A satellite sequence mission planning method and device based on greedy strategy

Abstract

The present application discloses a satellite sequence task planning method and device based on a greedy strategy. The method includes: determining the first time grid interval of the original meta-task according to the time grid and the power-on and power-off time corresponding to the original meta-task; determining the candidate time grid interval of each regression meta-task based on the target time grid interval and the task interval parameter, and determining the time grid interval of each regression meta-task from the candidate time grid interval based on the greedy strategy, so as to finally generate the planning scheme of the satellite sequence task based on the first time grid interval and the planning interval corresponding to each regression meta-task. The method can automatically detect and resolve conflicts during satellite sequence task planning, thereby improving the rationality and effectiveness of task planning.

Description

Technical Field

[0001] The present application relates to the field of remote sensing satellite technology, and in particular to a satellite sequence mission planning method, device, electronic equipment and medium based on a greedy strategy. Background Art

[0002] With the development of domestic independent radar satellites and differential interferometric synthetic aperture radar technology (D-InSAR) and the derived time series InSAR technology, the application of long-period deformation monitoring based on repeated observations in time series is increasing day by day. For the ground mission planning system of remote sensing satellites, planning and managing a large number of repetitive tasks with time series associations to maximize the observation requirements is the key to improving the operation and control efficiency of radar satellites with deformation monitoring as their mission.

[0003] However, the current research on satellite observation mission planning is mainly focused on short-term mission scheduling with only a single observation requirement. The planning algorithm of the existing ground mission planning system is also mainly aimed at resolving conflicts between single tasks, which makes it difficult to quickly and automatically detect and resolve conflicts for time-series InSAR sequence task groups. Summary of the invention

[0004] To this end, the present application provides a satellite sequence task planning method and device based on a greedy strategy to automatically detect and resolve conflicts in satellite sequence task groups, thereby improving the rationality and effectiveness of task planning.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a satellite sequence task planning method based on a greedy strategy, wherein the satellite sequence task includes an original meta-task and at least one regression meta-task, and the method includes: determining a first time grid interval of the original meta-task according to the time grid and the power-on and power-off time corresponding to the original meta-task; setting the initial value of the regression planning round to 1; determining a candidate time grid interval of the regression meta-task to be planned based on a target time grid interval and a task interval parameter; the target time grid interval is the time grid interval corresponding to the most recently successfully planned task in the satellite sequence task; the regression meta-task to be planned is a regression meta-task whose task number is the same as the value of the regression planning round; determining whether there is a conflict-free interval in the candidate time grid interval; if there is a conflict-free interval in the candidate time grid interval, scoring each conflict-free interval based on a preset scoring rule. The method comprises the steps of: scoring the conflict-free interval; determining whether there is a local optimal solution in the conflict-free interval according to the backoff count of the current round corresponding to the regression planning round; determining the local optimal solution according to the score and the backoff count of the current round if there is a local optimal solution, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; determining whether the regression planning round is equal to the maximum task sequence number of the regression meta-task; when the regression planning round is not equal to the maximum task sequence number, adding 1 to the value of the regression planning round, and returning the step of determining the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameter; and generating a planning scheme for the satellite sequence task based on the first time grid interval and the planning interval corresponding to each of the regression meta-tasks if the regression planning round is equal to the maximum task sequence number.

[0006] According to the first aspect, after determining whether there is a conflict-free interval in the candidate time grid interval, the method also includes: when there is no conflict-free interval in the candidate time grid interval, determining whether the value of the regression planning round is equal to 0 after being subtracted by 1; when the value of the regression planning round is not equal to 0 after being subtracted by 1, subtracting 1 from the value of the regression planning round, and adding 1 to the value of the current round backoff count corresponding to the regression planning round after the value is subtracted by 1, returning the current round backoff count corresponding to the regression planning round, and determining whether there is a local optimal solution in the conflict-free interval; the initial value of the current round backoff count is 0; when the value of the regression planning round is equal to 0 after being subtracted by 1, determining that the satellite sequence mission planning has failed.

[0007] According to the first aspect, or any implementation of the first aspect above, after determining whether there is a local optimal solution in the conflict-free interval based on the score and the current round backoff count corresponding to the regression planning round, the method also includes: in the case where there is no local optimal solution, determining whether the value of the regression planning round is equal to 0 after being subtracted by 1; in the case where the value of the regression planning round is not equal to 0 after being subtracted by 1, subtracting 1 from the value of the regression planning round, and adding 1 to the value of the current round backoff count corresponding to the regression planning round after the value is subtracted by 1, returning to the step of determining whether there is a local optimal solution in the conflict-free interval based on the current round backoff count corresponding to the regression planning round; the initial value of the current round backoff count is 0; in the case where the value of the regression planning round is equal to 0 after being subtracted by 1, determining that the satellite sequence mission planning has failed.

[0008] According to the first aspect, or any implementation method of the first aspect above, the first time grid interval of the original meta task is determined based on the time grid and the power on and off time corresponding to the original meta task, including: determining a first ratio of a first difference to a grid length of the time grid; the first difference is the difference between the power on time corresponding to the original meta task and time zero; rounding down the first ratio to determine it as the left endpoint of the first time grid interval; determining a second ratio of a second difference to the grid length of the time grid; the second difference is the difference between the power off time corresponding to the original meta task and time zero; rounding down the second ratio to determine it as the right endpoint of the first time grid interval.

[0009] According to the first aspect, or any implementation of the first aspect above, before determining the first time grid interval of the original meta-task, the method also includes: generating the time grid based on the regression period, the maximum imaging time of the system task, and the minimum time interval between two power on and power off imaging.

[0010] According to the first aspect, or any implementation of the first aspect above, the grid length of the time grid is the minimum value that meets preset conditions; the preset conditions include: the grid length is greater than the sum of the maximum imaging time of the system task and the minimum time interval between two power on and power off imaging; the grid length can divide the regression period.

[0011] According to the first aspect, or any implementation of the first aspect above, the task interval parameter includes: the minimum cycle interval and the maximum cycle interval of the satellite sequence task; the candidate time grid interval of the regression meta-task to be planned is determined based on the target time grid interval and the task interval parameter, including: the candidate time grid interval of the regression meta-task to be planned is: [a k ,b k ]; where a k =a+n*i,b k =b+n*i,i∈[imin , i max ]; k is the serial number of the candidate time grid interval; [a, b] is the first time grid interval, n is the number of grids contained in a regression cycle, i is the number of regression cycles, i min is the number of regression cycles contained in the minimum period interval, i max is the number of regression cycles contained in the maximum cycle interval.

[0012] According to the first aspect, or any implementation of the first aspect above, determining whether there is a conflict-free interval in the candidate time grid interval includes: determining a first extended interval corresponding to each candidate time grid interval; the first extended interval is [a k -1,b k -1]; for each of the first extension intervals, perform a first operation; after performing the first operation on all the first extension intervals, if there is a candidate time grid determined as a non-conflict interval, determine that there is a non-conflict interval in the candidate time grid interval; if all the candidate time grids are determined as conflicting intervals, determine that there is no non-conflict interval in the candidate time grid interval; the first operation includes: determining whether there is a grid occupancy conflict between a satellite observation task in the time grid and the first extension interval; if there is no grid occupancy conflict between a satellite observation task in the time grid and the first extension interval, determine the candidate time grid corresponding to the first extension interval as a non-conflict interval; if there is a satellite observation task in the time grid and there is a grid occupancy conflict between the first extension interval, determine whether there is a conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta task; if there is no conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta task, determine the candidate time grid corresponding to the first extension interval as a non-conflict interval; if there is a conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta task, determine the candidate time grid corresponding to the first extension interval as a conflicting interval.

[0013] According to the first aspect, or any implementation method of the first aspect above, each conflict-free interval is scored based on a preset scoring rule, including: for each conflict-free interval, based on the shortest interval time constraint between two tasks, determining the second extension interval corresponding to the conflict-free interval; based on a preset time range threshold, determining the third extension interval corresponding to the conflict-free interval; the preset time range threshold is greater than the shortest interval time constraint between the two tasks; determining the union of the second extension interval and the interval occupied by the successful task; the interval occupied by the successful task is the time interval occupied by the successfully planned satellite observation tasks in the third extension interval; determining the relative complement of the union in the third extension interval; inputting the relative complement into the scoring function to determine the score of the conflict-free interval.

[0014] According to the first aspect, or any implementation method of the first aspect above, the determination of whether there is a local optimal solution in the conflict-free interval is based on the round backoff count corresponding to the regression planning round, including: when the value of the round backoff count corresponding to the regression planning round is equal to the number of conflict-free intervals, determining that there is no local optimal solution in the conflict-free interval; when the value of the round backoff count corresponding to the regression planning round is less than the number of conflict-free intervals, determining that there is a local optimal solution in the conflict-free interval.

[0015] According to the first aspect, or any implementation method of the first aspect above, when a local optimal solution exists, the local optimal solution is determined based on the score and the round back-off count, including: when a local optimal solution exists, the value after the round back-off count is added by 1 is determined as the target value; and the scores are sorted into a conflict-free interval of the target value, and determined as the local optimal solution.

[0016] The second aspect of the present application provides a satellite sequence task planning device based on a greedy strategy, wherein the satellite sequence task includes an original meta-task and at least one regression meta-task, and the device includes: an interval determination module, which is used to determine the first time grid interval of the original meta-task according to the time grid and the power-on and power-off time corresponding to the original meta-task; a round configuration module, which is used to set the initial value of the regression planning round to 1; a candidate interval determination module, which is used to determine the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameter; the regression meta-task to be planned is a regression meta-task whose task sequence number is the same as the value of the regression planning round; a conflict interval determination module, which is used to determine whether there is a conflict-free interval in the candidate time grid interval; a scoring module, which scores each conflict-free interval based on a preset scoring rule when there is a conflict-free interval in the candidate time grid interval; an optimal solution determination module, which is used to determine the optimal solution according to the current time grid interval corresponding to the regression planning round. The method comprises the following steps: a first step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a second step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a third step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a third step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a fourth step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a fourth step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a fourth step of determining a local optimal solution in the conflict-free interval according to the score and the current round back count corresponding to the regression planning round, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; a fifth step of determining a local optimal solution in the conflict-free interval according to the target time grid interval and the task interval ...

[0017] The third aspect of the present application provides an electronic device, which includes: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement any one of the methods in the first aspect; one or more I / O interfaces, connected between the processor and the memory, and configured to implement information interaction between the processor and the memory.

[0018] A fourth aspect of the present application provides a computer-readable medium having a computer program stored thereon, which implements any one of the methods in the first aspect when the program is executed by a processor.

[0019] The present application has the following advantages: The embodiment of the present application provides a satellite sequence task planning method and device based on a greedy strategy to automatically detect and resolve conflicts in the satellite sequence task group, thereby improving the rationality and effectiveness of task planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide further understanding of the present application and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present application, but do not constitute a limitation to the present application.

[0021] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a satellite sequence mission planning method provided in an embodiment of the present application;

[0023] Figure 3 A flowchart of another satellite sequence mission planning method provided in an embodiment of the present application;

[0024] Figure 4 A structural block diagram of a satellite sequence mission planning device provided in an embodiment of the present application;

[0025] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0027] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0028] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0029] The terms used in this application are only used to describe specific embodiments and are not intended to limit the present application.As used in this application, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0030] When the terms “comprising” and / or “made of…” are used in this application, it specifies the existence of the stated features, integers, steps, operations, elements and / or components, but does not exclude the existence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0031] The embodiments described in the present application may be described with reference to plan views and / or cross-sectional views by means of ideal schematic diagrams of the present application. Therefore, the exemplary illustrations may be modified according to manufacturing techniques and / or tolerances.

[0032] Unless otherwise defined, all terms (including technical and scientific terms) used in this application have the same meaning as those commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this application, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined in this application.

[0033] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application. Figure 1 A planning framework for time-series InSAR type sequence tasks is presented. Based on this planning framework, conflict detection and resolution of long-period sequence tasks can be achieved, ensuring the efficiency and accuracy of automatic scheduling of time-series InSAR tasks.

[0034] Before describing the planning process of the time-series InSAR interferometry task, it is necessary to agree that the first observation task of the interferometry task is called the original meta-task (also called the original observation task or the original plan), and the subsequent ones are called regression meta-tasks (also called regression observation tasks or regression plans). In this planning framework, the planning process can be divided into the following steps.

[0035] Step 1: After the system receives the programming order, a programming order corresponds to a collection task in the demand pool of the planning system. During the order collection process, for time series InSAR type tasks, in addition to the basic collection parameters, additional sequence-related advanced parameter options are set, including minimum interval, maximum interval, collection frequency, etc.

[0036] The ground mission planning system receives differential interference mission orders (repeated mission orders) from the order system, and the orders enter the demand pool and become collection requirements. Compared with the collection requirements of regular orders, the collection requirements of repeated mission orders are supplemented with parameter information for repeated execution of tasks, mainly including the repeated collection time range, the number of collections, and the task time interval.

[0037] Step 2: After the acquisition task is combined with the orbit information to perform transit visit calculation, an observation meta-task is generated and entered into the planning task pool.

[0038] Step 3: The planning system regularly performs planning calculations on the observation meta-tasks in the planning task pool and generates observation plans after successful planning.

[0039] It should be noted that, in the embodiment of the present application, the original meta-task is planned in the planning task pool (conflict detection and resolution are completed), and there are no conflicts in the satellite observation tasks in the planning task pool.

[0040] In some embodiments, the planning system regularly performs planning calculations on the observation meta-tasks in the planning task pool based on the constellation resources (constraints), data transmission resources, and measurement and control resources, and generates an observation plan after the planning is successful.

[0041] Step 4: After the original task of the sequence task is successfully planned in step 3, it enters the sequence task group conflict detection and resolution phase. If a sequence execution plan that does not conflict with other existing sequence task groups in the system can be calculated, it means that the current sequence task planning is successful, and the original task planning result can be added to the observation plan. Otherwise, the InSAR sequence task planning fails, and the next transit calculation is required to enter step 4 again.

[0042] Step 5: The planning plan does not need to be adjusted. The observation plan is formed and satellite business instructions are generated and sent to the measurement and control system, waiting for the satellite to be uploaded.

[0043] In the application scenario provided by the embodiment of the present application, compared with conventional orders, before generating an observation plan, the order receiving, orbit prediction, access calculation, generation of observation meta-tasks entering the planning pool, and the process of task conflict resolution in the planning pool are consistent. However, the original meta-task for successful planning cannot be directly added to the planning plan, because the D-InSAR interference task group is transactional and atomic, and it is necessary to further increase the link of conflict detection and resolution of the interference task group. If the current interference task sequence execution plan that does not conflict with the existing interference task group in the system can be calculated, it can be indicated that the current interference task planning is successful, and the original task planning result can be added to the observation plan to generate instructions. The interference task sequence execution plan calculated at the same time is encapsulated into a sequence task model and managed by the planning system. For the sequence task of successful planning, its regression meta-task enters the planning pool regularly according to the interference task sequence execution plan calculated. It is worth noting that when calculating the interference task sequence execution plan, the conflict resolution between the regression tasks has been completed, so there is no conflict between the regression meta-tasks in the planning pool, and at the same time, the regression meta-task should have a higher priority than the conventional meta-task.

[0044] In the application scenario provided in the embodiment of the present application, the D-InSAR mission has strict requirements on the regression of the satellite orbit regression period and the freezing of the orbit period. Therefore, under the premise of ensuring the strict regression of the satellite orbit periodicity, ensuring that the incident angle of the target area is consistent during each D-InSAR mission planning is a prerequisite for the success of the D-InSAR mission. Since the SAR satellite needs to calculate the satellite radar payload parameters based on geometric parameters such as orbital altitude, slant range, satellite orbit data, and target position, it is necessary to use the imaging time T0 and the incident angle R0 of the first phase of the D-InSAR mission after the payload parameter calculation as the input parameters T1 and R1 for the parameter calculation of the later multi-phase mission, and perform parameter calculation again to obtain the payload parameters that finally meet the imaging requirements.

[0045] In practical applications, the planning framework is embedded in the actual system of the 4D Gaojing-2 satellite observation mission planning. Before the use of this framework, the 4D Gaojing-2 time-series InSAR mission mainly relied on manual judgment and task association. According to the above InSAR mission establishment process, the mission planning system manually records each repeated observation task of the time-series InSAR mission. It takes a long time to complete an observation mission arrangement, and as the number of tasks increases, the difficulty of task management and the manual workload will increase sharply.

[0046] Based on the planning framework of this application, the process of the existing planning system is: demand decomposition according to order requirements, regional target requirements are decomposed into single-point target requirements, access calculations are performed for each point target, and the planning results of the target area are based on the idle orbital resources or multi-star ground resources after the conflict is resolved, and then sub-task planning is performed according to the time period. If the task is a D-InSAR task, the system will create a time-series InSAR task according to the task parameters (minimum time interval, maximum time interval, acquisition frequency, etc.), and manage the time-series InSAR task as a whole. All imaging subtasks obtain the payload parameters of the daily imaging task through the parameter calculation tool and generate planning instructions, which are sent to the ground station.

[0047] The satellite sequence task planning method based on the greedy strategy provided in the embodiment of the present application mainly provides a specific processing method for the sequence task group conflict detection and resolution link in the aforementioned step four, so as to realize automatic conflict detection and resolution of the satellite sequence task group, and improve the rationality and effectiveness of task planning.

[0048] Figure 2 A satellite sequence task planning method based on a greedy strategy is provided in an embodiment of the present application. In the satellite sequence task planning method based on a greedy strategy, the satellite sequence task includes an original meta-task and at least one regression meta-task. Figure 2 The method shown includes: the following steps S201 to S213.

[0049] Step S201: Determine a first time grid interval of the original meta-task according to the time grid and the power on / off time corresponding to the original meta-task.

[0050] The time grid is essentially a one-dimensional coordinate axis. The time grid is used to quickly locate the execution time of the satellite observation task. The grid number of the starting time grid is 1, and the grid length is L. The execution time of the satellite observation task can be determined by the grid number in the time grid occupied by the satellite observation task. The execution time can be represented by the time grid interval. For example, the grid numbers in the time grid occupied by a satellite observation task are 3, 4, and 5, and the time grid interval of the satellite observation task is [3,5].

[0051] In one embodiment, the step of determining the first time grid interval of the original primitive task according to the time grid and the power on / off time corresponding to the original primitive task (step S201 ) includes the following steps 1 to 4.

[0052] Step 1: Determine a first ratio of the first difference value to the grid length of the time grid.

[0053] The first difference is the difference between the startup time corresponding to the original primitive task and the time zero.

[0054] Step 2: rounding down the first ratio to an integer and determining it as the left endpoint of the first time grid interval.

[0055] Step three: determine a second ratio of the second difference to the grid length of the time grid.

[0056] The second difference is the difference between the shutdown time corresponding to the original primitive task and time zero.

[0057] Step 4: round down the second ratio and determine it as the right endpoint of the first time grid interval.

[0058] In the above implementation, the first time grid interval of the original meta-task can be determined by the following formula.

[0059] Assuming that time zero is T0, the time grid interval occupied by the original task execution is for:

[0060] ;

[0061] ;

[0062] ;

[0063] Among them, the planned startup time of the original mission is Tori_start , the shutdown time is T ori_end The Floor(x) function is a function that rounds x down.

[0064] Similarly, the time grid interval occupied by the regression meta-task is for:

[0065] n = C / L;

[0066] ;

[0067] Among them, i represents the number of regressions, k i Indicates the number of regression cycles between this regression task and its original task.

[0068] In this embodiment, Figure 1 The timing of generating the sequence task model in is: after the original meta-task is successfully planned, the interference task sequence execution plan is generated, encapsulated into a sequence task model, and stored in the library. The task execution constraints of the original meta-task and the regression meta-task are described in the following table.

[0069] Serial number Recording Parameters 1 Longitude of scene center (°) 2 Scene center latitude (°) 3 Scene center height (m) 4 Imaging start time (s) 5 Imaging end time(s) 6 Strip rolling angle (°) .....

[0070] Figure 1 The design of the sequence task model in: mainly includes meta-task information and sequence information, that is, mainly , .

[0071] In one embodiment, before determining the first time grid interval of the original meta-task, the method further includes: based on the regression period (C), the maximum imaging time of the system task (T stmax ), the minimum time interval between two power-on and power-off imaging (T interval ), generate the time grid.

[0072] The key to generating the time grid is to determine the grid length (L) of the time grid. The grid length of the time grid is the minimum value under the preset conditions. The preset conditions include: the grid length is greater than the sum of the maximum imaging time of the system task and the minimum time interval between two power on and power off imaging, and the grid length can divide the regression period.

[0073] That is, L>T stmax +T interval , and L|C, in order to make the grid more accurate, L takes the smallest possible value.

[0074] In the embodiment of the present application, a relational database is selected to store the sequence task model instance, and the grid number related information obtained by the above calculation will be used to establish an index. Through the time grid, a fast search for satellite observation tasks is achieved.

[0075] After the first time grid interval of the original meta-task is determined, it is necessary to perform conflict detection and resolution on the first regression meta-task based on the first time grid interval.

[0076] It is understandable that repetitive tasks have batch and periodic characteristics, that is, repetitive tasks are a group of sequential tasks, and the planning is either all successful or all failed. It is not possible to only consider the success of some tasks in the sequence. Therefore, if the original meta-task planning of a repetitive task is successful, the conflict detection and resolution of subsequent regression meta-tasks should be performed based on the original meta-task, and the sequence planning should be generated and stored. If there is a reasonable solution for the sequence planning calculation, the repetitive task is considered to be successfully planned. Therefore, the conflict resolution algorithm needs to perform conflict detection on each task in the repetitive task.

[0077] Step S202: Set the initial value of the regression planning round to 1.

[0078] The regression planning round is used to indicate that the current task planning round is the round for which the regression meta-task is planned.

[0079] In an embodiment of the present application, there are multiple regression meta-tasks, and the task numbers of the regression meta-tasks are, for example, 1, 2, 3, 4, ..., m; then the regression planning round corresponds to the regression meta-task, the initial value of the regression planning round is 1, and the maximum value is the maximum task number of the regression meta-task.

[0080] Step S203: Determine the candidate time grid intervals for the regression meta-task to be planned based on the target time grid interval and the task interval parameter.

[0081] The target time grid interval is the time grid interval corresponding to the latest successfully planned task in the satellite sequence task. The regression meta-task to be planned is a regression meta-task whose task number is the same as the value of the regression planning round.

[0082] It can be understood that when planning the first regression meta-task (the first round of task planning), the latest successfully planned task in the satellite sequence mission is the original meta-task, that is, the target time grid interval is the aforementioned first time grid interval.

[0083] When planning the second regression meta-task (the second round of mission planning), the most recently planned mission in the satellite sequence mission is the first regression meta-task, that is, the target time grid interval is the time grid interval corresponding to the first regression meta-task. Similarly, in each round of mission planning, the target time grid interval is determined.

[0084] In one embodiment, the task interval parameter includes: the minimum cycle interval and the maximum cycle interval of the satellite sequence task. The task interval parameter is specified by the user, wherein the minimum cycle interval is i min cycles, the maximum cycle interval is i max cycle.

[0085] The step of determining the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameter (step S203) includes: the candidate time grid interval of the regression meta-task to be planned is: [a k ,b k ].

[0086] Among them, a k =a+n*i,b k =b+n*i,i∈[i min , i max ]; k is the serial number of the candidate time grid interval; [a, b] is the first time grid interval, n is the number of grids contained in a regression cycle, i is the number of regression cycles, i min is the number of regression cycles contained in the minimum period interval, i max is the number of regression cycles contained in the maximum cycle interval.

[0087] Step S204: Determine whether there is a conflict-free interval in the candidate time grid interval.

[0088] The measurement standard of the conflict-free interval is: when the regression meta-task is determined in this interval, there is no conflict when the satellite executes the regression meta-task and any satellite observation mission that has been successfully planned.

[0089] In one embodiment, determining whether there is a conflict-free interval in the candidate time grid interval includes: the following steps S2041 to S2043.

[0090] Step S2041: Determine the first extension interval corresponding to each candidate time grid interval.

[0091] In the embodiment of the present application, the candidate time grid interval is: k ,b k ], the first extension interval is [a k -1,b k -1]. In the embodiment of the present application, the candidate grid interval is expanded by 1 unit because conflicts may occur between tasks with occupancy conflicts within the expanded range of 1 unit.

[0092] Step S2042: Execute a first operation for each of the first expansion intervals;

[0093] Step S2043: After performing the first operation on all the first extended intervals, if there is a candidate time grid determined to be a non-conflict interval, it is determined that there is a non-conflict interval in the candidate time grid interval; if all the candidate time grids are determined to be conflicting intervals, it is determined that there is no non-conflict interval in the candidate time grid interval.

[0094] In the embodiment of the present application, the first operation includes: step S2042a-step S2042e.

[0095] Step S2042a: determine whether there is a satellite observation task in the time grid that conflicts with the grid occupancy of the first extension interval.

[0096] In an embodiment of the present application, if the time grid interval corresponding to the satellite observation task has an intersection with the first extension interval, it is considered that there is a grid occupancy conflict between the satellite observation task and the first extension interval; if the time grid interval corresponding to the satellite observation task has no intersection with the first extension interval, it is considered that there is no grid occupancy conflict between the satellite observation task and the first extension interval.

[0097] For example, the first extension interval is [10, 15], and the time grid interval of a certain satellite observation task is [9, 11], then there is a grid occupancy conflict between the satellite observation task and the first extension interval.

[0098] In the embodiment of the present application, by querying the sequence task model table, the regression task grid interval Greg and [a i -1,b i +1] to determine whether there is a satellite observation task in the time grid that has a grid occupancy conflict with the first extension interval.

[0099] Step S2042b: When there is no satellite observation task in the time grid that has a grid occupancy conflict with the first extended interval, determine the candidate time grid corresponding to the first extended interval as a non-conflict interval.

[0100] Step S2042c: when there is a satellite observation task in the time grid and there is a grid occupancy conflict with the first extension interval, determine whether there is a conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta-task.

[0101] Step S2042d: When there is no conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta-task, determine the candidate time grid corresponding to the first extended interval as a conflict-free interval.

[0102] In an embodiment of the present application, if there is no conflict between the task execution constraints of the satellite observation task and the task execution constraints of the original meta-task, it means that when the satellite performs the observation task, it can synchronously execute the satellite observation task and the regression meta-task corresponding to the original meta-task. Therefore, when there is no conflict between the task execution constraints of the satellite observation task and the task execution constraints of the original meta-task, the candidate time grid corresponding to the first extended interval can be determined as a conflict-free interval.

[0103] Step S2042e: when there is a conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta-task, determine the candidate time grid corresponding to the first extended interval as a conflicting interval.

[0104] In the embodiment of the present application, all conflict-free intervals in the candidate time grid intervals are determined through the above steps S2042a to S2042e.

[0105] Step S205: When there is a non-conflict interval in the candidate time grid interval, score each of the non-conflict intervals based on a preset scoring rule.

[0106] It is understandable that in the D-InSAR task order, the user will specify the time interval range for the expected task to be repeated. Therefore, for each regression task planning of the task, there may be multiple feasible planning schemes within the interval range. This method will design a scoring algorithm to calculate the score of each feasible scheme (the design of the scoring algorithm is based on the impact of the scheme on the flexibility of system planning. The smaller the impact, the higher the score. It can be further expressed as: based on the scheme, calculate the probability of no conflict when a new random task is inserted within a certain threshold time range associated with the execution time of the scheme before and after). This method is based on the idea of ​​a greedy algorithm. In each round of regression task planning, the scheme with the highest score among the currently planned feasible schemes is selected as the local optimal scheme. After all regression tasks are successfully planned, the generated local optimal scheme sequence is used as the overall local optimal solution for the repeated task.

[0107] Figure 3 A schematic diagram of a scoring method provided in an embodiment of the present application. Figure 3 As shown, the step of scoring each conflict-free interval based on a preset scoring rule includes: the following steps S301 to S305.

[0108] Step S301: for each task, based on the shortest interval constraint between two tasks, determine a second extension interval corresponding to the conflict-free interval.

[0109] In the embodiment of the present application, assuming that the conflict-free interval is [t0, t1], the second extended interval , where s represents the shortest interval time constraint between two tasks.

[0110] It can be understood that if the regression meta-task is planned in the conflict-free interval, the actual occupied time of the regression meta-task is the second extended interval.

[0111] Step S302: Determine a third extension interval corresponding to the conflict-free interval based on a preset time range threshold.

[0112] The preset time range threshold is greater than the shortest interval time constraint between the two tasks.

[0113] In the embodiment of the present application, the preset time range threshold is set to t. The third extension interval is .

[0114] Step S303: determine the union of the second extended interval and the interval occupied by the successful task.

[0115] The successful mission occupied interval is a time interval occupied by successfully planned satellite observation missions in the third extended interval.

[0116] In the embodiment of the present application, assuming that there is a successfully planned task within the third expansion interval, the time range occupied by the successfully planned task is respectively expressed as , where t i0 represents the start execution time of the i-th successfully planned task, t i1 Indicates the end execution time of the i-th successfully planned task.

[0117] Step S304: determine the relative complement of the union in the third extension interval.

[0118] The relative complement is the available time range interval within the third extension interval. The relative complement is: .

[0119] Step S305: input the relative complement into a scoring function to determine the score of the conflict-free interval.

[0120] Among them, the scoring function is based on the weight function of the distribution of day-level tasks on the time axis.

[0121] In the embodiment of the present application, each conflict-free interval is scored, and the score can represent the matching degree score of the conflict-free interval and the greedy strategy. The greedy strategy is to select the candidate grid with the highest flexibility in the planning cycle as the current local optimal solution.

[0122] It is understandable that the greedy strategy requires the electronic device to select the conflict-free interval with the highest score. However, if the regression meta-task cannot be planned in the next round after the conflict-free interval with the highest score, it is necessary to fall back to the round and reselect a suitable conflict-free interval as the local optimal solution. During the first fallback, the conflict-free interval with the second highest score will be selected as the local optimal solution; during the second fallback, the conflict-free interval with the third highest score will be selected as the local optimal solution... However, if there is no conflict-free interval to choose from during the zth fallback, the local optimal solution cannot be determined. Therefore, before selecting the local optimal solution, it is necessary to determine whether there is still a local optimal solution in the conflict-free interval based on the fallback count of the current round.

[0123] Step S206: Determine whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round.

[0124] The current round rollback count corresponding to the regression planning round is the number of times the regression planning round is rolled back. For example, if the regression planning round is 4 and the corresponding current round rollback count is 2, it means that the number of times the regression planning round is rolled back to the fourth round is 2.

[0125] It should be noted that, for each round of planning tasks, the initial value of the round back count is 0. When the electronic device backs off to the round once, the value of the round back count corresponding to the round is increased by 1.

[0126] In one embodiment, determining whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round includes: the following step 1 or step 2.

[0127] Step 1: When the value of the round back-off count corresponding to the regression planning round is equal to the number of the conflict-free intervals, it is determined that there is no local optimal solution in the conflict-free interval.

[0128] Among them, when the value of the round backoff count corresponding to the regression planning round is equal to the number of the conflict-free intervals, it means that in the previous multiple backoff processes, the conflict-free intervals have all been used as local optimal solutions, and none of them can make the subsequent processes proceed smoothly. There are no conflict-free intervals to be selected in this backoff process. Therefore, it is determined that there is no local optimal solution in the conflict-free interval.

[0129] Step 2: When the value of the round back-off count corresponding to the regression planning round is less than the number of the conflict-free intervals, it is determined that there is a local optimal solution in the conflict-free interval.

[0130] When the value of the round backoff count corresponding to the regression planning round is less than the number of the conflict-free intervals, it means that in the previous multiple backoff processes, no conflict-free interval has been used as the local optimal solution, or only some of the conflict-free intervals have been used as the local optimal solution. In this backoff process, there are still conflict-free intervals that can be selected. Therefore, it is determined that there is a local optimal solution in the conflict-free interval.

[0131] Step S207: When a local optimal solution exists, the local optimal solution is determined according to the score and the backoff count of the current round, and the local optimal solution is determined as the planning interval of the regression meta-task to be planned.

[0132] In an embodiment of the present application, when a local optimal solution exists, the local optimal solution is determined based on the score and the round back-off count, including: when a local optimal solution exists, the value of the round back-off count plus 1 is determined as the target value; and the scores are sorted into a conflict-free interval of the target value to be determined as the local optimal solution.

[0133] For example, if the regression planning round is 4, the corresponding backoff count for the current round is 2, and the target value = the backoff count for the current round plus 1 = 2 + 1 = 3. In this backoff process, the conflict-free interval with a score ranking of 3 is selected as the local optimal solution.

[0134] Step S208: Determine whether the regression planning round is equal to the maximum task sequence number of the regression meta-task.

[0135] The determination of whether the regression planning round is equal to the maximum task sequence number of the regression meta-task is to determine whether the task planning operations are completed for all regression meta-tasks.

[0136] Step S209: when the regression planning round is not equal to the maximum task sequence number, add 1 to the value of the regression planning round, and return to the step of determining the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameter.

[0137] Among them, when the regression planning round is not equal to the maximum task sequence number, it means that the task planning operation has not been completed for all regression meta-tasks. Therefore, the value of the regression planning round is increased by 1, and the step of determining the candidate time grid interval for the regression meta-task to be planned based on the target time grid interval and task interval parameters is returned to perform task planning for the next regression meta-task.

[0138] Step S210: When the regression planning round is equal to the maximum task number, a planning scheme for the satellite sequence task is generated based on the first time grid interval and the planning interval corresponding to each regression meta-task.

[0139] Among them, when the regression planning round is equal to the maximum task number, it means that the task planning operation has been completed for all regression meta-tasks, and the conflict detection and elimination of all regression meta-tasks in the satellite sequence task have been achieved. There is no conflict between all regression meta-tasks in the satellite sequence task and all existing satellite observation tasks in the planning task pool. Therefore, based on the first time grid interval and the planning interval corresponding to each regression meta-task, a planning scheme for the satellite sequence task is generated. The planning scheme is the overall local optimal solution for the satellite sequence task.

[0140] In an implementation provided by an embodiment of the present application, after determining whether there is a conflict-free interval in the candidate time grid interval (the aforementioned step S204), the satellite sequence task planning method further includes: steps S211 to S213. After steps S211 to S213, the fallback process is described.

[0141] Step S211: When there is no conflict-free interval in the candidate time grid interval, determine whether the value of the regression planning round minus 1 is equal to 0.

[0142] Step S212: if the value of the regression planning round is not equal to 0 after being subtracted by 1, the value of the regression planning round is subtracted by 1, and the value of the round backoff count corresponding to the regression planning round after the value is subtracted by 1 is added by 1, and the round backoff count corresponding to the regression planning round is returned to determine whether there is a local optimal solution in the conflict-free interval.

[0143] Among them, the initial value of the round back count is 0.

[0144] It is understandable that if the value of the regression planning round is not equal to 0 after being subtracted by 1, it means that there is a round to which rollback is currently available. The value of the current round rollback count corresponding to the regression planning round after the value is subtracted by 1 is added by 1 to record the number of times this rollback process has rolled back to the current rollback round.

[0145] Step S213: When the value of the regression planning round is equal to 0 after being subtracted by 1, it is determined that the satellite sequence mission planning has failed.

[0146] It can be understood that the value of the regression planning round minus 1 equals 0, indicating that the current round is the first round and there is no round to fall back to. In the first round, all conflict-free intervals cannot support the planning of subsequent regression meta-tasks, so it is determined that the satellite sequence mission planning has failed.

[0147] After determining that the satellite sequence mission planning has failed, the original meta-task of the satellite sequence mission can be replanned, for example, the observation time of the original meta-task is delayed by one or more regression cycles. After the original meta-task of the satellite sequence mission is replanned, the satellite sequence mission planning method based on the greedy strategy provided in the embodiment of the present application is executed based on the replanned original meta-task to realize the task planning of each regression meta-task in the satellite sequence mission.

[0148] In an implementation provided by an embodiment of the present application, after determining whether there is a local optimal solution in the conflict-free interval based on the score and the current round backoff count corresponding to the regression planning round (the aforementioned step S206), the method further includes: in the absence of a local optimal solution, executing the above steps S211-S213.

[0149] Among them, it is determined that there is no local optimal solution in the conflict-free interval, which means that the local optimal solution cannot be selected in this round. At this time, it is necessary to fall back to the previous round and reselect the local optimal solution of the previous round, so as to facilitate task planning for the regression meta-task corresponding to this round based on the local optimal solution reselected in the previous round in this round.

[0150] The step division of the above methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this application; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0151] In a second aspect, an embodiment of the present application provides a satellite sequence task planning device based on a greedy strategy. The satellite sequence task includes an original meta-task and at least one regression meta-task. Figure 4 As shown, the device includes the following modules.

[0152] The interval determination module 401 is used to determine the first time grid interval of the original meta-task according to the time grid and the power on / off time corresponding to the original meta-task.

[0153] The round configuration module 402 is used to set the initial value of the regression planning round to 1.

[0154] The candidate interval determination module 403 is used to determine the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameter; the regression meta-task to be planned is a regression meta-task whose task sequence number is the same as the value of the regression planning round.

[0155] The conflict interval determination module 404 is used to determine whether there is a conflict-free interval in the candidate time grid interval.

[0156] The scoring module 405 scores each of the non-conflict intervals based on a preset scoring rule when there is a non-conflict interval in the candidate time grid intervals.

[0157] The optimal solution determination module 406 is used to determine whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round.

[0158] The planning processing module 407 is used to determine the local optimal solution according to the score and the current round backoff count corresponding to the regression planning round when there is a local optimal solution, and determine the local optimal solution as the planning interval of the regression meta-task to be planned.

[0159] The planning decision module 408 is used to determine whether the regression planning round is equal to the maximum task sequence number of the regression meta-task.

[0160] The round configuration module 402 is also used to increase the value of the regression planning round by 1 when the regression planning round is not equal to the maximum task sequence number, and the planning decision module 408 is also used to control the return of the step of determining the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and task interval parameters.

[0161] The planning decision module 408 is further configured to generate a planning scheme for the satellite sequence task based on the first time grid interval and the planning interval corresponding to each regression meta-task when the regression planning round is equal to the maximum task sequence number.

[0162] The embodiment of the present application provides a satellite sequence task planning device based on a greedy strategy, which can automatically detect and resolve conflicts in a satellite sequence task group, thereby improving the rationality and effectiveness of task planning.

[0163] It should be clear that the present application is not limited to the specific configurations and processes described in the above embodiments and shown in the figures. For the convenience and brevity of description, a detailed description of the known methods is omitted here, and the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, which will not be repeated here.

[0164] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5The electronic device includes: one or more processors 501; a memory 502, on which one or more programs are stored. When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned satellite sequence mission planning methods based on a greedy strategy; one or more I / O interfaces 503, connected between the processor and the memory, and configured to realize information interaction between the processor and the memory.

[0165] Among them, the processor 501 is a device with data processing capabilities, including but not limited to a central processing unit (CPU) and the like; the memory 502 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 503 is connected between the processor 501 and the memory 502, and can realize information interaction between the processor 501 and the memory 502, including but not limited to a data bus (Bus) and the like.

[0166] In some embodiments, the processor 501 , the memory 502 , and the I / O interface 503 are connected to each other through a bus, and further connected to other components of the computing device.

[0167] This embodiment also provides a computer-readable medium on which a computer program is stored. When the program is executed by a processor, the satellite sequence mission planning method based on the greedy strategy provided in this embodiment is implemented. To avoid repeated description, the specific steps of the satellite sequence mission planning method based on the greedy strategy are not repeated here.

[0168] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods invented above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0169] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0170] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present embodiment and to form different embodiments.

[0171] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present application, but the present application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of the present application, and these modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A satellite sequence mission planning method based on a greedy strategy, characterized in that: The satellite sequence task includes an original meta-task and at least one regression meta-task, and the method includes: Determine a first time grid interval of the original meta-task according to the time grid and the power on / off time corresponding to the original meta-task; Set the initial value of the regression planning round to 1; Based on the target time grid interval and the task interval parameter, a candidate time grid interval of the regression meta-task to be planned is determined; the target time grid interval is the time grid interval corresponding to the latest successfully planned task in the satellite sequence task; the regression meta-task to be planned is a regression meta-task whose task sequence number is the same as the value of the regression planning round; Determining whether there is a conflict-free interval in the candidate time grid interval; In the case where there is a non-conflict interval in the candidate time grid intervals, scoring each of the non-conflict intervals based on a preset scoring rule; Determining whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round; In the case where a local optimal solution exists, determining the local optimal solution according to the score and the current round backoff count, and determining the local optimal solution as the planning interval of the regression meta-task to be planned; Determine whether the regression planning round is equal to the maximum task sequence number of the regression meta-task; In the case where the regression planning round is not equal to the maximum task sequence number, adding 1 to the value of the regression planning round, and returning to the step of determining the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameter; When the regression planning round is equal to the maximum task number, generating a planning scheme for the satellite sequence task based on the first time grid interval and the planning interval corresponding to each regression meta-task; Scoring each of the conflict-free intervals based on a preset scoring rule includes: For each of the conflict-free intervals, determining a second extended interval corresponding to the conflict-free interval based on a shortest interval time constraint between two tasks; Based on a preset time range threshold, determining a third extended interval corresponding to the conflict-free interval; the preset time range threshold is greater than the shortest interval time constraint between the two tasks; Determine a union of the second extended interval and the interval occupied by successful tasks; the interval occupied by successful tasks is a time interval occupied by successfully planned satellite observation tasks in the third extended interval; determining a relative complement of the union in the third extension interval; The relative complement is input into a scoring function to determine the score of the conflict-free interval.

2. The method according to claim 1, characterized in that After determining whether there is a conflict-free interval in the candidate time grid interval, the method further includes: In the case that there is no conflict-free interval in the candidate time grid interval, determining whether the value of the regression planning round minus 1 is equal to 0; If the value of the regression planning round is not equal to 0 after being subtracted by 1, the value of the regression planning round is subtracted by 1, and the value of the current round backoff count corresponding to the regression planning round after the value is subtracted by 1 is added by 1, and the step of determining whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round is returned; the initial value of the current round backoff count is 0; When the value of the regression planning round is equal to 0 after being subtracted by 1, it is determined that the satellite sequence mission planning has failed.

3. The method according to claim 1, characterized in that After determining whether there is a local optimal solution in the conflict-free interval according to the score and the current round backoff count corresponding to the regression planning round, the method further includes: In the absence of a local optimal solution, determining whether the value of the regression planning round minus 1 is equal to 0; If the value of the regression planning round is not equal to 0 after being subtracted by 1, the value of the regression planning round is subtracted by 1, and the value of the current round backoff count corresponding to the regression planning round after the value is subtracted by 1 is added by 1, and the step of determining whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round is returned; the initial value of the current round backoff count is 0; When the value of the regression planning round is equal to 0 after being subtracted by 1, it is determined that the satellite sequence mission planning has failed.

4. The method according to claim 1, characterized in that: The determining, according to the time grid and the on / off time corresponding to the original meta-task, the first time grid interval of the original meta-task comprises: Determine a first ratio of a first difference value to a grid length of a time grid; the first difference value is a difference between a startup time corresponding to the original meta-task and time zero; Rounding down the first ratio to an integer and determining it as the left endpoint of the first time grid interval; Determine a second ratio of the second difference to the grid length of the time grid; the second difference is the difference between the shutdown time corresponding to the original meta-task and the time zero; The second ratio is rounded down to an integer and determined as the right endpoint of the first time grid interval.

5. The method according to claim 4, characterized in that Before determining the first time grid interval of the original meta-task, the method further includes: The time grid is generated based on the regression cycle, the maximum imaging time of the system task, and the minimum time interval between two power-on and power-off imaging.

6. The method according to claim 5, characterized in that The grid length of the time grid is the minimum value that satisfies the preset conditions; the preset conditions include: The grid length is greater than the sum of the maximum imaging time of the system task and the minimum time interval between two power-on and power-off imaging; The grid length can divide the regression period.

7. The method according to claim 4, characterized in that The task interval parameters include: the minimum cycle interval and the maximum cycle interval of the satellite sequence task; the determining of the candidate time grid interval of the regression meta-task to be planned based on the target time grid interval and the task interval parameters includes: The candidate time grid intervals of the regression meta-task to be planned are: k ,b k ]; Among them, a k =a+n×i,b k =b+n×i,i∈[i min , i max ]; k is the serial number of the candidate time grid interval; [a, b] is the first time grid interval, n is the number of grids contained in a regression cycle, i is the number of regression cycles, i min is the number of regression cycles contained in the minimum period interval, i max is the number of regression cycles contained in the maximum cycle interval.

8. The method according to claim 7, characterized in that The determining whether there is a conflict-free interval in the candidate time grid intervals includes: Determine the first extension interval corresponding to each candidate time grid interval; the first extension interval is [a k -1,b k -1]; For each of the first extension intervals, perform a first operation; After performing the first operation on all the first extended intervals, if there is a candidate time grid determined as a non-conflict interval, it is determined that there is a non-conflict interval in the candidate time grid interval; if all the candidate time grids are determined as conflicting intervals, it is determined that there is no non-conflict interval in the candidate time grid interval; The first operation includes: Determine whether there is a satellite observation task in the time grid that has a grid occupancy conflict with the first extension interval; When there is no satellite observation task in the time grid and there is a grid occupancy conflict with the first extension interval, determining the candidate time grid corresponding to the first extension interval as a non-conflict interval; In the case where there is a grid occupancy conflict between a satellite observation task and the first extension interval in the time grid, determining whether there is a conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta-task; In the case where there is no conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta-task, determining the candidate time grid corresponding to the first extended interval as a conflict-free interval; When there is a conflict between the task execution constraint of the satellite observation task and the task execution constraint of the original meta-task, the candidate time grid corresponding to the first extended interval is determined as a conflicting interval.

9. The method according to claim 2 or 3, characterized in that: The determining whether there is a local optimal solution in the conflict-free interval according to the current round backoff count corresponding to the regression planning round includes: When the value of the round back-off count corresponding to the regression planning round is equal to the number of the conflict-free intervals, determining that there is no local optimal solution in the conflict-free interval; When the value of the round back-off count corresponding to the regression planning round is less than the number of the conflict-free intervals, it is determined that a local optimal solution exists in the conflict-free interval.

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