Traffic infrastructure cluster maintenance planning method, device and equipment and storage medium

Abstract

The invention is suitable for the field of traffic infrastructure cluster maintenance planning, and provides a traffic infrastructure cluster maintenance planning method, device and equipment and a storage medium, and the method comprises the steps: dividing a traffic infrastructure cluster, and obtaining a bottom planning layer and at least one upper planning layer, according to a solving rule of inter-layer serial solving from bottom to top and parallel solving in the same layer, a multi-target optimization algorithm is adopted to solve the multi-target maintenance planning models of the basic units in all the bottom planning layers and the composite units in all the upper planning layers, and a target non-inferior solution set of maintenance planning is obtained; wherein an optimization variable of the multi-target maintenance planning model of the composite unit is an index of a non-inferior solution set forming the unit. According to the scheme, the maintenance planning optimization calculation efficiency is improved, the resource consumption is reduced, association of maintenance planning results of different granularities in the traffic infrastructure cluster is systematically realized, and the applicability of maintenance planning problems of various granularities is improved.

Description

Technical Field

[0001] The present invention belongs to the field of facility cluster maintenance planning, and in particular relates to a transportation infrastructure cluster maintenance planning method, device, equipment and storage medium. Background Art

[0002] The service life of transportation infrastructure is as long as several decades or even hundreds of years. The coupling of disaster factors such as environmental erosion, material aging, and long-term effects of loads, fatigue effects, and mutation effects will inevitably lead to the accumulation of structural damage and attenuation of resistance, thereby reducing the ability to resist natural disasters and even normal environmental effects, and in extreme cases causing catastrophic accidents. Therefore, in order to ensure the safety, applicability and durability of the structure, the transportation infrastructure that has been built and used urgently needs to adopt effective means to assess its safety status, repair and control damage.

[0003] However, the maintenance of transportation infrastructure during the operation period involves two major conflicting aspects: safety and economy. How to ensure the operational safety of the growing facilities with limited resources is an urgent problem to be solved.

[0004] From the current application status, most of the early planning adopted a passive maintenance strategy, that is, maintenance actions were triggered after the performance reached a certain threshold. However, with the dual influence of the increase in facility volume and limited maintenance resources, one of the main solutions is to use quantitative indicators and the evolution law of structural performance to achieve scientific maintenance decisions during the facility operation period. This solution mainly includes performance prediction and decision-making. There are two main ways to predict the evolution of structural performance. One is to predict the trend of structural performance changes through historical statistical data of structural degradation. The other is to analyze the time-varying laws of factors affecting degradation and construct a multi-factor performance degradation evolution model. Commonly used prediction methods include experimental methods, deterministic curve models (such as regression model methods, time series methods, etc.), random degradation models (such as Markov chain models, methods based on reliability theory, etc.) and deep learning models. These models can all achieve structural performance prediction in specific scenarios. Based on the law of performance evolution, planning problems involving economic indicators, safety indicators and other objectives are constructed. Finding the optimal solution through optimization algorithms is a common way to solve decision-making problems. One of the solutions is decision optimization of multi-attribute utility target values ​​based on single-objective genetic algorithms, but this method cannot match the management preferences of multiple users. Although the current multi-objective optimization solution concept solves the problem of a single recommended solution, it does not open up the data link between facility-level and network-level planning. The planning of the network level and facility level is independent. In summary, the above methods have the problems of low calculation efficiency and high resource consumption in maintenance planning optimization, and cannot systematically solve the problem of the association of maintenance planning results of different granularities in transportation infrastructure clusters. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, equipment and storage medium for maintenance planning of a transportation infrastructure cluster, aiming to solve the problems of low optimization calculation efficiency and high resource consumption of maintenance planning of transportation infrastructure clusters in the prior art, and the inability to systematically solve the correlation between maintenance planning results of different granularities in the transportation infrastructure cluster.

[0006] In one aspect, the present invention provides a method for cluster maintenance planning of transportation infrastructure, the method comprising: The transportation infrastructure cluster is divided into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer, wherein the planning unit in the bottom planning layer is a basic unit, and the planning unit in each upper planning layer is a composite unit. Each planning layer is progressive, and in two adjacent planning layers, the composite unit of the upper planning layer is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit constituting the composite unit; According to the solution rules of bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of all planning units to obtain the target non-inferior solution set of the transportation infrastructure cluster maintenance planning; wherein the optimization variable of the multi-objective maintenance planning model of the composite unit is the index of the non-inferior solution set of the constituent unit.

[0007] Optionally, the optimization variables of the multi-objective maintenance planning model of each basic unit are the maintenance measures to be taken in each period within a preset maintenance planning cycle; The objective functions of the multi-objective maintenance planning models of each of the planning units include a safety index, an economic index and a maintenance frequency index; wherein the safety index is the extent of performance improvement after maintenance measures are taken within the preset maintenance planning cycle, the economic index is the total cost of maintenance measures taken within the preset maintenance planning cycle, and the maintenance frequency index is the number of maintenance measures taken within the preset maintenance planning cycle.

[0008] Optionally, the safety index of the basic unit is determined according to the difference between the integral area of ​​the improved performance prediction curve after the maintenance measures are taken and the integral area of ​​the initial performance prediction curve without taking the maintenance measures; The safety index of the composite unit is determined according to the weighted sum of the calculated values ​​of the safety indexes in the non-inferior solutions of the constituent units.

[0009] Optionally, the performance of each basic unit before and after taking maintenance measures is predicted using an adaptive performance prediction model; wherein the parameters of the adaptive performance prediction model are updated using a preset update mechanism after obtaining new performance evaluation data.

[0010] Optionally, the adaptive performance prediction model is an adaptive exponential performance degradation model based on a Bayesian update mechanism.

[0011] Optionally, the method further comprises: Obtaining m current structural condition levels of each of the basic units before taking maintenance measures and n improved structural condition levels after taking maintenance measures; Establish a cost-effectiveness matrix based on the maintenance measures corresponding to each group of current structural condition levels and improved structural condition levels, the utility and cost corresponding to each maintenance measure; The intercept difference between the improved performance prediction curve and the initial performance prediction curve is determined according to the utility corresponding to the taken maintenance measures, and the utility corresponding to the taken maintenance measures is determined according to the cost-effectiveness matrix.

[0012] Optionally, the maintenance measures in the cost-effectiveness matrix are represented by measure codes, and the maintenance measures in the optimization variables of the multi-objective maintenance planning model of each basic unit are represented by the measure codes; The utility corresponding to different maintenance measures is determined according to the utility interval of the corresponding improvement of the structural condition level and the level span before and after the maintenance measures are taken; The costs of different maintenance measures are obtained by multiplying the corresponding proportional factors by the preset costs.

[0013] In another aspect, the present invention provides a transportation infrastructure cluster maintenance planning device, the device comprising: A hierarchical division module: used to divide the traffic infrastructure cluster into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer, wherein the planning unit in the bottom planning layer is a basic unit, and the planning unit in each upper planning layer is a composite unit. Each planning layer is progressive, and in two adjacent planning layers, the composite unit of the upper planning layer is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit constituting the composite unit; and The multi-objective optimization module is used to solve the established multi-objective maintenance planning model of all planning units according to the solution rules of bottom-up serial solution between layers and parallel solution within the same layer, and adopt a multi-objective optimization algorithm to obtain the target non-inferior solution set of the transportation infrastructure cluster maintenance planning; wherein the optimization variable of the multi-objective maintenance planning model of the composite unit is the index of the non-inferior solution set of the constituent unit.

[0014] On the other hand, the present invention provides a transportation infrastructure cluster maintenance planning device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method when executing the computer program.

[0015] On the other hand, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0016] The present invention divides the transportation infrastructure cluster into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer. According to the bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of the basic units in all bottom planning layers and the composite units in all upper planning layers, so as to obtain a target non-inferior solution set for the maintenance planning of the transportation infrastructure cluster, wherein the optimization variable of the multi-objective maintenance planning model of each composite unit is the index of the non-inferior solution set of the constituent unit, thereby improving the optimization calculation efficiency of the maintenance planning, reducing resource consumption, and systematically realizing the association of maintenance planning results of different granularities in the transportation infrastructure cluster, thereby improving the applicability of maintenance planning problems of various granularities. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A is a flow chart for implementing the method for cluster maintenance planning of transportation infrastructure provided in the first embodiment of the present invention; Figure 1B This is a schematic diagram of the optimization calculation process of the transportation infrastructure cluster maintenance planning provided by the first embodiment of the present invention; Figure 1C It is the performance prediction curve corresponding to the adaptive exponential performance degradation model based on the Bayesian update mechanism before and after updating provided in the first embodiment of the present invention; Figure 1D This is an example diagram of the performance improvement provided by the first embodiment of the present invention; Figure 1E is an example diagram of the measure coding part of the cost-effectiveness matrix provided in the first embodiment of the present invention; Figure 1F This is an example diagram of the optimization calculation process of the transportation infrastructure cluster provided in the first embodiment of the present invention; Figure 2 is a schematic diagram of the structure of a transportation infrastructure cluster maintenance planning device provided in Embodiment 2 of the present invention; and Figure 3 It is a structural diagram of the transportation infrastructure cluster maintenance planning equipment provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0020] Terms and Definitions:

[0021] Maintenance planning: With structural safety and maintenance economy as the goals, reasonable maintenance opportunities and actions are proposed under constraints such as structural performance requirements and maintenance funding limits to achieve the optimal allocation of maintenance funds during the planning period.

[0022] Basic unit: In multi-granularity maintenance planning, the basic unit refers to the smallest granularity, non-dividable planning unit. The planning result of the basic unit is the basis for the planning result of the composite unit.

[0023] Composite unit: In multi-granularity maintenance planning, a composite unit refers to a meaningful collection of basic units or composite units with smaller granularity. The planning result of a composite unit is a combination of the planning results of the planning units it contains.

[0024] Structural condition level: refers to the structural technical condition level assessed according to regular inspection data or the comprehensive structural assessment level obtained by integrating multi-source data.

[0025] Non-inferior solution: The result of a multi-objective optimization problem is a set of non-inferior solutions. For any non-inferior solution, there is no possible solution that is better in all optimization objectives.

[0026] Non-inferior solution set: In multi-objective optimization, a non-inferior solution set (Pareto Optimal Set, also known as the Pareto frontier) refers to a solution set in which each solution cannot be further optimized without sacrificing other objectives. In a non-inferior solution set, any improvement of a solution will lead to the degradation of at least one objective, so these solutions form a balance between all objectives.

[0027] Embodiment 1:

[0028] Figure 1A The implementation process of the transportation infrastructure cluster maintenance planning method provided by the first embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0029] In step S101, the transportation infrastructure cluster is divided into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer, wherein the planning unit in the bottom planning layer is a basic unit, and the planning unit in each upper planning layer is a composite unit. The planning layers are progressive, and for two adjacent planning layers, the composite unit of the upper planning layer is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit that constitutes the composite unit.

[0030] The embodiment of the present invention is applicable to the maintenance planning of a transportation infrastructure cluster. In the embodiment of the present invention, the transportation infrastructure cluster can be divided into multiple levels, which can specifically include a bottom planning layer and an upper planning layer, and different planning layers are composed of planning units of different granularities. Among them, the planning unit of the bottom planning layer is defined as a basic unit, and the planning unit of the upper planning layer is defined as a composite unit, that is, the planning unit is a basic unit or a composite unit, and the composite unit of the upper planning layer in the two adjacent planning layers is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit that constitutes the composite unit. For example, in the maintenance planning of a bridge group, main beams, piers and other components are respectively used as basic units, and a bridge containing the above components or a bridge cluster containing multiple bridges can be used as a composite unit. The maintenance planning result of each composite unit can be the optimal combination obtained by screening and synthesizing the planning results of the planning objects it contains.

[0031] In step S102, according to the solution rules of bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of all planning units to obtain a target non-inferior solution set for the maintenance planning of the transportation infrastructure cluster; wherein the optimization variable of the multi-objective maintenance planning model of each composite unit is the index of the non-inferior solution set of the constituent unit.

[0032] In the embodiment of the present invention, the optimization variable of the multi-objective maintenance planning model of each composite unit is the index of the non-inferior solution set of all the constituent units of the composite unit, and the target non-inferior solution set is the non-inferior solution set corresponding to the top composite unit. The multi-objective optimization algorithm can adopt a multi-objective optimization algorithm such as a multi-objective genetic algorithm and a particle swarm optimization algorithm, which is not specifically limited in this embodiment.

[0033] As an example, Figure 1B As shown, the transportation infrastructure cluster includes the bottom planning layer and the upper planning layers L1 and L2. The bottom planning layer contains multiple basic units A 111 , A 112 , A 113 ..., the upper planning layer L1 contains composite unit A 11 ,……,A 1N The upper planning layer L2 contains composite unit A1, composite unit A 11 Contains basic unit A 111 , A 112 , A 113 ..., composite unit A 1N It is the basic unit A 1N1 , A 1N2 , A 1N3 ...combined unit A1 is a composite unit A 11 ,……,A1N When solving the established multi-objective maintenance planning model for each planning unit, firstly, each basic unit A 111 , A 112 , A 113 The multi-objective maintenance planning model of ... is optimized and solved in parallel, and then the composite unit A in the upper planning layer L1 is 11 ,……,A 1N The multi-objective maintenance planning model of the composite unit A1 is optimized and solved in parallel, and finally the multi-objective maintenance planning model of the composite unit A1 is optimized and solved to obtain the target non-inferior solution set. 11 The optimization variable of the multi-objective maintenance planning model is the basic unit A 111 , A 112 , A 113 Index of non-inferior solutions of multi-objective maintenance planning model, composite unit A 1N The optimization variable of the multi-objective maintenance planning model is the basic unit A 1N1 , A 1N2 , A 1N3 The index of the non-inferior solution set of the multi-objective maintenance planning model of ..., the optimization variable of the multi-objective maintenance planning model of composite unit A1 is composite unit A 11 ,……,A 1N The target non-inferior solution set is the index of the non-inferior solution set of the multi-objective maintenance planning model, where the target non-inferior solution set is the non-inferior solution set obtained by solving the multi-objective maintenance planning model of the top-level composite unit A1.

[0034] Considering that maintenance planning is essentially an optimization problem, structural safety and maintenance economy can be weighed by adjusting maintenance timing and maintenance measures. However, different users have different preferences for safety and economy. In order to meet the needs of different users and solve the problems of low computational efficiency and unscientific decision-making schemes in the maintenance decision of the transportation infrastructure cluster, the optimization variables of the multi-objective maintenance planning model of each basic unit are the maintenance measures to be taken in each period of the preset maintenance planning cycle, and the objective functions of the multi-objective maintenance planning models of each basic unit and each composite unit include safety index, economic index and maintenance frequency index, that is, a multi-objective optimization algorithm with structural safety, maintenance economy and maintenance frequency as optimization objectives is adopted in the maintenance planning, and the maintenance planning result is a set of maintenance strategies that cannot be optimized simultaneously in structural safety, maintenance economy and maintenance frequency, so that through the multi-objective hierarchical optimization strategy, the structural performance, maintenance cost and maintenance frequency are comprehensively considered, the number of optimization variables is reduced, the computational efficiency is improved, the business logic is smoother, and the business meaning and scientificity of the optimization result are stronger. Among them, the safety index can be the extent of performance improvement after maintenance measures are taken within the preset maintenance planning cycle, the economic index can be the total cost of maintenance measures taken within the preset maintenance planning cycle, and the maintenance frequency index can be the number of maintenance measures taken within the preset maintenance planning cycle. In specific implementations, the safety index of the basic unit can be determined based on the utility difference between not taking measures and taking maintenance measures, the safety index of the composite unit can be the sum of the calculated values ​​of the corresponding safety indexes in the non-inferior solutions of all the constituent units of the composite unit; the economic index of the basic unit can be the sum of the costs of all maintenance measures taken, and the economic index of the composite unit can be the sum of the calculated values ​​of the economic indexes in the non-inferior solutions of all the constituent units of the composite unit; the maintenance frequency index of the basic unit can be the sum of the number of all maintenance measures taken, and the maintenance frequency index of the composite unit can be the sum of the calculated values ​​of the maintenance frequency index in the non-inferior solutions of all the constituent units of the composite unit.

[0035] Optionally, the performance of each basic unit before and after taking maintenance measures is predicted using an adaptive performance prediction model, so that by updating the parameters of the adaptive performance prediction model, the adaptive performance prediction model can continuously approach the real law of performance degradation of each basic unit, effectively reducing the dependence of the adaptive performance prediction model on historical data and improving the accuracy of the prediction results. Among them, the parameters of the adaptive performance prediction model are updated using a preset update mechanism after obtaining new performance evaluation data. Optionally, the update mechanism is Bayesian update.

[0036] Optionally, the adaptive performance prediction model is an adaptive exponential performance degradation model based on the Bayesian update mechanism, so that the adaptive performance prediction model can be adaptively updated through the Bayesian update technology to fit the performance degradation law of the current structure. In specific implementation, the application of this model may involve two steps: initialization and adaptive update. Taking civil engineering as an example, we can analyze the performance evolution law of a large number of civil engineering structures and find that the performance of the structure degrades slowly in the early stage, and the degradation rate gradually accelerates with the increase of operating time. This law conforms to the evolution law of the exponential model. The expression of the adaptive exponential performance degradation model is as follows:

[0037]

[0038] The parameters represents the intercept, parameter and A random variable representing the parameter of the exponential term, represents random error, with a mean of 0 and a variance of .

[0039] When the target cluster facilities are just put into operation, the historical assessment data is insufficient. In order to make the model available, the model parameters can be initialized by the initial performance IP of the structure and the expected service life EL, without considering the random error term of the model. , assuming that the model parameters , the core parameters of the model are calculated as follows:

[0040]

[0041]

[0042] In the formula In order to make the model adaptively updateable, the Bayesian update technique can be used to update the model each time the assessment information is added. , the parameters of the model can be updated, and the above model can be transformed into:

[0043]

[0044] make , , then the above equation is transformed into:

[0045]

[0046] According to the Bayesian updating theory, we need to solve the problem when there is new structural state information, that is, Known, updated parameters and ,Right now:

[0047]

[0048] According to the formula , ;assumed They obey the joint normal distribution, and their prior means and variances are , and , , the correlation coefficient is , after derivation, the posterior mean, variance and correlation coefficient are:

[0049]

[0050]

[0051]

[0052] In the formula Representation parameters The posterior variance of Representation parameters The posterior variance of Representation parameters The posterior mean of Representation parameters The posterior mean of Representation parameters The posterior correlation coefficient of the model is as follows: Figure 1C As shown. Figure 1C It can be seen that compared with the initial performance curve, the updated performance curve and the actual measured points of the fitting structure performance are in line with the performance degradation law of the current structure.

[0053] Optionally, the safety index of the basic unit is determined based on the difference between the integral area of ​​the improved performance prediction curve after maintenance measures are taken and the integral area of ​​the initial performance prediction curve without maintenance measures taken, so as to calculate the safety index through the performance prediction curve.

[0054] Optionally, the safety index of the composite unit is determined according to the weighted sum of the calculated values ​​of the safety index in the non-inferior solutions of the constituent units, so as to realize the calculation of the safety index of the composite unit. In the specific implementation, considering the different importance of each constituent unit, the weighting of safety for different constituent units is different.

[0055] Further optionally, the intercept of the improved performance prediction curve is different from that of the initial performance prediction curve, and the intercept difference between the improved performance prediction curve and the initial performance prediction curve is determined according to the difference in utility before and after maintenance measures are taken, and the utility after maintenance measures are taken is determined according to an established cost-effectiveness matrix, so as to realize the calculation of safety indicators through the improved utility after maintenance measures are taken. In a specific implementation, the current structural status can be input into an adaptive performance prediction model to obtain an initial performance prediction curve before maintenance measures are taken, and the adaptive performance prediction model after maintenance measures are taken can be obtained by changing the parameters of the intercept of the curve corresponding to the model in the adaptive performance prediction model, and the parameters characterizing the intercept of the performance prediction curve corresponding to the model can be determined according to the difference between the utility value after maintenance measures are taken and the utility value before maintenance measures are taken. As an example, if Figure 1D As shown in the figure, the intercept of the performance prediction curve changes after the maintenance measures are implemented, and the corresponding gray area is the extent of performance improvement.

[0056] When establishing the above-mentioned cost-effectiveness matrix, it is considered that the cost and utility of maintenance measures are difficult to accurately define. Due to the influence of factors such as the form of the basic unit structure and the environment in which it is located, it is difficult to accurately quantify the cost and utility of specific maintenance measures to achieve the goal of application implementation. The specific details of the maintenance measures are not the focus of planning. Optionally, the m current structural condition levels of each basic unit without taking maintenance measures and the n improved structural condition levels after taking the corresponding maintenance measures are obtained. According to the maintenance measures corresponding to each group of current structural condition levels and improved structural condition levels, the utility and cost corresponding to each maintenance measure, a cost-effectiveness matrix is ​​established. The cost-effectiveness matrix abstracts the maintenance measures as the improvement of the structural condition level, which is convenient for realizing the accurate definition of the cost and utility of the maintenance measures, and is not affected by the specific structural working environment and the type of measures. It is in line with the goal that the medium- and long-term planning should focus on the allocation of funds, the timing of maintenance and the maintenance of structural safety performance. Among them, each element in the cost-effectiveness matrix can be a three-dimensional vector matrix containing maintenance measures, the utility and cost corresponding to the maintenance measures, or the cost-effectiveness matrix includes a maintenance measure matrix, a utility matrix and a cost matrix respectively.

[0057] Further, optionally, the maintenance measures in the cost-effectiveness matrix are represented by measure coding, and the maintenance measures in the optimization variables of the multi-objective maintenance planning model of each basic unit are represented by measure coding, so as to improve the efficiency of maintenance planning optimization calculation while solving the utility quantification problem corresponding to the maintenance measures. In a specific implementation, each group of current structural condition level and measures corresponding to the improved structural condition level can be sequentially coded. As an example, Figure 1EAs shown, the current structural condition level and the improved structural condition level are divided into 5 levels respectively. The measure code corresponding to the current structural condition level 1 to the improved structural condition level 1 is 1, and the measure code corresponding to the current structural condition level 2 to the improved structural condition level 1 is 2, and so on. There are 15 codes corresponding to the 15 maintenance measures.

[0058] The utility corresponding to different maintenance measures can be determined according to the improvement value of the utility after the maintenance measures are adopted, or according to the utility value corresponding to the improved structural condition level. Optionally, the utility corresponding to different maintenance measures is determined according to the utility interval of the corresponding improved structural condition level and the level span before and after the maintenance measures are taken, so as to improve the accuracy of the quantitative data while realizing the quantification of the utility. In a specific implementation, each improved structural condition level can correspond to a utility interval. If the structural condition level is improved at the same level, the utility corresponding to the maintenance measure is the first preset value within the utility interval corresponding to the improved structural condition level. If the condition level is improved across levels, the utility corresponding to the maintenance measure is the second preset value within the utility interval corresponding to the improved structural condition level. Taking the maintenance object as a highway bridge as an example, referring to the corresponding relationship between the grade and score of the "Highway Bridge Technical Condition Assessment Standard" JTG TH21-2011, Class 1 [95, 100], Class 2 [80, 95), Class 3 [60, 80), Class 4 [40, 60), Class 5 [0, 40). For the same grade improvement, it is believed that after the maintenance measure, the structural utility will reach the upper limit of the grade, such as from the current structural status grade 1 to the improved structural status grade 1, the improved utility is 100. For the cross-grade improvement, it is believed that after the maintenance measure, the structural utility will reach the middle of the utility range corresponding to the grade, such as from the current structural status grade 2 to the improved structural status grade 1, the improved utility is 97.5.

[0059] Optionally, the cost of different maintenance measures is obtained by multiplying the corresponding proportional factors by the preset costs. In specific implementations, the cost value of each maintenance measure is determined according to the cost of each maintenance measure. Taking civil engineering as an example, it can be obtained by multiplying the construction cost of each structure by the corresponding proportional factor. It should be noted here that the costs in the cost-effectiveness matrix can be represented by specific cost values ​​or by proportional factors of the measures, which is not specifically limited in this embodiment.

[0060] Figure 1F This is an example diagram of the optimization calculation process of the hierarchical maintenance planning of a transportation infrastructure cluster. Figure 1F The upper and middle planning layers contain only one composite unit, which is combined with Figure 1F As shown in the figure, the solution process of the multi-objective maintenance planning model of the basic unit and the composite unit is explained:

[0061] The purpose of maintenance planning for basic units is to determine the type of maintenance measures and the timing of implementation, and then determine the cost and performance changes of the structure during the planning period. The maintenance planning problem at this level is transformed into an optimization problem of which measures to implement each year during the planning period. Assuming that each basic unit will only implement one measure in a certain year, a multi-objective genetic algorithm is used to optimize the non-inferior solution set under certain constraints for safety, economy, and maintenance times. The algorithm is executed in the following steps:

[0062] Step 1: Determine the planning cycle and optimization variables of each basic unit such as the main beam and the cable system. The planning cycle can generally be in the range of 10 to 20 years. The maintenance measures in each year can be selected from any integer between 0 and 15, where 0 means no measures are taken in that year, and 1 to 15 are the above-mentioned measure codes. During initialization, the measure codes for each year are randomly generated to achieve individual initialization.

[0063] Step 2: Calculate the objective function of each individual. The calculation uses a multi-objective function, where safety is defined as the performance improvement after taking maintenance measures; economy is defined as the cost of maintenance measures. The cost of a certain measure is obtained according to the cost-effectiveness matrix, and then the total cost during the planning period is calculated; the number of maintenance times is defined as the total number of maintenance measures performed during the planning period.

[0064] Step 3: Determine whether there are unreasonable situations in the individual's chromosomes, such as failure to meet constraints (such as minimum performance constraints) or unreasonable measures taken (such as the current structural condition level is Class 2, and the measures taken are from Class 3 to Class 1). When unreasonable situations exist, correct the individual in a timely manner.

[0065] Step 4: Use the corrected individuals as the initial population. For the second and subsequent generations, the iterative process needs to merge the excellent individuals of the parent generation and the individuals of the offspring generation to form a new population.

[0066] Step 5: Perform fast non-dominated sorting on the population. Fast non-dominated sorting is used to calculate the Pareto frontier under multiple objectives, comparing the performance of P individuals and Q individuals under multiple objectives. If each objective of P is not weaker than Q, then P is said to dominate Q, otherwise Q dominates P. The individuals with 0 dominated numbers form the first layer of frontiers. After eliminating the first layer of frontiers, iterate in sequence to obtain the frontiers of each layer.

[0067] Step 6: Calculate the crowding degree of each individual (i.e., the sum of multiple target crowding distances). The crowding distance is defined as the distance between the target individual and its neighboring individuals under a certain target, and the formula is as follows:

[0068]

[0069] in represents the crowding distance of the i-th individual and the m-th target, represents i+1 individuals, and represents the boundary individual, Represents the value of the mth target of the i+1th individual.

[0070] Step 7: Select excellent individuals for crossover to generate offspring individuals, and set a certain mutation rate to mutate some individuals to generate offspring populations.

[0071] Step 8: Merge the parent population and the child population, and repeat steps 2 to 7 until the number of iterations meets the set limit, and then output the non-inferior solution set of the Pareto frontier.

[0072] The planning of the composite unit is based on the non-inferior solution set of its constituent unit outputs, constructs optimization variables, calls the multi-objective genetic algorithm, and calculates the non-inferior solution set that enables the composite unit planning. The multi-objective genetic algorithm of the composite unit is executed according to the following steps:

[0073] Step 1: Determine the optimization variables of the composite unit maintenance planning object. The maintenance planning cycle of the composite unit is consistent with that of the basic unit. The optimization variables are the indexes of the non-inferior solution sets of all the basic units contained in the composite unit. For example, the first value of the optimization variable of the composite unit is the index of the non-inferior solution set of the main beam of the basic unit. The value range is [0, set_len-1], where set_len is the length of the non-inferior solution set, and the values ​​are all integers. This is done by analogy to obtain the optimization variables of the composite unit.

[0074] Step 2: Calculate the individual objective function. The objective of the composite unit is consistent with that of the basic unit, both of which control the objectives of safety, economy, and maintenance frequency. Economy and maintenance frequency are the sum of the corresponding objectives of the non-inferior solutions of the constituent units of the composite unit. Due to the different importance of different basic units, the weighting of safety for different basic units is different. Taking bridges as an example, referring to the weights of parts and components in the "Highway Bridge Technical Condition Assessment Standard" JTG TH21-2011, the weight values ​​of each basic unit are calculated, such as the weight of the superstructure is 0.4, the weight of the main beam is 0.25, and if the upper node is a facility, then its weight is 0.4×0.25=0.1. By analogy, for the weights of each facility at the regional road network level, the weights can be determined by comprehensively considering multiple factors such as facility importance and structural fragility, and then the safety score can be calculated.

[0075] Steps 3 to 8 are the same as above, and the maintenance planning scheme of the composite unit is finally output. The output of the scheme is a non-inferior solution set, providing multiple options for different users. If the facilities of the regional road network are large, the area can be divided into areas for optimization on this basis, and then the facilities in an area can be divided, and finally a four-layer optimization scheme of the basic unit, i.e., component node → facility → area → area, is formed. This hierarchical scheme has strong scalability, high computational efficiency, reasonable results, and good application value.

[0076] In the embodiment of the present invention, by performing multi-level and multi-granularity division on the transportation infrastructure cluster, a bottom planning layer and at least one upper planning layer are obtained, and according to the solution rule of bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of the basic units in all bottom planning layers and the composite units in all upper planning layers, so as to obtain the target non-inferior solution set of the maintenance planning of the transportation infrastructure cluster, wherein the optimization variable of the multi-objective maintenance planning model of each composite unit is the index of the non-inferior solution set of the constituent unit, thereby improving the optimization calculation efficiency of the maintenance planning, reducing resource consumption, and systematically realizing the association of maintenance planning results of different granularities in the transportation infrastructure cluster, thereby improving the applicability of maintenance planning problems of various granularities; A multi-objective optimization algorithm with structural safety and maintenance economy as optimization objectives is adopted in maintenance planning. The optimization result is a set of maintenance strategies that cannot be optimized simultaneously in terms of structural safety and maintenance economy. The performance prediction model in this scheme adopts an adaptive performance prediction model based on a preset update mechanism. By updating the model parameters, the model can continuously approach the true law of performance degradation of each unit, and the model has low dependence on historical data and high prediction accuracy. The cost-effectiveness matrix adopted in this scheme abstracts maintenance measures into the improvement of the structural condition level, which solves the problem that the cost and utility of the measures are difficult to accurately define. The cost and utility of the maintenance measures are not affected by the specific unit working environment and the type of measures, which is in line with the goal of focusing on fund allocation, maintenance timing and maintaining structural safety performance in medium and long-term planning.

[0077] Embodiment 2:

[0078] Figure 2 The structure of the transportation infrastructure cluster maintenance planning device provided by the second embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, including: The hierarchical division module 21 is used to divide the traffic infrastructure cluster into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer, wherein the planning unit in the bottom planning layer is a basic unit, and the planning unit in each upper planning layer is a composite unit. Each planning layer is progressive, and in two adjacent planning layers, the composite unit of the upper planning layer is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit constituting the composite unit; and The multi-objective optimization module 22 is used to solve the established multi-objective maintenance planning model of all planning units according to the solution rules of bottom-up serial solution between layers and parallel solution within the same layer, using a multi-objective optimization algorithm to obtain the target non-inferior solution set of the transportation infrastructure cluster maintenance planning; wherein the optimization variable of the multi-objective maintenance planning model of the composite unit is the index of the non-inferior solution set of the constituent unit.

[0079] In the embodiment of the present invention, each module of the transportation infrastructure cluster maintenance planning device can be implemented by a corresponding hardware or software module. Each module can be an independent software or hardware module, or can be integrated into a software or hardware module, which is not intended to limit the present invention. The specific implementation of each module of the transportation infrastructure cluster maintenance planning device can refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0080] Embodiment three:

[0081] Figure 3 The structure of the transportation infrastructure cluster maintenance planning device provided in the third embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown.

[0082] The transportation infrastructure cluster maintenance planning device 3 of the embodiment of the present invention includes a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned method embodiments are implemented, for example Figure 1A Alternatively, when the processor 30 executes the computer program 32, the functions of each module in the above-mentioned device embodiments are implemented, for example Figure 2 The functions of modules 21 to 22 are shown.

[0083] In an embodiment of the present invention, a transportation infrastructure cluster is divided into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer. According to the bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of the basic units in all bottom planning layers and the composite units in all upper planning layers to obtain a target non-inferior solution set for the maintenance planning of the transportation infrastructure cluster, wherein the optimization variable of the multi-objective maintenance planning model of each composite unit is the index of the non-inferior solution set of the constituent unit, thereby improving the maintenance planning optimization calculation efficiency and reducing resource consumption, and systematically realizing the association of maintenance planning results of different granularities in the transportation infrastructure cluster, thereby improving the applicability of maintenance planning problems of various granularities.

[0084] The transportation infrastructure cluster maintenance planning device of the embodiment of the present invention can be a computer, a server, etc. The steps implemented when the processor 30 in the transportation infrastructure cluster maintenance planning device 3 executes the computer program 32 to implement the transportation infrastructure cluster maintenance planning method can refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0085] Embodiment 4:

[0086] In an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above method embodiment are implemented, for example, Figure 1A Alternatively, when the computer program is executed by a processor, the functions of each module in the above-mentioned device embodiments are realized, for example Figure 2 The functions of the units 21 to 22 are shown.

[0087] In an embodiment of the present invention, a transportation infrastructure cluster is divided into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer. According to the bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of the basic units in all bottom planning layers and the composite units in all upper planning layers to obtain a target non-inferior solution set for the maintenance planning of the transportation infrastructure cluster, wherein the optimization variable of the multi-objective maintenance planning model of each composite unit is the index of the non-inferior solution set of the constituent unit, thereby improving the maintenance planning optimization calculation efficiency and reducing resource consumption, and systematically realizing the association of maintenance planning results of different granularities in the transportation infrastructure cluster, thereby improving the applicability of maintenance planning problems of various granularities.

[0088] The computer-readable storage medium of the embodiment of the present invention may include any entity or device or recording medium capable of carrying computer program code, for example, ROM / RAM, magnetic disk, optical disk, flash memory and other memories.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A transportation infrastructure cluster maintenance planning method, characterized in that: The method comprises: The transportation infrastructure cluster is divided into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer, wherein the planning unit in the bottom planning layer is a basic unit, and the planning unit in each upper planning layer is a composite unit. Each planning layer is progressive, and in two adjacent planning layers, the composite unit of the upper planning layer is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit constituting the composite unit; According to the solution rules of bottom-up serial solution between layers and parallel solution within the same layer, a multi-objective optimization algorithm is used to solve the established multi-objective maintenance planning model of all planning units to obtain the target non-inferior solution set of the transportation infrastructure cluster maintenance planning; wherein the optimization variable of the multi-objective maintenance planning model of the composite unit is the index of the non-inferior solution set of the constituent unit.

2. The method according to claim 1, characterized in that The optimization variables of the multi-objective maintenance planning model of each basic unit are the maintenance measures to be taken in each period within the preset maintenance planning cycle; The objective functions of the multi-objective maintenance planning models of each of the planning units include a safety index, an economic index and a maintenance frequency index; wherein the safety index is the extent of performance improvement after maintenance measures are taken within the preset maintenance planning cycle, the economic index is the total cost of maintenance measures taken within the preset maintenance planning cycle, and the maintenance frequency index is the number of maintenance measures taken within the preset maintenance planning cycle.

3. The method according to claim 2, characterized in that The safety index of the basic unit is determined according to the difference between the integral area of ​​the improved performance prediction curve after the maintenance measures are taken and the integral area of ​​the initial performance prediction curve without taking the maintenance measures; The safety index of the composite unit is determined according to the weighted sum of the calculated values ​​of the safety indexes in the non-inferior solutions of the constituent units.

4. The method according to claim 3, characterized in that The performance of each basic unit before and after taking maintenance measures is predicted using an adaptive performance prediction model; wherein the parameters of the adaptive performance prediction model are updated using a preset update mechanism after obtaining new performance evaluation data.

5. The method according to claim 4, characterized in that The adaptive performance prediction model is an adaptive exponential performance degradation model based on a Bayesian update mechanism.

6. The method according to claim 3, characterized in that The method further comprises: Obtaining m current structural condition levels of each of the basic units before taking maintenance measures and n improved structural condition levels after taking maintenance measures; Establish a cost-effectiveness matrix based on the maintenance measures corresponding to each group of current structural condition levels and improved structural condition levels, the utility and cost corresponding to each maintenance measure; The intercept difference between the improved performance prediction curve and the initial performance prediction curve is determined according to the utility corresponding to the taken maintenance measures, and the utility corresponding to the taken maintenance measures is determined according to the cost-effectiveness matrix.

7. The method according to claim 6, characterized in that The maintenance measures in the cost-effectiveness matrix are represented by measure codes, and the maintenance measures in the optimization variables of the multi-objective maintenance planning model of each basic unit are represented by the measure codes; The utility corresponding to different maintenance measures is determined according to the utility interval of the corresponding improvement of the structural condition level and the level span before and after the maintenance measures are taken; The costs of different maintenance measures are obtained by multiplying the corresponding proportional factors by the preset costs.

8. A transportation infrastructure cluster maintenance planning device, characterized in that: The device comprises: A hierarchical division module: used to divide the traffic infrastructure cluster into multiple levels and multiple granularities to obtain a bottom planning layer and at least one upper planning layer, wherein the planning unit in the bottom planning layer is a basic unit, and the planning unit in each upper planning layer is a composite unit. Each planning layer is progressive, and in two adjacent planning layers, the composite unit of the upper planning layer is a set of constituent units of the lower planning layer, and the constituent unit is a basic unit or a composite unit constituting the composite unit; and The multi-objective optimization module is used to solve the established multi-objective maintenance planning model of all planning units according to the solution rules of bottom-up serial solution between layers and parallel solution within the same layer, and adopt a multi-objective optimization algorithm to obtain the target non-inferior solution set of the transportation infrastructure cluster maintenance planning; wherein the optimization variable of the multi-objective maintenance planning model of the composite unit is the index of the non-inferior solution set of the constituent unit.

9. A transportation infrastructure cluster maintenance planning device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images