Remote sensing satellite in-orbit imaging task autonomous management method, device and equipment

Abstract

The embodiment of the invention provides a remote sensing satellite in-orbit imaging task autonomous management method, device and equipment. The method is applied to the technical field of remote sensing satellite imaging. Based on a preset forecasting period, performing rough forecasting processing on all the tasks in each forecasting period, and adding the tasks which are successfully subjected to rough forecasting into a precise forecasting list; on the basis of task requirements, performing precise forecasting processing on the tasks in the precise forecasting list, and generating instruction sequences corresponding to the tasks; based on the priority and the resource occupation state of each task, performing conflict detection on the task corresponding to each instruction sequence and the task in the ready list; adding conflict-free tasks into a ready list based on a conflict detection result, and merging the tasks of the same type; and executing the tasks based on the task sequence in the optimized ready list. In this way, autonomous planning, autonomous execution and autonomous state monitoring of tasks on the satellite are achieved, and therefore abnormal conditions on the satellite can be adjusted in time.

Description

Technical Field

[0001] The present disclosure relates to the field of remote sensing satellite imaging technology, and further relates to the field of autonomous planning technology for remote sensing satellite imaging tasks, and particularly relates to a method, device and equipment for autonomous management of on-orbit imaging tasks of remote sensing satellites. Background Art

[0002] In traditional on-orbit imaging tasks of remote sensing satellites, a program control instruction sequence is generally generated through ground planning. When the satellite passes by, the program control instruction sequence is uploaded to the satellite, or a program control instruction sequence is generated on the satellite, and the program control instruction sequence is executed on the satellite on time to control the satellite to perform on-orbit imaging. This control method is not flexible enough, the amount of data to be uploaded for the instruction sequence is large, and a large number of imaging tasks cannot be preset; there is no effective task tracking mechanism for imaging tasks. After the task is planned, the task status cannot be tracked, and only the automatic execution of the task is realized, but the on-orbit task management and feedback are not realized. The generation and upload of the instruction sequence still rely on scattered tools and manual processing. A large amount of manpower is consumed to complete each step such as instruction production and station upload. In addition, the interaction with the SAR payload currently still relies on table and file transmission to exchange information, and the process is extremely inefficient and the interaction is complex.

[0003] The solution of the present invention proposes a method for realizing autonomous management of on-orbit imaging tasks of remote sensing satellites. This method only needs to upload the parameter data required for imaging tasks (including parameters such as the longitude, latitude, altitude of the imaging target point, and imaging duration) on the ground. The satellite can autonomously plan the imaging tasks, and at the same time, perform autonomous conflict detection and merging processing on the tasks according to the status of task priority, energy, etc., automatically generate an imaging task sequence, continuously monitor the satellite's own status and task status, and execute the task sequence until the task ends. It realizes the functions of autonomous planning and execution of tasks on the satellite, autonomous monitoring of status, and timely adjustment of tasks in case of anomalies on the satellite. Summary of the Invention

[0004] The present disclosure provides a method, device and equipment for autonomous management of on-orbit imaging tasks of remote sensing satellites, which solves the technical problem that traditional on-orbit imaging tasks of remote sensing satellites lack an effective task tracking mechanism. After the task is planned, the task status cannot be tracked, and only the automatic execution of the task is realized, but the on-orbit task management and feedback are not realized.

[0005] According to a first aspect of the present disclosure, a method for autonomous management of on-orbit imaging tasks of remote sensing satellites is provided. The method includes:

[0006] Obtain imaging tasks;

[0007] Based on a preset prediction period, perform rough prediction processing on all imaging tasks in each prediction period, and add the tasks with successful rough prediction to the fine prediction list;

[0008] Based on the task requirements, perform fine prediction processing on the tasks in the fine prediction list to generate instruction sequences corresponding to each task;

[0009] Based on the priorities and resource occupancy statuses of each task, perform conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list;

[0010] Based on the conflict detection results, add the non-conflicting tasks to the ready list and merge the tasks of the same type, update the ready list to obtain an optimized ready list;

[0011] Execute the tasks based on the task order in the optimized ready list.

[0012] In the above aspects and any possible implementation manners, a further implementation manner is provided. The ready list includes instruction sequences corresponding to each imaging task and the execution intervals between the instruction sequences.

[0013] In the above aspects and any possible implementation manners, a further implementation manner is provided. The performing rough prediction processing on all imaging tasks in each prediction period based on a preset prediction period and adding the tasks with successful rough prediction to the fine prediction list includes:

[0014] If the rough prediction fails, determine whether the task processing duration exceeds a preset time. If it does not exceed the time limit, continue with the rough prediction; if it exceeds the time limit, delete the task and mark the task as ended;

[0015] If the rough prediction is successful, add the task to the fine prediction list.

[0016] In the above aspects and any possible implementation manners, a further implementation manner is provided. The performing fine prediction processing on the tasks in the fine prediction list based on the task requirements to generate instruction sequences corresponding to each task includes:

[0017] If the fine prediction is not successful, determine whether the task processing duration exceeds a preset time. If it does not exceed the time limit, continue with the fine prediction; if it exceeds the time limit, delete the task and mark the task as ended;

[0018] If the fine prediction is successful, perform orbit prediction based on the parameter data of the imaging task and the acquired orbit information, and generate instruction sequences corresponding to each task.

[0019] In the above aspects and any possible implementation manners, a further implementation manner is provided. The performing conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list based on the priorities and resource occupancy statuses of each task includes:

[0020] Traverse the tasks corresponding to each instruction sequence and the tasks in the ready list, and determine whether the task types are the same. Among them,

[0021] If the task types are different, then determine whether the resource occupation at the same task time conflicts. If there is a conflict, delete the task with a lower priority or a later execution time, and make the deleted task re-enter the rough prediction list, and perform the next rough prediction process according to the preset prediction period; if there is no conflict, add the task to the ready list according to the priority;

[0022] If the task types are the same, then determine whether there is a conflict in the imaging time. If there is a conflict, delete the task with a lower priority or a later execution time, and make the deleted task re-enter the rough prediction list, and perform the next rough prediction process according to the preset prediction period; if there is no conflict, add the task to the ready list according to the priority and enter the task merging state.

[0023] In the above aspects and any possible implementation manners, a further implementation manner is provided. Adding the conflict-free tasks to the ready list based on the conflict detection result and performing merging of tasks of the same type, updating the ready list, and obtaining the optimized ready list includes:

[0024] If there is a conflict in the execution time of the instruction sequences among tasks of the same type, but there is no conflict in the imaging time, perform task merging processing, merge the tasks of the same type into one instruction sequence, and add the instruction sequence to the ready list according to the priority to obtain the optimized ready list;

[0025] If the tasks of the same type are not successfully merged into one instruction sequence, delete the task with a lower priority or a later execution time, and make the deleted task re-enter the rough prediction list, and perform the next rough prediction process according to the preset prediction period.

[0026] In the above aspects and any possible implementation manners, a further implementation manner is provided. Sequentially executing the tasks based on the task order in the optimized ready list includes:

[0027] Based on the execution interval between each instruction sequence in the optimized ready list, sequentially execute the instruction sequences in the optimized ready list, and during the task execution process, determine in real time whether the resource status is satisfied. If it cannot be satisfied, end the task in advance; if it can be satisfied, sequentially execute the instruction sequences in the optimized ready list.

[0028] In the above aspects and any possible implementation manners, a further implementation manner is provided. The method further includes:

[0029] When deleting a task, judge the task hierarchy of the task to be deleted: if the task to be deleted is a subtask, directly delete it, and make the deleted task re-enter the rough prediction list, and perform the next rough prediction process according to the preset prediction period; if the task to be deleted is the main task, promote the first subtask in the task sequence to be processed to the main task, and promote the levels of the subsequent subtasks in turn.

[0030] According to a second aspect of the present disclosure, there is provided an on-orbit imaging task autonomous management device for a remote sensing satellite. The device includes:

[0031] A data acquisition module for acquiring imaging tasks;

[0032] A rough prediction processing module for performing rough prediction processing on all imaging tasks in each prediction period based on a preset prediction period, and adding the tasks with successful rough prediction to the fine prediction list;

[0033] A fine prediction processing module for performing fine prediction processing on the tasks in the fine prediction list based on task requirements to generate an instruction sequence corresponding to each task;

[0034] A conflict detection module for detecting conflicts between the tasks corresponding to each instruction sequence and the tasks in the ready list based on the priorities and resource occupancy statuses of the tasks;

[0035] A merging processing module for adding conflict-free tasks to the ready list and merging tasks of the same type based on the conflict detection result, updating the ready list, and obtaining an optimized ready list;

[0036] A task execution module for executing the tasks in the order of the tasks in the optimized ready list.

[0037] According to a third aspect of the present disclosure, there is provided an electronic device. The electronic device includes: a memory and a processor, and a computer program is stored on the memory, and when the processor executes the program, the method described above is implemented.

[0038] In the embodiments of the present disclosure, first, by obtaining imaging tasks, and based on a preset prediction period, rough prediction processing is performed on all imaging tasks in each prediction period, and the tasks with successful rough prediction are added to the fine prediction list; secondly, based on task requirements, fine prediction processing can be performed on the tasks in the fine prediction list to generate instruction sequences corresponding to each task, and based on the priorities and resource occupancy statuses of each task, conflict detection is performed on the tasks corresponding to each instruction sequence and the tasks in the ready list; finally, based on the conflict detection results, the conflict-free tasks can be added to the ready list and the same-type tasks can be merged to update the ready list, obtaining an optimized ready list, and tasks are executed based on the task order in the optimized ready list. In this way, the satellite can autonomously plan imaging tasks, and at the same time perform autonomous conflict detection and merging processing on tasks through task priorities, energy, etc., automatically generate imaging task sequences, and continuously monitor the satellite's own status and task status, thereby saving human resources and on-board energy, improving the utilization rate of on-board resources and the execution efficiency of on-board tasks, and avoiding ineffective task execution.

[0039] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more obvious. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0041] Figure 1 shows a flowchart of a method for autonomous management of on-orbit imaging tasks of a remote sensing satellite provided by an embodiment of the present disclosure;

[0042] Figure 2 shows a flowchart of on-board autonomous task sequence generation provided by an embodiment of the present disclosure;

[0043] Figure 3 shows a flowchart of task conflict detection provided by an embodiment of the present disclosure;

[0044] Figure 4 shows a flowchart of task merging provided by an embodiment of the present disclosure;

[0045] Figure 5 shows a flowchart of on-board autonomous imaging tasks provided by an embodiment of the present disclosure;

[0046] Figure 6The structure diagram of the on-orbit imaging task autonomous management device for remote sensing satellites provided by the embodiments of the present disclosure is shown;

[0047] Figure 7 The structure diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure is shown. Detailed implementation manners

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0049] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0050] In the embodiments of the present disclosure, first, by obtaining imaging tasks, based on a preset forecast period, rough forecast processing is performed on all imaging tasks in each forecast period, and the tasks with successful rough forecasts are added to the fine forecast list; secondly, based on task requirements, fine forecast processing can be performed on the tasks in the fine forecast list to generate instruction sequences corresponding to each task, and based on the priorities and resource occupancy statuses of each task, conflict detection is performed on the tasks corresponding to each instruction sequence and the tasks in the ready list; finally, based on the conflict detection results, the non-conflicting tasks can be added to the ready list and the same-type tasks can be merged to update the ready list, obtaining an optimized ready list, and tasks are executed based on the task order in the optimized ready list. In this way, the satellite can autonomously plan imaging tasks, and at the same time perform autonomous conflict detection and merging processing on tasks through task priorities, energy, etc., automatically generate imaging task sequences, and continuously monitor the satellite's own state and task state, thereby saving human resources and on-board energy, improving the utilization rate of on-board resources and the execution efficiency of on-board tasks, and avoiding ineffective task execution.

[0051] The following will, with reference to the accompanying drawings, describe in detail the on-orbit imaging task autonomous management method, device, and equipment for remote sensing satellites provided by the embodiments of the present disclosure through specific embodiments.

[0052] Figure 1 The flowchart of an on-orbit imaging task autonomous management method for remote sensing satellites provided by the embodiments of the present disclosure is shown, asFigure 1 As shown in Figure 1 , the autonomous management method 100 for the on-orbit imaging mission of a remote sensing satellite may include the following steps:

[0053] S110, acquire the imaging mission.

[0054] Exemplarily, the imaging mission may come from the imaging target-related information (imaging duration, target location (longitude, latitude, altitude), imaging mode) uploaded from the ground according to user requirements.

[0055] The parameters uploaded from the ground are shown in the following table:

[0056]

[0057]

[0058] S120, based on a preset forecast period, perform a rough forecast process on all imaging missions in each forecast period, and add the missions with successful rough forecasts to the refined forecast list.

[0059] In some embodiments, if the rough forecast fails, it is determined whether the task processing duration exceeds the preset time. If it does not exceed the time limit, the rough forecast continues; if it exceeds the time limit, the task is deleted and the task end is marked;

[0060] If the rough forecast is successful, the task is added to the refined forecast list.

[0061] Exemplarily, before performing the rough forecast, it is also necessary to preset the forecast period length (for example, every hour, every day, etc.) and the task processing duration (for example, 30 minutes, 1 hour, etc.), and then retrieve all imaging missions within the current forecast period from the task queue or database, and ensure that each task contains necessary parameters (such as task ID, imaging target, imaging parameters, etc.). Traverse all imaging missions within the current forecast period, and execute the rough forecast algorithm for each task, which may involve a preliminary assessment of imaging conditions (such as weather, light, cloud cover, etc.).

[0062] Exemplarily, after the task is uploaded, there are tasks with high or even urgent priorities. The tasks with high priorities will be preferentially and autonomously arranged, and at the same time, an attempt will be made to perform a merge process with the existing uploaded tasks. Even if the existing tasks are deleted due to conflicts or failed merges caused by overlapping task execution times, they will enter the forecast queue for re-forecasting after being deleted, minimizing the impact of high-priority tasks on existing tasks.

[0063] S130, based on the task requirements, perform a refined forecast process on the tasks in the refined forecast list to generate the instruction sequences corresponding to the tasks.

[0064] In some embodiments, if the precise prediction fails, it is determined whether the task processing duration exceeds a preset time. If it does not exceed the time limit, the precise prediction continues; if it exceeds the time limit, the task is deleted and the task end is marked;

[0065] If the precise prediction is successful, orbit prediction is performed based on the parameter data of the imaging task and the acquired orbit information, and an instruction sequence corresponding to each task is generated.

[0066] Exemplarily, there are three sources of data for generating the instruction sequence on the satellite:

[0067] The first is the instruction template, that is, all the instructions that need to be sent for each imaging task. These instructions are grouped according to the operation type to form an instruction template. Each template contains one or more instructions to together implement an operation, such as a power-on sequence template, a power-off sequence template, an imaging sequence template, etc. For example, the power-on sequence template needs to send instructions for setting the attitude, powering on the antenna, powering on the payload, etc.

[0068] The instruction template is formed by one or more instructions, and each instruction exists in the form of instruction description + instruction content. The specific content form of the instruction template implemented by this method is as follows:

[0069] Type Content Instruction Description Description starting with # sign + instruction code, only used to identify instruction function Instruction Content Execution interval time with the previous instruction (4 bytes) + instruction content code

[0070] The information of the instruction sequence template is shown in the following table:

[0071]

[0072]

[0073] The instruction content code format consists of a fixed instruction header + instruction content + CRC, and the specific content is as follows:

[0074]

[0075]

[0076] The second is the imaging parameters uploaded from the ground, such as imaging duration, latitude / longitude / altitude information, etc. These parameters will be filled into the corresponding parameter fields of the specified template as the control parameters specific to this task;

[0077] The third is to obtain relevant information (such as target information, task execution information) from the ground imaging parameters, perform orbit prediction with the orbit information obtained by the current GPS. After the prediction is successful, these parameters will also be filled into the specified template as control parameters, and the execution interval between certain instructions will also be adjusted.

[0078] Finally, an imaging parameter instruction sequence is generated by combining the above three types of data.

[0079] Exemplarily, the parameter information output after successful prediction is shown in the following table:

[0080]

[0081]

[0082] Exemplarily, since the template file has been pre - set on the satellite, it is only necessary to upload the task parameter instructions. The satellite can automatically help users plan imaging parameters, adjust the imaging mode, adjust the imaging time interval, schedule multiple tasks on the satellite, and automatically execute according to the execution time, realizing the real - time display of the task execution status.

[0083] S140, based on the priorities and resource occupancy status of each task, perform conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list.

[0084] In some embodiments, traverse the tasks corresponding to each instruction sequence and the tasks in the ready list, and determine whether the task types are the same. Among them,

[0085] If the task types are different, then determine whether there is a conflict in resource occupancy at the same task time. If there is a conflict, delete the task with a lower priority or a later execution time, and make the deleted task re - enter the rough prediction list, and perform the next rough prediction process according to the preset prediction period; if there is no conflict, add the task to the ready list according to the priority;

[0086] If the task types are the same, then determine whether there is a conflict in the imaging time. If there is a conflict, delete the task with a lower priority or a later execution time, and make the deleted task re - enter the rough prediction list, and perform the next rough prediction process according to the preset prediction period; if there is no conflict, add the task to the ready list according to the priority and enter the task merging state.

[0087] Exemplarily, determining whether there is a conflict in the imaging time specifically includes: determining whether the start and end of the imaging times of two imaging tasks are within 2 minutes. If the imaging times of the two imaging tasks are within 2 minutes and the measurement swing directions are inconsistent, it is determined that there is a task conflict, and the task with a lower priority or a later execution time is deleted.

[0088] S150, based on the conflict detection results, add the non - conflicting tasks to the ready list and perform merging of tasks of the same type, update the ready list, and obtain an optimized ready list.

[0089] In some embodiments, if there are conflicts in the execution time of instruction sequences among tasks of the same type, but there are no conflicts in the imaging time, task merging processing is performed. The tasks of the same type are merged into one instruction sequence, and the instruction sequence is added to the ready list according to the priority to obtain an optimized ready list.

[0090] If the tasks of the same type are not successfully merged into one instruction sequence, the task with a lower priority or a later execution time is deleted, and the deleted task re-enters the rough prediction list for the next rough prediction process according to the preset prediction period.

[0091] In some embodiments, the method further includes: when deleting a task, judging the task level of the task to be deleted: if the task to be deleted is a subtask, it is directly deleted, and the deleted task re-enters the rough prediction list for the next rough prediction process according to the preset prediction period; if the task to be deleted is a main task, the first subtask in the task sequence to be processed is promoted to the main task, and the levels of the subsequent subtasks are promoted in turn.

[0092] Exemplarily, the sequence of merged tasks will be cleared, and the execution intervals between instructions will be readjusted. Instructions with some overlapping functions are merged to regenerate an instruction sequence that can continuously execute multiple tasks.

[0093] Exemplarily, for tasks to be executed or being executed, each task is arranged in ascending order of the imaging time of the task nodes and is hung on the first bus; at the same time, due to the merging of tasks, the merged tasks take the first executed task as the main task, and the subsequent tasks merged with it are subtasks, which are hung on the second bus. There is only one task bus (the first bus) to be executed or being executed on the satellite, while there are multiple second buses. Therefore, when each task is inserted, it can perform conflict checking and merging processing with all tasks to be executed on the satellite, and when a task is deleted due to conflict, only the conflicting node is deleted, without affecting other nodes in its sequence.

[0094] Exemplarily, the task conflict check on the satellite can eliminate mutually conflicting tasks and reduce the execution of useless tasks. After task merging, the merged continuous tasks are executed in one power-on, saving the imaging preparation and ending time, and avoiding frequent switching of the payload to delay imaging. It has energy anomaly protection for tasks, avoiding imaging failure and deteriorating the satellite's energy state when imaging occurs under low energy conditions.

[0095] Exemplarily, after the task is uploaded, it can be predicted and planned multiple times. Even if the conflict or merger fails, it will not be immediately deleted, but will return to the rough prediction list to continue prediction and planning. There are more opportunities for trial and error, which improves the success rate of task execution. Moreover, there is abnormal state monitoring throughout the process from task planning to execution, which can avoid inappropriate task execution times and also improve the success rate of task execution.

[0096] Exemplarily, in order to intuitively and completely display the status information of all on-board imaging tasks, the software collects the relevant status of each task every second and displays it in the telemetry. One page of the telemetry packet of an autonomous imaging task can display the status information of 12 imaging tasks. Through the page-by-page rotation display method, a total of 5 pages and 60 task information can be displayed; the specific telemetry content is shown in the following table:

[0097]

[0098]

[0099]

[0100] Exemplarily, the task is displayed in the telemetry in real time from being uploaded to the satellite to the end of execution, which not only facilitates timely viewing of the task status, understanding of the task arrangement status on the satellite, and adjustment of the task plan, but also facilitates post-event analysis of the task execution status, and can flexibly specify the deletion of pending task nodes.

[0101] S160. Execute the task based on the task order in the optimized ready list.

[0102] In some embodiments, based on the execution intervals between the instruction sequences in the optimized ready list, the instruction sequences in the optimized ready list are executed in order, and during the task execution process, it is determined in real time whether the resource status is satisfied. If not, the task is ended in advance; if satisfied, the instruction sequences in the optimized ready list are executed sequentially.

[0103] Exemplarily, after the task reaches the execution time, it will start to be executed item by item according to the time intervals in the instruction sequence until the task execution is completed. And the task from being uploaded from the ground to the end of execution (failed exit or successful completion) will be recorded in real time through the telemetry status word to facilitate timely manual intervention on the ground and post-event traceability. In addition, the ground can also manually delete the task nodes on the satellite at any time to cancel the current task.

[0104] Figure 2 Shows the flowchart of generating the on-board autonomous task sequence provided by the embodiment of the present disclosure. As Figure 2 shown:

[0105] Based on the instruction sequence template file, a list of template information structures is generated, and the specified templates are filled by obtaining imaging parameters uploaded from the ground, etc. Forecasting processing is performed on all tasks in each forecasting cycle on the satellite. If both the rough forecast and the fine forecast are successful, conflict detection and merging processing are performed with other ready tasks, and the parameters uploaded from the ground and forecast on the satellite are filled into the corresponding template variables to generate an instruction sequence. If the rough forecast and / or the fine forecast is not successful, it re-enters the rough forecast ready queue.

[0106] Figure 3 The flowchart of task conflict detection provided by the embodiment of the present disclosure is shown. As Figure 3 shown:

[0107] After the task forecast successfully generates a sequence, it is added to the on-satellite task ready list, and each task in the ready list needs to be compared one by one to check for conflicts. The conflicts between different types of tasks mainly involve determining whether there is a conflict in resource occupancy at the same task time, and the conflicts between the same type of tasks mainly involve determining whether there is a conflict in imaging time. If there is a conflict, the task node with a lower priority or a later execution time is deleted (the deleted task node re-enters the forecast list for the next rough forecast processing). The new task is compared with all task nodes in the ready list, and the conflict detection is considered completed only after the instruction sequence is successfully updated.

[0108] Exemplarily, the conflicts are mainly reflected in the overlap of resource occupancy or imaging time between two tasks. For example, Task A occupies resources or imaging time earlier than Task B, and Task A has not finished occupying resources or imaging when Task B has already started to occupy resources or image; Task A occupies resources or imaging time later than Task B, and Task B has not finished occupying resources or imaging when Task A has already started to occupy resources or image.

[0109] Figure 4 The flowchart of task merging provided by the embodiment of the present disclosure is shown. As Figure 4 shown:

[0110] There is an overlap in the execution time of task sequences among tasks of the same type, but the imaging times do not overlap. Task merging processing is required. After merging, the task nodes will be combined into an instruction sequence for execution. According to the imaging interval length between tasks, there is power-on and power-off of relevant single machines and payloads for tasks with an interval longer than 4 minutes. For tasks with an interval shorter than 4 minutes, the states of the single machine and payload are maintained during this period, which can avoid delaying task time and consuming energy due to frequent switching on and off. The merging process first determines whether the imaging times of tasks overlap. If the imaging times overlap, the merging fails; if they can be merged, they are added to the same continuous imaging task sequence, and an attempt is made to successfully generate a new merged imaging sequence. If the sequence fails to be generated, it also indicates that the merging fails. After the merging fails, it is necessary to delete the task nodes with lower priority or later execution time (the deleted task nodes re-enter the prediction list for the next rough prediction process). Then, the remaining task nodes regenerate the instruction sequence.

[0111] Figure 5 The flowchart of the on-board autonomous imaging task provided by the embodiment of the present disclosure is shown as Figure 5 follows:

[0112] Based on the imaging task parameter data uploaded from the ground (including parameters such as the longitude, latitude, altitude of the imaging target point, and imaging duration), the imaging tasks are autonomously predicted, and at the same time, autonomous conflict detection and merging processing are performed on the tasks through states such as task priority and energy, an imaging task sequence is automatically generated, and the satellite's own state and task state are continuously monitored, and the task sequence is executed until the task ends. Thus, it saves human resources and on-board energy, improves the utilization rate of on-board resources and the execution efficiency of on-board tasks, and avoids ineffective task execution.

[0113] Therefore, through the implementation of the conflict check, merging processing, task deletion and recycling mechanism, and abnormal monitoring processing mechanism of on-board tasks in the present disclosure, it not only ensures that the temporarily added urgent tasks can be executed preferentially, and the affected tasks can be recycled and returned to the ready prediction list for planning and scheduling, and attempt to generate a sequence to execute the tasks again, giving each task more opportunities to execute and try out, ensuring the success rate of task execution, and there is a conflict and merging processing mechanism between tasks, avoiding tasks that do not meet the conditions from being executed, optimizing the resource utilization of task execution, saving the energy consumption to the greatest extent, and improving the resource utilization rate. In this way, it not only simplifies the manual operation but also optimizes the on-board task arrangement, greatly improving the success rate of task execution in complex situations.

[0114] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0115] The above is the introduction of the method embodiments. The following further describes the solution of the present disclosure through device embodiments.

[0116] Figure 6 The structural diagram of the on-orbit imaging task autonomous management device for remote sensing satellites provided by the embodiments of the present disclosure is shown. As Figure 6 shown, the on-orbit imaging task autonomous management device 600 for remote sensing satellites includes:

[0117] A data acquisition module 610, configured to acquire imaging tasks;

[0118] A rough prediction processing module 620, configured to perform rough prediction processing on all imaging tasks in each prediction period based on a preset prediction period, and add the tasks with successful rough prediction to the fine prediction list;

[0119] A fine prediction processing module 630, configured to perform fine prediction processing on the tasks in the fine prediction list based on task requirements, and generate an instruction sequence corresponding to each task;

[0120] A conflict detection module 640, configured to perform conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list based on the priority and resource occupancy status of each task;

[0121] A merging processing module 650, configured to add the non-conflicting tasks to the ready list based on the conflict detection result and perform merging of tasks of the same type, update the ready list, and obtain an optimized ready list;

[0122] A task execution module 660, configured to execute the tasks in the order of the tasks in the optimized ready list.

[0123] It can be understood that Figure 6 each module / unit in the on-orbit imaging task autonomous management device 600 for remote sensing satellites shown has the function of implementing Figure 1 each step in the on-orbit imaging task autonomous management method 100 for remote sensing satellites shown, and can achieve its corresponding technical effects. For the sake of brevity, it will not be elaborated here.

[0124] Figure 7FIG. 0 shows a schematic block diagram of an electronic device 700 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0125] The electronic device 700 includes a computing unit 701 that can perform various appropriate actions and processes in accordance with a computer program stored in the ROM 702 or a computer program loaded from the storage unit 708 into the RAM 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. The I / O interface 705 is also connected to the bus 704.

[0126] A plurality of components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0127] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of method 100 described above can be executed. Alternatively, in other embodiments, the computing unit 701 can be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0128] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0129] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0130] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0132] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0133] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0134] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this disclosure can be achieved, and no limitation is imposed herein.

[0135] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. An autonomous management method for on-orbit imaging tasks of remote sensing satellites, characterized in that, Including: Obtain imaging tasks; Based on a preset forecast period, perform rough forecast processing on all imaging tasks in each forecast period, and add the tasks with successful rough forecasts to the fine forecast list; Based on task requirements, perform fine forecast processing on the tasks in the fine forecast list to generate instruction sequences corresponding to each task; Based on the priorities and resource occupancy status of each task, perform conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list; Based on the conflict detection results, add the non-conflicting tasks to the ready list and merge the tasks of the same type, update the ready list to obtain an optimized ready list; Execute the tasks based on the task order in the optimized ready list.

2. The method according to claim 1, characterized in that, The ready list includes the instruction sequences corresponding to each imaging task and the execution intervals between the instruction sequences.

3. The method according to claim 2, wherein The step of performing rough forecast processing on all imaging tasks in each forecast period based on a preset forecast period and adding the tasks with successful rough forecasts to the fine forecast list includes: If the rough forecast fails, determine whether the task processing duration exceeds the preset time. If it does not exceed the time limit, continue with the rough forecast; if it exceeds the time limit, delete the task and mark the task as ended; If the rough forecast is successful, add the task to the fine forecast list.

4. The method according to claim 2, wherein The step of performing fine forecast processing on the tasks in the fine forecast list based on task requirements to generate instruction sequences corresponding to each task includes: If the fine forecast is not successful, determine whether the task processing duration exceeds the preset time. If it does not exceed the time limit, continue with the fine forecast; if it exceeds the time limit, delete the task and mark the task as ended; If the fine forecast is successful, perform orbit forecasting based on the parameter data of the imaging task and the obtained orbit information, and generate instruction sequences corresponding to each task.

5. The method according to claim 2, wherein The step of performing conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list based on the priorities and resource occupancy status of each task includes: Traverse the tasks corresponding to each instruction sequence and the tasks in the ready list, and determine whether the task types are the same. Among them, If the task types are different, determine whether there is a conflict in resource occupancy at the same task time. If there is a conflict, delete the task with a lower priority or a later execution time, and make the deleted task re-enter the rough forecast list for the next rough forecast processing according to the preset forecast period; if there is no conflict, add the task to the ready list according to the priority; If the task types are the same, determine whether there is a conflict in imaging time. If there is a conflict, delete the task with a lower priority or a later execution time, and make the deleted task re-enter the rough forecast list for the next rough forecast processing according to the preset forecast period; if there is no conflict, add the task to the ready list according to the priority and enter the task merging state.

6. The method according to claim 2, wherein The step of adding the non-conflicting tasks to the ready list based on the conflict detection results and merging the tasks of the same type, updating the ready list to obtain an optimized ready list includes: If there are conflicts in the execution time of instruction sequences among tasks of the same type, but there are no conflicts in the imaging time, task merging processing is performed. The tasks of the same type are merged into one instruction sequence, and the instruction sequence is added to the ready list according to the priority to obtain an optimized ready list; If the tasks of the same type are not successfully merged into one instruction sequence, the task with a lower priority or a later execution time is deleted, and the deleted task re-enters the rough prediction list for the next rough prediction processing according to the preset prediction period.

7. The method according to claim 2, characterized in that, The executing the tasks according to the order of the tasks in the optimized ready list includes: Based on the execution interval between each instruction sequence in the optimized ready list, the instruction sequences in the optimized ready list are executed in order, and during the task execution process, it is judged in real time whether the resource status is satisfied. If it is not satisfied, the task is ended in advance; if it is satisfied, the instruction sequences in the optimized ready list are executed in order.

8. The method according to any one of claims 3 to 6, characterized in that, The method further includes: When deleting a task, judge the task level of the task to be deleted: if the task to be deleted is a subtask, it is directly deleted, and the deleted task re-enters the rough prediction list for the next rough prediction processing according to the preset prediction period; if the task to be deleted is a main task, the first subtask in the task sequence to be processed is promoted to the main task, and the levels of the subsequent subtasks are promoted in turn.

9. An on-orbit imaging mission autonomous management device for a remote sensing satellite, characterized in that, including: A data acquisition module, configured to acquire imaging tasks; A rough prediction processing module, configured to perform rough prediction processing on all imaging tasks in each prediction period based on a preset prediction period, and add the tasks with successful rough prediction to the fine prediction list; A fine prediction processing module, configured to perform fine prediction processing on the tasks in the fine prediction list based on task requirements to generate instruction sequences corresponding to each task; A conflict detection module, configured to perform conflict detection on the tasks corresponding to each instruction sequence and the tasks in the ready list based on the priorities and resource occupancy statuses of the tasks; A merging processing module, configured to add conflict-free tasks to the ready list and perform merging of tasks of the same type based on the conflict detection result, and update the ready list to obtain an optimized ready list; A task execution module, configured to execute the tasks according to the order of the tasks in the optimized ready list.

10. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-8.

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