Model-based flight test task dynamic optimization method
By constructing uncertain mathematical model and optimization model, combining knowledge mining and operation optimization theory, and designing and developing solutions algorithms, the problems of insufficient flexibility and deviation in execution during the execution of aircraft flight test mission planning are solved, and the flexibility and efficiency of test flight mission planning are improved to ensure that the test flight project meets the expected goals.
Patent Information
- Application Number
- CN202411951664.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-04-25
AI Technical Summary
There are major problems of insufficient flexibility and deviation in the implementation of aircraft flight test mission planning, which leads to the extension of the test flight cycle and affects the improvement of resource allocation efficiency and test flight efficiency.
The dynamic optimization method of aircraft flight test missions is adopted based on model, and by constructing uncertain mathematical model, proactive test flight task optimization model and reactive test flight task optimization model, combined with knowledge mining and operation optimization theory, corresponding solution algorithms are designed and developed to provide comprehensive and reliable task planning.
It improves the flexibility and efficiency of test flight mission planning, ensures that the test flight mission can better adapt to complex and changeable environments and needs, reduces the impact of changes and changes in test flight mission planning, and ensures that the test flight project meets the expected progress, cost and quality goals.
Smart Images
Figure CN119886674A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of aviation applications and relates to a model-based dynamic optimization method for aircraft flight test tasks. Background Art
[0002] Aircraft flight test mission planning is a process with clear goals and comprehensive optimization to arrange various test tasks and resources. In the flight test mission planning, it is necessary to fully consider the relevant work content, preconditions and resource guarantee capabilities involved. Considering the long cycle of aircraft flight test, the complex interaction between test elements, and the existence of many uncertain factors in the execution process, the planned flight test mission cannot be executed as planned, resulting in an extension of the flight test cycle, affecting the efficiency of resource allocation and the improvement of flight test benefits, thus causing huge losses to the flight test project.
[0003] Traditional flight test mission planning mainly focuses on the task relationship of the flight test project itself to formulate the division of labor and sequence of flight test tasks. However, during the execution of flight test tasks, many uncertain factors cause the flight test activities to be changeable and the resource allocation to be unbalanced. The existing control method of flight test planning adopts the adjustment strategy of plan rescheduling to deal with the risk interference in the flight test process, but the logical order between the test elements and the adjustment cost and cycle limit the adjustment space of the flight test mission. Summary of the invention
[0004] The present application provides a model-based dynamic optimization method for aircraft flight test tasks. Through the proactive flight test task optimization model and the corresponding solution algorithm, it can effectively solve the problems of insufficient flexibility in flight test task planning and large execution deviation. The corresponding solution algorithm of the reactive flight test task optimization model can provide a flight test task adjustment plan with the least loss in the case of deviation in the implementation of the flight test task.
[0005] The solution of the present invention is a model-based flight test task dynamic optimization method, the method comprising:
[0006] S10. Statistically analyze the flight test mission execution data and construct an uncertainty mathematical model based on the factors that affect the change of the flight test mission plan; the uncertainty mathematical model includes a mission uncertainty model, a mission execution time uncertainty model, and a resource uncertainty model:
[0007] S20, constructing a proactive flight test mission optimization model based on the uncertainty mathematical model to obtain a proactive flight test mission planning scheme, wherein the proactive flight test mission optimization model includes a proactive target model and a proactive constraint model;
[0008] S30. According to the uncertainty mathematical model, a reactive scheduling test flight task optimization model is constructed to obtain a reactive test flight task planning scheme, wherein the reactive scheduling test flight planning optimization model includes a reactive target model and a reactive constraint model.
[0009] Furthermore, the mission uncertainty model is used to characterize the uncertainty of the flight test mission sortie requirements;
[0010] The task execution time uncertainty model is used to characterize the uncertainty of task start time;
[0011] The flight test resource uncertainty model is used to characterize the technical status of the test aircraft, special environment, and time uncertainty of scarce resources.
[0012] Furthermore, step 20 includes:
[0013] S201: Construct a proactive target model based on uncertainty mathematical model Among them, imp i The impact of delayed execution of test flight mission i on the overall mission planning, tb i The buffer time added for test flight mission i, N is the total number of test flight missions;
[0014] S202: Construct a proactive constraint model based on the flight test mission logic, test capability and buffer time;
[0015] S203: Based on the characteristics of the proactive flight test mission optimization model, the variables involved in the proactive flight test mission optimization model are linearized and converted into a linear programming model. The model is solved using a linear programming solution method to obtain a proactive flight test mission planning scheme with the greatest robustness that meets the constraints.
[0016] Furthermore, the proactive constraint model includes a flight test mission logic proactive constraint model, a test capability proactive constraint model, and a buffer time proactive constraint model; wherein, the test capability proactive constraint model includes a flight test mission execution time constraint, a flight test mission execution personnel number constraint, and a flight test mission total resource consumption constraint.
[0017] Furthermore, the flight test mission logic proactive constraint model is: in and Add the buffered flight test task start time for flight test tasks i and j;
[0018] The proactive constraint model of the flight test mission execution time is: Among them, test ik Whether the test flight mission i is tested on the test aircraft k, ed i represents the execution time of the i-th test flight mission, x it Whether the test flight mission i starts at time t; tk represents the total number of test flights of the kth test aircraft in the tth month, and ped is the upper limit of the execution time of a single flight;
[0019] The proactive constraint model for the number of personnel performing the flight test mission is: Among them, y kt is whether the test machine k is tested at time t, and pcr is the number of available executors;
[0020] The proactive constraint model of the total resource consumption of the flight test mission is: Among them, fc i represents the resources consumed by the ith test flight mission, and pfc represents the upper limit of resource consumption;
[0021] The buffer duration proactive constraint model is: Among them, tb i The buffer time added for test flight mission i, the project period is T.
[0022] Furthermore, S30 includes:
[0023] S301: Building a Reactive Target Model Among them, ac i is the deviation loss of test flight mission i, and is the start time and end time of flight test mission i;
[0024] S302: Construct a reactive constraint model based on the flight test logic, test capability, and the acceptability of the flight test mission adjustment cost;
[0025] S303: According to the characteristics of the reactive flight test mission planning optimization model, the variables involved in the reactive flight test mission planning optimization model are linearized and converted into a linear programming model. The model is solved using a linear programming solution method to obtain a reactive flight test mission planning solution with the minimum deviation from the baseline flight test mission planning and the minimum adjustment cost.
[0026] Furthermore, the reactive constraint model includes a test flight logic reactive constraint model, a test capability reactive constraint model, and a test flight mission adjustment cost acceptability model; the test capability reactive constraint model includes a test flight mission execution time constraint, a test flight mission execution personnel number constraint, and a test flight mission total resource consumption constraint.
[0027] Furthermore, the flight test logic reactive constraint model is: in and Add the buffered flight test task start time for flight test tasks i and j;
[0028] The reactive constraint model of the flight test mission execution time is: Among them, test ik Whether the test flight mission i is tested on the test aircraft k, edi represents the execution time of the i-th test flight mission, x it Whether the test flight mission i starts at time t; tk represents the total number of test flights of the kth test aircraft in the tth month, and ped is the upper limit of the execution time of a single flight;
[0029] The reactive constraint model of the number of personnel performing the flight test mission is: Among them, y kt is whether the test machine k is tested at time t, and pcr is the number of available executors;
[0030] The reactive constraint model of the total resource consumption of the flight test mission is: Among them, fc i represents the resources consumed by the ith test flight mission, and pfc represents the upper limit of resource consumption;
[0031] The acceptable degree model of the adjusted cost of the flight test mission is: Where C is the acceptability level.
[0032] In summary, the present application provides a model-based dynamic optimization method for aircraft flight test tasks, which has the following advantages: combining theoretical methods such as knowledge mining and operations optimization, by constructing a flight test task optimization model that combines proactivity and reactivity, and designing and developing corresponding solution algorithms, it provides comprehensive and reliable task planning for flight tests, making flight test task planning more flexible and efficient, and ensuring that flight test task planning can better adapt to the complex and changeable environment and needs in flight tests. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 A schematic flow chart of a model-based dynamic optimization method for aircraft flight test tasks provided in this application.
[0034] Figure 2 A schematic diagram of a proactive test flight mission planning scheme provided for this application;
[0035] Figure 3 A schematic diagram of a reactive flight test mission planning scheme provided for this application. DETAILED DESCRIPTION
[0036] The present invention proposes a model-based dynamic optimization method for aircraft flight test tasks to ensure that the project is executed according to the benchmark flight test task plan as much as possible, minimize the changes in the flight test task plan and the impact of the changes, and enable the flight test project to achieve the expected progress, cost and quality goals.
[0037] Statistical analysis is performed on the factors that affect the execution of flight test missions, and they are classified and structured to form mission uncertainty models, mission execution time uncertainty models, and resource uncertainty models. This will improve the accuracy of estimation of related uncertain parameters, thereby more accurately and reliably optimizing the efficiency of flight test time and resource utilization, reducing resource utilization costs, and improving the scientific level of flight test mission planning and dynamic management and control.
[0038] The proactive flight test mission optimization method aims to maximize the robustness value of the overall mission planning. Under the constraints of flight test mission logic, test capability and buffer time, a mathematical model of the proactive flight test mission optimization objectives and constraints is established, and a variable neighborhood search heuristic algorithm is developed to obtain the flight test mission planning scheme with the greatest robustness that meets the constraints.
[0039] A reactive flight test mission optimization method was proposed. The goal was to minimize the time and resource costs of flight test planning adjustment, and the flight test logic, test capability, and the acceptability of the flight test mission adjustment cost were used as constraints. A mathematical model of the goal and constraints was established, and a taboo search heuristic algorithm was developed to obtain a flight test mission adjustment plan with the minimum loss that met the constraints.
[0040] Embodiment 1
[0041] like Figure 1 As shown, the present application provides a model-based dynamic optimization method for aircraft flight test tasks, including:
[0042] S10. Statistically analyze the flight test mission execution data and construct an uncertainty mathematical model based on the factors that affect the change of flight test mission planning;
[0043] Among them, the uncertainty mathematical model includes the task uncertainty model, the task execution time uncertainty model, and the resource uncertainty model:
[0044] Mission uncertainty model: The demand for test flight missions is uncertain.
[0045] Task execution time uncertainty model: The task start time is uncertain.
[0046] Flight test resource uncertainty model: uncertainty in the technical status of the test aircraft, special environment, and time of scarce resources.
[0047] It should be noted that there are various uncertainties in the process of executing flight test missions, such as test aircraft failure, delayed supply of external coordinated resources, and failure to meet the flight test environment window period. Through scientific identification and classification of flight test requirements, various uncertainties in complex dynamic environments are analyzed to determine the influence of these factors on flight test execution.
[0048] S20. According to the uncertainty mathematical model, a proactive flight test mission optimization model is constructed to obtain a proactive flight test mission planning scheme, wherein the proactive flight test mission optimization model includes a proactive target model and a proactive constraint model.
[0049] It should be added that, based on the information such as the required resource types and the total amount of various resources of the flight test project, the deadline for completion of the flight test project, the logical relationship constraints between the flight test tasks, the test capability constraints, the buffer time constraints, etc., a proactive scheduling optimization model is constructed with the goal of maximizing the robustness of proactive flight test task optimization, and a variable neighborhood search heuristic algorithm is designed to solve the model.
[0050] S201: Construct a proactive target model based on uncertainty mathematical model Among them, imp i The impact of delayed execution of test flight mission i on the overall mission planning, tb i The buffer time added for test flight mission i, N is the total number of test flight missions;
[0051] It should be noted that based on the increased buffer time and increased resource occupancy time due to the impact of delayed execution of test flight tasks on test flight task planning, the robustness of the entire baseline plan is defined as the sum of the time buffer contribution and resource occupancy time of all test flight tasks. The goal of the proactive model is to maximize the robustness value of the overall task planning and the maximum value of resource increase.
[0052] Among them, the proactive target model defines the robustness of the entire benchmark plan as the sum of the time buffer contributions of all test flight tasks. The goal of the proactive model is to maximize the robustness value of the overall task planning. For each test flight task, the more buffer is added, the more robustness of the task planning can be improved. Under the given constraints, this optimization model can allocate as much time buffer as possible to the test flight tasks that are more important to the overall planning tasks, so as to achieve the purpose of reasonable buffer configuration and dynamic task planning.
[0053] S202: Construct a proactive constraint model based on the flight test mission logic, test capability and buffer time.
[0054] It should be noted that the proactive constraint model includes three types of models. The first type is the logical constraints of the flight test mission for reasons such as flight test safety. The second type is the test capability constraints, including the technical status of the test aircraft, test resource guarantee capability constraints, etc. The third type is the buffer duration constraints. Based on the original flight test mission plan, the mission is constrained by the mission start time and the total planned time.
[0055] The proactive constraint model includes a flight test mission logic proactive constraint model, a test capability proactive constraint model, and a buffer time proactive constraint model;
[0056] Among them, the proactive constraint model of test capability includes constraints on the execution time of the flight test mission, constraints on the number of flight test mission executors, and constraints on the total amount of flight test mission resource consumption.
[0057] Specifically, the flight test mission logic proactive constraint model is: in and Add the buffered test flight task start time for test flight tasks i and j.
[0058] More specifically, the proactive constraint model for the flight test mission execution duration is: Among them, test ik Whether the test flight mission i is tested on the test aircraft k, ed i represents the execution time of the i-th test flight mission, x it Whether the test flight mission i starts at time t; tk It represents the total number of test flights of the kth test aircraft in the tth month, and ped is the upper limit of the execution time of a single flight.
[0059] The proactive constraint model of the number of flight test mission performers is: Among them, y kt is whether the test machine k is tested at time t, and pcr is the number of available executors.
[0060] The proactive constraint model of total resource consumption of flight test mission is: Among them, fc i It represents the resources consumed by the ith test flight mission, and pfc represents the upper limit of resource consumption.
[0061] Specifically, the buffer duration proactive constraint model is: Among them, tb i The buffer time added for test flight mission i, the project period is T.
[0062] S203: Based on the characteristics of the proactive flight test mission optimization model, the variables involved in the proactive flight test mission optimization model are linearized and converted into a linear programming model. The model is solved using a linear programming solution method to obtain a proactive flight test mission planning scheme with the greatest robustness that meets the constraints.
[0063] S30. According to the uncertainty mathematical model, a reactive scheduling test flight task optimization model is constructed to obtain a reactive test flight task planning scheme, wherein the reactive scheduling test flight planning optimization model includes a reactive target model and a reactive constraint model.
[0064] It should be noted that the optimal model of reactive scheduling flight test planning is set to minimize the time cost and resource cost of flight test planning adjustment. Based on the analysis of model characteristics, a taboo search heuristic algorithm is designed to solve the model and realize the dynamic adjustment plan of flight test tasks.
[0065] Specifically, S30 includes:
[0066] S301: Building a Reactive Target Model Among them, ac i is the deviation loss of test flight mission i, and is the start time and end time of flight test mission i.
[0067] The reactive target model aims to ensure that the actual planning arrangement of the test flight mission does not deviate from its baseline plan as much as possible when it has to be adjusted, with the minimum deviation loss relative to the baseline test flight plan as the goal.
[0068] S302: Constructing a reactive constraint model according to the flight test logic, the test capability and the acceptability of the flight test mission adjustment cost, wherein the reactive constraint model includes a flight test logic reactive constraint model, a test capability reactive constraint model and a flight test mission adjustment cost acceptability model.
[0069] Specifically, the flight test logic reactive constraint model is: in and Add the buffered test flight task start time for test flight tasks i and j.
[0070] Specifically, the test capability reactive constraint model includes constraints on the execution time of the flight test mission, the number of flight test mission executors, and the total amount of flight test mission resource consumption.
[0071] The reactive constraint model of the flight test mission execution time is: Among them, test ik Whether the test flight mission i is tested on the test aircraft k, ed i represents the execution time of the i-th test flight mission, x it Whether the test flight mission i starts at time t; tk It represents the total number of test flights of the kth test aircraft in the tth month, and ped is the upper limit of the execution time of a single flight.
[0072] The reactive constraint model of the number of flight test mission performers is: Among them, y kt is whether the test machine k is tested at time t, and pcr is the number of available executors.
[0073] The reactive constraint model of the total resource consumption of the flight test mission is: Among them, fc i It represents the resources consumed by the ith test flight mission, and pfc represents the upper limit of resource consumption.
[0074] Specifically, the acceptable degree model of the flight test mission adjustment cost is: Where C is the acceptability level.
[0075] S303: According to the characteristics of the reactive flight test mission planning optimization model, the variables involved in the reactive flight test mission planning optimization model are linearized and converted into a linear programming model. The model is solved using a linear programming solution method to obtain a reactive flight test mission planning solution with the minimum deviation from the baseline flight test mission planning and the minimum adjustment cost.
[0076] It should be noted that by analyzing and summarizing historical test flight mission data, building an uncertainty mathematical model, designing a proactive test flight mission optimization model, and obtaining a proactive test flight mission planning scheme, the robustness of the test flight mission planning is improved.
[0077] It should be noted that during the execution of the flight test mission, when it deviates from the baseline flight test mission plan, a reactive scheduling flight test mission optimization model is constructed to implement a reactive flight test mission planning scheme with the minimum deviation loss from the flight test mission adjustment.
[0078] Embodiment 2
[0079] The application provides a model-based dynamic optimization method for aircraft flight test tasks, which is illustrated by taking test flight task data as an example, including:
[0080] Step 1: Analyze the flight test experience of previous similar projects and construct the flight test mission uncertainty model, time uncertainty model and flight test resource uncertainty model.
[0081] Specific:
[0082] There are uncertainties in the execution of five test flight missions, with the uncertainty range being ±5 flights.
[0083] The execution time of three test flight missions is uncertain, with the time uncertainty range being ±2 months.
[0084] There are 5 test flight resources that are uncertain, and the resource uncertainty range is ±3 months. It should be noted that these 5 test flight resources affect the execution of 2 test flight missions.
[0085] Step 2: According to the aforementioned proactive flight test mission optimization method, construct the target model and constraint model, call the solution algorithm, and obtain the proactive flight test mission planning scheme.
[0086] Specific:
[0087] In the objective function model, the total period of the test flight mission is T = [6], and the degree of influence of the delayed execution of the uncertain test flight mission on the overall mission planning is imp i =[5, 1, 3, 4, 2, 1, 2, 8, 9, 3], there is a buffer time tb added for the uncertain test flight task i =[2, 1, 3, 4, 3, 2, 1, 3, 2, 1], the total number of test flight missions N =
[100] .
[0088] The constraint model parameters are as follows:
[0089] In the flight test mission execution logic model In the flight test mission execution time constraint model, ik =[1,1,0,0,1,0,0,1,1,0],ed i =[2,3,3,2,2,3,2,2,3,3],x it =[1,1,0,0,0,1,1,1,0,1], fl tk =[10,15,20,13], ped=[2].
[0090] In the flight test mission execution personnel quantity constraint model, y kt =[1,1,0,0,1,0,0,1,1,0], pcr=
[20] .
[0091] In the resource consumption total constraint model, fc i =[10,12,11,5,3,5,1,4,7,2], pfc=
[50] .
[0092] tb in the buffer time constraint model i =[1,2,2,1,1,3,1,2,1,3].
[0093] According to the model setting parameters, the proactive model solving algorithm is called to obtain the execution buffer time of each uncertain flight test task, such as Figure 2 As shown, a robust flight test mission planning scheme is formed.
[0094] Step 3: According to the above-mentioned reactive flight test mission optimization method, construct the target model and constraint model, call the solution algorithm, and obtain the reactive flight test mission planning plan.
[0095] Specific:
[0096] In the objective function, ac i =[10, 15, 6, 3, 5, 8, 2, 6, 3, 6], and It is consistent with the proactive test flight mission planning parameter settings.
[0097] The parameters of the flight test logic constraint model, test capability constraint model and proactive flight test mission optimization model in the constraint function model are consistent.
[0098] In the acceptability model of the flight test mission adjustment cost, C =
[200] .
[0099] According to the model setting parameters, the reactive model solving algorithm is called to obtain the reactive flight test mission adjustment time with the minimum deviation from the benchmark flight test mission plan and the minimum adjustment cost, such as Figure 3 As shown, a test flight mission planning scheme with minimal adjustment loss is formed.
[0100] In summary, the present invention relates to a model-based dynamic optimization method for aircraft flight test tasks. The present invention proposes a dynamic optimization method for test flight tasks based on proactivity and responsiveness, so as to ensure that the test flight tasks are executed according to the benchmark plan as much as possible, minimize the changes in the plan, and enable the project to achieve the expected progress, cost and quality goals. Based on the proactive test flight task planning method, the factors affecting the execution of the test flight task are statistically analyzed using data statistical analysis technology, and classified and structured to form a task uncertainty model, a task execution time uncertainty model, and a resource uncertainty model. A proactive test flight task optimization model with robustness is established and a heuristic algorithm is designed, which can provide dynamic planning optimization of the test flight task and ensure the robustness of the benchmark test flight task planning. In view of the deviation between the test flight implementation process and the benchmark test flight plan, a reactive test flight task optimization model and algorithm are established to obtain the best test flight task optimization solution for the interference of uncertain factors.
Claims
1. A model-based dynamic optimization method for flight test tasks, characterized in that the method include: S10. Statistically analyze the flight test mission execution data and construct an uncertainty mathematical model based on the factors that affect the change of the flight test mission plan; the uncertainty mathematical model includes a mission uncertainty model, a mission execution time uncertainty model, and a resource uncertainty model: S20, constructing a proactive flight test mission optimization model based on the uncertainty mathematical model to obtain a proactive flight test mission planning scheme, wherein the proactive flight test mission optimization model includes a proactive target model and a proactive constraint model; S30. According to the uncertainty mathematical model, a reactive scheduling test flight task optimization model is constructed to obtain a reactive test flight task planning scheme, wherein the reactive scheduling test flight planning optimization model includes a reactive target model and a reactive constraint model.
2. The method according to claim 1, characterized in that Mission uncertainty model, used to characterize the uncertainty of flight test mission sortie requirements; The task execution time uncertainty model is used to characterize the uncertainty of task start time; The flight test resource uncertainty model is used to characterize the technical status of the test aircraft, special environment, and time uncertainty of scarce resources.
3. The method according to claim 1, characterized in that Step 20 includes: S201: Construct a proactive target model based on uncertainty mathematical model Among them, imp i The impact of delayed execution of test flight mission i on the overall mission planning, tb i The buffer time added for test flight mission i, N is the total number of test flight missions; S202: Construct a proactive constraint model based on the flight test mission logic, test capability and buffer time; S203: Based on the characteristics of the proactive flight test mission optimization model, the variables involved in the proactive flight test mission optimization model are linearized and converted into a linear programming model. The model is solved using a linear programming solution method to obtain a proactive flight test mission planning scheme with the greatest robustness that meets the constraints.
4. The method according to claim 3, characterized in that The proactive constraint model includes a flight test mission logic proactive constraint model, a test capability proactive constraint model, and a buffer time proactive constraint model; wherein, the test capability proactive constraint model includes a flight test mission execution time constraint, a flight test mission execution personnel number constraint, and a flight test mission total resource consumption constraint.
5. The method according to claim 4, characterized in that The logical proactive constraint model of the flight test mission is: in and Add the buffered flight test task start time for flight test tasks i and j; The proactive constraint model of the flight test mission execution time is: Among them, test ik Whether the test flight mission i is tested on the test aircraft k, ed i represents the execution time of the i-th test flight mission, x it Whether the test flight mission i starts at time t; tk represents the total number of test flights of the kth test aircraft in the tth month, and ped is the upper limit of the execution time of a single flight; The proactive constraint model for the number of personnel performing the flight test mission is: Among them, y kt is whether the test machine k is tested at time t, and pcr is the number of available executors; The proactive constraint model of the total resource consumption of the flight test mission is: Among them, fc i represents the resources consumed by the ith test flight mission, and pfc represents the upper limit of resource consumption; The buffer duration proactive constraint model is: Among them, tb i The buffer time added for test flight mission i, the project period is T.
6. The method according to claim 1, characterized in that S30 includes: S301: Building a Reactive Target Model Among them, ac i is the deviation loss of test flight mission i, and is the start time and end time of flight test mission i; S302: Construct a reactive constraint model based on the flight test logic, test capability, and the acceptability of the flight test mission adjustment cost; S303: According to the characteristics of the reactive flight test mission planning optimization model, the variables involved in the reactive flight test mission planning optimization model are linearized and converted into a linear programming model. The model is solved using a linear programming solution method to obtain a reactive flight test mission planning solution with the minimum deviation from the baseline flight test mission planning and the minimum adjustment cost.
7. The method according to claim 6, characterized in that The reactive constraint model includes a test flight logic reactive constraint model, a test capability reactive constraint model, and a test flight mission adjustment cost acceptability model; the test capability reactive constraint model includes a test flight mission execution time constraint, a test flight mission execution personnel number constraint, and a test flight mission total resource consumption constraint.
8. The method according to claim 7, characterized in that The flight test logic reactive constraint model is: in and Add the buffered flight test task start time for flight test tasks i and j; The reactive constraint model of the flight test mission execution time is: Among them, test ik Whether the test flight mission i is tested on the test aircraft k, ed i represents the execution time of the i-th test flight mission, x it Whether the test flight mission i starts at time t; tk represents the total number of test flights of the kth test aircraft in the tth month, and ped is the upper limit of the execution time of a single flight; The reactive constraint model of the number of personnel performing the flight test mission is: Among them, y kt is whether the test machine k is tested at time t, and pcr is the number of available executors; The reactive constraint model of the total resource consumption of the flight test mission is: Among them, fc i represents the resources consumed by the ith test flight mission, and pfc represents the upper limit of resource consumption; The acceptable degree model of the adjusted cost of the flight test mission is: Where C is the acceptability level.