milling and resurfacing area Am = 1000 square meters. Then the total required filler quantity:

[0209] Qc = Ac×Mc;

[0210] Qp = Ap×Mp;

[0211] Qm = Am×Mm;

[0212] If Mc = 1.5 kg / m2, Mp = 20 kg / m2, and Mm = 50 kg / m2, the calculations are as follows:

[0213] Qc = 6000 × 1.5 = 9000;

[0214] Qp = 3000 × 20 = 60000;

[0215] Qm = 1000 × 50 = 50000;

[0216] According to the applicable ranges of each construction process, match the construction methods. For crack repair, use sealant perfusion; for pothole filling, use hot mix asphalt filling; for milling and resurfacing, use mechanical milling + hot mix asphalt paving to obtain the construction matching results for each section.

[0217] The construction scheduling optimization sub-module uses the construction matching results for each section, combines with the road traffic demand, evaluates the impact degree of construction on traffic flow, adjusts the construction sequence, divides the construction areas, optimizes the construction schedule, and obtains the highway maintenance scheduling results;

[0218] Combined with the road traffic demand, evaluate the impact degree of construction on traffic flow, adjust the construction sequence, and divide the construction areas to optimize the construction schedule. Analyze the traffic flow data of each section. The average daily traffic volume of section A is VA = 15000 vehicles, that of section B is VB = 12000 vehicles, and that of section C is VC = 18000 vehicles. If the construction affects the traffic capacity by 50%, the remaining traffic capacity during construction is estimated as follows:

[0219] V′i = Vi × (1 - Is);

[0220] Where Is = 0.5 is the construction impact rate, and the calculations are as follows:

[0221] V′A = 15000 × (1 - 0.5) = 7500;

[0222] V′B = 12000 × (1 - 0.5) = 6000;

[0223] V′C = 18000 × (1 - 0.5) = 9000;

[0224] Calculate the construction impact index Ti on traffic flow:

[0225]

[0226] The calculations are as follows:

[0227]

[0228] If the influence indices of each section are the same, the construction of the section with a higher maintenance priority shall be arranged first, where B > C > A. Combining the time required for construction, the construction area is divided and the construction sequence is arranged. For example, the construction of section B takes 10 days, section C takes 8 days, and section A takes 7 days, to obtain the highway maintenance scheduling result.

[0229] Please refer to Figure 8 , the highway maintenance allocation method based on data analysis is executed based on the above-mentioned highway maintenance allocation system based on data analysis, and includes the following steps:

[0230] S1: Obtain the initial composition data of the highway pavement materials, set the monitoring period, collect the changes in the components of asphalt mixtures, the composition of mineral aggregate particles and the adhesion within different time periods, calculate the mass loss ratio per unit time within each period, analyze the antioxidant rate, volatilization loss rate and surface peeling trend, compare the component attenuation curves, and obtain the material component attenuation characteristics;

[0231] S2: Based on the material component attenuation characteristics, record the traffic flow, vehicle load distribution and average driving speed of the road sections, calculate the tire contact pressure per unit area, analyze the material wear rate under different axle load levels, calculate the damage expansion rate of the road surface layer, and obtain the distribution trend of highway surface layer damage;

[0232] S3: Utilize the distribution trend of highway surface layer damage, record the heavy-duty freight vehicle flow and the peak load period, analyze the contact stress concentration degree of the load distribution state on the different damage areas, and obtain the load influence evaluation result;

[0233] S4: According to the load influence evaluation result, combine the highway maintenance fund allocation and the road damage expansion data, calculate the deviation ratio between the damage rate and the fund demand of different sections, adjust the fund allocation priority, set the budget adjustment period, and obtain the maintenance budget adjustment result;

[0234] S5: Through the maintenance budget adjustment result, with reference to the road surface layer damage trend and the construction resource requirements, combine the construction technology and the material replacement method, adjust the construction sequence, and obtain the highway maintenance scheduling result.

[0235] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. A highway maintenance allocation system based on data analysis, characterized in that The system includes: The material aging assessment module collects the initial composition data of highway pavement materials, sets a time-segmented monitoring window, compares the changes in the core components of the materials in different time periods, calculates the mass loss ratio per unit time, and analyzes the attenuation trend by combining the antioxidant capacity, volatility characteristics, and surface structure stability of the materials to obtain the material composition attenuation characteristics; Based on the material composition attenuation characteristics, the road wear assessment module analyzes the wear trend range per unit area of the road, evaluates the damage expansion of the highway per unit time, and obtains the surface damage distribution trend of the highway by referring to the highway traffic flow, vehicle load distribution, and driving speed; Using the surface damage distribution trend of the highway, the traffic load impact analysis module records the heavy-duty freight vehicle flow and load peak time periods of the highway, analyzes the load distribution state, evaluates the impact degree of different loads on road wear, and obtains the load impact assessment result; Using the load impact assessment result, the budget allocation module compares the budget allocation for highway maintenance, changes in maintenance requirements, and road wear trends, adjusts the priority of fund allocation, sets the budget adjustment period, and obtains the maintenance budget adjustment result.

2. The highway maintenance deployment system based on data analysis according to claim 1, characterized in that The material composition attenuation characteristics include the oxidation rate of chemical components and the change in temperature stability. The surface damage distribution trend of the highway includes surface crack distribution, pothole occurrence areas, and material shedding. The load impact assessment result includes the degree of damage aggravation in the load concentration area, the wear condition in the load area, and the impact of periodic peak loads. The maintenance budget adjustment result includes the newly added budget amount, the items with priority adjustment, and the highway areas with budget adjustment.

3. The highway maintenance allocation system based on data analysis according to claim 1, wherein The material aging assessment module includes: The composition monitoring sub-module collects the initial composition data of highway pavement materials, sets a time-segmented monitoring window, detects the concentration values of the core components of the materials in multiple time periods, and analyzes the change range of the core components in different time periods to obtain the change situation of the key components; Based on the change situation of the key components, the concentration change analysis sub-module analyzes the concentration change of the core components of highway pavement materials, calculates the overall mass loss of the materials per unit time, screens the material categories with mass loss, and obtains the material mass loss situation; Using the material mass loss situation, the attenuation trend analysis sub-module analyzes the attenuation trend of the material composition in different time periods by referring to the antioxidant capacity, volatility characteristics, and surface structure stability of the materials to obtain the material composition attenuation characteristics.

4. The highway maintenance deployment system based on data analysis according to claim 1, wherein The road wear assessment module includes: The wear trend analysis sub-module uses the material composition attenuation characteristics, records the highway traffic flow, vehicle load distribution, and driving speed, analyzes the stress situation per unit area of the highway under different traffic conditions, and obtains the wear identification result per unit area of the highway; Based on the wear identification result per unit area of the highway, the damage expansion assessment sub-module combines the change situation of the wear trend per unit time, calculates the damage expansion rate of the highway surface layer, and obtains the highway damage expansion situation; Through the highway damage expansion situation, the highway damage distribution sub-module extracts the traffic flow and vehicle load characteristics in different regions, analyzes the change situation of the damage distribution in multiple regions, and obtains the surface damage distribution trend of the highway.

5. The highway maintenance deployment system based on data analysis according to claim 1, characterized in that The traffic load impact analysis module includes: The load flow monitoring sub-module records the number of freight vehicles passing through and the corresponding axle load information within multiple time periods through the distribution trend of the highway surface damage, identifies the total number of vehicles in different time periods, calculates the load peak value, and arranges the data according to the time sequence to obtain the load flow sequence data; The formula for calculating the load peak value is as follows: Among them, N t is the peak load, n represents the number of passes of freight vehicles, and a i represents the axle load when the i-th vehicle passes, and w i represents the axle load value when the i-th vehicle passes, represents the average axle load value, represents the absolute difference between the axle load value when the i-th vehicle passes and the average axle load value; The load distribution calculation sub-module uses the load flow sequence data to calculate the load cumulative value within multiple time periods, counts the occurrence frequency of different load levels, determines the load peak period, and obtains the load distribution interval; The load wear assessment sub-module analyzes the highway wear degree under multiple load intervals according to the load distribution interval, evaluates the contribution degree of the load concentration area to the pavement damage degree, combines the time series trend to analyze the cumulative impact of load differences on pavement damage, and obtains the load impact assessment result.

6. The highway maintenance deployment system based on data analysis according to claim 1, characterized in that, The budget allocation module includes: The budget demand calculation sub-module obtains the maintenance budget demand by using the load impact assessment result, referring to the maintenance requirements of different road sections, counting the pavement wear amount of multiple sections and comparing the real-time maintenance budget configuration; The fund priority adjustment sub-module, based on the maintenance budget demand, compares the road wear trend, screens the sections with highway maintenance budget gaps, calculates the fund demand ratio, adjusts the fund allocation order, determines the fund priority of the demand sections, and obtains the fund priority allocation situation; The budget cycle setting sub-module uses the fund priority allocation situation to analyze the periodic fluctuation of the fund demand, evaluates the rationality of fund allocation under different budget cycles, screens the optimal budget adjustment cycle, sets the fund adjustment interval of multiple sections, and obtains the maintenance budget adjustment result.

7. The highway maintenance deployment system based on data analysis according to claim 6, wherein The formula for calculating the fund demand ratio is: Among them, R o represents the capital demand ratio of the o-th section, D om represents the budget demand of the o-th section for the m-th maintenance project, W m represents the weight coefficient of the m-th maintenance project, M represents the total number of maintenance projects, P o represents the road wear trend index of the o-th section, represents the mean value of the road wear trend index.

8. The highway maintenance allocation system based on data analysis according to claim 1, wherein, The system further includes a maintenance adjustment module: The maintenance adjustment module, based on the maintenance budget adjustment result, combines the distribution trend of the highway surface damage, evaluates the maintenance priority, matches the construction process, material replacement method and road traffic demand, adjusts the construction sequence and divides the construction area, and obtains the highway maintenance scheduling result; The highway maintenance scheduling result includes the emergency repair area, the schedule of highway maintenance, and the phased traffic adjustment measures.

9. The highway maintenance deployment system based on data analysis according to claim 8, wherein The maintenance adjustment module includes: The maintenance priority evaluation sub-module uses the maintenance budget adjustment result, combines the distribution trend of the highway surface damage, evaluates the damage degree of different road sections, and obtains the maintenance priority ranking; The damage type analysis sub-module, based on the maintenance priority ranking, analyzes the damage types of multiple sections, calculates the required material replacement amount, and matches the construction with the material replacement method to obtain the section construction matching result; The construction scheduling optimization sub-module uses the section construction matching result, combines the road traffic demand, evaluates the impact degree of construction on traffic flow, adjusts the construction sequence, divides the construction area, optimizes the construction schedule, and obtains the highway maintenance scheduling result.

10. A highway maintenance allocation method based on data analysis, characterized in that Executed by the highway maintenance allocation system based on data analysis according to any one of claims 1-9, including the following steps: S1: Obtain the initial composition data of highway pavement materials, set the monitoring period, collect the component changes of asphalt mixtures, the mineral aggregate particle composition, and the adhesion change values within different time periods, calculate the mass loss ratio per unit time within each period, analyze the antioxidant rate, volatilization loss rate, and surface peeling trend, compare the component attenuation curves, and obtain the material component attenuation characteristics; S2: Based on the material component attenuation characteristics, record the traffic flow, vehicle load distribution, and average driving speed of the road section, calculate the tire contact pressure per unit area, analyze the material wear rate under different axle load levels, calculate the damage propagation rate of the road surface layer, and obtain the highway surface damage distribution trend; S3: Use the highway surface damage distribution trend to record the heavy-duty freight vehicle flow and the peak load time period, analyze the contact stress concentration degree of the load distribution state on different damaged areas, and obtain the load impact assessment result; S4: According to the load impact assessment result, combine the highway maintenance fund allocation and the road damage propagation data, calculate the deviation ratio of the damage rate to the fund demand in different sections, adjust the priority of fund allocation, set the budget adjustment period, and obtain the maintenance budget adjustment result; S5: Through the maintenance budget adjustment result, refer to the road surface damage trend and construction resource requirements, combine the construction process and material replacement method, adjust the construction sequence, and obtain the highway maintenance scheduling result.

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