Rural road pavement intelligent maintenance decision method based on automatic detection data

Abstract

The application discloses a kind of rural road pavement intelligent maintenance decision-making methods based on automatic detection data, information matching is carried out to rural road pavement automatic detection data and road annual report data, and automatic detection disease data set is formed;Select road age, pavement performance index, pavement type, disease type and intensity index, determine threshold, form maintenance decision-making logic and give reference maintenance measures, build rural road pavement maintenance decision tree;Set rural road network maintenance benefit maximization, maintenance cost minimization target, set up maintenance funds, index performance, maintenance frequency, maintenance mileage constraint condition, and consider maintenance priority and measure benefit, cost, build rural road multi-objective intelligent maintenance decision-making model to obtain decision result, through road section merging and integration, form multi-year rural road maintenance decision scheme.The present application provides strong technical support for scientific, reasonable and efficient rural road maintenance decision-making scheme formulation and fund allocation.

Description

Technical Field

[0001] This invention belongs to the field of rural road maintenance technology, specifically relating to an intelligent maintenance decision-making method for rural road surfaces based on automated detection data. Background Technology

[0002] By the end of 2022, the total mileage of rural roads in my country reached 4.5314 million kilometers, accounting for 84.62% of the total national road mileage. Significant progress has been made in large-scale construction, and the focus of development is gradually shifting towards structural quality. The task and pressure of rural road maintenance have increased, and given the large mileage of rural roads, manual inspection is labor-intensive and has limited accuracy. Automated inspection has emerged and is gradually being promoted and applied, accumulating massive amounts of data whose asset value urgently needs to be explored and utilized.

[0003] Therefore, how to make scientific, reasonable, and efficient rural road maintenance decisions based on automated detection data is one of the urgent problems to be solved in the field of rural road maintenance.

[0004] According to existing literature, in recent years, with the development of practical needs and related technologies, comprehensive evaluation models, multi-objective optimization algorithms, and machine learning algorithms have been widely used in pavement maintenance decision-making. Patent CN115271565B discloses a DEA-based evaluation method, device, and equipment for highway pavement maintenance measures. By analyzing the historical pavement quality and repair history of the highway, it estimates the unit maintenance cost and changes in pavement performance indicators for each affected section, establishes a DEA evaluation model for pavement maintenance measures, evaluates the benefits of each maintenance measure, and provides maintenance decision recommendations. This invention provides a reference for highway maintenance departments to decide on the main types of defects to repair when funds are limited; however, its evaluation object is the defect type, not the precise section-level maintenance decision.

[0005] Meanwhile, most existing road maintenance decision-making methods are designed for high-grade trunk highways, while low-grade rural roads differ significantly from trunk highways in terms of detection data, road characteristics, disease distribution, and road traffic flow characteristics, making it impossible to directly apply existing methods for maintenance decisions. Summary of the Invention

[0006] Purpose of the invention: This invention proposes an intelligent maintenance decision-making method for rural road surfaces based on automated detection data, providing strong technical support for the formulation of scientific, reasonable, and efficient rural road maintenance decision-making schemes and the allocation of funds.

[0007] Technical Solution: The present invention provides an intelligent maintenance decision-making method for rural road surfaces based on automated detection data, which specifically includes the following steps:

[0008] (1) The pre-acquired automated detection data of rural road pavement and the annual road report data are matched to form a dataset of automated detection defects of rural road pavement;

[0009] (2) Based on the relevant characteristics of rural roads, select the decision tree branching criteria and determine the thresholds for each; combine the criteria to form the maintenance decision logic and construct the rural road surface maintenance decision tree;

[0010] (3) Based on the rural road pavement automated detection defect dataset, and based on the maintenance decision tree, combined with road section performance prediction and maintenance decision characteristic analysis, the objective function is determined, constraints are set, and maintenance priority, measure benefits and costs are considered to build a multi-objective intelligent maintenance decision model for rural roads.

[0011] (4) Based on the model decision results, the optimal maintenance plan and the optimal allocation plan of funds at the project level are obtained by merging and integrating road sections, and finally a multi-year rural road maintenance decision plan is formed.

[0012] Furthermore, the information matching process in step (1) includes the city or county to which the road belongs, the road number, the road name, and the station number.

[0013] Furthermore, the rural road pavement automated detection defect dataset in step (1) includes the district or county to which the road belongs, road number, road name, station number, road grade, pavement type, road section length, number of lanes, detection area of ​​various defects, damage rate DR, pavement smoothness index, pavement damage status index, pavement rutting depth index and pavement technical condition index.

[0014] Furthermore, the decision tree branching criteria selected in step (2) include four types: road age, pavement performance index, pavement type, and distress type and intensity; the threshold selection is as follows:

[0015] The road age threshold is set to p years, and maintenance decisions are made for rural roads with an age exceeding p years.

[0016] For asphalt pavement, the PCI value is selected as the decision threshold for judging the strength of maintenance measures, and the RQI value is selected as the decision threshold for the auxiliary decision node RQI. For cement pavement, the PCI value is selected as the decision threshold for judging the strength of maintenance measures. For other pavement types, the PCI value is selected as the decision threshold for judging the strength of maintenance measures, and the PSSI value is selected as the decision threshold for the auxiliary decision node PSSI.

[0017] The road surface type threshold is used to make decision branches based on the road characteristics of different road surface types, considering asphalt pavement, cement pavement, and other common rural road surface types.

[0018] Disease type and intensity threshold: minor diseases are generally not subject to maintenance, but timely repairs are arranged for broken board diseases; the severity of diseases is classified based on the current disease data, combined with K-means clustering method and relevant evaluation criteria.

[0019] Furthermore, the rural road pavement maintenance decision tree in step (2) includes a general decision tree and branch decision trees for asphalt pavement, cement pavement, and other pavement types. The general decision tree is based on the road age, pavement type, and pavement damage index (PCI). Road sections with an age of less than p years do not require maintenance. For road sections with an age of p years or more, decisions are made according to the pavement type and the PCI, based on the branch decision tree for each pavement type.

[0020] Furthermore, the implementation process of step (3) is as follows:

[0021] (31) Set objectives for maximizing road maintenance benefits and minimizing maintenance costs:

[0022] MaxF = MaxF1 - MinF2

[0023] Among them, MaxF is the overall objective, MaxF1 is the objective of maximizing pavement benefits, and MinF2 is the objective of minimizing maintenance costs;

[0024] (32) The objective of maximizing road surface benefits is:

[0025]

[0026] Among them, w i x represents the importance weight of path element i; ijt A variable ranging from 0 to 1, indicating whether maintenance measures are taken; i Let A be the mileage of path element i; ijt This represents the total benefit that road element i can achieve by taking maintenance measures j in year t within the planning period T:

[0027] A ijt =A PT(ijmt) -A DN(im)

[0028] Among them, A PT(ijmt) This represents the sum of the m-th indicator over the planning period when maintenance measure j is taken in year t of road element i; A DN(im) This represents the sum of the m-th indicator during the planning period if no maintenance measures are taken for road element i.

[0029] (33) The goal of minimizing maintenance costs is:

[0030]

[0031] Among them, c j The maintenance cost per square meter corresponding to maintenance measures j; d i Let i be the maintenance area of ​​road surface i;

[0032] (34) Set constraints on maintenance funds, performance indicators, maintenance frequency, and maintenance mileage:

[0033] The budget constraints for the overall maintenance of rural road networks are as follows:

[0034]

[0035] C represents the upper limit of overall maintenance funding during the road network planning period;

[0036] The minimum acceptable level of road surface performance indicators is constrained as follows:

[0037] ft′ imt ≥a im

[0038] Among them, ft′ imt When maintenance measures are taken in year t′, the value of the m-th performance index of road element i in year t is given by a. im Let m be the minimum acceptable level of the m-th indicator value of path element i;

[0039] The constraint on the maximum number of maintenance sessions for a single road element during the planning period refers to the requirement that each road element undergoes maintenance at most once within the planning period T, in order to save funds.

[0040]

[0041] The constraint on annual maintenance mileage of rural road network during the planning period refers to the requirement that the annual maintenance mileage should not be less than the total mileage. And not exceeding the total mileage The calculation formula is as follows:

[0042]

[0043] (35) Consider maintenance priorities, the effectiveness of measures, and costs:

[0044] Different maintenance priorities are assigned to road elements based on their road classification, and different weights are assigned to road elements of different road classifications. i The maintenance benefits are represented by the improvement values ​​of PCI and RQI after maintenance, and the costs of each maintenance measure are determined.

[0045] Furthermore, the method of merging and integrating road segments in step (4) is to merge adjacent road segments with the same maintenance measures to obtain a project-level maintenance plan.

[0046] Furthermore, the multi-year rural road maintenance decision-making scheme described in step (4) includes a project-level maintenance decision-making scheme and an optimal fund allocation plan; specifically, it includes the project maintenance year, road number, project start and end station numbers, maintenance measures, project length, maintenance area, and project maintenance cost.

[0047] Furthermore, the types of defects mentioned in step (2) include asphalt pavement defects such as cracking, longitudinal cracks, transverse cracks, and potholes; and cement pavement defects such as broken slabs, cracks, potholes, and exposed aggregate.

[0048] Furthermore, the road age threshold is three years; the pavement performance index threshold is 60, 70 or 80 for asphalt pavement and 70 for RQI; the PCI threshold for cement pavement is 50, 60 or 70; the PCI threshold for other pavements is 60 or 70 and the PSSI threshold is 70.

[0049] Beneficial Effects: Compared with existing technologies, the beneficial effects of this invention are as follows: 1. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data utilizes maintenance decision tree technology to determine the applicable decision-making branch basis for rural roads, taking into account the differences between rural roads and trunk roads. Based on a large amount of automated detection data on rural road surface performance, it determines various decision thresholds, forming a clear and reasonable maintenance decision-making logic that can be directly used to judge the intensity of rural road maintenance measures. 2. This invention utilizes multi-objective optimization technology to make multi-year rural road maintenance decisions from a global perspective, determining maintenance sections, optimal maintenance time points, and maintenance measures, thus possessing high practical value. 3. This invention fills the gap in the field of intelligent rural road maintenance, enabling the full integration of maintenance resources under limited funding constraints. It plays an important auxiliary role in maintaining the overall efficiency of the road network and reducing long-term maintenance costs, thus aiding in the scientific maintenance decision-making of rural roads. Attached Figure Description

[0050] Figure 1 This is a flowchart of the present invention;

[0051] Figure 2 A schematic diagram of the overall decision tree for rural road surface maintenance;

[0052] Figure 3 This is a schematic diagram of a branching decision tree for asphalt pavement.

[0053] Figure 4 A schematic diagram of a branching decision tree for cement pavement;

[0054] Figure 5 This is a schematic diagram of the decision tree for other road surface branches. Detailed Implementation

[0055] The present invention will now be described in further detail with reference to the accompanying drawings:

[0056] like Figure 1 As shown, this invention provides a smart maintenance decision-making method for rural road surfaces based on automated detection data, specifically including the following steps:

[0057] Step 1: Perform information matching processing on the automated detection data of rural road pavement and the annual road report data to form a dataset of automated detection defects of rural road pavement.

[0058] The information matching fields include: city / county (district) to which the road belongs, road number, road name, and mileage marker. The resulting automated rural road surface defect detection dataset contains the following fields: district / county to which the road belongs, road number, road name, mileage marker, road grade, pavement type, road segment length (m), number of lanes, and area detected for each type of defect (m²). 2 The indicators include: Dysfunction Rate (DR) (%), Road Surface Roughness Index (IRI), Road Surface Damage Index (PCI), Road Rutting Depth Index (RQI), Road Surface Technical Condition Index (PQI), and Road Surface Structural Strength Index (PSSI). The types of defects are as follows: for asphalt pavements, defects include cracking, longitudinal cracks, transverse cracks, and potholes; for cement pavements, defects include broken slabs, cracks, potholes, and exposed aggregate.

[0059] To verify the effectiveness of the multi-objective maintenance decision model for rural road surfaces, this embodiment uses automated inspection data of rural road surfaces in Z City, Henan Province, in 2022 and 2023 for case verification. The total length of the verification section is 204.74 km, including 102.31 km of asphalt pavement and 102.43 km of cement pavement, mainly single-lane and two-lane roads.

[0060] Step 2: Based on the relevant characteristics of rural roads and a large amount of automated pavement inspection data from rural roads in Henan Province, select indicators such as road age, pavement performance index, pavement type, and type and intensity of defects to determine thresholds, form a maintenance decision logic, and provide reference maintenance measures, thus constructing a rural road pavement maintenance decision tree, such as... Figure 2 As shown. The specific steps include:

[0061] (2.1) Decision tree branching is selected based on a threshold, specifically including:

[0062] 1) Road age: Road age is the primary basis for deciding whether maintenance is necessary. Maintenance decisions are made for rural roads that are 3 years or older, while rural roads that are less than 3 years old are considered not to require maintenance at present.

[0063] 2) Pavement Performance Index: PCI and RQI are selected as the main decision-making criteria. For asphalt pavement, PCI = 60 / 70 / 80 is used as the decision threshold; for cement pavement, PCI = 50 / 60 / 70 is used as the decision threshold; for other pavements, PCI = 60 / 70 is used as the decision threshold, and a PSSI value of 70 is selected as the decision threshold for the auxiliary decision node PSSI. In addition, asphalt roads generally have heavier traffic loads, and considering their requirements for smoothness, RQI is included as an auxiliary decision node. When PCI ∈ (70, 80], medium repair is performed when RQI ∈ (0, 70], and minor repair is performed when RQI ∈ (70, 100].

[0064] 3) Road surface type: The road surface type is divided into asphalt road surface, cement road surface and other road surface. The characteristics of different road surface types are then considered to make decision branches to determine different decision thresholds and specific applicable maintenance measures for each road surface material.

[0065] 4) Type and intensity of defects: When the PCI is greater than 70, for lightly broken slabs of cement pavement, which are prone to developing into severe defects that can only be repaired by replacing the slabs, repair should be arranged in a timely manner; those with other light defects do not need to be treated. The severity of defects is classified based on the current defect data, combined with the K-means clustering method and relevant evaluation criteria.

[0066] (2.2) Based on the decision branching criteria and thresholds obtained in step (2.1), combine indicators to determine the maintenance decision logic, and provide reference maintenance measures. Construct a rural road pavement maintenance decision tree, including the overall decision tree and branch decision trees for asphalt pavement, cement pavement, and other pavements (gravel, brick masonry, etc.). Figures 2 to 5 As shown; the reference maintenance measures for each road surface type are as follows:

[0067] For asphalt pavements, sections requiring no maintenance are routinely swept and drained during rain and snow. Minor repair sections are repaired for potholes, subsidence, and undulations, and issues such as bleeding, bulging, cracks, and looseness are addressed promptly. Medium repair sections undergo asphalt surface treatment, slurry seal, asphalt macadam, and concrete treatment. Major repair sections undergo pavement reinforcement and major repairs, or reconstruction projects are implemented.

[0068] For cement pavements, sections requiring no maintenance are routinely swept and drained during rain and snow. Minor repairs involve filling cracks, addressing subsidence, bulging, and peeling. Medium repairs may include laying wear-resistant layers, applying slurry seals, sealing joints, grouting under slabs, repairing, and grooving. Major repairs involve complete slab overhauls, adding asphalt or cement concrete pavement, or implementing reconstruction projects.

[0069] For other road sections, some sections require no maintenance or only partial repairs; medium-repair sections require overlaying, resurfacing, or repair of road cambers, potholes, ruts, etc.; major-repair sections may require widening or thickening of the road surface, or repaving or reconstruction.

[0070] Step 3: Build a multi-objective intelligent maintenance decision-making model for rural roads and conduct multi-objective decision-making. Consider resource allocation, technological changes, and road reconstruction, and set the maintenance planning period to 3 years; analyze maintenance needs and constraints to determine the objective function and constraints; specific steps include:

[0071] The objective function is determined. Since rural road maintenance funds are often limited, maintenance decisions primarily focus on how to rationally allocate these limited funds to ensure the overall pavement performance of the road network to the greatest extent possible throughout the planning period. Therefore, the objectives of maximizing pavement maintenance benefits and minimizing maintenance costs are set as follows:

[0072] MaxF = MaxF1 - MinF2

[0073] MaxF1 represents the objective of maximizing road maintenance benefits:

[0074]

[0075] Among them, w i Let A be the importance weight of path element i. ijt The total maintenance benefits during the planning period.

[0076] MinF2 represents the objective of minimizing maintenance costs:

[0077]

[0078] Among them, c j The maintenance cost per square meter corresponding to maintenance measures j; d i Let x be the maintenance area of ​​road surface i; ijt It is a 0-1 variable, indicating whether maintenance measures are taken.

[0079] The method for calculating the total maintenance benefits is as follows:

[0080] A ijt =A PT(ijmt) -A DN(im)

[0081] Among them, A ijt This represents the total benefit that road element i can achieve by taking maintenance measures j in year t within the planning period T; A PT(ijmt) This represents the sum of the m-th indicator over the planning period when maintenance measure j is taken in year t of road element i; A DN(im)This represents the sum of the m-th indicator during the planning period, assuming no maintenance is implemented for road element i. i Let i be the mileage of path element i.

[0082] Constraints were determined. Considering the characteristics of rural road maintenance and taking the overall road network into account, the model was designed with constraints on maintenance funding, performance indicators, maintenance frequency, and maintenance mileage.

[0083] The budget for overall road network maintenance is constrained as follows:

[0084]

[0085] Among them, c j The maintenance cost per square meter is represented by maintenance measure j, and C is the upper limit of the overall maintenance funds during the road network planning period, specifically 2.4 million yuan for asphalt pavement and 2 million yuan for cement pavement.

[0086] Minimum acceptable levels of pavement performance indicators are constrained:

[0087] ft′ imt ≥a im

[0088] Among them, ft′ imt When maintenance measures are taken in year t′, the value of the m-th performance index of road element i in year t is given by a. im Let m be the minimum acceptable level of the m-th index value of road element i (70 for Class I, II, and III highways, and 60 for Class IV highways).

[0089] The maximum number of maintenance sessions for a single road element is constrained during the planning period. Within the planning period T, each road element will be maintained at most once to save funds.

[0090]

[0091] During the planning period, upper and lower limits will be imposed on the annual maintenance mileage of the road network, requiring that the annual maintenance mileage be no less than a certain percentage of the total mileage. And not exceeding the total mileage

[0092]

[0093] Maintenance priority and the determination of the benefits and costs of measures. The priority of maintenance must be considered during planning, and road elements should be assigned a certain maintenance priority according to their road grade. This means that a corresponding weight is added to the base weight of 1, as shown in Table 1.

[0094] Table 1. Maintenance Priority Weight Rules for Multi-Objective Maintenance Decision Model of Rural Roads

[0095]

[0096]

[0097] The benefits of maintenance measures are represented by the improvement in PCI and RQI after maintenance when making decisions in the model. The benefits and costs of each maintenance measure (minor repair, medium repair, major repair) are shown in Table 2:

[0098] Table 2. Benefits and Costs of Rural Road Maintenance Measures

[0099]

[0100] Specifically, the CPLEX programming solver is used to solve the constructed multi-objective decision-making model to obtain the road sections to be maintained, maintenance measures, maintenance years, and the resulting maintenance benefits.

[0101] Step 4: Output the maintenance decision plan. Integrate the results obtained in Step 3, merge adjacent road sections with the same maintenance measures into one project, and calculate the project length, maintenance area, and project cost, etc., to obtain the project-level decision plan and the optimal allocation plan of funds. Finally, output the multi-year rural road maintenance decision plan, as shown in Table 3:

[0102] Table 3. Decision-making scheme for multi-objective maintenance of rural roads.

[0103]

[0104] As can be seen from the above description of the specific embodiments in conjunction with the accompanying drawings, this invention, tailored to the characteristics of rural roads, utilizes maintenance decision tree technology to establish a concise and clear maintenance decision logic, determining the required intensity of maintenance measures for each road segment; and employs multi-objective optimization technology to conduct multi-year rural road maintenance decisions from a global perspective, identifying maintenance sections, optimal maintenance times, and maintenance measures. It has high practical value, enabling the full integration of maintenance resources under limited financial constraints, maintaining the overall benefits of the road network, and reducing long-term maintenance costs, thus playing a significant auxiliary role in the scientific maintenance decision-making of rural roads.

[0105] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent maintenance decision-making of rural road surfaces based on automated detection data, characterized in that, Includes the following steps: (1) The information matching process is performed on the pre-acquired rural road pavement automated detection data and road annual report data to form a rural road pavement automated detection defect dataset; (2) Based on the relevant characteristics of rural roads, select the decision tree branching indicators and determine the thresholds respectively; form the maintenance decision logic by combining the indicators and construct the rural road surface maintenance decision tree; (3) Based on the rural road pavement automated detection defect dataset, and based on the maintenance decision tree, combined with road section performance prediction and maintenance decision characteristic analysis, determine the objective function, set constraints, and consider maintenance priority, measure benefits, and costs to build a multi-objective intelligent maintenance decision model for rural roads. (4) Based on the model decision results, the optimal maintenance plan and the optimal fund allocation plan at the project level are obtained through road segment merging and integration, and finally a multi-year rural road maintenance decision plan is formed. The decision tree branching criteria selected in step (2) include four types: road age, pavement performance index, pavement type, and distress type and intensity; the thresholds are selected as follows: Road age threshold, select Using years as a threshold, for roads exceeding [a certain age] Decisions were made regarding the maintenance of rural roads in 2018. Pavement performance index threshold: For asphalt pavement types, the PCI value is selected as the decision threshold for judging the intensity of maintenance measures, and the RQI value is selected as the decision threshold for the auxiliary decision node RQI. For cement pavement, the PCI value is selected as the decision threshold for judging the intensity of maintenance measures; For other pavement types, the PCI value is selected as the decision threshold for judging the intensity of maintenance measures, and the PSSI value is selected as the decision threshold for the auxiliary decision node PSSI. The road surface type threshold is used to make decision branches based on the road characteristics of different road surface types, considering asphalt pavement, cement pavement, and other common rural road surface types. Disease type and intensity threshold: minor diseases generally do not require maintenance, but timely repair should be arranged for broken board diseases; The rural road pavement maintenance decision tree in step (2) includes a general decision tree and branch decision trees for asphalt pavement, cement pavement, and other pavements; wherein, the general decision tree is based on the road age, pavement type, and pavement damage index (PCI) to make judgments, and for roads with an age of less than [missing information], [missing information]. Road sections that are [age] years old require no maintenance; for roads with an age of [age] years... For road sections aged 3 years or older, decisions are made based on the Pavement Damage Index (PCI) according to the different pavement types and the branch decision tree for each pavement type. The implementation process of step (3) is as follows: (31) Set the objectives of maximizing road maintenance benefits and minimizing maintenance costs: in, For the overall goal, To maximize road surface benefits, To minimize maintenance costs; (32) The objective of maximizing road surface benefits is: in, For Lu Yuan Importance weights; A variable ranging from 0 to 1, indicating whether maintenance measures are taken; For Lu Yuan Mileage; Indicating during the planning period The first of the year Year, Lu Yuan Take maintenance measures Total benefits that can be achieved: in, Represents the path element During the planning period Annual maintenance measures At that time, the sum of the m-th indicator during the planning period; Represents the path element The sum of the m-th indicator during the planning period when no maintenance measures are taken; (33) The goal of minimizing maintenance costs is: in, For maintenance measures Corresponding maintenance cost per square meter; For road surface The maintenance area; (34) Set constraints on maintenance funds, performance indicators, maintenance frequency, and maintenance mileage: The budget constraints for the overall maintenance of rural road networks are as follows: in, This represents the upper limit of overall maintenance funding during the road network planning period; The minimum acceptable level of road surface performance indicators is constrained as follows: in, In the first When maintenance measures are taken in the year, the first Nian Luyuan The Performance index value, For Lu Yuan The The minimum acceptable level for each indicator value; The constraint on the maximum number of maintenance times for a single road element during the planning period refers to the constraint on the maximum number of maintenance times for a single road element during the planning period. Each road element will be maintained at most once per year to save money: The constraint on annual maintenance mileage of rural road network during the planning period refers to the requirement that the annual maintenance mileage should not be less than the total mileage. And not exceeding the total mileage The calculation formula is as follows: ； (35) Consider maintenance priorities and the benefits and costs of measures: Different maintenance priorities are assigned to road elements based on their road classification, and different weights are assigned to road elements of different road classifications. The maintenance benefits are represented by the improvement values ​​of PCI and RQI after maintenance, and the costs of each maintenance measure are determined.

2. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data according to claim 1, characterized in that, The information matching process in step (1) includes the city or county to which the road belongs, the road number, the road name, and the station number.

3. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data according to claim 1, characterized in that, The rural road pavement automated detection defect dataset in step (1) includes the district or county to which the road belongs, road number, road name, station number, road grade, pavement type, road section length, number of lanes, detection area of ​​various defects, damage rate DR, pavement smoothness index, pavement damage status index, pavement rutting depth index and pavement technical condition index.

4. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data according to claim 1, characterized in that, The method of merging and integrating road segments in step (4) is to merge adjacent road segments with the same maintenance measures to obtain a project-level maintenance plan.

5. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data according to claim 1, characterized in that, The multi-year rural road maintenance decision-making scheme in step (4) includes a project-level maintenance decision-making scheme and an optimal fund allocation plan; specifically, it includes the project maintenance year, road number, project start and end station numbers, maintenance measures, project length, maintenance area, and project maintenance cost.

6. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data according to claim 1, characterized in that, The types of defects mentioned in step (2) include asphalt pavement defects such as cracking, longitudinal cracks, transverse cracks, and potholes; and cement pavement defects such as broken slabs, cracks, potholes, and exposed aggregate.

7. The intelligent maintenance decision-making method for rural road surfaces based on automated detection data according to claim 1, characterized in that, The road age threshold is three years; the pavement performance index threshold is 60, 70 or 80 for asphalt pavement PCI and 70 for RQI. The PCI threshold for cement pavement is 50, 60, or 70; the PCI threshold for other pavements is 60 or 70, and the PSSI threshold is 70.

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