Satellite-borne SAR (Synthetic Aperture Radar) constellation task planning method and device for maritime ship rescue

Abstract

The invention provides a satellite-borne SAR constellation task planning method and device for maritime ship rescue, and belongs to the field of satellite-borne SAR, and the method comprises the steps: building a target ship movement range prediction model under physical constraints according to the maximum navigation speed and the maximum movement radius of a target ship in combination with the calculation of a geographic grid; constructing an adaptive priority strategy, analyzing geographic grids which a target ship may pass through, designing a trajectory prediction method based on Gaussian probability distribution according to the course and speed of the ship, and adaptively adjusting the priority of the geographic grids according to the probability; if the coverage rate of the geographic grids exceeds a set threshold value, assigning different priorities to each geographic grid according to the predicted track, otherwise, adopting equal priorities; and designing an MSDDEE algorithm, and respectively executing a multi-cross individual generation strategy, a feasibility-based individual selection strategy, a local left shift strategy and a population restart strategy to optimize task planning of search and rescue ships. The search efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of spaceborne SAR, and in particular relates to a spaceborne SAR constellation mission planning method and device for marine ship rescue. Background Art

[0002] Maritime rescue operations face many challenges, especially when ships are in distress, in adverse weather conditions or in complex sea environments, timely assistance is particularly urgent and important. In this case, spaceborne synthetic aperture radar (SAR) technology, with its all-weather and all-day observation capabilities, provides key technical support for maritime rescue. The SAR constellation system can continuously cover the sea area on different orbits, improving the speed and effectiveness of rescue response.

[0003] In order to improve the success rate of maritime rescue missions, satellite constellations need to be able to efficiently scan and image vast sea areas in a short period of time. Especially when the distressed ship is in complex sea conditions or extreme weather environments, and ground rescue forces are difficult to arrive quickly, the SAR constellation system can achieve comprehensive coverage of vast sea areas through rapid mission planning, providing accurate spatial information for maritime rescue decision-making.

[0004] 12 hours after the ship lost contact, the urgent problem to be solved was how to reasonably allocate constellation resources and optimize orbital coverage to effectively image and monitor the possible locations of the ship in the vast ocean to ensure that rescue operations could respond quickly within the critical time after the loss of contact.

[0005] This problem is essentially an optimization problem, and the optimization goal is how to optimize the satellite task sequence so that the total time to find the ship is the shortest. However, there is no analytical solution to such an optimization problem, and the traditional exact solution method cannot obtain an exact solution from it, which means that researchers can only search for the best possible solution through continuous iterative trial and error. For this type of NP-hard problem, evolutionary algorithms have a natural advantage, and through continuous iterative trial and error, the optimal solution is gradually found. The evolutionary algorithms include the following:

[0006] The Composite Differential Evolution (C2ODE) algorithm was proposed by Wang et al. in 2018. They first designed three different mutation strategies, namely DE / current-to-rand / 1, modified DE / rand-to-best / 1 / bin, and DE / current-to-best / 1 / bin. The three mutation strategies were designed from different perspectives to balance diversity and convergence as well as constraints and objective functions. They also proposed a new constraint processing strategy that combines feasibility rules and constraint methods. The feasibility rule pre-selects the best one from the offspring as the test vector. The constraint method compares each target vector with the trial vector, and the feasibility rule preference constraints. The constraint method integrates the information of the objective function to a certain extent, and the combination of the two further promotes the balance between the constraint and the objective function. In order to deal with some extremely complex constrained optimization problems, they also proposed a restart strategy to help the population jump out of the local optimal solution in the infeasible area. The algorithm that combines the improved search algorithm, the improved constraint processing technology, and the restart strategy is called the C2ODE algorithm.

[0007] Incorporating Objective Function Information Into the Feasibility Rule for Constrained Evolutionary Optimization (FROFI) was proposed by Yong Wang et al. in 2016. Considering the constraints, the paper uses the feasibility rule to compare the parent and offspring in the differential evolution algorithm (DE). Considering the objective function, if the offspring cannot survive to the next generation and the objective function value of the offspring is better than that of the parent, the offspring is stored in a predefined archive. Subsequently, based on the replacement mechanism of the objective function value, the individuals in the archive are used to replace some individuals in the population. In addition, a mutation strategy is proposed to help the population jump out of the local optimal value in the infeasible area. Therefore, the information of the objective function is also used to generate offspring in DE. This paper achieves an effective balance between constraints and objective functions.

[0008] The Adaptive Tradeoff Model combined with Evolutionary Strategies (ATMES) was proposed by Yong Wang et al. in 2008. This study first proposed an adaptive strategy (ATM) for establishing a balance between the objective function and constraint violations, focusing on solving the following three key problems: 1) How to evaluate the effectiveness of these solutions when the population contains only infeasible individuals; 2) How to balance the selection between feasible and infeasible individuals when the population contains both feasible and infeasible individuals; 3) How to effectively select the optimal solution when the population consists of only feasible individuals. In order to deal with these problems in the search process, the article designed corresponding trade-off mechanisms at different stages, thereby achieving a balanced optimization of the objective function and constraint violations. Evolutionary strategy (ES) is used as the core search engine, and the ATMES algorithm is finally formed, which solves the constrained optimization problem by integrating ATM and ES.

[0009] The Voting-Mechanism based Ensemble Framework for Constraint Handling Techniques (VMCH) was proposed by Guohua Wu et al. in 2021. Given that the various existing constraint handling techniques (such as penalty functions, feasible solution priorities, and β-constraints) each have their own scope of application, and their effectiveness changes with different stages of optimization, this study proposed a method to integrate multiple constraint handling techniques through a voting mechanism. In this mechanism, each constraint handling technique acts as a voter, and all voters vote for each solution. The solution with the most votes and the highest weight is considered to be the optimal solution. In addition, the method introduces an adaptive strategy that can dynamically adjust the weight according to the historical performance of each voter. VMCH was also embedded in the best algorithm for three constrained single-objective real parameter optimization at CEC 2018, demonstrating its good adaptability and optimization effect.

[0010] Although the aforementioned methods are constrained single-objective optimization methods, because their encoding methods are all continuous encoding, the above methods can only handle continuous optimization problems and cannot handle the discrete optimization problems in the present invention. Therefore, an optimization method suitable for handling discrete optimization problems is urgently needed. Summary of the invention

[0011] To solve the above technical problems, the present invention provides a satellite-borne SAR constellation mission planning method and device for maritime ship rescue, which combines a track prediction model with physical constraints, integrates the maximum navigation speed and maximum moving radius of the target ship, and combines geographic grid calculation to propose a target ship movement range prediction model under physical constraints, which can effectively narrow the search range and improve the search efficiency 12 hours after the ship is lost; by analyzing the grids that the target ship may pass through, the priority of the grid is adaptively adjusted according to different situations; the operation of the evolutionary algorithm is used to select the optimal offspring in combination with the feasibility criterion to optimize the mission planning, effectively improve the global search capability of the search and rescue mission, and improve the success rate of finding the target ship through the evolution of the population.

[0012] In order to achieve the above object, the present invention adopts the following technical solution:

[0013] A spaceborne SAR constellation mission planning method for marine ship rescue comprises the following steps:

[0014] Step 1: Based on the maximum sailing speed and maximum moving radius of the target ship and the calculation of the geographic grid, a target ship moving range prediction model under physical constraints is constructed;

[0015] Step 2: Construct an adaptive priority strategy, analyze the geographic grids that the target ship may pass through, design a trajectory prediction method based on Gaussian probability distribution according to the ship's heading and speed, and adaptively adjust the priority of the geographic grid according to the probability; if the coverage of the geographic grid exceeds the set threshold, assign different priorities to each geographic grid according to the predicted track, otherwise, use equal priority;

[0016] Step 3, design the MSDCDE algorithm, which respectively executes the multi-crossover individual generation strategy, the feasibility-based individual selection strategy, the local left shift strategy and the population restart strategy to optimize the mission planning of the search and rescue vessel; MSDCDE stands for multi-strategy discrete constrained differential evolution.

[0017] The present invention also provides a space-borne SAR constellation mission planning device for marine ship rescue, comprising the following modules:

[0018] The model building module builds a prediction model of the target ship's moving range under physical constraints based on the target ship's maximum sailing speed and maximum moving radius combined with the calculation of the geographic grid;

[0019] The priority acquisition module builds an adaptive priority strategy, analyzes the geographic grids that the target ship may pass through, designs a trajectory prediction method based on Gaussian probability distribution according to the ship's heading and speed, and adaptively adjusts the priority of the geographic grid according to the probability; if the coverage of the geographic grid exceeds the set threshold, different priorities are assigned to each geographic grid according to the predicted track, otherwise equal priorities are used;

[0020] The optimization module designs the MSDCDE algorithm, which respectively executes the multi-crossover individual generation strategy, the feasibility-based individual selection strategy, the local left shift strategy and the population restart strategy to optimize the mission planning of the search and rescue vessel.

[0021] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method for spaceborne SAR constellation mission planning for marine ship rescue are implemented.

[0022] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for spaceborne SAR constellation mission planning for marine ship rescue.

[0023] Beneficial effects:

[0024] 1. The present invention combines the track prediction model of physical constraints, integrates the maximum navigation speed and maximum moving radius of the target ship, and combines geographic grid calculation to propose a target ship movement range prediction model under physical constraints, which can effectively narrow the search range and improve the search efficiency 12 hours after the ship is lost.

[0025] 2. The present invention designs an adaptive priority strategy. By analyzing the grids that the target ship may pass through, the algorithm adaptively adjusts the priority of the grid according to different situations. If the grid coverage exceeds the set threshold, different priorities are assigned to each geographic grid according to the predicted track, otherwise equal priority distribution is adopted. This mechanism can dynamically optimize the search strategy and enhance the flexibility and accuracy of the search and rescue mission.

[0026] 3. In order to better adapt to the track prediction model and adaptive priority strategy combined with physical constraints of the present invention, the present invention optimizes task planning based on the designed multi-strategy discrete constrained difference evolutionary algorithms (MSDCDE). The strategies in MSDCDE include: 1) multi-crossover individual generation strategy; 2) individual selection strategy based on feasibility; 3) local left shift strategy; 4) population restart strategy. The multi-crossover individual generation strategy designs three crossover methods, each of which has its own advantages, helping to generate feasible individuals more effectively. The individual selection strategy based on feasibility gives priority to task sequences with small constraint violations, aiming to quickly select task sequences that meet all constraints. The local left shift operation optimizes the latest task in the feasible task sequence, so that the observation task can be completed as early as possible. Finally, the population restart strategy reinitializes the entire population when the algorithm falls into the local optimal situation, in the hope of obtaining a better task sequence than the current optimal task sequence. Through the designed MSDCDE algorithm, the observation task can be effectively realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a space-borne SAR constellation mission planning method for marine ship rescue according to the present invention;

[0028] Figure 2 It is a schematic diagram of the local left shift strategy;

[0029] Figure 3 For overall performance comparison; (a) is the result of all algorithms, (b)-(f) are the results of 20 rounds of running of a single algorithm, including the optimal value and average value of the latest task execution time of the task sequence, and (g) is the feasible task sequence obtained by all algorithms in 20 rounds of running results;

[0030] Figure 4 This is a graph showing the impact of population size on algorithm performance;

[0031] Figure 5 This is a graph showing the impact of population size on algorithm performance;

[0032] Figure 6 This is a graph showing the impact of mutation probability on algorithm performance. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] In the description of the present invention, it should be understood that the terms "upper", "lower", "top", "bottom", "inner", "outer", etc. used in the present invention to indicate the orientation or position relationship is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0035] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0036] like Figure 1 As shown, a spaceborne SAR constellation mission planning method for marine ship rescue according to an embodiment of the present invention includes the following steps:

[0037] Step 1: Based on the maximum sailing speed and maximum moving radius of the target ship and the calculation of the geographic grid, a target ship moving range prediction model under physical constraints is constructed;

[0038] Step 2: Construct an adaptive priority strategy, analyze the geographic grids that the target ship may pass through, design a trajectory prediction method based on Gaussian probability distribution according to the ship's heading and speed, and adaptively adjust the priority of the geographic grid according to the probability; if the coverage of the geographic grid exceeds the set threshold, assign different priorities to each geographic grid according to the predicted track, otherwise, use equal priority;

[0039] Step 3: Design the MSDCDE algorithm to respectively execute the multi-crossover individual generation strategy, the feasibility-based individual selection strategy, the local left shift strategy and the population restart strategy to optimize the mission planning of the search and rescue vessel.

[0040] Specifically, the step 1 includes:

[0041] Given that the radius of the earth is 6371 km, first calculate the maximum sailing distance of the target ship in 12 hours to be 480 km, and use the Haversine formula to convert the straight-line distance into radians. :

[0042] ;

[0043] in, Indicates the arc converted from the straight-line distance. Lat indicates the latitude of the center point of the target sea area in degrees. is the difference between the latitude of the center point of the geographic grid where the target ship is initially located and the latitude of the center point of the geographic grid that it may pass through. , is the difference between the longitude of the center point of the grid where the target ship is initially located and the longitude of the center point of the grid that it may pass through, and are the initial longitude and latitude and the farthest grid longitude and longitude of the point the ship passes, respectively.

[0044] Then, using radians Calculate the central angle c:

[0045] ;

[0046] Among them, the central angle The unit is radians.

[0047] pass The maximum possible moving radius R of the target ship is calculated, and based on this maximum moving radius R, all possible geographical grids that the ship may pass through within two hours are calculated based on past experience. Indicates distance.

[0048] When the unit grid size is 100km At 100 km, in this scenario, the optimization goal of the present invention can be constructed as follows:

[0049] ;

[0050] ;

[0051] in, Indicates that the satellite observation platform (WX) is from The total time to observe (search + confirm) all geographic grids at least once from the moment, Indicates WX in the geographic grid The observation time on It represents the total idle time when WX observes all geographic grids at least once. N represents the total number of geographic grids. Represents the observation time of a single geographic grid (unit: minute). Indicates the observation start time of the t-th geographic grid (unit: minute). Indicates satellite Task Sequence Middle The start time of observation for each satellite mission; Indicates satellite Task Sequence Middle The start time of observation for each satellite mission; Indicates satellite Task Sequence The The geographic grid corresponding to each task. Indicates satellite The work sequence is extracted from the entire task sequence and sorted in ascending time order. Observation satellite search grid The working sequence, , for Observation satellite search grid The length of the working sequence, Indicates taking the geographic grid The time value at which the observation starts. Indicates satellite Task Sequence The The observation time of the geographic grid corresponding to the task.

[0052] Since WX has a The observation time is known and fixed, so the optimization goal of the present invention is how to adjust the task sequence so that , that is, the total idle time ( ) and the latest task end time ( The advantage of such an objective function is that it takes into account both the rationality of the arrangement of all tasks and the requirement that tasks should be completed as early as possible, thus achieving a balance.

[0053] In addition, the constraints that need to be met include:

[0054] 1) The value constraints of the elements of the satellite task sequence are:

[0055] ;

[0056] in, is the satellite number, is the geographic grid number, is the satellite's cycle number, is the observation matrix, size is n N; is the grid task sequence, i.e., the corresponding individual in the Multi-strategy Discrete Constrained Differential Evolution algorithms (MSDCDE); Represents a grid task sequence Elements of Indicated in Cycles to start searching the grid The moment point, Indicates a geographic grid;

[0057] 2) For the same satellite, the idle time constraints of adjacent tasks are:

[0058] ;

[0059] in, and In fact, it means the start observation time of two adjacent tasks in the same satellite mission sequence after time sorting. Indicates satellites in geographic grid Observation time on ;

[0060] 3) The working time constraint of a satellite in one cycle is:

[0061] ;

[0062] in, represents the working time of all grids observed by the same satellite i in the same period r, Indicates the upper limit of the cycle.

[0063] 4) The constraint that the satellite can find the target ship in less than two hours is:

[0064] ;

[0065] in, Indicates the start time of the latest task. Indicates the time when the task is issued, in seconds.

[0066] The optimization objective and constraint conditions constitute a target vessel movement range prediction model under the physical constraints.

[0067] Specifically, the step 2 includes:

[0068] Step 2.1. Calculate the maximum moving radius R of the target ship within 12 hours according to the formula;

[0069] Step 2.2. Calculate 100km based on the calculated maximum moving radius 100km longitude and latitude grid Whether all grid centers are within the range of the maximum moving radius R;

[0070] Step 2.3. According to step 2.2, obtain all the geographical grids that the target ship may pass through within 2 hours ;

[0071] Step 2.4. If | |>λ, that is, the number of longitude and latitude grids is greater than the threshold λ; predict the track of the target ship and give each geographic grid a priority;

[0072] Step 2.5. If | |≤λ, giving each geographic grid the same priority.

[0073] Specifically, in step 2.4, the priority of the grids that the ship may pass through is calculated using Gaussian probability. For each geographic grid, the probability of the ship passing through the grid can be described by Gaussian distribution. The mean (μ) of the Gaussian distribution corresponds to the predicted position of the current position of the ship, and the covariance matrix (Σ) describes the possible errors and uncertainties of the ship during its movement. Assuming that the ship will move to a new position in the future time t, its future position can be predicted based on the current heading and speed:

[0074] ;

[0075] in, are the current coordinates of the ship, It's the speed. is the heading. The covariance matrix Σ of the Gaussian distribution can be adjusted according to the relationship between the ship's speed, heading and time. Usually, the elements of the covariance matrix can reflect factors such as ship speed and heading instability, and t represents time. Represents the predicted grid position.

[0076] For each geographic grid, the probability of a ship entering the geographic grid can be calculated. Let the geographic grid The coordinates of , then the probability of the ship entering the geographic grid It can be represented by the probability density function of the Gaussian distribution:

[0077] ;

[0078] in, is the determinant of the covariance matrix, is the inverse of the covariance matrix, is the predicted grid position, is the difference between the grid position and the predicted position. Finally, the corresponding grid probability can be obtained, and the priority is obtained according to the probability. exp() represents the exponential function, and the superscript T represents the transpose of the matrix.

[0079] Specifically, the step 3 includes:

[0080] Step 3.1. Initialize the population;

[0081] Step 3.2. Calculate the fitness value;

[0082] Step 3.3. Execute the multi-crossover individual generation strategy;

[0083] Step 3.4. Select the best offspring according to the feasibility criterion;

[0084] Step 3.5. When the task sequence is feasible, execute the local left shift strategy;

[0085] Step 3.6. If the population diversity is lower than the threshold, execute the population restart strategy;

[0086] Step 3.7. If the termination condition is reached, stop; if the termination condition is not reached, repeat steps 3.1 to 3.6.

[0087] Specifically, in step 3.2, the fitness value is calculated according to the optimization target, that is, the task sequence time obtained by the current population is calculated.

[0088] Specifically, in step 3.3, the multi-crossover individual generation strategies include: single-point crossover, uniform crossover and sequential crossover.

[0089] 1) The single-point crossover is: Assume that there are parent individuals A and B, where parent individual A is the best individual in the population, and parent individual B is an individual randomly selected from the population that is not equal to parent individual A. Their genes are represented by two sequences:

[0090] ;

[0091] in, and is an element in the gene sequence, and n represents the length of the gene sequence. Then, a random crossover point k is selected, which is located between positions 1 and n-1 in the gene sequence. According to the crossover point k, the two parent individuals are crossed to generate two child individuals C and D:

[0092] ;

[0093] Among them, the gene sequence of child individual C is the same as that of parent individual A at the intersection point k, and the same as that of parent individual B after the intersection point k. The gene sequence of child individual D is the opposite, the same as that of parent individual B at the intersection point k, and the same as that of parent individual A after the intersection point k. The mathematical formula is as follows:

[0094] ;

[0095] ;

[0096] in, represents the pth gene of child individual C, represents the pth gene of child individual D, represents the pth gene of parent individual A, represents the pth gene of parent individual B, and k represents the crossover point. The task sequence is updated through the above single-point crossover.

[0097] 2) Uniform crossover is an extension of single-point crossover, which is used to combine the genes of two parent individuals to generate child individuals. It selects genes from two parent individuals at each position according to a certain probability, thereby increasing the diversity of the population. Parent individuals can be crossovered at multiple crossover points to generate two child individuals. Similarly, suppose there are parent individuals A and parent individuals B, parent individual A is the best individual in the population, and parent individual B is a randomly selected individual that is not equal to parent individual A. Their genes are represented as two sequences:

[0098] ;

[0099] in, and is an element in the gene sequence, and n represents the length of the gene sequence. For child individual C, genes are selected from the same position in parent individual A and parent individual B with a certain probability. If the gene of parent individual A is selected at position p, then the pth gene of C is , otherwise , and use the same method to generate the child individual D:

[0100] The formula is as follows:

[0101] ;

[0102] ;

[0103] in, represents the pth gene of child individual C, represents the pth gene of child individual D, represents the pth gene of parent individual A, It represents the p-th gene of the parent individual B, and rand represents a random number generated within the range of [0, 1]. Uniform crossover can increase the diversity between individuals and help the algorithm jump out of the local optimal solution.

[0104] 3) The order crossover is as follows:

[0105] Two random positions are selected in the parent individual, and then the gene segment between these two positions is retained, and the remaining positions of the child individual are filled in according to the order of the other parent individual. Suppose there are parent individual A and parent individual B, and their genes are represented as two permutations:

[0106] ;

[0107] Among them, and are elements in the permutation, and n represents the length of the permutation. Then, two random positions k and l are selected in parent individual A, ensuring k < l. Next, the gene segment between positions k and l in parent individual A is copied to the corresponding positions of child individual C, and then the remaining positions of child individual C are filled in according to the order of parent individual B. Similarly, in the present invention, the gene segment between positions k and l in parent individual B is copied to the corresponding positions of child individual D, and then the remaining positions of child individual D are filled in according to the order of parent individual A. The corresponding formula is as follows:

[0108] ;

[0109] ;

[0110] Among them, represents the p-th gene of child individual C, represents the p-th gene of child individual D, represents the p-th gene of parent individual A, represents the p-th gene of parent individual B, represents the q-th gene of parent individual A, represents the q-th gene of parent individual B, and k and l represent the starting position and ending position of the crossover. Order crossover can retain the order information of the parent individual and increase the diversity of the population by obtaining gene segments from another parent individual, which helps the algorithm find a better solution in the permutation problem.

[0111] Specifically, in step 3.4, after three new individuals (one new individual is obtained from single-point crossover, one from uniform crossover, and one from order crossover) are generated through the above-designed multi-crossover individual generation strategy, first, the three offspring individuals need to be selected. The constraint handling technology based on the superiority of feasible solutions can quickly select a better solution, including:

[0112] A feasible solution is superior to an infeasible solution;

[0113] If both are infeasible solutions, the solution with the smaller constraint violation is the better solution;

[0114] If both are feasible solutions, the solution with the smaller fitness value is the better solution;

[0115] By using the superiority criterion of the above feasible solutions, the best offspring individual among the three offspring individuals is selected.

[0116] Specifically, in step 3.5, in order to further improve the performance of the algorithm in processing the above optimization problem, the present invention finds that another major factor restricting the task performance is the end time of the last task. Therefore, if the end time of the last task can be advanced as much as possible, the solution that can be obtained will be better.

[0117] Based on the above considerations, the present invention is designed based on the task characteristics. Figure 2 The local left-shift strategy shown in Figure 1 performs a left-shift operation on the last task of each satellite when task training is feasible, where t represents the task and n represents the number of tasks.

[0118] like Figure 2 As shown, for the last task of each satellite, it is inserted into the adjacent position one by one. If the inserted new task sequence satisfies the constraint condition (i.e. Figure 2 If the constraints in the task sequence are satisfied, stop, otherwise continue to insert to the previous position and judge. If there is no feasible position after traversing the entire task sequence, the left shift operation is not performed. The above left shift operation can effectively save the time required for the entire search for the identification point.

[0119] Specifically, in step 3.6, in order to prevent the population from falling into the local optimum, a population restart mechanism is designed, and a diversity detection method as shown in the following formula is designed. When the population diversity is lower than a certain level, it is considered that the population has fallen into the local optimum. At this time, the population needs to be restarted, individuals are regenerated, and the best individuals in the population are retained.

[0120] ;

[0121] in, Represents a task sequence The ath point and the task sequence The Euclidean distance between the bth points in , d represents the total number of positions. The individual similarity of the entire population can be calculated through the above. When the average similarity between individuals in the population is lower than 1e-6, it is considered to have fallen into a local optimum, and the population is restarted.

[0122] The following diagrams are used to illustrate the comparison between the present invention and other algorithms.

[0123] (1) The present invention is compared with four comparison algorithms respectively:

[0124] The performance of re-tracking the target ship in the case of a large grid is shown in Table 1 (Performance table of re-tracking the target ship of the present invention and the comparative algorithm), where the task start time indicates the task issuance time for the target ship to start tracking. It can be found that under different task issuance times, MSDCDE can find the target ship in the large grid scenario, and is the best in four different task issuance scenarios. The task start time is the issuance time for re-tracking, the task end time is the time to find the ship, and the time spent is the time spent from the task issuance time to the time to find the ship. The smaller the time spent, the better the performance. It can be found that MSDCDE only needs 3 seconds to find the target ship, which proves the effectiveness of MSDCDE. dC2ODE also shows good competitiveness, and can find feasible optimal solutions, and the performance is consistent with MSDCDE in both cases. However, dFROFI and ATM can only find feasible solutions when the task start time is 200 seconds, and cannot find feasible solutions in other cases. This is mainly because when the task start time is 200 seconds, the starting condition may be able to better help the search. Both dC2ODE and VMCH can find feasible solutions, mainly because the constraint processing technology of these methods can effectively find the optimal solution when facing the current task. However, in more difficult tasks, such as when the search range is larger, other methods are difficult to find feasible solutions, while the method designed by the present invention can still effectively find feasible solutions, and the task planning efficiency is high.

[0125] Table 1

[0126] (2) Overall comparison with several other algorithms:

[0127] Compare the task sequences obtained by MSDCDE with those obtained by several other methods. Figure 3 As shown, Figure 3 (a) is the result of all algorithms. Figure 3 (b)- Figure 3 (f) is the result of 20 rounds of running of a single algorithm, including the optimal value and average value of the latest task execution time of the task sequence. Figure 3 (g) is the feasible task sequence obtained by all algorithms after 20 rounds of operation. From the overall experimental results, it can be found that MSDCDE can obtain feasible solutions 19 times in 20 rounds, while VMCH, C2ODE and ATM only found feasible solutions 13, 9 and 2 times respectively, and FROFI cannot even find a feasible solution. In addition, MSDCDE can obtain the optimal task sequence and the optimal average task sequence, which proves the effectiveness of MSDCDE.

[0128] (3) Parameter sensitivity analysis:

[0129] Further analysis is conducted on various parameters that affect the algorithm, including population size, mutation probability, crossover probability, etc.

[0130] 1) Population size

[0131] Test the effect of population size on algorithm performance. Figure 4 The graph of the effect of population size on algorithm performance shows that the algorithm performance increases with the population size. When the population size is 100, the algorithm performance is basically optimal, and when the population size is 30, the algorithm performance is basically the worst. In addition, Table 2 shows the results of the latest task start time in the task sequence obtained by each algorithm under different populations. It can be found that MSDCDE is optimal in both the minimum and maximum values, and the maximum value is close to the minimum value and far from other methods, which proves the effectiveness and stability of MSDCDE.

[0132] Table 2

[0133] 2) Crossover probability

[0134] Test the effect of crossover probability on algorithm performance. Figure 5 The figure below is the influence of crossover probability on algorithm performance. It can be found that different crossover probabilities have different effects on algorithm performance, but in general, the best result is obtained when the crossover probability is between 0-5-0.8. In addition, Table 3 shows the results of the latest task start time in the task sequence obtained by each algorithm under different crossovers. It can be found that, like the results of population size, MSDCDE is optimal in both the minimum and maximum values, and the maximum value is close to the minimum value and far from the minimum value. Due to other methods, it further illustrates the effectiveness of the local left shift strategy. When the task sequence is feasible, performing a local left shift operation on the latest starting task sequence can effectively reduce the latest task start time, further proving the effectiveness and stability of MSDCDE.

[0135] Table 3

[0136] 3) Mutation probability

[0137] In this section, the effect of mutation probability on algorithm performance is tested. Figure 6The figure below is the influence of mutation probability on algorithm performance. Similar to the population size, the algorithm performance improves with the increase of mutation probability. In addition, Table 4 shows the results of the latest task start time in the task sequence obtained by each algorithm under different mutation probabilities. Unlike population size and crossover probability, mutation probability has a greater impact on algorithm performance. It can be found that except for MSDCDE, the other methods also achieved better performance than the other parameter settings, indicating that the impact of mutation probability on algorithm performance is the greatest. In addition, MSDCDE still shows the best performance, which is the best in both minimum and maximum values, and the maximum value is close to the minimum value and far away from other methods, proving the effectiveness and stability of MSDCDE.

[0138] Table 4

[0139] The present invention also provides a space-borne SAR constellation mission planning device for marine ship rescue, comprising the following modules:

[0140] The model building module builds a prediction model of the target ship's moving range under physical constraints based on the target ship's maximum sailing speed and maximum moving radius combined with the calculation of the geographic grid;

[0141] The priority acquisition module builds an adaptive priority strategy, analyzes the geographic grids that the target ship may pass through, designs a trajectory prediction method based on Gaussian probability distribution according to the ship's heading and speed, and adaptively adjusts the priority of the geographic grid according to the probability; if the coverage of the geographic grid exceeds the set threshold, different priorities are assigned to each geographic grid according to the predicted track, otherwise equal priorities are used;

[0142] The optimization module designs the MSDCDE algorithm, which respectively executes the multi-crossover individual generation strategy, the feasibility-based individual selection strategy, the local left shift strategy and the population restart strategy to optimize the mission planning of the search and rescue vessel.

[0143] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method for spaceborne SAR constellation mission planning for marine ship rescue are implemented.

[0144] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for spaceborne SAR constellation mission planning for marine ship rescue.

[0145] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A spaceborne SAR constellation mission planning method for marine ship rescue, characterized in that: The steps include: Step 1: Based on the maximum sailing speed and maximum moving radius of the target ship and the calculation of the geographic grid, a target ship moving range prediction model under physical constraints is constructed; Step 2: Construct an adaptive priority strategy, analyze the geographic grids that the target ship may pass through, design a trajectory prediction method based on Gaussian probability distribution according to the ship's heading and speed, and adaptively adjust the priority of the geographic grid according to the probability; if the coverage of the geographic grid exceeds the set threshold, assign different priorities to each geographic grid according to the predicted track, otherwise, use equal priority; Step 3, design the MSDCDE algorithm, and respectively implement the multi-crossover individual generation strategy, the feasibility-based individual selection strategy, the local left shift strategy, and the population restart strategy to optimize the mission planning of the search and rescue vessel; MSDCDE stands for Multi-Strategy Discrete Constrained Differential Evolution.

2. The method for spaceborne SAR constellation mission planning for marine ship rescue according to claim 1, characterized in that: The step 1 comprises: Calculate the maximum distance the target ship can travel in 12 hours, using the Haversing formula to convert the straight-line distance into radians , using radians Calculate the central angle c using The maximum possible moving radius R of the target ship is calculated, and based on this maximum moving radius R, all the geographical grids that the ship may pass through within two hours are calculated; where d represents the distance.

3. The method for spaceborne SAR constellation mission planning for marine ship rescue according to claim 2, characterized in that: The step 1 also includes constructing an optimization target as follows: ； ； in, Indicates that the satellite observation platform The total time from the moment of time to observe all geographic grids at least once, including search and confirmation, Indicates the satellite observation platform in the geographic grid The observation time on It represents the total idle time when WX observes all geographic grids at least once; N represents the total number of geographic grids; Represents the observation time of a single geographic grid, in minutes; Indicates the observation start time of the tth geographic grid, in minutes; Indicates satellite Task Sequence Middle The start time of observation for each satellite mission; Indicates satellite Task Sequence Middle The start time of observation for each satellite mission; Indicates satellite Task Sequence The The geographical grid corresponding to each task; Indicates satellite The work sequence is extracted from the entire task sequence and sorted in ascending time order. Observation satellite search grid The working sequence, , for Observation satellite search grid The length of the working sequence, Indicates taking the geographic grid The time value at which the observation starts. Indicates satellite Task Sequence The The observation time of the geographic grid corresponding to the task; The optimization goal is to make the total idle time and the latest task end time The sum is the smallest.

4. The method for spaceborne SAR constellation mission planning for marine ship rescue according to claim 3, characterized in that: The step 1 also includes constructing the constraint conditions as follows: The value constraints of the elements of the satellite work task sequence are: ； in, is the satellite number, is the geographic grid number, is the satellite's cycle number, is the observation matrix, size is n N; is the grid task sequence, i.e., the corresponding individual in the MSDCDE algorithm; Represents a grid task sequence Elements of Indicated in Cycles to start searching the grid The moment point, Indicates a geographic grid; For the same satellite, the idle time constraints of adjacent tasks are: ； in, Indicates satellites in geographic grid Observation time on ; The working time constraint of a satellite in one cycle is: ； in, represents the working time of all grids observed by the same satellite i in the same period r, Indicates the upper limit of the cycle; The constraint that the satellite can find the target ship in less than two hours is: ； in, Indicates the start time of the latest task. Indicates the time when the task was issued, in seconds; The optimization objective and constraint conditions constitute a target vessel movement range prediction model under the physical constraints.

5. The method for spaceborne SAR constellation mission planning for marine ship rescue according to claim 2, characterized in that: The step 2 comprises: Step 2.

1. Calculate the maximum moving radius R of the target ship within 12 hours according to the formula; Step 2.

2. Calculate 100km based on the calculated maximum moving radius R 100km longitude and latitude grid Whether all grid centers are within the range of the maximum moving radius R; Step 2.

3. According to step 2.2, obtain all the geographical grids that the target ship may pass through within 2 hours ; Step 2.

4. If | |>λ, that is, the number of longitude and latitude grids is greater than the threshold λ; predict the track of the target ship and assign a priority to each geographic grid; Step 2.

5. If | |≤λ, giving each geographic grid the same priority.

6. The method for spaceborne SAR constellation mission planning for marine ship rescue according to claim 5, characterized in that: In step 2.4, for each geographic grid, a Gaussian distribution is used to describe the probability of the target ship passing through the geographic grid.

7. The method for spaceborne SAR constellation mission planning for marine ship rescue according to claim 1, characterized in that: The step 3 comprises: Step 3.

1. Initialize the population; Step 3.

2. Calculate the fitness value; Step 3.

3. Execute the multi-crossover individual generation strategy; Step 3.

4. Select the best offspring according to the feasibility criterion; Step 3.

5. When the task sequence is feasible, execute the local left shift strategy; Step 3.

6. If the population diversity is lower than the threshold, execute the population restart strategy; Step 3.

7. If the termination condition is reached, stop; if the termination condition is not reached, repeat steps 3.1 to 3.

6.

8. A spaceborne SAR constellation mission planning device for marine ship rescue, characterized in that: Includes the following modules: The model building module builds a prediction model of the target ship's moving range under physical constraints based on the target ship's maximum sailing speed and maximum moving radius combined with the calculation of the geographic grid; The priority acquisition module builds an adaptive priority strategy, analyzes the geographic grids that the target ship may pass through, designs a trajectory prediction method based on Gaussian probability distribution according to the ship's heading and speed, and adaptively adjusts the priority of the geographic grid according to the probability; if the coverage of the geographic grid exceeds the set threshold, different priorities are assigned to each geographic grid according to the predicted track, otherwise equal priorities are used; The optimization module designs the MSDCDE algorithm, which respectively implements the multi-crossover individual generation strategy, the feasibility-based individual selection strategy, the local left shift strategy and the population restart strategy to optimize the mission planning of the search and rescue vessel; MSDCDE stands for Multi-Strategy Discrete Constrained Differential Evolution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a space-borne SAR constellation mission planning method for marine ship rescue as described in any one of claims 1-7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for planning a space-borne SAR constellation mission for marine ship rescue as described in any one of claims 1 to 7 are implemented.

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