Maintenance decision rule optimization method and system integrated with road network performance degradation simulation

Abstract

The invention relates to the related technical field of data analysis and processing, in particular to a maintenance decision rule optimization method and system integrated with road network performance degradation simulation, and the method comprises the steps: extracting degradation simulation features, and judging whether there is an identification road section with a maintenance strategy replacement demand; analyzing a maintenance strategy track, and extracting a maintenance strategy main line of the corresponding road section; and a maintenance strategy principal line consistency rule is configured, and a maintenance strategy optimal solution obtained through decision making is obtained through decision making constraint. The technical problems that the maintenance decision is caught in short-term real-time guidance and the system collaboration is reduced due to frequent switching of the maintenance strategies due to lack of a constraint and guidance mechanism for a maintenance strategy time evolution path are solved, and on the basis of integrated road network performance degradation simulation, by analyzing the degradation simulation characteristics of each road section, the system collaboration is improved. And the evolution path of the maintenance strategy is constrained and guided, so that the replacement frequency of the maintenance strategy is remarkably reduced on the premise of meeting the performance control target, and the technical effect of delicacy management of the whole life cycle of the road network is realized.

Description

Technical Field

[0001] This invention relates to the field of data analysis and processing technology, specifically to a method and system for optimizing maintenance decision rules based on integrated road network performance degradation simulation. Background Technology

[0002] As the core infrastructure of the transportation system, the road network's capacity and service status are directly related to regional traffic efficiency and travel safety. With the increase in the years of operation of the road network, the surge in traffic flow, and the erosion of the natural environment, the performance of road sections will inevitably deteriorate. If maintenance decisions are not timely or reasonable, it can easily lead to a decline in the quality of road network traffic, an increase in maintenance costs, and even safety hazards.

[0003] Current road network maintenance decisions are mostly based on real-time performance status. The same road segment frequently switches maintenance strategies at different time scales, lacking time continuity and road network stability. The switching of maintenance strategies is not explicitly modeled, resulting in the neglect of implicit switching costs. The lack of systematic control over maintenance decision switching easily leads to problems such as fragmented decision-making and contradictory maintenance decisions, which cannot guarantee the integrity and continuity of road network maintenance.

[0004] In summary, the existing technology lacks a constraint and guidance mechanism for the time evolution path of maintenance strategies, which leads to maintenance decisions being driven by short-term immediacy and a decline in system synergy caused by frequent switching of maintenance strategies. Summary of the Invention

[0005] This application provides a method and system for optimizing maintenance decision rules by integrating road network performance degradation simulation. It aims to solve the technical problem in the prior art that the lack of a constraint and guidance mechanism on the time evolution path of maintenance strategies leads to maintenance decisions being driven by short-term immediacy and frequent switching of maintenance strategies, resulting in a decline in system synergy.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: In a first aspect, this application provides a method for optimizing maintenance decision rules based on integrated road network performance degradation simulation. The method includes: constructing a road network topology model; performing road segment performance degradation simulation on the road network topology model to obtain a road segment performance degradation simulation dataset; extracting degradation simulation features from the road segment performance degradation simulation dataset; and determining whether there are identified road segments requiring maintenance strategy replacement by analyzing the degradation simulation features. The degradation simulation features include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features; analyzing the maintenance strategy trajectory of the road network topology model; extracting the maintenance strategy mainline for the corresponding road segment from the maintenance strategy trajectory; configuring a maintenance strategy mainline consistency rule; and applying decision constraints based on the maintenance strategy candidate space to the identified road segments based on the maintenance strategy mainline consistency rule to obtain the optimal maintenance strategy solution obtained from the decision.

[0007] In a possible implementation, the degradation rate stability feature is used to measure the stability of the performance degradation rate fluctuation variance at adjacent time points within a time window; the trend slope change feature is used to measure the magnitude of the degradation trend slope change at adjacent time periods within a time window; the strategy threshold change probability feature is used to measure the probability of changing the threshold corresponding to the current maintenance strategy within a time window; and the degradation fluctuation amplitude feature is used to measure the difference between peaks and valleys within a time window. If the vector value of any feature in the current road segment degradation simulation features does not meet the corresponding preset threshold, the current road segment is output as an identified road segment.

[0008] In a possible implementation, the road segment set of the road network topology model is analyzed, and the time axis corresponding to each road segment in the road segment set is extracted; the historical maintenance strategy records are mapped and output as a maintenance strategy time series according to the time axis; the maintenance strategy time series is continuously processed to obtain the merged strategy segment; each strategy segment in the merged strategy segment is recorded to obtain the maintenance strategy trajectory of each road segment.

[0009] In a possible implementation, the strategy feature vector of each strategy segment in the maintenance strategy trajectory is extracted. The strategy feature vector includes maintenance intensity level, construction process type, resource consumption distribution, and pavement structure influence. The strategy feature similarity between any two strategy segments is calculated to obtain a set of strategy feature similarity indicators. Each strategy segment is used as a node, and connections between nodes are established using the set of strategy feature similarity indicators to construct a strategy similarity relationship graph. Multiple candidate strategy main lines are searched from the strategy similarity relationship graph, and a maintenance strategy main line is selected from the multiple candidate strategy main lines.

[0010] In a possible implementation, the multiple candidate strategy lines include strategy lines whose strategy feature similarity index is greater than a preset similarity threshold; a resource change cost function is introduced, the resource change cost index of the multiple candidate strategy lines is calculated according to the resource change cost function, and the maintenance strategy line is selected according to the magnitude of the resource change cost index.

[0011] In possible implementations, the mainline strategy feature vector corresponding to the identified road segment is extracted, including the high-frequency type distribution of the strategy, average intervention intensity, temporal continuity characteristics, and historical stable duration; the mainline consistency evaluation is performed based on the strategy feature vector of any candidate maintenance strategy in the maintenance strategy candidate space and the mainline strategy feature vector, and the mainline consistency evaluation index is output; the optimal solution of the maintenance strategy obtained from the decision is obtained by comparing the mainline consistency rule and the mainline consistency evaluation index.

[0012] In a possible implementation, the maintenance strategy mainline consistency rule includes a first threshold and a second threshold; candidate maintenance strategies with a mainline consistency evaluation index greater than or equal to the first threshold are integrated into a first search space, and candidate maintenance strategies with a mainline consistency evaluation index less than the first threshold but greater than or equal to the second threshold are integrated into a second search space; wherein, the first threshold is greater than the second threshold, and the decision priority of the first search space is greater than the decision priority of the second search space.

[0013] In a second aspect, this application provides a maintenance decision rule optimization system integrating road network performance degradation simulation, wherein the system comprises: a road network topology model construction module: constructing a road network topology model, performing road segment performance degradation simulation on the road network topology model, and obtaining a road segment performance degradation simulation dataset; a judgment module: extracting degradation simulation features from the road segment performance degradation simulation dataset, and determining whether there are marked road segments requiring maintenance strategy replacement by analyzing the degradation simulation features, wherein the degradation simulation features include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features; a maintenance strategy mainline extraction module: analyzing the maintenance strategy trajectory of the road network topology model, and extracting the maintenance strategy mainline of the corresponding road segment from the maintenance strategy trajectory; and a maintenance strategy acquisition module: configuring maintenance strategy mainline consistency rules, applying decision constraints based on the maintenance strategy candidate space to the marked road segments based on the maintenance strategy mainline consistency rules, and obtaining the optimal maintenance strategy solution obtained from the decision.

[0014] In summary, one or more technical solutions provided in this application, based on integrated road network performance degradation simulation, analyze the degradation simulation characteristics of each road segment, constrain and guide the evolution path of maintenance strategies, thereby significantly reducing the frequency of maintenance strategy replacement while meeting performance control objectives, and achieving the technical effect of refined management of the entire life cycle of the road network. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This application provides a flowchart illustrating the optimization method for maintenance decision rules based on integrated road network performance degradation simulation.

[0017] Figure 2 This application provides a schematic diagram of the structure of a maintenance decision rule optimization system that integrates road network performance degradation simulation.

[0018] Explanation of reference numerals in the attached diagram: Road network topology model construction module M100, judgment module M200, maintenance strategy main line extraction module M300, maintenance strategy acquisition module M400. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0020] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides a method for optimizing maintenance decision rules based on integrated road network performance degradation simulation, wherein the method includes: S1: Construct a road network topology model, perform road segment performance degradation simulation on the road network topology model, and obtain a road segment performance degradation simulation dataset; S2: Extract degradation simulation features from the road segment performance degradation simulation dataset, and determine whether there are any marked road segments that require maintenance strategy replacement by analyzing the degradation simulation features. The degradation simulation features include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features.

[0021] Specifically, a road network topology model is an abstract representation of the actual road network structure. It describes the connection relationships and topological structure of the road network through the combination of nodes and edges. The purpose of constructing a road network topology model is to systematically analyze and simulate the performance changes of each road segment in the road network. Furthermore, nodes correspond to intersections and entrances / exits, and edges correspond to road segments. Road segment performance degradation simulation refers to using mathematical models and algorithms to simulate the degradation process of road segment performance over time in order to predict the performance status of road segments at different points in the future. It needs to consider a variety of factors, including traffic flow, environmental factors, and road material characteristics.

[0022] Degradation simulation features are parameters extracted from simulation data that reflect the performance degradation characteristics of road sections. These include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features. Furthermore, degradation rate stability features measure the stability of degradation rate fluctuations, trend slope change features measure the amount of change in the degradation trend slope, strategy threshold change probability features measure the probability of threshold changes under the current strategy, and degradation fluctuation amplitude features measure the peak-to-valley difference during the degradation process. Identified road sections are those that, after analyzing degradation simulation features, are determined to require maintenance strategy replacement.

[0023] Execution steps: Construct a road network topology model. Specifically, by abstractly modeling the actual road network, the connection relationship between each road segment in the road network can be clearly described, providing a structural framework for performance degradation simulation. During the simulation process, the performance degradation of each road segment is dynamically simulated by combining actual traffic flow data, road material characteristics, and environmental factors. Specifically, in an urban road network containing multiple road segments, the performance degradation dataset of each road segment over a period of time can be obtained through simulation, including changes in performance indicators such as road surface smoothness and skid resistance.

[0024] The degradation simulation features extracted from the degradation simulation dataset are key to determining the need for maintenance strategy replacement. Specifically, taking the degradation rate stability feature as an example, if the variance of the degradation rate fluctuation of a certain road segment exceeds a preset threshold in a continuous simulation over a period of time, it indicates that the degradation process of that road segment is unstable and may require a change in maintenance strategy. Similarly, the trend slope change feature can reflect the acceleration or deceleration of the degradation trend. If the slope change exceeds a certain threshold, it also indicates that attention is needed. By comprehensively analyzing these features, the road segments that need to be replaced with maintenance strategies can be accurately identified, providing precise targets for maintenance strategy optimization and avoiding misjudgment caused by a single indicator.

[0025] S3: Analyze the maintenance strategy trajectory of the road network topology model, and extract the maintenance strategy main line of the corresponding road segment from the maintenance strategy trajectory; S4: Configure the consistency rule of the maintenance strategy main line, and apply the decision constraint based on the maintenance strategy candidate space to the identified road segment based on the consistency rule of the maintenance strategy main line, and obtain the optimal solution of the maintenance strategy obtained by the decision.

[0026] Specifically, the maintenance strategy trajectory refers to the sequence of changes in maintenance strategies adopted by each road segment during historical maintenance in the road network topology model. It records information such as the maintenance measures, strategy types, and durations applied by the road segment in different time periods. The maintenance strategy mainline is the most representative and stable maintenance strategy path extracted from the maintenance strategy trajectory, reflecting the most important evolution trend of maintenance strategies in the historical maintenance process of the road segment. The maintenance strategy mainline consistency rule is a series of rules used to evaluate the similarity and matching degree between candidate maintenance strategies and historical maintenance strategy mainlines. By setting thresholds and evaluation indicators, the maintenance strategy mainline consistency rule ensures that new maintenance strategies maintain a certain degree of continuity and consistency with historical strategies.

[0027] The maintenance strategy candidate space refers to the set of all possible maintenance strategies available during the decision-making process. These maintenance strategies are generated based on the current performance status, degradation characteristics, and historical maintenance data of the road segment, providing multiple alternatives for decision-making. Decision constraints refer to the process of screening and evaluating candidate strategies according to the consistency rule of the maintenance strategy main line when selecting maintenance strategies. By constraining candidate strategies, it is ensured that the final selected maintenance strategy meets current needs and is consistent with historical strategies.

[0028] Execution steps: By performing time series analysis on the historical maintenance strategies of each road segment, the evolution of maintenance strategies in different time periods can be clearly understood. Furthermore, by analyzing the historical data of a road segment, the main maintenance strategy of that road segment is extracted. After configuring the consistency rules of the main maintenance strategy, decision constraints are applied to the candidate space of the maintenance strategy for the identified road segment to determine the characteristics of the main maintenance strategy of the identified road segment. When selecting a new maintenance strategy, strategies that are highly consistent with these characteristics are selected from the candidate space.

[0029] Specifically, the first threshold for the mainline consistency evaluation index is set to 0.8, and the second threshold is set to 0.6. If the mainline consistency evaluation index of a candidate strategy is 0.85, it enters the first search space with higher priority; strategies with an evaluation index of 0.7 enter the second search space. By comparing these indices, the optimal solution is selected from the first search space to ensure that the new maintenance strategy meets the current performance requirements and is highly consistent with historical strategies. In the best case, by constraining candidate strategies, the fragmentation of decisions and policy contradictions caused by the lack of historical data constraints are avoided, which significantly improves the level of refined management of the entire life cycle of the road network.

[0030] Furthermore, by analyzing the degradation simulation characteristics to determine whether there are marked road sections requiring maintenance strategy replacement, the method of this application includes: The degradation rate stability feature is used to measure the stability of the performance degradation rate fluctuation variance at adjacent time points within the time window. The trend slope change feature is used to measure the magnitude of the degradation trend slope change at adjacent time periods within the time window. The strategy threshold change probability feature is used to measure the probability of changing the threshold corresponding to the current maintenance strategy within the time window. The degradation fluctuation amplitude feature is used to measure the difference between peaks and valleys within the time window. If the vector value of any feature in the current road segment degradation simulation features does not meet the corresponding preset threshold, the current road segment will be output as an identified road segment.

[0031] Specifically, a time window refers to a specific period of time used to analyze and calculate degradation characteristics during degradation simulation, and to observe changes in road segment performance within that specific period. The performance degradation rate fluctuation variance refers to the degree of fluctuation between performance degradation rates at adjacent time points within the time window. By calculating the variance of these fluctuations, the stability of the degradation rate is quantified; the smaller the variance, the more stable the degradation rate. The degradation trend slope change refers to the degree of change between the degradation trend slopes at adjacent time points within the time window; the greater the slope change, the more obvious the acceleration or deceleration of the degradation trend.

[0032] The probability of a change in the strategy threshold refers to the likelihood that the threshold corresponding to the current maintenance strategy will change within a time window. It reflects whether the current strategy needs to be adjusted to adapt to changes in road segment performance. The threshold corresponding to the current maintenance strategy is the critical value of the performance index. The degradation fluctuation amplitude refers to the difference between the maximum and minimum values ​​during the road segment performance degradation process within a time window. It reflects the fluctuation range during the degradation process. The preset threshold refers to the standard value set based on experience and actual needs to judge whether the degradation characteristics are abnormal. If the vector value of the degradation simulation characteristics exceeds these thresholds, the road segment is considered to have potential problems and needs further attention.

[0033] Execution steps: Obtain performance degradation data of road segments within a time window through simulation. Specifically, for the stability characteristics of degradation rate, calculate the variance of degradation rate fluctuations at adjacent time points. If the variance exceeds a preset threshold, it indicates that the degradation rate of the road segment is unstable and there may be potential problems. Similarly, for the trend slope change characteristics, calculate the change in degradation trend slope between adjacent time periods. If the change exceeds a preset threshold, it indicates that the degradation trend has changed significantly and needs attention.

[0034] Regarding the probability characteristic of strategy threshold changes, if the performance threshold of the current maintenance strategy is a pavement smoothness index that is less than a certain limit, and the probability of this threshold changing exceeds a preset threshold within a specific time interval, it indicates that the current strategy may no longer be applicable and needs adjustment. The degradation fluctuation amplitude characteristic measures the fluctuation amplitude by calculating the difference between the maximum and minimum values ​​of the performance index within a time window. If the fluctuation amplitude exceeds a preset threshold, the degradation process is considered to be too volatile. Preferably, a comprehensive evaluation accurately identifies the marked road sections that require a change in maintenance strategy, reducing resource waste caused by misjudgment.

[0035] Furthermore, the method of this application for analyzing the maintenance strategy trajectory of the road network topology model includes: Analyze the road segment set of the road network topology model and extract the time axis corresponding to each road segment in the road segment set; map the historical maintenance strategy records according to the time axis to output the maintenance strategy time series; obtain the merged strategy segment by continuously processing the maintenance strategy time series; record each strategy segment in the merged strategy segment to obtain the maintenance strategy trajectory of each road segment.

[0036] Specifically, the road segment set refers to the collection of all road segments in the road network topology model, each with its unique performance characteristics and maintenance history; the time axis refers to the time series that records the changes in maintenance strategies for each road segment, reflecting the maintenance strategies adopted by the road segment at different points in time and their duration; the historical maintenance strategy record refers to all maintenance measures and strategy information recorded in the past maintenance process of the road segment, including maintenance time, maintenance type, and maintenance intensity.

[0037] Maintenance strategy time series is a sequence formed by arranging historical maintenance strategy records in chronological order, used to analyze the evolution of road segment maintenance strategies; continuous processing refers to analyzing the maintenance strategy time series by merging adjacent and similar strategy segments into a larger strategy segment to simplify the analysis process and extract the main maintenance strategy evolution trends; merged strategy segments refer to time intervals with similar maintenance strategy characteristics obtained after continuous processing; maintenance strategy trajectory refers to the evolution path of maintenance strategies formed by each road segment in the historical maintenance process, reflecting the main maintenance strategies and their changes in different time periods of the road segment.

[0038] Execution steps: A city road network contains multiple road segments. Specifically, the road segment set in the road network topology model is analyzed, and the time axis corresponding to each road segment is extracted, including the maintenance measures and strategies at each time node. By mapping these historical maintenance strategy records in chronological order, a maintenance strategy time series is formed. The maintenance strategy time series is continuously processed to obtain merged strategy segments. Furthermore, if the maintenance strategy types of adjacent time periods are the same or similar, they are merged into a larger strategy segment. Detailed information of each merged strategy segment is recorded to form the maintenance strategy trajectory of each road segment.

[0039] The maintenance strategy trajectory reveals the evolution of the main maintenance strategies over a period of time, reflecting the maintenance needs and strategy adjustments of the road segment at different time periods. Preferably, through systematic analysis and organization of historical maintenance strategies, the evolution path of maintenance strategies for each road segment can be clearly understood. Further analysis of these maintenance strategy trajectories reveals that the main problem lies in the lack of effective constraints on the temporal evolution path of maintenance strategies. This provides support for introducing a consistency rule for the main maintenance strategy line, which helps optimize maintenance decisions, reduce the frequency of strategy switching, and improve the overall efficiency and stability of road network maintenance.

[0040] Furthermore, the method of this application includes extracting the main maintenance strategy line for the corresponding road segment from the maintenance strategy trajectory: Extract the strategy feature vector of each strategy segment in the maintenance strategy trajectory. The strategy feature vector includes maintenance intensity level, construction process type, resource consumption distribution, and pavement structure influence. Calculate the strategy feature similarity between any two strategy segments to obtain a set of strategy feature similarity indicators. Using each strategy segment as a node, establish connections between nodes based on the set of strategy feature similarity indicators to construct a strategy similarity relationship graph. Search for multiple candidate strategy main lines from the strategy similarity relationship graph, and select a maintenance strategy main line from the multiple candidate strategy main lines.

[0041] Specifically, the strategy feature vector refers to a multi-dimensional vector describing the core features of each strategy segment, including maintenance intensity level, construction process type, resource consumption distribution, and pavement structure impact. Furthermore, the maintenance intensity level reflects the intensity or frequency of maintenance measures, the construction process type refers to the specific maintenance construction method, including repair and renovation, the resource consumption distribution refers to the use of resources during the maintenance process, including materials and manpower, and the pavement structure impact refers to the degree of influence of maintenance measures on the pavement structure. Strategy feature similarity is a quantitative index obtained by calculating the similarity between the strategy feature vectors of two strategy segments. The higher the similarity, the closer the two strategy segments are in terms of maintenance features.

[0042] In the strategy similarity graph, each node represents a strategy segment, and the connections between nodes represent the similarity relationships between strategy segments. The weight of the connections is determined by the strategy feature similarity index. The candidate strategy main line refers to the possible maintenance strategy evolution path found by the search algorithm in the strategy similarity graph. The maintenance strategy evolution path reflects the possible main strategy evolution trend of the road segment in the historical maintenance process. The strategy main line is not the single most frequently occurring strategy, but rather the dominant strategy path that minimizes additional resource changes is selected from multiple candidate strategy main lines by analyzing the similarity main lines between strategies.

[0043] Execution steps: The maintenance strategy trajectory contains multiple strategy segments. The strategy feature vector of each strategy segment is extracted, and the similarity of strategy features between any two strategy segments is calculated. These similarity indicators are aggregated to obtain a complete similarity indicator set. Using each strategy segment as a node, connections are established between nodes based on the similarity indicator set to construct a strategy similarity graph. Multiple candidate strategy mainlines are searched from the strategy similarity graph. Furthermore, multiple candidate strategy mainlines are found using a graph search algorithm. Based on rules such as minimizing resource change costs, the maintenance strategy mainline is selected from the candidate strategy mainlines.

[0044] Preferably, by constructing a strategy similarity relationship graph and selecting the optimal maintenance strategy main line, the main strategy evolution trend of road segments in the historical maintenance process can be clearly reflected. Furthermore, after analyzing the maintenance strategy main lines of multiple road segments, it was found that the optimal strategy main line of a certain type of road segment is a cyclical pattern of "preventive maintenance → restorative maintenance → preventive maintenance". This finding provides a reference for subsequent maintenance decisions, helps to optimize the selection of maintenance strategies, reduce unnecessary strategy switching, improve the utilization efficiency of maintenance resources, and thus realize the systematic and refined management of road network maintenance decisions.

[0045] Furthermore, the method of this application for selecting a maintenance strategy main line from the multiple candidate strategy main lines includes: The multiple candidate strategy lines include strategy lines whose strategy feature similarity index is greater than a preset similarity threshold; a resource change cost function is introduced, and the resource change cost index of the multiple candidate strategy lines is calculated according to the resource change cost function, and the maintenance strategy line is selected according to the magnitude of the resource change cost index.

[0046] Specifically, the preset similarity threshold refers to a standard value used to screen candidate strategy lines in strategy feature similarity analysis. Only when the similarity index between two strategy segments is greater than this threshold will they be connected to form a candidate strategy line. The resource change cost function is a mathematical model used to quantify the resource change cost required to switch from one maintenance strategy to another. It considers various factors, such as material costs, construction equipment adjustments, and manpower reallocation, to calculate the cost of resource change. The resource change cost index refers to the specific value calculated by the resource change cost function, used to measure the resource cost required to switch from one strategy segment to another. The smaller the resource change cost index, the lower the resource change cost.

[0047] Execution steps: By analyzing the strategy similarity graph, all strategy segment connection paths with similarity indices greater than a preset similarity threshold are selected, forming multiple candidate strategy main lines. A resource change cost function is introduced to calculate the resource change cost index for each candidate strategy main line. Specifically, the resource change cost function considers factors such as material costs, construction equipment adjustments, and manpower reallocation. For main line A, the calculated resource change cost index for switching from segment 1 to segment 3 is 100 unit cost; for main line B, the resource change cost index for switching from segment 1 to segment 2 and then to segment 3 is 150 unit cost.

[0048] Based on the magnitude of the resource change cost index, the candidate strategy line with the lowest resource change cost is selected as the final maintenance strategy line. Referring to the example above, the resource change cost index of line A is 100, which is less than that of line B (150). Therefore, line A is selected as the maintenance strategy line for this road segment. Preferably, by introducing a resource change cost function, the resource change costs between different candidate strategy lines are quantified. This allows for the selection of the strategy line with the lowest resource change cost while meeting maintenance requirements. This not only improves the economic efficiency of maintenance decisions but also reduces resource waste and additional costs caused by strategy switching, significantly improving the overall efficiency and sustainability of road network maintenance.

[0049] Furthermore, based on the consistency rule of the maintenance strategy main line, the method of this application applies decision constraints based on the maintenance strategy candidate space to the identified road segments. Extract the mainline strategy feature vector corresponding to the identified road segment, including the high-frequency type distribution of the strategy, average intervention intensity, temporal continuity characteristics, and historical stable duration; perform mainline consistency evaluation based on the strategy feature vector of any candidate maintenance strategy in the maintenance strategy candidate space and the mainline strategy feature vector, and output the mainline consistency evaluation index; compare the mainline consistency rules and the mainline consistency evaluation index to obtain the optimal solution of the maintenance strategy obtained from the decision.

[0050] Specifically, the main strategy feature vector refers to a multi-dimensional vector describing the core features of the main maintenance strategy, including the distribution of high-frequency types of the strategy, the average intervention intensity, the temporal continuity feature, and the historical stable duration. Furthermore, the distribution of high-frequency types of the strategy refers to the types that appear most frequently in the maintenance strategy, the average intervention intensity refers to the average intensity or frequency of the maintenance measures, the temporal continuity feature is used to characterize the continuity of the maintenance strategy over time, and the historical stable duration refers to the duration for which the maintenance strategy remains stable.

[0051] Mainline consistency evaluation refers to assessing the degree of consistency between candidate maintenance strategies and the mainline strategy by comparing the similarity between the strategy feature vectors of candidate maintenance strategies and the feature vectors of the mainline strategy; this is the mainline consistency evaluation index. Mainline consistency rules for maintenance strategies are a series of rules used to determine whether the mainline consistency evaluation index meets the requirements. These rules typically include set thresholds and priority standards, and are used to select candidate strategies that are highly consistent with the mainline strategy. The optimal maintenance strategy obtained from the decision is the best maintenance strategy selected from the candidate maintenance strategies, provided that the mainline consistency rules are met. This optimal maintenance strategy not only meets the current maintenance needs of the road segment but also maintains a high degree of consistency with historical maintenance strategies.

[0052] Execution steps: Extract the mainline strategy feature vector of the identified road segment; select a candidate maintenance strategy from the maintenance strategy candidate space and extract its strategy feature vector; based on the mainline consistency evaluation, calculate the consistency between the candidate strategy and the mainline strategy feature vector. Specifically, the evaluation indicators include strategy type distribution similarity, intervention intensity difference, temporal continuity matching degree, and stable duration difference; obtain the mainline consistency evaluation index of the candidate strategy through calculation.

[0053] According to the consistency rule of the maintenance strategy mainline, if the evaluation index of the candidate strategy is higher than the threshold corresponding to the consistency rule of the maintenance strategy mainline, it means that the candidate strategy is highly consistent with the mainline strategy. Therefore, the candidate strategy is taken as the optimal solution of the maintenance strategy obtained from the decision. Preferably, through the mainline consistency evaluation, it is ensured that the new maintenance strategy not only meets the performance requirements of the current road segment, but also maintains a high degree of consistency with the historical maintenance strategy, avoiding the discontinuity and resource waste caused by strategy switching.

[0054] Furthermore, based on the comparison of the maintenance strategy mainline consistency rules and mainline consistency evaluation indicators, the method of this application includes: The maintenance strategy mainline consistency rule includes a first threshold and a second threshold; candidate maintenance strategies with a mainline consistency evaluation index greater than or equal to the first threshold are integrated into a first search space, and candidate maintenance strategies with a mainline consistency evaluation index less than the first threshold but greater than or equal to the second threshold are integrated into a second search space; wherein, the first threshold is greater than the second threshold, and the decision priority of the first search space is greater than the decision priority of the second search space.

[0055] Specifically, the first threshold and the second threshold are two standard values ​​used to divide the range of the main line consistency evaluation index. Furthermore, the first threshold is higher than the second threshold, which is used to screen out candidate maintenance strategies that are highly consistent with the main line strategy. The first search space refers to the set of candidate maintenance strategies whose main line consistency evaluation index is greater than or equal to the first threshold. These candidate maintenance strategies are highly consistent with the main line strategy and have a high decision priority.

[0056] The second search space refers to the set of candidate maintenance strategies whose consistency evaluation index is less than the first threshold but greater than or equal to the second threshold. These candidate maintenance strategies have a certain degree of consistency with the main strategy, but the degree of consistency is lower than that of the strategies in the first search space. Decision priority refers to the order of priority of candidate strategies in different search spaces when selecting the optimal maintenance strategy. The higher the decision priority, the closer the strategy in that search space is to the main strategy, and the more likely it is to be selected as the final maintenance strategy.

[0057] Execution steps: First, evaluate the consistency of all candidate maintenance strategies to obtain an evaluation index for each strategy. Second, classify these candidate strategies into different search spaces according to the maintenance strategy consistency rule. For example, candidate strategy A has an evaluation index of 0.92, which is greater than or equal to the first threshold of 0.9, and is therefore assigned to the first search space. Candidate strategy B has an evaluation index of 0.85, which is less than the first threshold but greater than or equal to the second threshold of 0.7, and is therefore assigned to the second search space. Candidate strategy C has an evaluation index of 0.75, which is also less than the first threshold but greater than or equal to the second threshold, and is also assigned to the second search space.

[0058] In the decision-making process, the optimal maintenance strategy is selected first from the first search space. If no suitable strategy is found in the first search space, a second search space is then selected, as the decision priority of the first search space is higher than that of the second search space. Preferably, a first threshold, a second threshold, a first search space, and a second search space are set to more efficiently filter out candidate strategies that are highly consistent with the main strategy, while providing alternatives for suboptimal selections. On the one hand, this hierarchical decision-making mechanism improves the scientific nature and accuracy of the decision-making, ensuring the flexibility and adaptability of maintenance strategy selection. On the other hand, the implementation effect of the maintenance strategy is more stable, improving the overall performance of the road network.

[0059] In summary, the beneficial effects of the embodiments of this application are: By constructing a road network topology model and performing road segment performance degradation simulation on the model, a road segment performance degradation simulation dataset is obtained. Degradation simulation features are extracted from this dataset, and analysis of these features determines whether there are road segments requiring maintenance strategy replacement. These features include degradation rate stability, trend slope change, strategy threshold change probability, and degradation fluctuation amplitude. The maintenance strategy trajectory of the road network topology model is analyzed, and the maintenance strategy mainline for the corresponding road segment is extracted. Consistency rules for the maintenance strategy mainline are configured, and decision constraints based on the maintenance strategy candidate space are applied to the identified road segments based on these rules to obtain the optimal maintenance strategy solution. This application provides a method and system for optimizing maintenance decision rules by integrating road network performance degradation simulation. Based on integrated road network performance degradation simulation, by analyzing the degradation simulation features of each road segment, the evolution path of maintenance strategies is constrained and guided. This significantly reduces the frequency of maintenance strategy replacement while meeting performance control objectives, achieving the technical effect of refined management throughout the entire lifecycle of the road network.

[0060] Example 2, based on the same inventive concept as the maintenance decision rule optimization method integrating road network performance degradation simulation in the aforementioned examples, such as... Figure 2 As shown in the embodiment of this application, a maintenance decision rule optimization system integrating road network performance degradation simulation is provided, wherein the system includes: Road network topology model construction module M100: Constructs a road network topology model, performs road segment performance degradation simulation on the road network topology model, and obtains a road segment performance degradation simulation dataset.

[0061] Judgment module M200: Extracts degradation simulation features from the road segment performance degradation simulation dataset, and determines whether there are identified road segments requiring maintenance strategy replacement by analyzing the degradation simulation features. The degradation simulation features include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features.

[0062] Maintenance strategy main line extraction module M300: Analyzes the maintenance strategy trajectory of the road network topology model and extracts the maintenance strategy main line of the corresponding road segment from the maintenance strategy trajectory.

[0063] Maintenance strategy acquisition module M400: Configures maintenance strategy mainline consistency rules, performs decision constraints on the identified road segments based on the maintenance strategy candidate space based on the maintenance strategy mainline consistency rules, and obtains the optimal solution of maintenance strategy obtained from the decision.

[0064] Furthermore, the judgment module M200 is used to execute the following method: The degradation rate stability feature is used to measure the stability of the performance degradation rate fluctuation variance at adjacent time points within the time window. The trend slope change feature is used to measure the magnitude of the degradation trend slope change at adjacent time periods within the time window. The strategy threshold change probability feature is used to measure the probability of changing the threshold corresponding to the current maintenance strategy within the time window. The degradation fluctuation amplitude feature is used to measure the difference between peaks and valleys within the time window. If the vector value of any feature in the current road segment degradation simulation features does not meet the corresponding preset threshold, the current road segment will be output as an identified road segment.

[0065] Furthermore, the maintenance strategy main line extraction module M300 is used to perform the following method: Analyze the road segment set of the road network topology model and extract the time axis corresponding to each road segment in the road segment set; map the historical maintenance strategy records according to the time axis to output the maintenance strategy time series; obtain the merged strategy segment by continuously processing the maintenance strategy time series; record each strategy segment in the merged strategy segment to obtain the maintenance strategy trajectory of each road segment.

[0066] Furthermore, the maintenance strategy main line extraction module M300 is used to perform the following method: Extract the strategy feature vector of each strategy segment in the maintenance strategy trajectory. The strategy feature vector includes maintenance intensity level, construction process type, resource consumption distribution, and pavement structure influence. Calculate the strategy feature similarity between any two strategy segments to obtain a set of strategy feature similarity indicators. Using each strategy segment as a node, establish connections between nodes based on the set of strategy feature similarity indicators to construct a strategy similarity relationship graph. Search for multiple candidate strategy main lines from the strategy similarity relationship graph, and select a maintenance strategy main line from the multiple candidate strategy main lines.

[0067] Furthermore, the maintenance strategy main line extraction module M300 is used to perform the following method: The multiple candidate strategy lines include strategy lines whose strategy feature similarity index is greater than a preset similarity threshold; a resource change cost function is introduced, and the resource change cost index of the multiple candidate strategy lines is calculated according to the resource change cost function, and the maintenance strategy line is selected according to the magnitude of the resource change cost index.

[0068] Furthermore, the maintenance strategy acquisition module M400 is used to perform the following method: Extract the mainline strategy feature vector corresponding to the identified road segment, including the high-frequency type distribution of the strategy, average intervention intensity, temporal continuity characteristics, and historical stable duration; perform mainline consistency evaluation based on the strategy feature vector of any candidate maintenance strategy in the maintenance strategy candidate space and the mainline strategy feature vector, and output the mainline consistency evaluation index; compare the mainline consistency rules and the mainline consistency evaluation index to obtain the optimal solution of the maintenance strategy obtained from the decision.

[0069] Furthermore, the maintenance strategy acquisition module M400 is used to perform the following method: The maintenance strategy mainline consistency rule includes a first threshold and a second threshold; candidate maintenance strategies with a mainline consistency evaluation index greater than or equal to the first threshold are integrated into a first search space, and candidate maintenance strategies with a mainline consistency evaluation index less than the first threshold but greater than or equal to the second threshold are integrated into a second search space; wherein, the first threshold is greater than the second threshold, and the decision priority of the first search space is greater than the decision priority of the second search space.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The optimization method and specific examples of the maintenance decision rules for integrated road network performance degradation simulation in Example 1 are also applicable to the optimization system of maintenance decision rules for integrated road network performance degradation simulation in this embodiment. Through the foregoing detailed description of the optimization method of maintenance decision rules for integrated road network performance degradation simulation, those skilled in the art can clearly understand the optimization system of maintenance decision rules for integrated road network performance degradation simulation in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0071] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0072] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing maintenance decision rules based on integrated road network performance degradation simulation, characterized in that, The method includes: Construct a road network topology model, perform road segment performance degradation simulation on the road network topology model, and obtain a road segment performance degradation simulation dataset; Degradation simulation features are extracted from the road segment performance degradation simulation dataset. By analyzing the degradation simulation features, it is determined whether there are road segments that require maintenance strategy replacement. The degradation simulation features include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features. Analyze the maintenance strategy trajectory of the road network topology model, and extract the maintenance strategy main line of the corresponding road segment from the maintenance strategy trajectory; Configure a maintenance strategy mainline consistency rule, and apply decision constraints based on the maintenance strategy candidate space to the identified road segments based on the maintenance strategy mainline consistency rule to obtain the optimal solution of the maintenance strategy obtained from the decision.

2. The method as described in claim 1, characterized in that, The method for determining whether there are road sections requiring maintenance strategy replacement by analyzing the degradation simulation characteristics includes: The degradation rate stability feature is used to measure the stability of the performance degradation rate fluctuation variance at adjacent time points within the time window. The trend slope change feature is used to measure the magnitude of the degradation trend slope change at adjacent time periods within the time window. The strategy threshold change probability feature is used to measure the probability of changing the threshold corresponding to the current maintenance strategy within the time window. The degradation fluctuation amplitude feature is used to measure the difference between peaks and valleys within the time window. If the vector value of any feature in the current road segment degradation simulation features does not meet the corresponding preset threshold, the current road segment will be output as an identified road segment.

3. The method as described in claim 1, characterized in that, The method for analyzing the maintenance strategy trajectory of the road network topology model includes: Analyze the road segment set of the road network topology model and extract the time axis corresponding to each road segment in the road segment set; The historical maintenance strategy records are mapped and output as a time series of maintenance strategies according to the time axis. By continuously processing the time series of the maintenance strategy, the merged strategy segment is obtained; Record each strategy segment in the merged strategy segment to obtain the maintenance strategy trajectory for each road segment.

4. The method as described in claim 3, characterized in that, The method for extracting the main maintenance strategy line for the corresponding road segment from the maintenance strategy trajectory includes: Extract the strategy feature vector of each strategy segment in the maintenance strategy trajectory. The strategy feature vector includes maintenance intensity level, construction process type, resource consumption distribution and pavement structure influence. Calculate the similarity of strategy features between any two strategy segments to obtain a set of strategy feature similarity indices; Using each strategy segment as a node, a connection is established between nodes based on the set of strategy feature similarity indicators to construct a strategy similarity relationship graph; Multiple candidate strategy lines are searched from the strategy similarity graph, and a maintenance strategy line is selected from the multiple candidate strategy lines.

5. The method as described in claim 4, characterized in that, Method for selecting a maintenance strategy main line from the multiple candidate strategy main lines include: Among them, the multiple candidate strategy lines include strategy lines whose strategy feature similarity index is greater than a preset similarity threshold; A resource change cost function is introduced, and the resource change cost index of the multiple candidate strategy lines is calculated based on the resource change cost function. The maintenance strategy line is selected based on the magnitude of the resource change cost index.

6. The method as described in claim 1, characterized in that, The method for applying decision constraints based on the maintenance strategy candidate space to the identified road segments according to the consistency rule of the maintenance strategy main line includes: Extract the main strategy feature vector of the maintenance strategy main line corresponding to the identified road segment, including the high frequency type distribution of the strategy, average intervention intensity, temporal continuity characteristics, and historical stable duration; Based on the strategy feature vector of any candidate maintenance strategy in the maintenance strategy candidate space and the feature vector of the main strategy, a main line consistency evaluation is performed, and the main line consistency evaluation index is output. The optimal solution for the maintenance strategy is obtained by comparing the main line consistency rules and the main line consistency evaluation index.

7. The method as described in claim 6, characterized in that, The comparison is performed based on the consistency rules and evaluation indicators of the maintenance strategy main line, including the following methods: The maintenance strategy mainline consistency rule includes a first threshold and a second threshold; Candidate maintenance strategies with a mainline consistency evaluation index greater than or equal to the first threshold are integrated into a first search space, and candidate maintenance strategies with a mainline consistency evaluation index less than the first threshold but greater than or equal to the second threshold are integrated into a second search space. Wherein, the first threshold is greater than the second threshold, and the decision priority of the first search space is greater than the decision priority of the second search space.

8. A maintenance decision rule optimization system integrating road network performance degradation simulation, characterized in that, The system is used for implementing the maintenance decision rule optimization method for integrated road network performance degradation simulation according to any one of claims 1-7, wherein the system comprises: Road network topology model construction module: Constructs a road network topology model, performs road segment performance degradation simulation on the road network topology model, and obtains a road segment performance degradation simulation dataset; Judgment module: Extract degradation simulation features from the road segment performance degradation simulation dataset, and determine whether there are road segments that require maintenance strategy replacement by analyzing the degradation simulation features. The degradation simulation features include degradation rate stability features, trend slope change features, strategy threshold change probability features, and degradation fluctuation amplitude features. Maintenance strategy main line extraction module: Analyzes the maintenance strategy trajectory of the road network topology model and extracts the maintenance strategy main line of the corresponding road segment from the maintenance strategy trajectory; Maintenance strategy acquisition module: Configure maintenance strategy main line consistency rules, apply decision constraints based on the maintenance strategy candidate space to the identified road segments based on the maintenance strategy main line consistency rules, and obtain the optimal solution of maintenance strategy obtained from the decision.

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