Hybrid mission-driven constellation autonomous mission planning heat map generation and update method

Abstract

The present application relates to a kind of mixed task-driven constellation autonomous task planning heat map generation and updating method, it is related to multi-satellite task planning field.The method includes: input initial data containing static task and dynamic task and generate initial heat map;According to update time period, periodically self-update heat map using time-driven method;Heat map is generated planning result by task planning method and is executed by satellite;Static task execution result, dynamic task execution result after being fused by target association and new task are respectively input event-driven heat map updating method and update;Finally, update result is re-input task planning and forms closed loop.The present application provides uniform task-driven input and decision-making reference model for spaceborne task collaborative decision-making by heat map, combines time driving with event driving, realizes the automation and intelligentization of constellation task planning, significantly improves planning efficiency and system adaptive capacity.

Description

Technical Field

[0001] This invention relates to the field of multi-satellite mission planning technology, and more specifically to a method for generating and updating heatmaps for autonomous mission planning of satellite constellations driven by hybrid missions. Background Technology

[0002] In recent years, breakthroughs in low-Earth orbit satellite technology and the rapid development of commercial spaceflight have accelerated the construction of large-scale global remote sensing constellations and continuously expanded their application scenarios. As constellation size increases, application scenarios become more complex, and the number of missions rises dramatically, the traditional "one mission, one model" and "one model, one algorithm" approach to mission management can no longer meet the needs of autonomous and collaborative planning for constellations. Specifically, existing technologies have the following limitations:

[0003] First, existing constellation mission planning systems lack a unified task input and decision reference model for hybrid missions (including static missions such as regional targets and point targets, as well as dynamic missions such as moving point targets). Different mission types often employ independent data structures and processing flows, resulting in complex system architectures, redundant data processing, and difficulties in achieving efficient collaborative decision-making.

[0004] Secondly, traditional task update mechanisms mostly adopt a single-drive mode, either time-driven updates based on fixed periods or reactive updates triggered by specific events. The update mechanism is relatively simple and fails to organically combine time-driven with multiple event-driven (such as task execution feedback, dynamic target changes, and new task injection) mechanisms. This results in insufficient adaptive capability of the system when facing complex and ever-changing observation tasks, making it difficult to achieve accurate and timely dynamic adjustments.

[0005] Furthermore, existing technologies have shortcomings in handling task execution feedback. Most systems lack a quantitative evaluation mechanism for task execution effectiveness and cannot dynamically adjust task priorities and resource allocation strategies based on actual task completion (such as completion time and data quality).

[0006] Therefore, there is an urgent need in this field for a constellation autonomous mission planning method that can uniformly handle mixed tasks, integrate multiple driving update mechanisms, and has the ability to adapt to task execution feedback, so as to solve the technical problems of inconsistent models, lag response, and weak adaptability in the existing technology, thereby comprehensively improving the mission planning efficiency and autonomous operation level of constellations. Summary of the Invention

[0007] In view of the above-mentioned technical problems, this invention proposes a hybrid task-driven method for generating and updating heatmaps for autonomous mission planning of constellations. By utilizing heatmaps, geographic information systems and other methods and modeling tools, this invention studies technologies such as autonomous generation and updating of meta-tasks based on heatmaps, providing a unified task-driven input and decision reference model for collaborative decision-making of spaceborne missions, thereby improving the autonomous mission planning capabilities of constellations.

[0008] The technical solution to the technical problem of this invention is: a method for generating and updating heatmaps for autonomous constellation mission planning driven by hybrid missions, comprising the following steps:

[0009] Step S1: Input initial task data;

[0010] Step S2: Generate a heatmap based on the input initial task data;

[0011] Step S3: According to the heatmap update calculation time period, the generated heatmap is periodically and autonomously updated using a time-driven heatmap update method;

[0012] Step S4: Generate task planning results from the initially generated heatmap or the periodically updated heatmap using task planning methods;

[0013] Step S5: Based on the mission planning results, the satellite executes the mission and acquires information. The mission planning results include static mission execution results and dynamic mission execution results.

[0014] Step S6: Input the new task into the event-driven heatmap update method and update the heatmap based on the new task injection;

[0015] Step S7: Input the event-driven heatmap update result into the task planning method, and repeat steps S4 to S7 to perform autonomous heatmap update.

[0016] According to one technical solution of the present invention, in step S1, the input initial task data includes: the regular static tasks of regional targets and point targets, the dynamic tasks of moving point targets, and the heat map update calculation time period data.

[0017] According to a technical solution of the present invention, in step S5, the satellite performs a mission and acquires information based on the mission planning results, specifically including:

[0018] Step S51: Input the static task execution result into the event-driven heatmap update method, and update the heatmap based on the task execution feedback;

[0019] Step S52: Perform target association and fusion on the dynamic task execution results, and input the heatmap update method based on event-driven method to update the heatmap based on the dynamic target state.

[0020] According to one technical solution of the present invention, in step S2, generating a heatmap based on the input initial task data includes:

[0021] Step S21: Input the predicted trajectory point data and heat map calculation time period corresponding to the regional target, point target, and moving point target;

[0022] Step S22: Convert the regional targets into spatial point targets using a spatial sampling method;

[0023] Step S23: Combine the predicted trajectory points corresponding to the sampled spatial point targets, the original point targets, and the moving point targets in the region to generate the total spatial point task set;

[0024] Step S24: Divide the task set area into H3 grids and grid center points to generate a heatmap task set within the time period;

[0025] Step S25: The sum of the comprehensive values ​​assigned to each task in each H3 grid within the heatmap time period is used as the heatmap value to generate the heatmap;

[0026] Step S26: Combine the point task set within the heatmap time period with the heatmap data to generate an initial point task set with added grid codes and heat value attributes.

[0027] According to a technical solution of the present invention, in step S3, the generated heatmap is periodically and autonomously updated using a time-driven heatmap update method according to the heatmap update calculation time period, including:

[0028] Step S31: Input the total spatial point task set after the previous cycle update and the heat map calculation time period, wherein the total spatial point task set includes the predicted trajectory of the moving target;

[0029] Step S32: Divide the task set area into H3 grids and corresponding center points to generate the task set for the next time period of the heatmap;

[0030] Step S33: The sum of the task comprehensive assignments of each point in each H3 grid within the next time period is used as the heat value to generate the heat map for the next time period;

[0031] Step S34: Combine the data generated in steps S32 and S33 to generate a time-driven task set for the next time period with added grid coding and thermal value attributes.

[0032] According to a technical solution of the present invention, in step S51, the static task execution result is input into the event-driven heatmap update method, and the heatmap is updated based on the task execution feedback, including:

[0033] Step S511: Input the satellite's mission execution and information acquisition results, as well as the previous time point mission set data with added grid coding and thermal value attributes, and determine whether the executed mission is a static mission;

[0034] Step S512: If it is a static task, calculate the satisfaction level of the executed task. The formula for calculating task satisfaction level is as follows:

[0035] in, It represents the degree to which the k-th task order plan meets the requirements of the observation task; It is the actual completion time of the task order plan; It is the expected completion time required for the observation mission; It is the resolution of the actual observation data when the task order is completed; This refers to the data resolution required for the observation mission;

[0036] like This does not meet the requirements; if To meet the needs;

[0037] Step S513: Count the number of observations that meet the task requirements for each task;

[0038] Step S514: Combining the total spatial point task set data after updating attributes in the previous time period with the statistical results of step S513, update the attributes of the executed tasks to generate the total spatial point task set with updated attributes. The attribute update formula is as follows:

[0039] in, The number of observations required for the task. The number of times it has been observed;

[0040] Among them, the number of observations required is the number of observations required for the task, and the number of observations already made is the number of observations already made;

[0041] Step S515: After comprehensively updating the attributes, the total spatial point task set, the heat map of the previous time period, and the heat map calculation time period are used to calculate the sum of the comprehensive assignment values ​​of each point task in each H3 grid in the next time period as the heat value, and generate the heat map of the next time period.

[0042] Step S516: Combine the updated total spatial point task set and the heatmap of the next time period to generate a task set for the next time period based on static task feedback, with added grid coding and heat value attributes.

[0043] According to a technical solution of the present invention, in step S52, the dynamic task execution results are fused with target association and input into an event-driven heatmap update method to update the heatmap based on the dynamic target state, including:

[0044] Step S521: Perform target association and fusion on the dynamic task execution results;

[0045] Step S522: Input the satellite's mission execution and information acquisition results, as well as the previous time point mission set data with added grid coding and thermal value attributes, and determine whether the executed mission is a dynamic mission;

[0046] Step S523: If it is a dynamic task, generate an updated total spatial point task set by combining the result of the integrated target association and the total spatial point task set after updating the attributes in the previous time period.

[0047] Step S524: Divide the task set area into H3 grids and corresponding center points to generate the task set for the next time period of the heatmap;

[0048] Step S525: The sum of the task comprehensive assignments of each point in each H3 grid within the next time period is used as the heat value to generate the heat map for the next time period;

[0049] Step S526: Combine the data generated in steps S524 and S525 to generate the next time-point task set based on dynamic task fusion, which includes grid coding and thermal value attributes.

[0050] According to a technical solution of the present invention, in step S6, the new task is input into the event-driven heatmap update method, and the heatmap is updated based on the injection of the new task, including:

[0051] Step S61: Input the predicted trajectory point data and heat map calculation time period corresponding to the regional target, point target, and moving point target;

[0052] Step S62: Convert the regional targets into spatial point targets using a spatial sampling method;

[0053] Step S63: Combine the spatial point targets after regional sampling, the original point targets, the predicted trajectory points corresponding to the moving point targets, and the total spatial point task set updated in the previous cycle to generate the total spatial point task set.

[0054] Step S64: Divide the task set area into H3 grids and grid center points to generate a heatmap task set within the time period;

[0055] Step S65: The sum of the comprehensive values ​​assigned to each task in each H3 grid within the heatmap time period is used as the heat value to generate the heatmap;

[0056] Step S66: Combine the point task set within the heatmap time period with the heatmap data to generate an initial point task set based on the new task input, with added grid codes and heat value attributes.

[0057] According to one aspect of the present invention, an electronic device is provided, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the hybrid mission-driven constellation autonomous mission planning heatmap generation and update method as described in any of the above technical solutions.

[0058] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement a hybrid task-driven method for generating and updating heatmaps for autonomous constellation mission planning as described in any of the above technical solutions.

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] This invention presents a method for generating and updating heatmaps for autonomous mission planning of constellations driven by hybrid missions. It integrates multi-source data from static and dynamic missions, utilizing heatmaps and geographic information system (GIS) technologies to achieve spatial visualization and dynamic adjustment of mission priorities. Through a dual update mechanism driven by both time and events (including static mission execution results, dynamic mission association and fusion, and new mission input), it ensures synchronization between the heatmap and real-time mission requirements, forming a closed-loop autonomous optimization. This provides a unified mission-driven input and decision reference model for collaborative decision-making onboard missions, significantly improving the autonomous mission planning capability, resource utilization efficiency, and response speed of constellations in complex environments. Furthermore, standardized processing (such as H3 grids) enhances the system's adaptability, scalability, and robustness, providing reliable technical support for large-scale constellation applications. Ultimately, it achieves full-process automation and intelligence from event recognition to mission execution, improving the overall performance and practicality of aerospace systems. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating a method for generating and updating heatmaps for cluster autonomous mission planning driven by hybrid missions, according to one embodiment of the present invention.

[0062] Figure 2 This is a detailed flowchart illustrating a method for generating and updating heatmaps for cluster autonomous mission planning driven by hybrid missions, according to one embodiment of the present invention.

[0063] Figure 3 This is a flowchart illustrating a heatmap update method driven by an event (static task execution result event) in one embodiment of the present invention. Detailed Implementation

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0065] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The embodiments cannot be described in detail here, but the embodiments of the present invention are not limited to the following embodiments.

[0066] like Figures 1 to 3 As shown, the present invention provides a method for generating and updating heatmaps for autonomous constellation mission planning driven by a hybrid mission, comprising the following steps:

[0067] Step S1: Input initial task data;

[0068] The initial input task data includes: conventional static tasks such as regional targets and point targets, dynamic tasks such as moving point targets, and heatmap update calculation time period data. Static tasks refer to observation needs at fixed geographical locations (such as urban area monitoring), while dynamic tasks refer to predicted trajectory points of moving targets (such as ships and vehicles). The heatmap update calculation time period defines the time window for the initial generation and subsequent updates of the heatmap, for example, with a cycle of 6 hours.

[0069] By explicitly defining the initial task data as including static tasks such as regional targets and point targets, as well as dynamic tasks such as moving point targets, and data for the heatmap update calculation period, it can cover multi-source heterogeneous task types and spatiotemporal dimension requirements, ensuring the comprehensiveness and accuracy of the basic data for heatmap generation. It achieves standardized preprocessing and unified fusion of mixed tasks, enabling the constellation system to simultaneously handle diverse needs such as fixed geographical location observation and moving target tracking. It has the advantages of enhancing system compatibility, reducing data conflicts, and improving the reliability of task planning, providing solid data support for subsequent dynamic updates of the heatmap.

[0070] Step S2: Generate a heatmap based on the input initial task data, including:

[0071] Step S21: Input the predicted trajectory point data and heat map calculation time period corresponding to the regional target, point target, and moving point target;

[0072] Step S22: Convert the regional targets into spatial point targets using a spatial sampling method;

[0073] Step S23: Combine the predicted trajectory points corresponding to the sampled spatial point targets, the original point targets, and the moving point targets in the region to generate the total spatial point task set;

[0074] Step S24: Divide the task set area into H3 grids and grid center points to generate a heatmap task set within the time period;

[0075] Step S25: The sum of the comprehensive values ​​assigned to each task in each H3 grid within the heatmap time period is used as the heatmap value to generate the heatmap;

[0076] Step S26: Combine the point task set within the heatmap time period with the heatmap data to generate an initial point task set with added grid codes and heat value attributes.

[0077] Through detailed heatmap generation steps, including inputting task data, spatial sampling transformation, task set fusion, H3 grid division, heat value calculation, and attribute set generation, complex geographic tasks can be transformed into standardized spatial point sets and grid heat distributions. By using a comprehensive assignment model to quantify task priorities, the heatmap is constructed as a unified decision-making reference model. It has the advantages of improving task visualization, optimizing resource allocation, and supporting on-board autonomous planning, thereby enabling the constellation to quickly identify high-priority areas based on heat values ​​and improve observation efficiency.

[0078] Among them, the H3 grid is a hexagonal-based global grid partitioning system used to spatially partition the task set region.

[0079] Step S3: According to the heatmap update calculation time period, the generated heatmap is periodically and autonomously updated using a time-driven heatmap update method, including:

[0080] Step S31: Input the total spatial point task set after the previous cycle update and the heat map calculation time period, wherein the total spatial point task set includes the predicted trajectory of the moving target;

[0081] Step S32: Divide the task set area into H3 grids and corresponding center points to generate the task set for the next time period of the heatmap;

[0082] Step S33: The sum of the task comprehensive assignments of each point in each H3 grid within the next time period is used as the heat value to generate the heat map for the next time period;

[0083] Step S34: Combine the data generated in steps S32 and S33 to generate a time-driven task set for the next time point, which includes grid coding and thermal value attributes.

[0084] By employing a time-driven method for periodically updating heatmaps, which includes inputting the previous cycle's task set, processing time offsets, recalculating heatmap values, and outputting updated results, the heatmap can be automatically adjusted according to a preset time period (e.g., every 6 hours) to ensure it is synchronized with real-time task requirements. This achieves continuous optimization of the heatmap and alignment with the satellite orbit cycle, offering advantages such as preventing task obsolescence, reducing planning delays, and enhancing the long-term operational stability of the system. It is particularly suitable for long-term monitoring tasks of large-scale constellations.

[0085] Step S4: Generate task planning results from the initially generated heatmap or the periodically updated heatmap using task planning methods;

[0086] The task planning algorithm includes genetic algorithm, greedy algorithm or dynamic programming algorithm, with genetic algorithm being preferred.

[0087] Step S5: Based on the mission planning results, the satellite executes the mission and acquires information. The mission planning results include static mission execution results and dynamic mission execution results.

[0088] In step S5, based on the mission planning results, the satellite executes the mission and acquires information, specifically including:

[0089] Step S51: Input the static task execution result into the event-driven heatmap update method, and update the heatmap based on the task execution feedback, including:

[0090] Step S511: Input the satellite's mission execution and information acquisition results, as well as the previous time point mission set data with added grid coding and thermal value attributes, and determine whether the executed mission is a static mission;

[0091] Step S512: If it is a static task, calculate the satisfaction level of the executed task. The formula for calculating task satisfaction level is as follows:

[0092] in, It represents the degree to which the k-th task order plan meets the requirements of the observation task; It is the actual completion time of the task order plan; It is the expected completion time required for the observation mission; It is the resolution of the actual observation data when the task order is completed; This refers to the data resolution required for the observation mission;

[0093] In the task satisfaction calculation formula, if This does not meet the requirements; if To meet the needs;

[0094] Step S513: Count the number of observations that meet the task requirements for each task;

[0095] Step S514: Combining the total spatial point task set data after updating attributes in the previous time period with the statistical results of step S513, update the attributes of the executed tasks to generate the total spatial point task set with updated attributes. The attribute update formula is as follows:

[0096] in, The number of observations required for the task. Number of observations

[0097] Among them, the number of observations required is the number of observations required for the task, and the number of observations already made is the number of observations already made;

[0098] Step S515: After comprehensively updating the attributes, the total spatial point task set, the heat map of the previous time period, and the heat map calculation time period are used to calculate the sum of the comprehensive assignment values ​​of each point task in each H3 grid in the next time period as the heat value, and generate the heat map of the next time period.

[0099] Step S516: Combine the updated total spatial point task set and the heatmap of the next time period to generate a task set of the next time period based on static task feedback, with added grid coding and heat value attributes;

[0100] By employing an event-driven update method based on static task execution results, including inputting execution results, calculating task satisfaction, counting observations, updating attributes, and regenerating heatmaps, the method can dynamically adjust heatmap values ​​according to the actual task completion status. It utilizes satisfaction formulas and attribute update formulas to achieve adaptive adjustment of task priorities, enabling real-time evaluation and feedback of task execution effects. This method has the advantages of improving resource utilization, reducing redundant observations, and enhancing task completion quality, thereby optimizing the overall performance of the constellation.

[0101] Step S52: Perform target association and fusion on the dynamic task execution results, and input them into the event-driven heatmap update method to update the heatmap based on the dynamic target state, including:

[0102] Step S521: Perform target association and fusion on the dynamic task execution results;

[0103] Step S522: Input the satellite's mission execution and information acquisition results, as well as the previous time point mission set data with added grid coding and thermal value attributes, and determine whether the executed mission is a dynamic mission;

[0104] Step S523: If it is a dynamic task, generate an updated total spatial point task set by combining the result of the integrated target association and the total spatial point task set after updating the attributes in the previous time period.

[0105] Step S524: Divide the task set area into H3 grids and corresponding center points to generate the task set for the next time period of the heatmap;

[0106] Step S525: The sum of the task comprehensive assignments of each point in each H3 grid within the next time period is used as the heat value to generate the heat map for the next time period;

[0107] Step S526: Combine the data generated in steps S524 and S525 to generate a task set for the next time period based on dynamic task fusion, which includes grid coding and thermal value attributes.

[0108] By employing an event-driven update method based on dynamic task execution results, including target association fusion, task type judgment, task set update, and heatmap regeneration, multi-source fusion of moving target observation data can be performed to eliminate data fragmentation. This enables accurate updates of dynamic task trajectories and continuous adjustments to heatmaps, improving moving target tracking accuracy, enhancing system response speed, and adapting to sudden task changes, thus ensuring efficient planning of the constellation in dynamic environments.

[0109] Step S6: Input the new task into the event-driven heatmap update method, and update the heatmap based on the new task injection, including:

[0110] Step S61: Input the predicted trajectory point data and heat map calculation time period corresponding to the regional target, point target, and moving point target;

[0111] Step S62: Convert the regional targets into spatial point targets using a spatial sampling method;

[0112] Step S63: Combine the spatial point targets after regional sampling, the original point targets, the predicted trajectory points corresponding to the moving point targets, and the total spatial point task set updated in the previous cycle to generate the total spatial point task set.

[0113] Step S64: Divide the task set area into H3 grids and grid center points to generate a heatmap task set within the time period;

[0114] Step S65: The sum of the comprehensive values ​​assigned to each task in each H3 grid within the heatmap time period is used as the heat value to generate the heatmap;

[0115] Step S66: Combine the point task set within the heatmap time period with the heatmap data to generate an initial point task set based on the new task input, with added grid codes and heat value attributes;

[0116] By adopting an event-driven update method based on new task input, including inputting new task data, task fusion and deduplication, and heatmap regeneration, new tasks can be integrated into the existing task set in real time. Data consistency is ensured through union operation and deduplication processing, enabling the heatmap to respond quickly to new requirements. This method has the advantages of improving system flexibility, supporting dynamic task expansion, and reducing manual intervention, allowing the constellation to adapt to constantly changing task scenarios.

[0117] Step S7: Input the event-driven heatmap update result into the task planning method, i.e., the updated task set and / or heatmap for the next time period, and repeat steps S4 to S7 to perform autonomous heatmap update.

[0118] According to one aspect of the present invention, an electronic device is provided, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform a hybrid task-driven constellation autonomous mission planning heatmap generation and update method as described in any of the above technical solutions.

[0119] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0120] The memory can be an internal storage unit of the terminal device, such as a hard drive or RAM. Alternatively, it can be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal and external storage units. The memory is used to store the computer program and other programs and data required by the terminal device. It can also be used to temporarily store data that has been output or will be output.

[0121] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement a hybrid task-driven method for generating and updating a constellation autonomous mission planning heatmap as described in any of the above technical solutions.

[0122] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), read-only optical disc (CD-ROM), magnetic tape, floppy disk, and optical data storage devices. They can be implemented using computer-executable program code, thus allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, this invention is not limited to any particular hardware and software combination.

[0123] In summary, the present invention provides a method for generating and updating heatmaps for autonomous constellation mission planning driven by hybrid missions. This method integrates multi-source data from both static and dynamic missions, utilizing heatmaps and geographic information system (GIS) technologies to achieve spatial visualization and dynamic adjustment of mission priorities. Through a dual update mechanism driven by both time and events (including static mission execution results, dynamic mission association and fusion, and new mission input), it ensures the synchronization of the heatmap with real-time mission requirements, forming a closed-loop autonomous optimization. This method provides a unified mission-driven input and decision reference model for collaborative decision-making onboard missions, significantly improving the autonomous mission planning capability, resource utilization efficiency, and response speed of constellations in complex environments. Furthermore, standardized processing enhances the system's adaptability, scalability, and robustness, providing reliable technical support for large-scale constellation applications. Ultimately, it achieves full-process automation and intelligence from event recognition to mission execution, improving the overall performance and practicality of aerospace systems.

[0124] Furthermore, it should be noted that the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0125] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0128] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for generating and updating heatmaps for autonomous constellation mission planning driven by hybrid missions, characterized in that, Includes the following steps: Step S1: Input initial task data; Step S2: Generate a heatmap based on the input initial task data; Step S3: According to the heatmap update calculation time period, the generated heatmap is periodically and autonomously updated using a time-driven heatmap update method; Step S4: Generate task planning results from the initially generated heatmap or the periodically updated heatmap using task planning methods; Step S5: Based on the mission planning results, the satellite executes the mission and acquires information. The mission planning results include static mission execution results and dynamic mission execution results. In step S5, based on the mission planning results, the satellite executes the mission and acquires information, specifically including: Step S51: Input the static task execution result into the event-driven heatmap update method, and update the heatmap based on the task execution feedback, including: Step S511: Input the satellite's mission execution and information acquisition results, as well as the previous time point mission set data with added grid coding and thermal value attributes, and determine whether the executed mission is a static mission; Step S512: If it is a static task, calculate the satisfaction level of the executed task. The formula for calculating task satisfaction level is as follows: in, It represents the degree to which the k-th task order plan meets the requirements of the observation task; It is the actual completion time of the task order plan; It is the expected completion time required for the observation mission; It is the resolution of the actual observation data when the task order is completed; This refers to the data resolution required for the observation mission; like This does not meet the requirements; if To meet the needs; Step S513: Count the number of observations that meet the task requirements for each task; Step S514: Combining the total spatial point task set data after updating attributes in the previous time period with the statistical results of step S513, update the attributes of the executed tasks to generate the total spatial point task set with updated attributes. The attribute update formula is as follows: in, The number of observations required for the task. The number of times it has been observed; Among them, the number of observations required is the number of observations required for the task, and the number of observations already made is the number of observations already made; Step S515: After comprehensively updating the attributes, the total spatial point task set, the heat map of the previous time period, and the heat map calculation time period are used to calculate the sum of the comprehensive assignment values ​​of each point task in each H3 grid in the next time period as the heat value, and generate the heat map of the next time period. Step S516: Combine the updated total spatial point task set and the heatmap of the next time period to generate a task set of the next time period based on static task feedback, with added grid coding and heat value attributes; Step S52: Perform target association and fusion on the dynamic task execution results, and input them into the event-driven heatmap update method to update the heatmap based on the dynamic target state. Step S6: Input the new task into the event-driven heatmap update method and update the heatmap based on the new task injection; Step S7: Input the event-driven heatmap update result into the task planning method, and repeat steps S4 to S7 to perform autonomous heatmap update.

2. The method according to claim 1, characterized in that, In step S1, the input initial task data includes: the regular static tasks of regional targets and point targets, the dynamic tasks of moving point targets, and the data of the heat map update calculation time period.

3. The method according to claim 1, characterized in that, In step S2, generating a heatmap based on the input initial task data includes: Step S21: Input the predicted trajectory point data and heat map calculation time period corresponding to the regional target, point target, and moving point target; Step S22: Convert the regional targets into spatial point targets using a spatial sampling method; Step S23: Combine the predicted trajectory points corresponding to the sampled spatial point targets, the original point targets, and the moving point targets in the region to generate the total spatial point task set; Step S24: Divide the task set area into H3 grids and grid center points to generate a heatmap task set within the time period; Step S25: The sum of the comprehensive values ​​assigned to each task in each H3 grid within the heatmap time period is used as the heatmap value to generate the heatmap; Step S26: Combine the point task set within the heatmap time period with the heatmap data to generate an initial point task set with added grid codes and heat value attributes.

4. The method according to claim 1, characterized in that, In step S3, the generated heatmap is periodically and autonomously updated according to the heatmap update calculation time period using a time-driven heatmap update method, including: Step S31: Input the total spatial point task set after the previous cycle update and the heat map calculation time period, wherein the total spatial point task set includes the predicted trajectory of the moving target; Step S32: Divide the task set area into H3 grids and corresponding center points to generate the task set for the next time period of the heatmap; Step S33: The sum of the task comprehensive assignments of each point in each H3 grid within the next time period is used as the heat value to generate the heat map for the next time period; Step S34: Combine the data generated in steps S32 and S33 to generate a time-driven task set for the next time period with added grid coding and thermal value attributes.

5. The method according to claim 3, characterized in that, In step S52, the dynamic task execution results are fused with target association and input into an event-driven heatmap update method to update the heatmap based on the dynamic target state, including: Step S521: Perform target association and fusion on the dynamic task execution results; Step S522: Input the satellite's mission execution and information acquisition results, as well as the previous time point mission set data with added grid coding and thermal value attributes, and determine whether the executed mission is a dynamic mission; Step S523: If it is a dynamic task, generate an updated total spatial point task set by combining the result of the integrated target association and the total spatial point task set after updating the attributes in the previous time period. Step S524: Divide the task set area into H3 grids and corresponding center points to generate the task set for the next time period of the heatmap; Step S525: The sum of the task comprehensive assignments of each point in each H3 grid within the next time period is used as the heat value to generate the heat map for the next time period; Step S526: Combine the data generated in steps S524 and S525 to generate the next time-point task set based on dynamic task fusion, which includes grid coding and thermal value attributes.

6. The method according to claim 1, characterized in that, In step S6, the new task is input into the event-driven heatmap update method, and the heatmap is updated based on the new task injection, including: Step S61: Input the predicted trajectory point data and heat map calculation time period corresponding to the regional target, point target, and moving point target; Step S62: Convert the regional targets into spatial point targets using a spatial sampling method; Step S63: Combine the spatial point targets after regional sampling, the original point targets, the predicted trajectory points corresponding to the moving point targets, and the total spatial point task set updated in the previous cycle to generate the total spatial point task set. Step S64: Divide the task set area into H3 grids and grid center points to generate a heatmap task set within the time period; Step S65: The sum of the comprehensive values ​​assigned to each task in each H3 grid within the heatmap time period is used as the heat value to generate the heatmap; Step S66: Combine the point task set within the heatmap time period with the heatmap data to generate an initial point task set based on the new task input, with added grid codes and heat value attributes.

7. An electronic device, characterized in that, include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the hybrid mission-driven constellation autonomous mission planning heatmap generation and update method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, implement the hybrid task-driven constellation autonomous mission planning heatmap generation and update method as described in any one of claims 1 to 6.

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