Knowledge-guided evolutionary optimization method for resource scheduling of aircraft measurement and control equipment

Abstract

The invention discloses a knowledge-guided evolutionary optimization method for resource scheduling of aircraft measurement and control equipment, and the method comprises the following steps: building a joint optimization model which meets various constraint conditions and maximizes comprehensive evaluation indexes based on the task scheduling and path planning of measurement and control equipment with maximized task completion; from the perspective of searching a combination scheme of measurement and control point positions and equipment, introducing decision variables capable of representing the combination scheme and a maneuvering scheme; optimization targets: considering measurement and control operation constraints, measurement and control equipment requirement constraints, measurement and control equipment performance constraints and measurement and control performance parameter calculation; and solving the model by using a knowledge-guided evolutionary optimization method. According to the method, the global optimization characteristic of the evolutionary algorithm and the local search characteristic of the heuristic algorithm are combined to find the high-quality solution of the problem; a knowledge-guided evolutionary algorithm can find a high-quality solution in a global range, and a rule-based heuristic algorithm ensures the quality of a solution in a local range.

Description

Technical Field

[0001] The present invention belongs to the technical field of measurement and control scheduling, and particularly relates to a knowledge-guided evolutionary optimization method for the resource scheduling of aircraft measurement and control equipment. Background Art

[0002] In order to ensure that the technical indicators of the aircraft reach the expected standards, multiple flight tests need to be carried out. These flight tests are not only a test of the aircraft's performance but also a strict assessment of relevant measurement and control technologies and strategies.

[0003] In the flight test of an aircraft, the measurement and control system plays a crucial role. It is responsible for collecting, transmitting, and analyzing various data during the flight of the aircraft, providing key support for the development, testing, optimization, and evaluation of the aircraft. However, with the increase in the number and complexity of flight test tasks, how to effectively schedule and deploy measurement and control resources to ensure the efficient, accurate, and safe completion of the tests has become an urgent problem to be solved.

[0004] Currently, the scheduling and deployment of measurement and control resources mostly rely on experience and intuition, which may lead to waste of resources and low efficiency to a certain extent. Therefore, how to use modern optimization technologies and algorithms to achieve scientific and efficient scheduling and deployment of measurement and control resources is one of the research goals in this field. Summary of the Invention

[0005] To achieve the above object, the knowledge-guided evolutionary optimization method for the resource scheduling of aircraft measurement and control equipment disclosed in this application includes the following steps: Step 1, model construction: Based on the measurement and control equipment task scheduling and path planning that maximize the task completion degree, construct a joint optimization model that satisfies various constraint conditions and maximizes the comprehensive evaluation index; Step 2, introducing decision variables: Starting from the perspective of finding the combination scheme of measurement and control points and equipment, introduce decision variables that can represent the combination scheme and maneuvering scheme; Step 3, optimization objective: Considering two aspects of effective coverage and balance, and then maximizing the comprehensive evaluation of the test effect, the optimization objective is to maximize the comprehensive evaluation index ; Step 4, analyzing constraint conditions: Consider measurement and control operation constraints, measurement and control equipment requirement constraints, measurement and control equipment performance constraints, and measurement and control performance parameter calculation; Step 5, establishing a model solving algorithm: Use the knowledge-guided evolutionary optimization method to solve the model.

[0006] Furthermore, various constraint conditions are accurately classified and simplified, including: task matching constraints and working and maneuvering time constraints of equipment; according to the specific constraint conditions of each TT&C equipment, all possible matching combinations of "aircraft - arc segment - TT&C point - TT&C equipment" are pre-calculated, and based on the TT&C equipment task scheduling and path planning that maximizes the task completion degree, a joint optimization model that meets various constraint conditions and maximizes the comprehensive evaluation index is constructed.

[0007] Furthermore, the decision variables include: : a 0 - 1 variable, which is 1 if the th task is measured and controlled by equipment for the th arc segment, and 0 otherwise; : a 0 - 1 variable, which is 1 if equipment should be deployed to point to measure and control task , and 0 otherwise; : a 0 - 1 variable, which is 1 if equipment needs to move to the corresponding TT&C point on the th day to complete task , and 0 otherwise; : a 0 - 1 variable, which is 1 if equipment moves from TT&C point to TT&C point , and 0 otherwise; : the maneuvering time of the equipment from TT&C point to TT&C point ; : the departure time of equipment from TT&C point to TT&C point ; : the arrival time of equipment at the supply point for the th time.

[0008] Furthermore, the optimization objective is to maximize the comprehensive evaluation index :

[0009] The calculation formula of the comprehensive evaluation index is: ; The comprehensive evaluation index is the task effective coverage and the balance degree is the weighted average of; The calculation process of the task effective coverage is as follows: A single task contains several arcs, which are divided into critical arcs and regular arcs; The effective coverage of the critical arc is defined as: ; Among them, the critical arc is the th arc of the th task, is the effective coverage, is the duration of the arc , is the actual tracking duration; The effective coverage of the regular arc is defined as: ; Among them, the regular arc is the th arc of the th task, is the effective coverage, is the duration of the arc , is the actual tracking duration; Suppose the th task has arcs, then the effective coverage of this task is: ; Among them, is the th arc of the th task; the arcs of different types of equipment need to be calculated separately; there are tasks, then the total task effective coverage is: ; The calculation formula of the balance degree is: ; In the formula, is the total duration of the planned period, is the total number of mobile equipment, is the th mobile equipment's total working time during the entire planned period, which refers to the total duration of the planned period minus the supply time; mobile equipment that is not mobile during the entire planned period is not included in the calculation scope of the balance degree.

[0010] Furthermore, various constraints include: Device task constraint: ; In the formula, is a 0-1 variable, indicating that if the th task's th arc segment is measured and controlled by the measurement and control device it is 1, otherwise it is 0; this constraint ensures that each device can only be assigned to one time arc of one task at the same time; Device position constraint: ; In the formula, is a 0-1 variable, indicating that if the measurement and control device is assigned to the position to complete the task it is 1, otherwise it is 0; this constraint ensures that each device can only be assigned to one point at the same time; Device time constraint: ; In the formula, is a 0-1 variable, indicating that if the measurement and control device needs to move to the corresponding measurement and control position on the th day to complete the task it is 1, otherwise it is 0; this constraint ensures that each device can measure and control at most one task per day; Time position constraint: ; This constraint ensures the time and position relationship of the device performing the measurement and control task. If it goes to perform the measurement and control task, the time and position are determined simultaneously; Device maneuver constraint: ; In the formula, is a 0-1 variable, indicating that if the device maneuvers from the measurement and control position to the measurement and control position it is 1, otherwise it is 0; these two constraints ensure that each device can only maneuver from one position to another, and this position cannot be the device's current position; Measurement and control device reaching the measurement and control position time constraint: ; In the formula, represents the launch date of the aircraft for task , represents the task The cycle; this constraint ensures that the devices participating in the mission must arrive at the corresponding TT&C points by 24:00 on the specified date; Time constraint for the TT&C equipment to leave the TT&C point after completing the mission: ; In the formula, represents the time when the device moves from the TT&C point to the TT&C point and departs; this constraint ensures that the devices participating in the mission must leave after 24:00 on the specified date; Longest continuous working time constraint: ; ; In the formula, represents the time when the device arrives at the supply point for the th time, represents the longest continuous working time of the device ; this constraint ensures that the time from when the mobile device leaves the supply point during the last supply to when it arrives at the supply point for the next supply does not exceed its longest continuous working time; Maneuvering time constraint: Use the Floyd algorithm to solve for the shortest path from the TT&C point to ; the maneuvering time from point to point is the sum of the passing times of each section of the road in its shortest path; Type and frequency band constraint:

[0011] In the formula, represents the type of TT&C equipment required for the th task and the th arc segment, represents the type of the device ; this constraint ensures that the TT&C equipment required for each arc segment in each task must be of the required type, and if the required device type is telemetry, it must further meet its frequency band requirements: ; In the formula, represents the frequency band required for the th task and the th arc segment, Indicates the receiving frequency band of the device ; This constraint ensures that the telemetry device can only complete the task if it has the receiving function of the same frequency band as the spacecraft under TT&C. Constraint requirements for "characteristic measurement": ; In the formula, Indicates the th task and the th arc segment of other TT&C requirements. 0 represents "none", 1 represents "characteristic measurement", 2 represents "dual coverage", and 3 represents "telemetry and safety control"; is a 0-1 variable indicating whether the device has the characteristic measurement function. If it has, it is 1; otherwise, it is 0. This constraint ensures that if there is a "characteristic measurement" requirement, the radar device for TT&C of this arc segment must have the characteristic measurement function; Constraint requirements for "dual coverage": ; In the formula, is the actual tracking duration of the device, is the duration of multi-device joint tracking. This constraint ensures that if there is a "dual coverage" requirement, the tracking duration of this arc segment is the period of joint tracking by two or more similar devices; Constraint requirements for "telemetry and safety control": ; In the formula, is a 0-1 variable indicating whether the device has the safety control function. If it has, it is 1; otherwise, it is 0. This constraint ensures that if there is a "telemetry + safety control" requirement, then this arc segment is a key arc segment, and the telemetry device for TT&C of this arc segment must have the safety control function; Constraint on the level of TT&C point position: ; In the formula, is the level of the TT&C point position , is the minimum point position level required for the device ; Constraint on the distance R of the target relative to the device: ; In the formula, is the distance of the position at the th task and the th arc segment and the th moment relative to the device , is the device Operating distance; when the distance between the target and the device is within the operating distance of the device, the device tracks and measures the target at that moment; Azimuth angle A constraint of the target relative to the device: ; In the formula, is the azimuth angle of the th task at the th arc segment at the th moment relative to the device , , are the lower and upper limits of the azimuth angle working range of the device ; when the azimuth angle of the target relative to the device is within the working range of the device, the device tracks and measures the target at that moment; Elevation angle E and masking angle SA constraints of the target relative to the device: ; In the formula, is the elevation angle of the th task at the th arc segment at the th moment relative to the device , , are the lower and upper limits of the elevation angle working range of the device , is the masking angle of the measurement and control point ; when the device observes the target, it is required that the elevation angle is within the working range of the device elevation angle index and greater than the masking angle of this point; Azimuth angular velocity VA constraint of the target relative to the device: ; In the formula, is the azimuth angular velocity of the position transformation between the th task at the th arc segment at the th moment and its previous moment relative to the device , is the maximum azimuth angular velocity of the device ; when the azimuth angular velocity of the target relative to the device is within the working range of the device, the device tracks and measures the target at that moment; Elevation angular velocity VE constraint of the target relative to the device: ; In the formula, is the th task at the th arc segment at the The position transformation of a moment relative to its previous moment with respect to the device is the pitch angular velocity of the device with respect to the device is the maximum pitch angular velocity of the device; when the pitch angular velocity of the target relative to the device is within the working range of the device, the device tracks and measures the target at this moment.

[0012] Furthermore, the measurement and control performance parameters are calculated as follows: The coordinates of the measurement and control device in the geocentric rectangular coordinate system are ([[]] , , ), and the coordinates in the geodetic coordinate system are ([[]] , , ). The coordinate point ([[]] , , ) in the geocentric rectangular coordinate system has the following coordinates in the station rectangular coordinate system ([[]] , , ): ; The formula for converting the coordinates ([[]] , , ) in the station rectangular coordinate system to the coordinates in the station RAE coordinate system is: ; ; ; ; To convert the velocity ([[]] , , ) in the geocentric rectangular coordinate system to the velocity in the station RAE coordinate system, first determine the position vector of the target point relative to the station, and then calculate of the three orthogonal unit vectors R, A, and E to obtain the RAE coordinate system; specifically: Calculate the position vector : ; Calculate the orthogonal unit vectors of R, A, and E: ; ; ; Among them, The three components are the velocities in the R, A, and E directions, and the latter two components are the azimuth angular velocity and the pitch angular velocity .

[0013] Furthermore, the knowledge-guided evolutionary optimization method includes the following steps: S1 The knowledge guidance mechanism guides the generation and selection of solutions by extracting the conflict information between current individual tasks: extracting valuable knowledge from the solutions, and these insights are used to evaluate and improve the quality of the solutions; secondly, using a method based on feasible rules and combining this knowledge to formulate an environmental selection strategy; Specifically, first record the number and location of the TT&C task conflicts of the current individual. The number of conflicts can be regarded as the distance between the current individual and the feasible region, and the conflict location indicates the search direction; when selecting a solution, give priority to selecting individuals with fewer conflict numbers; in the crossover and mutation operations, increase the crossover and mutation probability at the conflict location, but to avoid falling into local optima, avoid directly operating at the conflict location; this strategy guides the solution to search in a direction closer to the feasible region; To minimize the number of devices that do not meet the constraints as much as possible, a pair of data is used to mark the conflicts, that is, <(i, j, k)> indicates that in the i-th multiple coverage measurement, the TT&C device of the j-th arc segment k of the aircraft cannot meet the constraint conditions, and this conflict information is transmitted to the crossover and mutation operators to guide the evolution of these conflicts; S2 Encoding mechanism: A two-layer encoding method is adopted, namely the main encoding and the double coverage encoding; specifically, first calculate the matching combination of "aircraft - arc segment - TT&C point - TT&C device". The main encoding records an integer value in each variable of the two-dimensional matrix, and this integer value represents the index value of the combination of "TT&C point - TT&C device" that can provide observations under this aircraft and arc segment; for the double coverage encoding, the selected "aircraft - arc segment - TT&C point - TT&C device" combination is used for the observation task; S3 Knowledge-guided crossover and mutation operator: The crossover and mutation operator guides the crossover and mutation operations by increasing the probability of selecting those points that contain conflict information, so that the offspring are more likely to be improved; S4 Environment selection strategy based on knowledge guidance and feasibility criterion: The feasibility criterion is used to handle the constraint conditions, and an environment selection strategy based on knowledge guidance and feasibility criterion is adopted; among them, the feasibility criterion is: First compare the feasibility of the solutions: Feasible solutions are always better than infeasible solutions; If both solutions are feasible, then they are compared according to the values of the objective function; If both solutions are infeasible, they are compared based on the degree of violation of the constraints; Specifically, first consider the degree of constraint violation and fitness value of an individual. When there are differences in fitness values among individuals, preferentially select the solution with a larger fitness value; take the number of conflicts within an individual as the second criterion. When there are no differences in fitness values, preferentially select the individual with fewer conflict times. S5: Solve the path planning problem of TT&C equipment based on a rule-based heuristic algorithm, including the following steps: S51: Calculate the distance, azimuth angle, pitch angle, azimuth angular velocity, pitch angular velocity ; S52: Based on the above information and other constraint conditions, screen out the set of feasible solutions that can be measured and controlled for each arc segment in each task, that is ; S53: Perform a loop iteration for each solution of each arc segment in each task: S54: Calculate and detect whether there is a situation where the equipment can reach before the start time of the task in the corresponding combined solution set of the arc segment ; if not, the coverage rate of this arc segment is 0; the number of conflicts +1; if so, determine whether movement is required. If the equipment does not need to move, calculate the coverage rate of this arc segment. If movement is required, the equipment needs to maneuver to other positions to perform TT&C tasks, and implement maneuvering and resupply operations according to the preset equipment resupply and maneuver rules, and calculate the coverage rate of this arc segment; S55: If there is a double coverage requirement, repeat step S4; S56 Calculate comprehensive evaluation indicators: effective coverage and balance.

[0014] The equipment resupply and maneuver rules include: When both the starting point and the target point are resupply points: The equipment directly reaches the target point from the starting point because the longest maneuvering time between these two points is less than the maximum continuous working time of the equipment. After arrival, the equipment is resupplied at the target point; When the starting point is a resupply point but the target point is not: The equipment directly reaches the target point from the starting point; however, since the target point is not a resupply point, the equipment does not perform resupply after arrival; When the starting point is not a supply point but the target point is: First, determine whether the device will exceed its maximum continuous working time before reaching the target point; if not, the device directly reaches and replenishes there, and if it times out, the device selects the nearest supply point on the way for replenishment; in addition, determine whether the device can still reach the target point in time to complete the task after replenishment on the way; if it can, the device will replenish at both the nearest supply point and the target point, and if not, then this solution violates the constraints and becomes infeasible; When neither the starting point nor the target point is a supply point: Since neither point is a supply point, the device must replenish on the way to ensure that it does not exceed the maximum working duration; it is also necessary to determine whether the device can still reach the target point in time to complete the task after replenishment on the way. If it can, the device will replenish at the nearest supply point on the way, and if not, then this solution will violate the constraints and become infeasible.

[0015] The beneficial effects of this application are as follows: This application constructs a joint optimization model for the task scheduling and path planning of measurement and control equipment based on maximizing the task completion degree for the problem of task scheduling and path planning of measurement and control equipment. This model fully considers the actual background of the problem and various constraint conditions, ensuring the integrity and practicality of the model; Through the decoupling strategy, the model is divided into a main problem and a sub-problem, making the problem-solving process clearer and more operable. In the algorithm design, the global optimization characteristics of the evolutionary algorithm and the local search characteristics of the heuristic algorithm are combined to aim at finding a high-quality solution to the problem.

[0016] The proposed knowledge-guided evolutionary algorithm can find high-quality solutions globally, while the rule-based heuristic algorithm ensures the quality of solutions locally. The combination of the two provides an efficient and practical solution strategy for the problem of task scheduling and path planning of measurement and control equipment. Brief Description of the Drawings

[0017] Figure 1 Flowchart of the knowledge-guided evolutionary algorithm; Figure 2 Explanation of the knowledge-guided mechanism; Figure 3 Solution coding method (the dark part is the arc segment that needs to be covered twice); Figure 4 Knowledge-guided crossover and mutation operator (the dark part is the selected crossover point); Detailed Implementation Manner

[0018] The present invention will be further described below with reference to the accompanying drawings, but the present invention is not limited in any way. Any transformation or replacement made based on the teachings of the present invention falls within the protection scope of the present invention.

[0019] Table 1 Symbol Explanation: ; ; 。

[0020] It involves the deployment and scheduling of measurement and control resources in aircraft flight tests. It is necessary to formulate appropriate deployment and maneuvering plans for measurement and control equipment according to the performance of the measurement and control equipment and the specific requirements of the test. In addition, the flight test plan of the test department includes multiple tasks, and each task is further divided into several arcs. Each arc has specific measurement and control requirements, including the required equipment type and other specific requirements such as "characteristic measurement" or "dual coverage". The core of the problem is to find the optimal deployment and maneuvering plan to maximize the weighted average of the effective coverage and balance of the tasks.

[0021] Solution idea: 1. Model construction: First, a joint optimization model for measurement and control equipment task scheduling and path planning should be constructed. This model should aim to maximize the overall completion effect of the tasks, with measurement and control task allocation and path planning as the main decision variables. In addition, the model needs to consider various actual constraints; 2. Constraint classification: To simplify the solution process, the constraint conditions can be classified and simplified, mainly including: (1) Measurement and control operation constraints: This involves the matching of "aircraft - arc - time - measurement and control point - measurement and control equipment". Considering that it may be difficult to directly mathematize the constraints such as the working time and maneuvering time of the measurement and control equipment, a heuristic method is considered to ensure the feasibility and high quality of the solution; (2) Measurement and control equipment performance constraints: Before optimization, first calculate the relevant parameters of each measurement and control point and the theoretical trajectory of each task, such as distance, azimuth angle, pitch angle, etc. Then, according to the working environment requirements of the measurement and control equipment, filter out the feasible matching combinations and calculate their coverage rates. This can greatly reduce the decision space and improve the solution efficiency; (3) Measurement and control equipment requirement constraints: Different arc segments of different tasks require different types of measurement and control equipment, and some arc segments also need to meet other requirements, namely "characteristic measurement", "dual coverage" and "telemetry and safety control".

[0022] To simplify the problem, in one embodiment, the present application makes the following assumptions for the model: 1. Equipment limitation at supply points: Assume that there is no quantity limitation on the supply of measurement and control equipment at each supply point, that is, multiple pieces of equipment can be supplied at the same supply point at the same time; 2. Refueling time: Assume that the refueling time for each time is at least 1 day, that is, when the equipment stays at the refueling point for 1 day or more, it is considered that the equipment has completed refueling. During refueling, the equipment cannot perform other operations; 3. Radar technical characteristics: To simplify the model, assume that the angular velocity of the radar when observing the target is not restricted by any means, and all radar equipment adopts advanced phased array technology, enabling it to have a fast response time and high flexibility; 4. Actions of the equipment after tasks: Once the equipment has completed all assigned tasks, no longer consider whether it needs to be refueled again and its subsequent position changes; 5. Parallelism of equipment refueling and measurement and control: In the model, it is allowed for the equipment to perform measurement and control tasks simultaneously during refueling, but this may affect its measurement and control effect; 6. Environmental stability: Assume that during the flight test of the aircraft, environmental factors such as weather and electromagnetic interference remain stable and will not affect the operation of the measurement and control equipment; 7. Equipment reliability: To simplify the model, assume that all measurement and control equipment works normally during the test, and there are no equipment failures or emergencies.

[0023] The knowledge-guided evolutionary optimization method for resource scheduling of aircraft measurement and control equipment disclosed in this application includes the following steps: Step 101, model construction: Based on the task scheduling and path planning of measurement and control equipment to maximize the task completion degree, construct a joint optimization model that meets various constraint conditions and maximizes the comprehensive evaluation index; Step 102, introduce decision variables: From the perspective of finding the combination scheme of measurement and control points and equipment, decision variables that can represent the combination scheme and maneuvering scheme need to be introduced; Step 103, optimization objective: Consider two aspects of effective coverage and balance, and then maximize the comprehensive evaluation test effect. The optimization objective is to maximize the comprehensive evaluation index ; Step 104, analyze constraint conditions: Consider measurement and control operation constraints, measurement and control equipment requirement constraints, measurement and control equipment performance constraints, and measurement and control performance parameter calculation; Step 105, establish a model solution algorithm: Considering that the above model has non-linear constraint conditions and simulation calculation processes, use evolutionary calculation methods to solve the problem, decouple the problem into a main problem and a sub-problem, and adopt heuristic algorithms.

[0024] Step 1, model construction: Based on the task scheduling and path planning of measurement and control equipment to maximize the task completion degree, construct a joint optimization model that meets various constraint conditions and maximizes the comprehensive evaluation index.

[0025] Step 2, Introduce decision variables: From the perspective of finding the combined scheme of measurement and control points and equipment, it is necessary to introduce decision variables that can represent the combined scheme and the maneuvering scheme.

[0026] 2.1 From the perspective of finding the combined scheme of measurement and control points and equipment, it is necessary to introduce decision variables that can represent the combined scheme and the maneuvering scheme, as follows: : A 0-1 variable, if the th task is measured and controlled by equipment for the th arc segment, it is 1, otherwise it is 0; : A 0-1 variable, if equipment should be deployed to point to measure and control task it is 1, otherwise it is 0; : A 0-1 variable, if equipment needs to move to the corresponding measurement and control point on the th day to complete task it is 1, otherwise it is 0; : A 0-1 variable, if equipment maneuvers from measurement and control point to measurement and control point it is 1, otherwise it is 0; : The maneuvering time of the equipment from measurement and control point to measurement and control point ; : The equipment starts from measurement and control point and maneuvers to measurement and control point ; : The time when the equipment reaches the supply point for the th time.

[0027] Step 3, Optimization objective: Considering two aspects of effective coverage and balance, and then maximizing the comprehensive evaluation test effect, the optimization objective is to maximize the comprehensive evaluation index .

[0028] 3.1 From the perspective of finding the combined scheme of measurement and control points and equipment, it is necessary to introduce decision variables that can represent the combined scheme and the maneuvering scheme, as follows: : A 0-1 variable, if the th task is measured and controlled by equipment for the Measurement and control is 1, otherwise it is 0; : A 0-1 variable. If the device should be deployed to the point for the measurement and control task is 1, otherwise it is 0; : A 0-1 variable. If the device needs to move to the corresponding measurement and control point on the day to complete the task is 1, otherwise it is 0; : A 0-1 variable. If the device moves from the measurement and control point to the measurement and control point is 1, otherwise it is 0; : The time for the device to move from the measurement and control point to the measurement and control point ; : The device moves from the measurement and control point to the measurement and control point departure time; : The device the time when it reaches the supply point for the first time.

[0029] Step 4, analyze the constraints: Consider measurement and control operation constraints, measurement and control equipment requirements constraints, measurement and control equipment performance constraints, and measurement and control performance parameter calculations.

[0030] The effects of measurement and control equipment participating in test tasks are mainly considered in terms of effective coverage and balance. To maximize the comprehensive evaluation of test effects, the optimization goal is to maximize the comprehensive evaluation index : (1); Comprehensive evaluation index The calculation formula is: (2); Comprehensive evaluation index is the weighted average of the task effective coverage and the balance ;

[0031] The calculation process of the task effective coverage is as follows: A single task contains several arcs, which can be divided into key arcs and regular arcs.

[0032] The effective coverage of the key arc is defined as: (3); Among them, the key arc segment is the th arc segment of the th task, is 's effective coverage, is the duration of the arc segment , is 's actual tracking duration.

[0033] The definition of the effective coverage of the conventional arc segment is: (4); Among them, the conventional arc segment is the th arc segment of the th task, is 's effective coverage, is the duration of the arc segment , is 's actual tracking duration.

[0034] Suppose the rd task has arc segments, then the effective coverage of this task is: (5); Among them, is the weight of the th arc segment of the th task. The arc segments of different types of devices should be calculated separately and cannot be substituted for each other. Suppose there are (6); The calculation formula for the balance degree is: (7); In the formula, is the total duration of the planned period (calculated as 30 days for Questions 1 and 2, and 28 days for Question 3), is the total number of mobile devices, is the total working time of the th mobile device during the entire planned period, which refers to the total duration of the planned period minus the supply time (if the TT&C task is executed at the supply point, the time of the task cycle is included in the working time). Mobile devices that are not mobile during the entire planned period are no longer included in the calculation scope of the balance degree.

[0035] Equipment task constraints: (8); Wherein, is a 0-1 variable, indicating that if the th task's th arc segment is measured and controlled by the measurement and control equipment the measurement and control is 1, otherwise it is 0. This constraint ensures that each device can only be assigned to one time arc of one task at the same time.

[0036] Device location constraint: (9); Wherein, is a 0-1 variable, indicating that if the measurement and control equipment is assigned to the location to complete the task it is 1, otherwise it is 0. This constraint ensures that each device can only be assigned to one point at the same time Device time constraint: (10); Wherein, is a 0-1 variable, indicating that if the measurement and control equipment needs to move to the corresponding measurement and control location on the th day to complete the task it is 1, otherwise it is 0. This constraint ensures that each device can measure and control at most one task per day.

[0037] Time and location constraint: (11); This constraint ensures the relationship between the time and location of the device performing the measurement and control task. If it goes to perform the measurement and control task, the time and location are determined simultaneously.

[0038] Device mobility constraint: (12); (13); Wherein, is a 0-1 variable, indicating that if the device moves from the measurement and control location to the measurement and control location it is 1, otherwise it is 0. These two constraints ensure that each device can only move from one location to another, and this location cannot be the device's current location.

[0039] Time constraint for the measurement and control device to reach the measurement and control location: (14); Wherein, Indicates the mission The launch date of the aircraft Indicates the mission The period. This constraint ensures that the equipment participating in the mission must arrive at the corresponding TT&C point by 24:00 on the corresponding day

[0040] Time constraint for the TT&C equipment to leave the TT&C point after completing the mission: (15); Wherein, Indicates the equipment From the TT&C point Maneuver to the TT&C point The departure time. This constraint ensures that the equipment participating in the mission must leave after 24:00 on the corresponding day

[0041] Longest continuous working time constraint: (16); (17); Wherein, Indicates the equipment The th time to reach the supply point, Indicates the equipment The longest continuous working time. This constraint ensures that the mobile equipment The time from leaving the supply point during the last supply to reaching the supply point for the next supply does not exceed its longest continuous working time

[0042] Maneuver time constraint: Use the Floyd algorithm to solve the shortest path from the TT&C point To The shortest path from Point to The maneuver time Is the sum of the passing times of each section of the road in its shortest path

[0043] Type and frequency band constraint: (18); Wherein, Indicates the th mission th arc segment, the required TT&C equipment type, Indicates the equipment Type. This constraint ensures that for each arc segment in each mission The required TT&C equipment must be of the required type. And if the required equipment type is telemetry, it must further meet its frequency band requirements: (19); Wherein, represents the frequency band required for the th arc segment of the th task, represents the receiving frequency band of the device . This constraint ensures that the telemetry device can complete the task only if it has the receiving function of the same frequency band as the spacecraft under TT&C.

[0044] "Characteristic measurement" requirement constraint: (20); Wherein, represents the other TT&C requirements for the th arc segment of the th task, 0 represents "none", 1 represents "characteristic measurement", 2 represents "dual coverage", and 3 represents "telemetry + safety control". is a 0-1 variable, indicating whether the device has the characteristic measurement function. If it has, it is 1, otherwise it is 0. This constraint ensures that if there is a "characteristic measurement" requirement, the radar device for TT&C of this arc segment must have the characteristic measurement function.

[0045] "Dual coverage" requirement constraint: (21); Wherein, is the actual tracking duration of the device, is the duration of joint tracking by multiple similar devices. This constraint ensures that if there is a "dual coverage" requirement, the tracking duration of this arc segment is the period of joint tracking by two or more similar devices.

[0046] "Telemetry + safety control" requirement constraint: (22); Wherein, is a 0-1 variable, indicating whether the device has the safety control function. If it has, it is 1, otherwise it is 0. This constraint ensures that if there is a "telemetry + safety control" requirement, then this arc segment is a critical arc segment, and the telemetry device for TT&C of this arc segment must have the safety control function.

[0047] (3) TT&C equipment performance constraint TT&C point position level constraint: (23); Wherein, is the level of the TT&C point position , is the device The minimum required point level. Different measurement and control devices have different requirements for infrastructure and need to work at corresponding points or higher-level points.

[0048] The distance (R) constraint of the target relative to the device: (24); In the formula, is the distance of the position of the th task at the th arc segment at the th moment relative to the device , is the operating distance of the device . When the distance of the target relative to the device is within the operating distance of the device, the device may be able to track and measure the target at this moment.

[0049] The azimuth angle (A) constraint of the target relative to the device: (25); In the formula, is the azimuth angle of the position of the th task at the th arc segment at the th moment relative to the device , , are the lower and upper limits of the azimuth angle working range of the device . When the azimuth angle of the target relative to the device is within the working range of the device, the device may be able to track and measure the target at this moment.

[0050] The elevation angle (E) and masking angle (SA) constraints of the target relative to the device: (26); In the formula, is the elevation angle of the position of the th task at the th arc segment at the th moment relative to the device , , are the lower and upper limits of the elevation angle working range of the device , is the masking angle of the measurement and control point . When observing the target with the device, in addition to requiring the elevation angle to be within the working range of the device elevation angle index and greater than the masking angle of this point.

[0051] The azimuth angular velocity (VA) constraint of the target relative to the device: (27); In the formula, For the th task, the th arc segment, the th moment's position transformation relative to the previous moment and the azimuth angular velocity of the device is the maximum azimuth angular velocity of the device. When the azimuth angular velocity of the target relative to the device is within the working range of the device, the device may be able to track and measure the target at this moment.

[0052] Target relative to the elevation angular velocity (VE) constraint of the device: (28); In the formula, is the th task, the th arc segment, the th moment's position transformation relative to the previous moment and the elevation angular velocity of the device, is the maximum elevation angular velocity of the device. When the elevation angular velocity of the target relative to the device is within the working range of the device, the device may be able to track and measure the target at this moment.

[0053] Only when all the measurement and control performance constraints of the device are satisfied, it is considered that the device can track and measure the target at this moment.

[0054] (4) Calculation of measurement and control performance parameters ( , , , , ) Suppose the coordinates of the measurement and control device in the geocentric rectangular coordinate system are ( , , ), and the coordinates in the geodetic coordinate system are ( , , ). For the coordinate point ( , , ) in the geocentric rectangular coordinate system, its coordinates ( , , ) in the station rectangular coordinate system are: (29); The formula for converting the coordinates ( , , ) in the station rectangular coordinate system to the coordinates in the station RAE coordinate system is: (30); (31); (32); (33); To convert the velocity in the geocentric rectangular coordinate system ([[]] , , ) to the velocity in the station RAE coordinate system, it is necessary to first determine the position vector [[[]] of the target point relative to the station. Then, the RAE coordinate system can be obtained by calculating the three orthogonal unit vectors R, A, and E of [[[]] . Specifically: [[[]] Calculate the position vector [[[]] : [[[]] (34); Calculate the orthogonal unit vectors of R, A, and E: [[[]] (35); (36); (37); Among them, [[[]] The three components of are the velocities in the directions of R, A, and E respectively, and the latter two components are the azimuth angular velocity [[[]] and the pitch angular velocity [[[]] . [[[]]

[0055] Step 5, establish a model solution algorithm: Considering the non-linear constraint conditions and simulation calculation process of the above model, use the evolutionary calculation method to solve the problem, decouple the problem, and adopt a heuristic algorithm. [[[]]

[0056] The evolutionary algorithm combines with the heuristic algorithm to solve the joint optimization model of the TT&C equipment task scheduling and path planning. The parameter settings of the evolutionary algorithm are as follows: [[[]] Population size: 100[[[]] Number of iterations: 400[[[]] Crossover and mutation probabilities: 0.6, 0.05. [[[]]

[0057] Considering the non-linear constraint conditions and simulation calculation process of the above model, this application considers using the evolutionary calculation method to solve the problem. At the same time, considering the separable characteristics of the problem, this application decouples the problem into a main problem and a sub-problem. Among them, the optimal solution of the sub-problem needs to be obtained when evaluating the index value of the main problem solution. In order to quickly obtain the optimal solution of the sub-problem, this application proposes a rule-based heuristic algorithm. [[[]]

[0058] S1 Problem Decoupling: For complex combinatorial optimization problems, such as the task scheduling and path planning problems of measurement and control equipment, there are usually multiple interrelated decision-making levels. For such problems, if solved directly as a whole, it will not only bring huge computational overhead, but also may be difficult to find the global optimal solution. To solve this problem, this application adopts a problem decoupling strategy.

[0059] (1) Evolutionary Computation and Decoupling Strategy Considering the non-linear constraints and simulation calculations involved in the model, evolutionary computation methods are preferred due to their powerful global search ability and processing ability for non-linear problems. Especially in scenarios with a large number of constraints and multi-objective optimization, evolutionary algorithms show their superiority. Combining the characteristics of the problem, this application divides the problem into a main problem and sub-problems.

[0060] (2) Definition and Relationship of the Main-Sub Problems Main Problem: Mainly focuses on the task scheduling of measurement and control equipment, with the goal of maximizing comprehensive evaluation indicators, such as the effective coverage and balance of tasks; Sub-Problems: Concentrate on the path planning of equipment to determine how the equipment maneuvers to meet task requirements. Although the decision space is relatively small, it needs to meet various constraints, such as interactions between equipment, energy limitations, etc.; To efficiently solve the sub-problems, referring to Figure 1 , this application proposes a rule-based heuristic algorithm. This algorithm can quickly find the optimized path that meets the constraints. Once the solution of the sub-problems is determined, it can provide feedback on the main problem to help the evolutionary algorithm better optimize the task scheduling.

[0061] (3) Advantages of Decoupling Simplify Computation: By decomposing complex problems into smaller and more manageable parts, each sub-problem can be solved more intensively and efficiently; Enhance Flexibility: For different scenarios and requirements, we can independently adjust and optimize the solution strategies of the main problem and sub-problems; Improve the Solving Quality: The decoupling strategy allows us to choose the most suitable algorithm for each sub-problem, thus obtaining a better overall solution; Through the decoupling strategy, this application not only improves the solving efficiency of the problem, but also ensures the quality of the solution, laying a solid foundation for subsequent practical applications.

[0062] S2: Solving the Task Scheduling Problem of Measurement and Control Equipment Based on Knowledge-Guided Constrained Evolutionary Algorithm Evolutionary computation is a simulation of the natural selection mechanism and can effectively handle large-scale, multi-objective, and highly non-linear optimization problems. Its main advantages can be expressed as follows: 1. Global search ability. Evolutionary computation has a powerful global search ability and can widely search in the solution space, thereby increasing the chance of finding the global optimal solution. 2. Parallelism. Multiple solutions can evolve simultaneously, leveraging the power of the population to explore multiple possible solution directions. 3. Self-adaptability. Through natural selection and genetic operations, evolutionary computation can adaptively adjust the search strategy to better fit the characteristics of the problem. 4. Independence from specific problems. It does not require too much prior knowledge of the specific form of the problem, making it widely applicable.

[0063] This application proposes a knowledge-guided constrained evolutionary algorithm to solve the task scheduling problem of measurement and control equipment. The following will specifically introduce each component.

[0064] (1) Knowledge-guided mechanism: Traditional evolutionary algorithms usually use random crossover and mutation operators to explore the decision space. This randomness often leads to solutions without a clear direction when solving complex problems. This means that the quality of the solutions may be negatively affected because there is a lack of guiding information to direct the search process; To address this issue, this application introduces a knowledge-guided method. The key differences from traditional evolutionary algorithms are as follows: First, this application extracts valuable knowledge from the solutions, and this knowledge can provide insights for evaluating and improving the quality of the solutions. The introduction of this knowledge helps improve the quality of the solutions and makes the algorithm more targeted. Second, this application proposes a method based on feasible rules and combines this knowledge to formulate an environmental selection strategy. This means not only focusing on the quality of the solutions but also considering the feasibility of the solutions, thus better meeting the constraints of the problem; Figure 2 is a schematic diagram of the knowledge-guided method proposed in this application, where the dots represent individuals during the iterative search using the evolutionary algorithm. Usually, when the evolutionary algorithm generates new individuals through crossover and mutation, its direction is random, as shown by point E in Figure 2 Although point E may exchange genes with high-quality individuals in the current population, such an exchange is often inefficient. Especially in the case of a complex decision space and a narrow feasible region, the population individuals may conduct aimless searches in the infeasible region, resulting in a huge waste of computing resources. For the joint optimization problem of task scheduling and path planning of measurement and control equipment studied in this application, since the equipment needs to consider the maximum continuous working time, movement time limit, etc., the problem feasible region becomes even narrower, which makes it difficult for the evolutionary algorithm to obtain feasible solutions when solving such problems; The knowledge-guided method proposed in this application aims to guide the generation and selection of solutions by extracting the conflict information among current individual tasks. Specifically, first, record the number and location of the measurement and control task conflicts of the current individual. The number of conflicts can be regarded as the distance between the current individual and the feasible region, while the conflict location indicates the search direction. When selecting a solution, give priority to selecting individuals with fewer conflict numbers, such as Figure 2 points D and C in . In the crossover and mutation operations, increase the crossover and mutation probability at the conflict location, but to avoid falling into a local optimum, avoid operating directly at the conflict location. This strategy guides the solution to search in a direction closer to the feasible region, thereby greatly improving the algorithm efficiency;

[0065] (2) Encoding mechanism: Given the characteristics of the measurement and control equipment task scheduling problem, in order to be able to fully represent the information of an individual, this application adopts a two-layer encoding method, namely the main encoding and the double coverage encoding, as Figure 3 shown; Specifically, first, it is necessary to calculate the matching combinations of "aircraft-arc section-measurement and control point-measurement and control equipment". As can be seen from Figure 3 , each variable in the two-dimensional matrix records an integer value, which represents the index value of the combination of "measurement and control point-measurement and control equipment" that can provide observations under this aircraft and arc section. For example, the combinations that can be selected for the first arc section of aircraft M1 are: <C57,L2><C50,L10><C50,L11><C50,L12><C50,L13>. Then, at this time, the first arc section of aircraft M1 will be observed by the fifth combination, that is, C50 and L13. Similarly, for the double coverage encoding, the observation task will also be carried out by the selected combination.

[0066] (3) Crossover and mutation algorithm based on knowledge guidance: In evolutionary algorithms, crossover and mutation are two core genetic operators. The importance of crossover and mutation operators in evolutionary algorithms lies in that they jointly ensure the exploration and exploitation capabilities of the algorithm. The exploration ability enables the algorithm to search the entire solution space, while the exploitation ability enables the algorithm to conduct a detailed search after finding a promising region. Correctly balancing these two abilities is the key to obtaining an efficient evolutionary algorithm; Based on the encoding method proposed in this application and the extracted knowledge, crossover and mutation operators are designed, specifically as Figure 4 shown. Knowledge guidance increases the probability of selecting points that contain conflicting information to guide the crossover and mutation operations, thereby making it more likely for the offspring to be improved. This approach helps to improve the performance of the algorithm and enables the solution to converge to a better state more quickly.

[0067] (4) Environment selection strategy based on knowledge guidance and feasibility criteria: The selection of offspring plays a crucial role in evolutionary algorithms, directly affecting the convergence speed and search efficiency of the algorithm. In this algorithm, a knowledge-guided selection method is proposed to more comprehensively evaluate the quality of offspring. During the evolutionary process, evaluating the quality of solutions solely from the dimension of the fitness value of individuals may lead to the elimination of some offspring that have the potential to reach the optimal solution. In addition, the existence of constraint conditions makes it more difficult for the algorithm to obtain the optimal solution. Therefore, this application adopts feasibility criteria to handle constraint conditions and proposes an environment selection strategy based on knowledge guidance and feasibility criteria. Among them, the feasibility criteria can be described as follows: First, compare the feasibility of solutions: Feasible solutions are always considered superior to infeasible solutions; If both solutions are feasible, then they are compared according to the values of the objective function; If both solutions are infeasible, they can be compared based on the degree of constraint violation; First, consider the degree of constraint violation and fitness value of individuals. When there are differences in the fitness values among individuals, we will preferentially select solutions with larger fitness values because these solutions are usually closer to the optimal solution. However, in some cases, the fitness values among individuals may be very close or equal, and in this case, a more refined evaluation criterion needs to be introduced. Therefore, we also consider the number of conflicts within an individual as the second criterion. Generally speaking, individuals with fewer conflict times have more potential to approach the optimal solution because they may have more feasible task combination methods. Therefore, when there is no difference in fitness values, individuals with fewer conflict times will be preferentially selected, and this selection strategy helps to accelerate the convergence of the algorithm and guide the search process towards a more promising direction.

[0068] S3: Solve the path planning problem of TT&C equipment based on a rule-based heuristic algorithm When the above-mentioned evolutionary algorithm solves the main problem, to evaluate the fitness of the solution, it is necessary to solve the maneuvering paths of each device to meet the constraints of the maximum continuous working time of the device and the time to reach the working point. The planning of the maneuvering path can be regarded as an optimization problem. Therefore, after decoupling the problem, this application designs a heuristic algorithm to calculate the optimal device maneuvering route under a given TT&C equipment scheduling scheme, so as to provide services for the optimization of the main problem, as shown in Table 2. Generally speaking, this method can also be regarded as a decoding algorithm for the coding scheme.

[0069] Table 2 Rule-based heuristic algorithm

[0070] S1: Calculate the distance ( ), azimuth angle ( ), elevation angle ( ), azimuth angular velocity ( ), and elevation angular velocity ( ) of each TT&C point during the flight of each aircraft according to the input information; S2: Based on the above information and other constraints (such as equipment type constraints, point level constraints, etc.), screen out the set of feasible solutions ( ) that can be TT&C'ed for each arc segment in each mission, that is, , and each set of feasible solutions ( ) may have multiple alternative point and equipment combination solutions.

[0071] S3: Perform a loop iteration for each solution of each arc segment in each mission: S4: Calculate and detect whether there is a device that can arrive before the start time of the mission in the corresponding combination solution set of the arc segment ; if not, the coverage rate of this arc segment is 0; the conflict count +1; if so, determine whether movement is required. If the device does not need to move, calculate the coverage rate of this arc segment. If movement is required, the device needs to maneuver to other positions to perform TT&C tasks, and implement maneuvering and replenishment operations according to preset rules, and calculate the coverage rate of this arc segment; S5: If there is a double coverage requirement, repeat step S4; S6 Calculate comprehensive evaluation indicators: effective coverage and balance.

[0072] Table 3 Equipment replenishment and maneuver rules:

[0073] (1) Algorithm overview: As can be seen from Table 2, the algorithm first needs to traverse all tasks and all arcs according to the chronological order of task launch times. For the current arc, first, it is detected whether the device can reach the predetermined TT&C point within the task cycle (if it cannot arrive on time, then this information is recorded as "knowledge"); second, it is checked according to the rules whether it needs to be resupplied, and a maneuver plan for resupply is given; if successful resupply cannot be achieved, then the maximum continuous working time of the device is exceeded, and the plan does not meet the constraints, and this information is recorded as "knowledge".

[0074] (2) Device resupply and maneuver rules: The maneuver situation of the device during the mission can be classified according to the nature of its starting point and arrival point. This classification method can be viewed in Table 3. To prevent the device from exceeding its maximum working duration, this application adopts two strategies. Strategy ①: When the device reaches a resupply point, it will perform a resupply operation. Strategy ②: If the device needs to be resupplied on the way, it will choose the nearest resupply point to perform this operation.

[0075] The following is a specific description of the maneuver situation: 1. When both the starting point and the target point are resupply points: The device can directly reach the target point from the starting point because the longest maneuver time between these two points is less than the maximum continuous working time of the device. After arrival, the device will be resupplied at the target point; 2. When the starting point is a resupply point, but the target point is not: The device can directly reach the target point from the starting point. However, since the target point is not a resupply point, the device does not need to be resupplied after arrival; 3. When the starting point is not a resupply point, but the target point is: In this case, it is first necessary to determine whether the device will exceed its maximum continuous working time before reaching the target point (resupply point). If not, then the device can directly reach and be resupplied there. But if it will time out, the device needs to choose the nearest resupply point on the way for resupply. In addition, it is also necessary to determine whether the device can still reach the target point in time to complete the task after mid - way resupply. If it can, the device will be resupplied at both the nearest resupply point and the target point. If not, then this plan will violate the constraints and become infeasible; 4. When neither the starting point nor the target point is a resupply point: Since neither point is a resupply point, the device must be resupplied on the way to ensure that it does not exceed the maximum working duration. Similarly, we also need to determine whether the device can still reach the target point in time to complete the task after mid - way resupply. If it can, the device will perform mid - way resupply at the nearest resupply point. If not, then this plan will violate the constraints and become infeasible.

[0076] For all feasible combinations, without considering the equipment maneuvering plan, there are a total of 5.1005e+76 measurement and control plans. If 10,000 plans can be calculated per second, it will still take 1.6174e+65 years to complete all evaluations. Therefore, it is crucial to design a reasonable and powerful optimization algorithm.

[0077] Table 4 Maximum measurement and control coverage rates under different aircraft - arcs:

[0078] The beneficial effects of this application are as follows: This application constructs a joint optimization model for measurement and control equipment task scheduling and path planning based on maximizing the task completion degree for the measurement and control equipment task scheduling and path planning problem. This model fully considers the actual background of the problem and various constraints, ensuring the integrity and practicality of the model; Through the decoupling strategy, the model is divided into a main problem and a sub - problem, making the problem - solving process clearer and more operable. In algorithm design, it combines the global optimization characteristics of the evolutionary algorithm and the local search characteristics of the heuristic algorithm, aiming to find a high - quality solution to the problem.

[0079] The proposed knowledge - guided evolutionary algorithm can find high - quality solutions globally, while the rule - based heuristic algorithm ensures the quality of solutions locally. The combination of the two provides an efficient and practical solution strategy for the measurement and control equipment task scheduling and path planning problem.

[0080] As used herein, the term "preferred" is intended to be used as an example, illustration, or instance. Any aspect or design described as "preferred" herein need not be construed as more advantageous than other aspects or designs. Instead, the use of the term "preferred" is intended to present concepts in a specific manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X uses A or B" means any one of the permutations is naturally included. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing examples.

[0081] Moreover, although the present disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art based on a reading and understanding of this specification and the drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the above-described components (e.g., elements, etc.), the terms used to describe such components are intended to correspond to any component that performs the specified function of the component (e.g., it is functionally equivalent), unless otherwise indicated, even if it is not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations of the present disclosure shown herein. In addition, although a particular feature of the present disclosure has been disclosed with respect to only one of several implementations, such a feature may be combined with one or other features of other implementations as may be desired and advantageous for a given or particular application. Moreover, insofar as the terms "comprise", "have", "contain" or variations thereof are used in the detailed description or claims, such terms are intended to include in a manner similar to the term "include".

[0082] In the embodiments of the present invention, each functional unit may be integrated into a processing module, may exist physically alone for each unit, or may be integrated into one module with two or more units. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. The above-mentioned various devices or systems may execute the storage method in the corresponding method embodiments.

[0083] In summary, the above embodiments are an implementation manner of the present invention, but the implementation manner of the present invention is not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement manners and are all included in the protection scope of the present invention.

Claims

1. A knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling, characterized in that: The following steps are involved: Step 1, model construction: Based on the task scheduling and path planning of measurement and control equipment to maximize task completion, a joint optimization model that meets various constraints and maximizes comprehensive evaluation indicators is constructed; Step 2, introduce decision variables: from the perspective of finding a combination of measurement and control points and equipment, introduce decision variables that can represent combination plans and maneuver plans; Step 3, optimization goal: Consider the two aspects of effective coverage and balance, and then maximize the comprehensive evaluation test effect. The optimization goal is to maximize the comprehensive evaluation index ; Step 4: Analyze the constraints: consider the measurement and control operation constraints, measurement and control equipment requirement constraints, measurement and control equipment performance constraints, and measurement and control performance parameter calculations; Step 5, establish a model solving algorithm: use the knowledge-guided evolutionary optimization method to solve the model, the knowledge-guided evolutionary optimization method includes: guiding the generation and selection of solutions by extracting the conflict information between the current individual tasks; using a two-layer coding mechanism for encoding; using a crossover mutation operator to increase the probability of selecting points containing conflicting information; using feasibility criteria to deal with constraints, using an environment selection strategy based on knowledge guidance and feasibility criteria; and solving the measurement and control equipment path planning problem based on a rule-based heuristic algorithm.

2. The knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling according to claim 1, characterized in that: Accurately classify and simplify various constraints, including task matching constraints and equipment working and maneuvering time constraints; according to the specific constraints of each measurement and control equipment, pre-calculate all possible matching combinations of "aircraft-arc-measurement and control point-measurement and control equipment", and build a joint optimization model that meets various constraints and maximizes comprehensive evaluation indicators based on the task scheduling and path planning of measurement and control equipment that maximizes task completion.

3. The knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling according to claim 1, characterized in that: The decision variables include: : 0-1 variable, if the Task No. Arcs by device 1 if measurement and control is enabled, 0 otherwise; : 0-1 variable, if the device Should be deployed to the point Measurement and control tasks is 1, otherwise it is 0; : 0-1 variable, if the device Need to Move to the corresponding measurement and control point to complete the task is 1, otherwise it is 0; : 0-1 variable, if the device From the measurement and control point Maneuver to the measurement and control point is 1, otherwise it is 0; :The equipment is located at the measurement and control point To the measurement and control point Maneuvering time; :equipment From the measurement and control point Maneuver to the measurement and control point Time of departure; :equipment No. The time to reach the supply point.

4. The knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling according to claim 1, characterized in that: The optimization goal is to maximize the comprehensive evaluation index : ; Comprehensive evaluation indicators The calculation formula is: ； Comprehensive evaluation indicators Effective coverage for tasks and balance The weighted average of Effective coverage of tasks The calculation process is as follows: A single task contains several arcs, which are divided into key arcs and regular arcs; The effective coverage of the key arc is defined as: ； Among them, the key arc For the Task No. arc segments, for The effective coverage of For arc segment Length of time, for The actual tracking time; The effective coverage of a conventional arc segment is defined as: ； Among them, the conventional arc For the Task No. arc segments, for The effective coverage of For arc segment Length of time, for The actual tracking time; Set up Tasks include arcs, the effective coverage of the task is: ； in, for Task No. The weight of each arc segment; arc segments of different types of equipment should be calculated separately; tasks, the total effective coverage of tasks is: ； Balance The calculation formula is: ； In the formula, is the total duration of the planning cycle, is the total number of mobile equipment, For the The total working time of a mobile equipment in the entire planning cycle refers to the total duration of the planning cycle minus the replenishment time; mobile equipment that has not been mobilized in the entire planning cycle is not included in the calculation of balance.

5. The knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling according to claim 4, characterized in that: The constraints include: Equipment task constraints: ； In the formula, is a 0-1 variable, indicating that if Task No. The arc segment is controlled by the measurement and control equipment 1 if measurement and control is used, otherwise 0; this constraint ensures that each device can only be assigned to one time arc of a task at the same time; Equipment point constraints: ； In the formula, Is a 0-1 variable, indicating if the measurement and control equipment Assign to point To complete the task is 1, otherwise it is 0; this constraint ensures that each device can only be assigned to one point at the same time; Equipment time constraints: ； In the formula, Is a 0-1 variable, indicating if the measurement and control equipment Need to Move to the corresponding measurement and control point to complete the task is 1, otherwise it is 0; this constraint ensures that each device can only measure and control one task at most in a day; Time point constraints: ； This constraint condition ensures the time and location relationship of the equipment in performing the measurement and control task. If the equipment performs the measurement and control task, the time and location are determined at the same time. Equipment mobility constraints: ； In the formula, Is a 0-1 variable, indicating that if the device From the measurement and control point Maneuver to the measurement and control point is 1, otherwise it is 0; these two constraints ensure that each device can only maneuver from one position to another, and this position cannot be the current position of the device; Time constraints for measurement and control equipment to reach measurement and control points: ； In the formula, Indicates the task The launch date of the spacecraft, Indicates the task This constraint ensures that the devices participating in the task must be Arrive at the corresponding measurement and control point before 24:00 on the day; Time constraints for the measurement and control equipment to leave the measurement and control point after completing the task: ； In the formula, Indicates the device From the measurement and control point Maneuver to the measurement and control point Departure time; this constraint ensures that the equipment participating in the task must be You can leave after 24:00 on the day; Maximum continuous working time constraints: ； ； In the formula, Indicates the device No. The time to reach the supply point, Indicates the device The maximum continuous working time of the motorized equipment The time from the last time the supply station left the supply point to the next time it arrived at the supply point for supply shall not exceed its longest continuous working time; Maneuver time constraints: Use Floyd's algorithm to solve the problem of using the position of the measurement and control points arrive The shortest path from Click to Mobility time of point is the sum of the travel time of each road segment in its shortest path; Type and frequency band constraints: ； In the formula, Indicates Task No. The type of measurement and control equipment required for each arc segment, Indicates the device This constraint ensures that each arc in each task The required measurement and control equipment must be of the required type, and if the required equipment type is telemetry, it must further meet the frequency band requirements: ； In the formula, Indicates Task No. The frequency band required by each arc segment, Indicates the device This constraint ensures that the telemetry equipment can only complete the task if it has the receiving function of the same frequency band as the measurement and control aircraft; "Characteristic measurement" requirement constraints: ； In the formula, Indicates Task No. Arc Other requirements for measurement and control, 0 means "none", 1 means "characteristic measurement", 2 means "double coverage", and 3 means "telemetry and security control"; It is a 0-1 variable, indicating the device Whether it has the characteristic measurement function, if yes, it is 1, otherwise it is 0; this constraint ensures that if there is a "characteristic measurement" requirement, then the radar equipment measuring and controlling this arc segment must have the characteristic measurement function; "Double coverage" requirement constraints: ； In the formula, The actual tracking time of the device. The duration of joint tracking of multiple similar devices. This constraint ensures that if there is a "double coverage" requirement, the tracking duration of the arc segment is the period of joint tracking of two or more similar devices. "Telemetry and security control" requirements and constraints: ； In the formula, It is a 0-1 variable, indicating the device Whether it has security control function, if yes, it is 1, otherwise it is 0; this constraint ensures that if there is a "telemetry and security control" requirement, then this arc segment is a key arc segment, and the telemetry equipment for measuring and controlling this arc segment must have security control function; Measurement and control point level constraints: ； In the formula, For measurement and control points The level, For equipment The minimum point level required; The distance R of the target relative to the device is constrained: ； In the formula, For the Task No. In the arc The position at a moment relative to the device The distance For equipment The device will track and measure the target only when the distance between the target and the device is within the device's range. The target's relative azimuth angle A constraint: ； In the formula, For the Task No. In the arc The relative position of the device at that moment The azimuth of , For equipment The azimuth working lower and upper limits of the device; when the azimuth of the target relative to the device is within the working range of the device, the device will track and measure the target at that moment; The pitch angle E and shielding angle SA constraints of the target relative to the device: ； In the formula, For the Task No. In the arc The relative position of the device at that moment The pitch angle, , For equipment The lower and upper limits of the pitch angle are: For measurement and control points When the device observes the target, the pitch angle is required to be within the working range of the device's pitch angle indicator and greater than the shielding angle of the point; VA constraint of the target's azimuth velocity relative to the device: ； In the formula, For the Task No. In the arc The position change of a moment relative to the previous moment The azimuth velocity, For equipment The device will track and measure the target only when the azimuth velocity of the target relative to the device is within the working range of the device; The target's pitch angular velocity VE constraint relative to the device: ； In the formula, For the Task No. In the arc The position change of a moment relative to the previous moment The pitch angular velocity, For equipment The device tracks and measures the target only when the pitch angular velocity of the target relative to the device is within the working range of the device.

6. The knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling according to claim 5, characterized in that: The measurement and control performance parameters are calculated as follows: The coordinates of the measurement and control equipment in the geocentric rectangular coordinate system are ( , , ), the coordinates in the geodetic coordinate system are ( , , ), the coordinate point in the geocentric rectangular coordinate system ( , , ), the coordinates in the rectangular coordinate system of the station ( , , )have: ； From the station rectangular coordinate system coordinates ( , , ) The formula for the coordinates of the RAE coordinate system of the transfer station is: ； ； ； ； The velocity in the geocentric rectangular coordinate system ( , , ) is converted into the velocity in the RAE coordinate system of the measuring station. First, the position vector of the target point relative to the measuring station is determined. , then, calculate The RAE coordinate system is obtained by using three orthogonal unit vectors R, A, and E; specifically: Calculate the position vector : ； Calculate the orthogonal unit vectors of R, A, and E: ； ； ； in, The three components are the velocities in the R, A and E directions, and the last two components are the azimuthal velocities and pitch angular velocity .

7. The knowledge-guided evolutionary optimization method for aircraft measurement and control equipment resource scheduling according to claim 6, characterized in that: In the knowledge-guided evolutionary optimization method, the generation and selection of solutions are guided by extracting conflict information between current individual tasks, including: extracting valuable knowledge from solutions, which provides insights into the evaluation and improvement of the quality of the solutions; secondly, using a feasible rule-based method and combining this knowledge to formulate an environmental selection strategy; Specifically, firstly, the number and position of the measurement and control task conflicts of the current individual are recorded, where the number of conflicts can be regarded as the distance between the current individual and the feasible domain, and the conflict position indicates the search direction; when selecting a solution, individuals with fewer conflicts are given priority; in the crossover and mutation operations, the crossover and mutation probability of the conflict position is increased, but in order to avoid falling into the local optimum, operations are avoided directly at the conflict position; this strategy guides the search for solutions closer to the feasible domain; In order to minimize the number of devices that do not meet the constraints, a pair of data is used to mark the conflict, that is, <(i, j, k)> indicates that in the i-th coverage measurement, the measurement and control equipment of the j-th arc segment k of the aircraft cannot meet the constraints. These conflict information is passed to the crossover and mutation operators to guide the evolution of these conflicts; The two-layer coding is respectively the main coding and the double-cover coding; Specifically, the matching combination of "aircraft-arc-measurement and control point-measurement and control equipment" is first calculated, and the main coding records an integer value in each variable of the two-dimensional matrix, and the integer value represents the index value of the combination of "measurement and control point-measurement and control equipment" that can provide observation under the aircraft and arc; for the double-cover coding, the combination of "aircraft-arc-measurement and control point-measurement and control equipment" selected by it performs the observation task; The feasibility criteria are: First compare the feasibility of the solutions: a feasible solution is always better than an infeasible solution; If both solutions are feasible, they are compared based on the value of the objective function; If both solutions are infeasible, they are compared based on the extent to which the constraints are violated; Specifically, the constraint violation degree and fitness value of individuals are considered first. When there are differences in fitness values ​​between individuals, the solution with a large fitness value is preferred. The number of conflicts within individuals is used as the second criterion. When there is no difference in fitness values, individuals with fewer conflicts are preferred. The rule-based heuristic algorithm solves the measurement and control equipment path planning problem, including the following steps: S51: Calculate the distance of each aircraft to each measurement and control point during flight based on the input information , azimuth , Pitch angle , azimuth velocity , pitch angular velocity ; S52: Based on the above information and other constraints, select each arc segment in each task A set of solutions that can be measured and controlled ,Right now ; S53: Iterate each solution of each arc segment in each task in a loop: S54: Calculate and detect arc segments In the corresponding combination solution set, whether there is equipment that can arrive before the task start time; if not, the coverage rate of the arc segment is 0; the number of conflicts +1; if it exists, determine whether it needs to be moved, the equipment does not need to be moved, and calculate the coverage rate of the arc segment. If it needs to be moved, the equipment needs to maneuver to other locations to perform the measurement and control task, and implement the maneuver and supply operations according to the preset equipment supply and maneuver rules, and calculate the coverage rate of the arc segment; S55: If double coverage is required, repeat step S4; S56 calculates comprehensive evaluation indicators: effective coverage and balance; The equipment replenishment and mobility rules include: When both the starting point and the target point are replenishment points: the equipment directly reaches the target point from the starting point, because the longest maneuvering time between the two points is less than the maximum continuous working time of the equipment. After arriving, the equipment is replenished at the target point; When the starting point is a replenishment point, but the target point is not: the device goes directly from the starting point to the target point; but since the target point is not a replenishment point, the device does not replenish after arriving; When the starting point is not a replenishment point, but the target point is a replenishment point: first determine whether the device will exceed its maximum continuous working time before reaching the target point; if not, the device will directly reach there and recharge there; if it exceeds the time limit, the device will choose the nearest recharge point on the way for recharge; in addition, determine whether the device can still reach the target point in time to complete the task after recharging midway; if so, the device will recharge at both the nearest recharge point and the target point; if not, then this solution violates the constraints and becomes infeasible; When neither the starting point nor the target point is a supply point: Since neither point is a supply point, the equipment must be replenished on the way to ensure that the maximum working time is not exceeded; it is also necessary to determine whether the equipment can reach the target point in time to complete the task after the mid-way replenishment. If so, the equipment will be replenished at the nearest supply point. If not, then this plan will violate the constraints and become infeasible.

Images