A decision method and electronic device suitable for long-term maintenance planning of airport pavement

Abstract

The application discloses a decision-making method and electronic equipment suitable for long-term maintenance planning of an airport pavement, and the method comprises the following steps: combining periodic detection of the airport pavement and historical maintenance data to establish a pavement maintenance decision-making model, a pavement performance prediction model and a long-term maintenance decision-making multi-objective optimization model of the airport pavement under non-stop construction, repeatedly performing the steps of decision-making and prediction in a decision-making planning period to generate pavement maintenance alternative schemes, solving the optimization model by using a multi-objective evolutionary algorithm, and obtaining an optimal solution of the long-term maintenance planning of the airport pavement. The method can make decision optimization according to the operation demand of non-stop construction of the airport pavement under the consideration of the influence of specific maintenance measures on the pavement performance degradation, realize the optimal pavement service condition and the minimum maintenance cost in the decision-making period, and provide a practical reference for the long-term maintenance planning of the airport pavement.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of airport pavement maintenance decision optimization, and particularly relates to a decision method suitable for long-term maintenance planning of airport pavement and an electronic device. BACKGROUND

[0002] The rapid development of the economic society has led to an increasing demand for air transportation, and the number of large and medium-sized aircrafts in air transportation has also increased year by year. High-load operation puts the airport pavement under unprecedented pressure, and the early construction of the airport pavement has more diseases and safety hazards, and the maintenance demand is increasing. However, unlike highways, the maintenance operation of the airport pavement is often limited by the operation of the pavement, so non-stop construction has always been the focus of airport pavement maintenance work. Therefore, based on the operation requirements of non-stop construction of the airport pavement, the optimization of pavement maintenance decision is the primary task of current airport pavement maintenance management.

[0003] Currently, the airport pavement maintenance work mainly relies on expert decision, which is limited by the technical level of the decision maker and is highly subjective. In addition, the post-maintenance for existing pavement diseases not only increases the maintenance cost due to the delay of the optimal maintenance time, but also makes the aircraft operation have unpredictable risks. Therefore, in combination with the performance degradation law of the airport pavement, timely preventive maintenance is the key to maintaining the long-term high-quality operation of the airport pavement.

[0004] Although some scholars have tried to explore the performance degradation law of the airport pavement and proposed some preventive maintenance indicators in the current airport pavement management research, they mainly give the maintenance time, and the optimization of maintenance decision is always superficial, and there are few cases of long-term pavement maintenance decision with comprehensive benefit and cost analysis. In the highway maintenance management, Chinese patent application No. CN201911280981.9 proposes to use a planning method for pavement maintenance decision, but this method does not consider the long-term performance degradation of the pavement, and ignores the impact of specific maintenance measures in the decision, which is difficult to realize the fine long-term pavement maintenance planning. Chinese patent application No. CN202111330804.4 proposes a highway maintenance decision method considering the performance prediction of micro-units, but only optimizes the pavement performance in the decision, and does not consider the cost, which is an important constraint factor in the maintenance decision.

[0005] In summary, there is a lack of systematic decision optimization method in the current airport pavement maintenance management, and although there are some decision methods in the highway pavement maintenance, the considered factors are relatively single, which is limited by the different emphases in the highway and airport pavement management, and it is also difficult to apply to the airport pavement maintenance decision. SUMMARY

[0006] In order to solve the technical problems mentioned in the background art, the present application proposes a decision-making method and electronic equipment suitable for long-term maintenance planning of airport pavement.

[0007] In order to achieve the above technical purpose, the technical scheme of the present application is:

[0008] A decision-making method suitable for long-term maintenance planning of airport pavement, comprising the following steps:

[0009] S1, establishing an airport pavement maintenance decision-making model: inputting airport pavement performance indicators and outputting suitable maintenance measures under the condition of the indicators; the airport pavement maintenance decision-making model comprises establishing an airport pavement maintenance countermeasure library and establishing an airport pavement maintenance decision-making logic;

[0010] S2, collecting airport pavement periodic detection and maintenance historical data, and establishing an airport pavement performance indicator prediction model: including different prediction models for each indicator under different maintenance measures; inputting the pavement performance indicators and maintenance measures into the airport pavement performance indicator prediction model, and outputting the pavement performance indicator prediction value of the next quarter;

[0011] S3, making a single maintenance decision for the airport pavement, taking a quarter as the shortest decision period for the airport pavement, setting a decision planning period T, and repeating steps S1 and S2 in each quarter within the decision planning period to generate maintenance planning for each unit of the airport pavement;

[0012] S4, establishing a multi-objective optimization model for long-term maintenance decision-making of the airport pavement under non-stop construction, including an objective function and constraint conditions;

[0013] S5, taking the maintenance planning of each unit generated in step S3 as its multi-objective optimization feasible region, and using the multi-objective optimization model established in step S4 to optimize and solve, to obtain an optimal solution set of the long-term maintenance planning of the airport pavement.

[0014] Preferably, the airport pavement performance indicators in step S1 include: pavement condition index PCI, pavement classification number PCN, pavement slab void coefficient T, international roughness index IRI, and pavement friction coefficient μ.

[0015] Preferably, step S1 of establishing an airport pavement maintenance countermeasure library specifically refers to the types of airport pavement maintenance measures and the applicable conditions, unit price estimation, and construction time of each type of maintenance measure determined in combination with the Civil Aviation Administration of China <Technical Guidelines for Maintenance of Airport Pavement Flight Area and Site> and actual airport pavement maintenance historical data, wherein the maintenance measures are divided into three categories, a total of 10 items: preventive maintenance, targeted maintenance, and structural maintenance; the preventive maintenance includes daily maintenance and grooving; the targeted maintenance includes crack filling, slab grinding, foundation grouting, shallow repair, partial thickness repair, and full thickness repair; and the structural maintenance includes whole slab replacement and overlay.

[0016] Preferably, step S1, establishing the airport pavement maintenance decision logic, specifically refers to: after establishing the airport pavement maintenance countermeasures database, combining the airport pavement periodic inspection and maintenance historical data, determining the applicable maintenance measures under various indicator levels.

[0017] Preferably, the expression for the airport pavement performance index prediction model in step S2 is:

[0018]

[0019] In the formula, y(t) is the performance index prediction curve, t represents time, y0 is the initial performance value, and a and b are model parameters. The calculation formula is as follows:

[0020]

[0021]

[0022]

[0023] In the formula, sequence These are pavement performance index values ​​for n consecutive years, selected from historical pavement performance data. It is a weighted moving average sequence, derived from the sequence A single accumulation sequence It is generated after performing a weighted moving average, where α is the weighting coefficient, and its value ranges from 0 to 1.

[0024] Preferably, step S3, which makes a decision on a single maintenance operation of the airport pavement, specifically includes the following steps:

[0025] S31. Set the decision-making and planning period T;

[0026] S32. Input airport pavement performance index data, make decisions using the pavement maintenance decision logic established in step S1, and generate applicable maintenance measures.

[0027] S33. Based on the decision results of step S32, select the corresponding performance prediction model for each maintenance measure to calculate and output the performance indicators of the airport pavement for the next quarter.

[0028] S34. Input the predicted performance indicators for the next quarter, and repeat steps S32 to S33 until the decision-making and planning period T is completed and all maintenance plans for the decision-making and planning period are output.

[0029] Preferably, in step S4, the multi-objective optimization model for airport pavement maintenance decision-making takes maximizing the average PCI of the pavement and minimizing the maintenance cost during the decision-making planning period as the model objective function, and presets the maintenance construction window B for each quarter. jThe constraint of the maintenance scheme guarantees the operation requirement of non-stop construction of the airport pavement, and is expressed as follows:

[0030]

[0031]

[0032]

[0033] Wherein, maxF1 represents the maximization of the average PCI of the pavement in the decision planning period, M is the total number of decision units of the airport pavement, T is the decision planning period, PCI it is the pavement PCI prediction value of unit i in the tth quarter, Area i is the area of unit i, and Area is the total area of the pavement of the decision airport; minF2 represents the minimization of the maintenance cost, N is the total number of maintenance measures in the maintenance countermeasure library, x ijt represents whether the kth maintenance measure is used in the tth quarter of unit i, is a binary variable, and takes the value 1 to represent the use and takes the value 0 to represent the non-use, P k is the unit price of the kth maintenance measure; s.t. is the constraint condition of the model, C k is the construction time of the kth maintenance measure, B j is the maintenance construction window period of the decision airport in the jth quarter, and D is the lower limit of the pavement PCI in the decision planning period.

[0034] Preferably, step S5 specifically comprises the following steps:

[0035] S51, establishing an objective function equation of the optimization model, selecting the maximization of the average PCI of the pavement in the decision planning period and the minimization of the maintenance cost as two optimization objectives based on the maintenance requirement of the airport pavement, and determining the objective function according to the quantitative relationship between the optimization objectives and the maintenance scheme;

[0036] S52, determining the constraint condition of the optimization model, in addition to setting the lower limit of the pavement PCI to constrain the optimization scheme, the operation requirement of non-stop construction of the airport pavement is also used as the constraint condition, and the maintenance construction window period is used to constrain the decision optimization process according to the actual maintenance construction window period of each quarter in the airport operation; according to steps S51-S52, a long-term maintenance decision multi-objective optimization model of the airport pavement under non-stop construction is finally established;

[0037] S53, solving the multi-objective optimization model obtained in step S52 to obtain a non-dominated optimal solution set in the decision planning period;

[0038] S54, determining the corresponding pavement maintenance scheme according to the optimal solution set of the decision optimization problem obtained by solving step S53, in combination with the current year operation and maintenance plan of the airport.

[0039] Preferably, step S53 specifically means: using a non-dominated sorting genetic algorithm with elitism to solve the constructed multi-objective optimization model, first, randomly generating a certain number of initial populations in the decision space, wherein each individual represents a set of solutions of the optimization problem, second, screening the individuals in the population through fast non-dominated sorting and crowding calculation, thereby uniformly distributing the individuals in the decision space while ensuring the population fitness, and finally, obtaining a new generation of population by using selection, crossover and mutation operations, and when the population is iterated, the elite individuals in the parent population are retained by merging the parent population and the child population to improve the optimization efficiency.

[0040] An electronic device comprising a memory and a processor, the memory storing a computer program executable by the processor, and the processor implements the above-mentioned decision method for long-term maintenance planning of airport pavement when executing the computer program.

[0041] The above technical scheme brings the beneficial effects:

[0042] The present application provides a decision method for long-term maintenance planning of airport pavement and an electronic device, which takes the performance degradation law of airport pavement under different maintenance measures into the decision-making process, and establishes a multi-objective optimization model in the decision-making process according to the operation demand of non-stop construction of airport pavement, and gives the optimal solution set of maximizing the pavement performance level and minimizing the maintenance cost in the decision period through intelligent algorithm, which answers the questions of whether to maintain, when to maintain, and what maintenance measures to take in the decision-making of airport pavement maintenance, and has a very positive significance for realizing the operation and maintenance management of airport pavement in the whole life cycle. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 Figure 1 is a flowchart of the decision method for long-term maintenance planning of airport pavement of the present application;

[0044] Figure 2 Figure 2 is a schematic diagram of pavement maintenance decision logic;

[0045] Figure 3 Figure 3 is a schematic diagram of single maintenance decision flow;

[0046] Figure 4 Figure 4 is a schematic diagram of decision optimization non-dominated solution set in the embodiment of the present application;

[0047] Figure 5 Figure 5 is a schematic diagram of maintenance workload of each scheme in the optimization result in the embodiment of the present application. DETAILED DESCRIPTION

[0048] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings.

[0049] ReferenceFigure 1 Taking the optimization of airport cement concrete pavement maintenance decision as an example, the steps of the embodiment of the application are as follows:

[0050] Step S1, establishing an airport pavement maintenance decision model:

[0051] Step S1.1, establishing an airport pavement maintenance countermeasure library: combining the Civil Aviation Administration of China “Guidelines for Maintenance of Runway and Apron Pavements of Civil Airports” (AC-140-CA-2010-3) and historical data of airport pavement maintenance, building a pavement maintenance countermeasure library, determining the types of airport pavement maintenance measures and the applicable conditions, unit price estimates and construction time of each type of maintenance measure, as shown in Table 1.

[0052] Table 1: Pavement maintenance countermeasure library

[0053]

[0054]

[0055] Step S1.2, establishing an airport pavement maintenance decision logic: combining periodic detection of airport pavement and historical data of maintenance, establishing a decision tree for airport pavement maintenance. “Technical Specifications for Evaluation and Management of Pavements of Civil Airports” (MH / T5024-2019) classifies the pavement evaluation indicators (in this embodiment, the classification results of the indicators are shown in Table 2), and then combines the periodic detection of airport pavement and historical data of maintenance to determine the applicable maintenance measures for all situations (a total of 5*3*4*4*3=720) in the arrangement combination of different levels of these indicators, which are used as the decision logic for airport pavement maintenance. For specific pavement unit decision, only the performance indicators of the unit need to be input, and the applicable maintenance measures for the unit can be obtained by judging which situation it belongs to, as shown in Table 3. Figure 2

[0056] Table 2: Classification results of pavement rating indicators

[0057] Indicator Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 PCI PCI > 85 70 < PCI < 85 55 < PCI < 70 40 < PCI < 55 PCI < 40 PCN PCN > 80 50 < PCN < 80 PCN < 50 / / T T≤1.5 1.5<T≤2.5 2.0<T≤3.0 T>3.0 / IRI IRI < 2.5 2.5 < IRI < 3.5 3.5 < IRI < 4.5 IRI > 4.5 / μ μ > 0.60 0.50 < μ < 0.60 μ < 0.50 / /

[0058] Step S2, establishing an airport pavement performance prediction model:

[0059] Step S2.1, screening airport pavement performance and maintenance historical data: from the collected airport pavement performance and maintenance historical data, classifying according to the used maintenance measures, and screening out the time series of each performance indicator of the airport pavement after using each maintenance measure

[0060] Step S2.2, establishing an airport pavement performance prediction system: using the results of data screening, respectively establishing performance prediction models, and sequentially calculating the first cumulative sequence weighted moving average sequence ​and model parameters a, b:

[0061]

[0062]

[0063]

[0064]

[0065] wherein the sequence is the pavement performance index value of consecutive n years, is a cumulative sequence, is a weighted moving average sequence, a is a weighting coefficient, in the embodiment, a = 0.5, y(t) is a performance index prediction curve, t represents time (quarter), y0 is a performance initial value, a, b are model parameters.

[0066] Step S3: single maintenance decision of airport pavement, the flow is as shown in Figure 3 .

[0067] Step S3.1: set decision planning period T: in the embodiment, 3 years, i.e. 12 quarters, are taken as the decision planning period;

[0068] Step S3.2: airport pavement maintenance decision: input airport pavement performance index data (current value or predicted value), use the pavement maintenance decision logic established in step S1.2 to make a decision, and generate applicable maintenance measures;

[0069] Step S3.3: airport pavement performance prediction: for each maintenance measure generated in step S3.2, select the corresponding performance prediction model to calculate the performance index of the next quarter;

[0070] Step S3.4: input the predicted performance index of the next quarter, repeat the steps of decision-making and prediction (S3.2 and S3.3) until the decision-making of each quarter in the decision planning period is completed and the entire maintenance planning in the decision planning period is output.

[0071] Taking a single pavement unit as an example, it is assumed that two available measures can be generated according to the current index data, the index data is predicted according to the two measures respectively, that is, two possible situations of pavement performance in the next quarter can be obtained, and then the two situations are decided respectively, and a plurality of available measures can be generated under each situation, and the possible situations in the third quarter can be obtained by prediction again, and the process is repeated to finally generate a plurality of maintenance plans of the pavement unit in the decision planning period (which maintenance measure is used in each quarter), which are used as the alternative schemes of the unit in long-term decision optimization. In this embodiment, a total of 13730 maintenance plans are generated for 100 units of the airport pavement by the above steps, and the long-term decision optimization of the airport pavement will be selected from the permutation and combination of these schemes.

[0072] Step S4: long-term decision optimization of the airport pavement

[0073] Step S4.1: establishing an objective function equation of the optimization model: based on the maintenance demand of the airport pavement, two optimization objectives of maximizing the average PCI of the pavement in the decision planning period and minimizing the maintenance cost are selected in this embodiment, and the objective function is determined according to the quantitative relationship between the optimization objectives and the maintenance schemes;

[0074] Step S4.2: determining the constraint conditions of the optimization model: in addition to setting the lower limit of the pavement PCI to constrain the optimization scheme, the operation demand of the non-stop construction of the airport pavement is also considered as a constraint condition in this application, and the maintenance construction window period is used as the constraint condition, and the decision optimization process is constrained according to the maintenance construction window period of each quarter in the actual operation of the airport.

[0075] According to steps S4.1-S4.2, the long-term maintenance decision multi-objective optimization model of the airport pavement under non-stop construction is finally established:

[0076]

[0077]

[0078]

[0079] Wherein, M is the total number of decision units of the airport pavement, T is the decision planning period (quarter), PCI it is the pavement PCI prediction value of unit i in the tth quarter, Area i is the area of unit i, Area is the total area of the pavement of the decision airport, N is the total number of maintenance measures in the maintenance countermeasure library, x ijt represents whether the kth maintenance measure is used by unit i in the tth quarter, which is a binary variable, taking value 1 to represent use and value 0 to represent non-use, P k is the unit price of the kth maintenance measure, C kis the construction time of the kth maintenance measure, B j is the decision-making airport maintenance construction window period (days) in the jth quarter, and the value in this embodiment is shown in Table 3, D is the pavement PCI lower limit in the decision-making planning period, and in this embodiment, D = 80;

[0080] Table 3: Airport construction window period in each quarter in the decision-making planning period

[0081]

[0082] Step S4.3: Solving the multi-objective optimization model to obtain a set of non-dominated optimal solutions in the decision-making planning period: In this embodiment, the non-dominated sorting genetic algorithm with elitism (NSGA-II algorithm) is used to solve the constructed multi-objective optimization model. When solving, firstly, a certain number of initial populations are randomly generated in the decision space, wherein each individual represents a set of solutions to this optimization problem, secondly, the individuals in the population are screened through fast non-dominated sorting and congestion degree calculation, so that the individuals are uniformly distributed in the decision space while ensuring the fitness of the population, and finally, the new population is obtained by using the operations of selection, crossover and mutation, and the elite individuals in the parent population are retained by merging the parent and child populations to improve the optimization efficiency when the population is iterated;

[0083] Step S4.4: Obtaining the decision optimization result: The solution obtained in step S4.3 is a series of non-dominated solutions, i.e., the optimal solution set of the decision optimization problem, including the specific maintenance scheme, performance index, cost estimate, and construction time of each unit in each quarter, and the pavement manager can refer to the above solutions to determine the corresponding pavement maintenance scheme combined with the airport operation and maintenance plan in the current year. In this embodiment, 18 non-dominated solutions are obtained, as shown in Table 4, the maintenance cost and PCI prediction results of each scheme are shown in Table 5, and the maintenance engineering quantity of each scheme is shown in Table 6. Figure 4 Figure 5

[0084] Table 4: Construction time, cost and performance prediction statistics of alternative schemes

[0085]

[0086]

[0087] The embodiments only illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, and any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the present application.​​

Claims

1. A decision-making method suitable for long-term maintenance planning of airport pavements, characterized in that, The method comprises the following steps: S1, establishing an airport pavement maintenance decision model: inputting an airport pavement performance index, and outputting a suitable maintenance measure under the index; the airport pavement maintenance decision model comprises establishing an airport pavement maintenance countermeasure library and establishing an airport pavement maintenance decision logic; S2, collecting airport pavement periodic detection and maintenance historical data, and establishing an airport pavement performance index prediction model: including different prediction models of each index under different maintenance measures; inputting the pavement performance index and the maintenance measure into the airport pavement performance index prediction model, and outputting a predicted value of the pavement performance index in the next quarter; S3, making a single airport pavement maintenance decision, taking a quarter as the shortest decision period of the airport pavement, setting a decision planning period T, and repeatedly generating a maintenance plan for each unit of the airport pavement in the decision planning period by the steps S1 and S2; S4, establishing a multi-objective optimization model for long-term airport pavement maintenance under non-stop construction, including an objective function and a constraint condition; S5, taking the maintenance plan of each unit generated in the step S3 as a multi-objective optimization feasible region, and adopting the multi-objective optimization model established in the step S4 to perform optimization solving, so as to obtain an optimal solution set of the long-term airport pavement maintenance plan; In the step S2, the expression of the airport pavement performance index prediction model is as follows: In the formula, y(t) is a performance index prediction curve, t represents time, y0 is a performance initial value, and a and b are model parameters, and the calculation formula is as follows: In the formula, sequence is the pavement performance index value of consecutive n years, selected from pavement performance historical data, is a weighted moving average sequence, generated by taking a weighted moving average of the sequence , and is a first cumulative sequence of the sequence , and α is a weighting factor, with a value ranging from 0 to 1. The multi-objective optimization model of the airport pavement maintenance decision in the step S4 takes the maximization of the average PCI of the pavement in the planning period and the minimization of the maintenance cost as the model objective function, and the maintenance construction window period B of each quarter is preset j The operation demand of non-stop construction of the airport pavement is guaranteed by the constraint of the maintenance scheme, and is expressed by the following formula: Where, maxF1 represents the maximization of the average PCI of the pavement in the decision planning period, M is the total number of decision units of the airport pavement, T is the decision planning period, PCI it is the pavement PCI prediction value of unit i in the tth quarter, Area i is the area of unit i, Area is the total area of the pavement of the decision airport; minF2 represents the minimization of the maintenance cost, N is the total number of maintenance measures in the maintenance countermeasure library, x ijt represents whether the kth maintenance measure is used in the tth quarter of unit i, is a binary variable, and takes the value 1 to represent use and the value 0 to represent non-use, P k is the unit price of the kth maintenance measure; s.t. is the constraint condition of the model, C k is the construction time of the kth maintenance measure, B j is the maintenance construction window period of the decision airport in the jth quarter, D is the lower limit of the pavement PCI in the decision planning period.

2. The decision-making method suitable for long-term maintenance planning of airport pavement according to claim 1, characterized in that, In the step S1, the airport pavement performance index comprises a pavement condition index PCI, a pavement grade number PCN, a pavement slab void coefficient T, an international roughness index IRI, and a pavement friction coefficient μ.

3. The decision-making method suitable for long-term maintenance planning of airport pavement according to claim 1, characterized in that, In the step S1, the establishment of the airport pavement maintenance countermeasure library specifically refers to determining the types of airport pavement maintenance measures, the applicable conditions of various maintenance measures, the unit price estimation, and the construction time in combination with the Civil Aviation Administration of China “Technical Guidelines for Maintenance of Airport Pavement Flight Area and Site” and the actual airport pavement maintenance historical data, wherein the maintenance measures are divided into three categories, i.e., 10 items, including preventive maintenance, targeted maintenance, and structural maintenance; the preventive maintenance comprises daily maintenance and grooving; the targeted maintenance comprises crack filling, slab grinding, foundation grouting, shallow repair, partial thickness repair, and full thickness repair; and the structural maintenance comprises whole slab replacement and overlay.

4. The decision-making method suitable for long-term maintenance planning of airport pavement according to claim 1, characterized in that, In the step S1, the establishment of the airport pavement maintenance decision logic specifically refers to, after the establishment of the airport pavement maintenance countermeasure library, determining the suitable maintenance measures under the index grade in combination with the airport pavement periodic detection and maintenance historical data.

5. The decision-making method for long-term maintenance planning of airport pavements according to claim 1, characterized in that, The step S3 of making a single airport pavement maintenance decision specifically comprises the following steps: S31, setting the decision planning period T; S32, inputting the airport pavement performance index data, and making a decision by using the pavement maintenance decision logic established in the step S1 to generate a suitable maintenance measure; S33, based on the decision result of the step S32, selecting a corresponding performance prediction model for each maintenance measure to calculate and output the performance index of the airport pavement in the next quarter. S34, input the predicted next quarter performance index, repeat steps S32-S33 until the decision-making period T is completed every quarter decision and output decision-making period within the entire maintenance planning.

6. The decision-making method suitable for long-term maintenance planning of airport pavement according to claim 1, characterized in that, Step S5 specifically includes the following steps: S51, establish the objective function equation of the optimization model, based on the airport pavement maintenance needs, select the two optimization objectives of maximizing the average PCI of the pavement in the decision-making period and minimizing the maintenance cost, and determine the objective function according to the quantitative relationship between the optimization objective and the maintenance scheme; S52, determine the constraint conditions of the optimization model, in addition to setting the lower limit of the pavement PCI to constrain the optimization scheme, for the operation demand of the airport pavement non-stop construction, the pavement maintenance construction window period is taken as the constraint condition, and the decision optimization process is constrained according to the maintenance construction window period of each quarter in the actual operation of the airport; according to steps S51-S52, a long-term maintenance decision multi-objective optimization model of the airport pavement under non-stop construction is finally established; S53, solve the multi-objective optimization model obtained in step S52 to obtain a non-dominated optimal solution set in the decision-making period; S54, according to the optimal solution set of the decision optimization problem obtained by solving step S53, determine the corresponding pavement maintenance scheme combined with the airport operation and maintenance plan this year.

7. The decision-making method suitable for long-term maintenance planning of airport pavement according to claim 6, characterized in that, Step S53 specifically refers to: the non-dominated sorting genetic algorithm with elitist strategy is used to solve the constructed multi-objective optimization model, first, a pre-set number of initial populations are randomly generated in the decision space, each individual represents a set of solutions to this optimization problem, second, the individuals in the population are screened through fast non-dominated sorting and crowding degree calculation, so that the individuals are uniformly distributed in the decision space while ensuring the fitness of the population, and finally the new population is obtained by using selection, crossover and mutation operations, when the population is iterated, the elite individuals in the parent population are reserved by merging the parent and child populations to improve the optimization efficiency.

8. An electronic device, comprising: Comprise: a memory and a processor, the memory stores a computer program executable by the processor, and the processor implements the decision-making method for long-term maintenance planning of airport pavement according to any one of claims 1-7 when executing the computer program.

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