Satellite ground system multi-agent cooperative scheduling control method, device and equipment and computer readable storage medium

Abstract

The invention relates to the technical field of cooperative scheduling control, and discloses a satellite ground system multi-agent cooperative scheduling control method, device and equipment and a computer readable storage medium. Remote sensing data, task requirements and the like are input into the main agent and the standby agent respectively, and tasks and job execution results output by the main agent and tasks and job execution results output by the standby agent are obtained. And judging whether task and job execution results output by the main agent reach preset results or not. And if yes, inputting the task and job execution results output by the main agent into a remote sensing data service system. And if not, inputting the task and job execution result output by the standby agent into the remote sensing data service system. And when the main agent has the task and the operation execution result cannot reach the preset result, the standby agent can quickly take over the task, and continuous and stable operation of the satellite ground system is ensured.

Description

Technical Field

[0001] The present invention relates to the field of collaborative scheduling control technology, and in particular to a satellite ground system multi-agent collaborative scheduling control method, device, equipment and computer-readable storage medium. Background Art

[0002] At present, with the development of satellite technology, there are more and more satellites in the sky capturing remote sensing data related to the earth. These remote sensing data contain rich geographical information, so data analysis and utilization based on remote sensing data are increasingly valued by people.

[0003] However, as the observation systems on satellites and the processing requirements of remote sensing data become increasingly complex, after ground stations receive remote sensing data transmitted by satellites, the ground systems have found it difficult to meet the increasingly complex processing requirements, and are unable to achieve reliable and efficient job scheduling control, ensure reliable and efficient execution of operations, and realize the intelligence of the scheduling decision-making process.

[0004] To this end, the present invention provides a satellite ground system multi-agent collaborative scheduling control method. Summary of the invention

[0005] The present invention provides a satellite ground system multi-agent collaborative scheduling control method, device, equipment and computer-readable storage medium to partially solve the above-mentioned problems existing in the prior art.

[0006] The present invention adopts the following technical solutions: The present invention provides a satellite ground system multi-agent collaborative scheduling control method, comprising: Acquire remote sensing data transmitted by satellite and task requirements corresponding to the remote sensing data; Input the remote sensing data and the task requirements into the main intelligent agent and the backup intelligent agent respectively, and obtain the task and operation execution results output by the main intelligent agent and the task and operation execution results output by the backup intelligent agent; Determine whether the task and operation execution results output by the master agent meet the preset results; If yes, the task and operation execution results output by the master agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the master agent; If not, the task and operation execution results output by the standby agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the standby agent.

[0007] Optionally, the main intelligent agent includes a positioning and registration system, a calibration processing system, and a product generation system; Inputting the remote sensing data and the task requirements into the main agent, and obtaining the task and operation execution results output by the main agent, specifically including: Inputting the remote sensing data and the task requirements into the positioning and registration system in the master agent to obtain first task scheduling data; Inputting the first task scheduling data into the calibration processing system to obtain second task scheduling data; The second task scheduling data is input into the product generation system to obtain task and operation execution results.

[0008] Optionally, judging whether the task and operation execution results output by the master agent reach the preset results specifically includes: Determine a state vector array of the remote sensing data, the first task scheduling data, the second task scheduling data, and the task and job execution results; Determine the reinforcement value of the main agent according to the state vector array and the action information of the main agent; Determine the maximum expected value of the master agent according to the reinforcement value of the master agent, the state vector array and the preset strategy; Determine whether the maximum expected value reaches a preset expected result.

[0009] Optionally, the state vector array is represented as follows: ; Among them, L0 represents remote sensing data, L1A represents the first task scheduling data, L1 represents the second task scheduling data, and L2 represents the task and job execution results. Indicates L0 data integrity flag, Indicates the timeliness of L0 data. Indicates the L1A data quality inspection results, Indicates the timeliness of L1A data. Indicates the L1 data quality inspection results, Indicates the timeliness of L1 data. Indicates the L2 data quality inspection results, Indicates the timeliness of data.

[0010] Optionally, the calculation formula of the enhancement value of the main agent is: ; in, represents the i-th agent, represents the current state of the agent, represents the current action of the agent, is the preset learning rate, r is the preset reward value, γ is the preset discount factor, represents the next state of the agent, Represents the next action of the agent.

[0011] Optionally, the calculation formula of the maximum expected value of the master agent is: ; Among them, A is the joint action space, is the probability of taking joint action a in state s.

[0012] Optionally, the constraint formula of the maximum expected value of the master agent is: .

[0013] The present invention provides a satellite ground system multi-agent collaborative scheduling control device, comprising: An acquisition module, used to acquire remote sensing data transmitted by satellite and task requirements corresponding to the remote sensing data; An output module, used to input the remote sensing data and the task requirements into the main intelligent agent and the backup intelligent agent respectively, and obtain the task and operation execution results output by the main intelligent agent and the task and operation execution results output by the backup intelligent agent; A judgment module is used to judge whether the task and operation execution results output by the main intelligent agent reach the preset results; if so, the task and operation execution results output by the main intelligent agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the main intelligent agent; if not, the task and operation execution results output by the backup intelligent agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the backup intelligent agent.

[0014] The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the multi-agent collaborative scheduling control method of the satellite ground system is implemented.

[0015] The present invention provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a multi-agent collaborative scheduling control method for a satellite ground system when executing the program.

[0016] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects: The satellite ground system multi-agent collaborative scheduling control method provided by the present invention first obtains the remote sensing data transmitted by the satellite and the task requirements corresponding to the remote sensing data. The remote sensing data and the task requirements are respectively input into the main agent and the backup agent to obtain the tasks and job execution results output by the main agent and the tasks and job execution results output by the backup agent. It is judged whether the tasks and job execution results output by the main agent reach the preset results. If so, the task scheduling results output by the main agent are input into the remote sensing data service system, so that the remote sensing data service system performs the job execution of remote sensing data processing according to the tasks output by the main agent. If not, the tasks and job execution results output by the backup agent are input into the remote sensing data service system, so that the remote sensing data service system performs the job execution of remote sensing data processing according to the tasks output by the backup agent.

[0017] The satellite ground system multi-agent collaborative scheduling control method designed by the present invention can effectively improve the reliability of the satellite ground system. When the main agent is abnormal, the performance is degraded, or the task scheduling result does not meet the preset result, the backup agent can quickly take over the task to ensure the continuous and stable operation of the satellite ground system. Through the global control strategy and efficient information transmission mechanism, the system can respond quickly to complex business changes and make the best decision autonomously, thereby enhancing the adaptability and robustness of the system. In addition, through the working mode that combines local control with global coordination, the system can dynamically adjust resource allocation to ensure the maximum utilization of resources and greatly improve the system operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings: Figure 1 A schematic diagram of a flow chart of a satellite ground system multi-agent collaborative scheduling control method provided by an embodiment of the present invention; Figure 2 A schematic diagram of the active-standby collaborative operation of a ground system provided by the present invention; Figure 3 A multi-agent collaborative scheduling control business flow chart provided in the present invention; Figure 4 A schematic diagram of a satellite ground system multi-agent collaborative scheduling control device provided by the present invention; Figure 5 The present invention provides a method corresponding to Figure 1 Schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0021] Figure 1 A flowchart of a satellite ground system multi-agent collaborative scheduling control method provided in an embodiment of this specification includes the following steps: S100: Acquire remote sensing data transmitted by satellite and task requirements corresponding to the remote sensing data.

[0022] In this specification, the process of satellite ground system multi-agent collaborative scheduling control is performed. In the embodiment of this specification, the process of satellite ground system multi-agent collaborative scheduling control can be performed by a server. Of course, this specification does not limit which device is used to perform the process of satellite ground system multi-agent collaborative scheduling control. For example, personal computers and mobile terminals can also be used to perform the process of satellite ground system multi-agent collaborative scheduling control. For the convenience of description, the following description is based on the control server as the execution subject.

[0023] In one or more embodiments of the present specification, after the satellite collects remote sensing data, the remote sensing data can be transmitted to the ground station, and the ground station transmits the remote sensing data to the control server. After the server receives the remote sensing data, it needs to process the remote sensing data according to the task plan. For some tasks with high real-time requirements, the server needs to process them in real time, while for some non-real-time tasks, the server schedules resources according to task requirements to execute these tasks with low real-time requirements.

[0024] Therefore, after the server obtains the remote sensing data transmitted by the satellite, it can also obtain the task requirements set for these remote sensing data, so as to distinguish which data in the remote sensing data needs to be processed in real time and which data needs to be processed non-real time, so that the server can allocate resources on demand to process the remote sensing data.

[0025] In one or more embodiments of this specification, the server can control the satellite ground system, and the ground system may include several systems that perform different functions, such as operation control system, data reception and command control system, calibration processing system, positioning and registration system, observation simulation system, product generation system, quality assessment system, etc. In this specification, it is not limited that the ground system controlled by the server may include how many systems that perform different functions, and it can be set according to the actual situation. Among them, the server can formulate a unified task plan through the operation control system in the ground system, and the satellite and ground system use the task schedule assigned in the task plan as the driving force for automated intelligent operation, and can use the integration and classification combination to perform integrated scheduling control on each system in the ground system. For business systems and operations with high timeliness requirements, integrated scheduling is adopted, and for business systems and operations with low real-time requirements, hierarchical scheduling is performed. For data reception and command control systems, positioning and registration systems, calibration processing systems, and product generation systems (key products) with high real-time requirements, integrated real-time scheduling control is adopted. For product generation systems (secondary products), observation simulation systems, quality assessment systems, etc. with low real-time requirements, hierarchical scheduling control is adopted. Integrated scheduling and centralized control are easy to optimize system performance and facilitate unified management of the entire process. Hierarchical scheduling does not affect each other, and has the ability to reprocess scheduling control. According to the system operation requirements, the scheduling method can also be appropriately adjusted to achieve intelligent and refined scheduling and management.

[0026] S102: Input the remote sensing data and the task requirements into the main intelligent agent and the backup intelligent agent respectively, and obtain the tasks and job execution results output by the main intelligent agent and the tasks and job execution results output by the backup intelligent agent.

[0027] In one or more embodiments of the present specification, the server may input the acquired remote sensing data and task requirements into the main agent, obtain the task output by the main agent, and obtain the task execution result obtained by executing the task as a job. The server may input the acquired remote sensing data and task requirements into the standby agent, obtain the task output by the standby agent, and obtain the task execution result obtained by executing the task as a job.

[0028] Specifically, the server can input the remote sensing data and task requirements into the positioning and registration system in the main intelligent agent to obtain the first task scheduling data. The first task scheduling data is input into the calibration processing system to obtain the second task scheduling data. The second task scheduling data is input into the product generation system to obtain the task and job execution results. Similarly, the process of obtaining the task and job execution results output by the standby intelligent agent can also refer to the above process.

[0029] It is worth noting that, whether in the main agent or in the backup agent, after the remote sensing data is input from the server, the integrated scheduling control management in the above-mentioned ground system can be triggered. The main agent is used as an example for explanation. In the main agent, the integrated scheduling control management mainly realizes the integrated scheduling control management of real-time jobs, and the strategic configuration of resources required for the operation of the business system to meet the efficient operation of jobs and resources. The job scheduling strategies in the task scheduling results include immediate execution, one-time execution, periodic execution, the parallelism of the job flow, and the priority of the job flow can be adjusted dynamically. According to the definition of the system external interface, integrated real-time scheduling control is adopted for the positioning and registration system, the calibration processing system, and the product generation system. Among them, the integrated scheduling control management completes the task planning according to the task requirements and generates the load task schedule on a daily basis. Each system involved in the integrated scheduling provides job information, job parameters, required resources, input data to be monitored, output products, job process topology diagrams, scheduling strategy information, completes the job integration configuration, and forms input configuration, product configuration, and flow template configuration. Scheduled scheduling starts the plan generation process on a daily basis according to the job integration configuration information to generate input plans, product plans, and scheduling plans. Real-time scheduling is divided into positioning and registration scheduling, calibration processing scheduling, and product generation scheduling according to the systems involved in the integrated scheduling. According to the scheduling plan, input plan, and product plan scheduling, the business processing flow of each system is completed, and the jobs of each system are submitted to the system for resource scheduling, and the job operation results returned by the system are collected and recorded. Internal distribution pushes the products of all levels generated by each system in the integrated scheduling to the atmospheric remote sensing data service system. The business process execution status of each system in the integrated scheduling and the status of product distribution results are collected to realize the monitoring of the business process of the whole system.

[0030] As for hierarchical scheduling control management, hierarchical scheduling control mainly realizes hierarchical scheduling control and management of jobs with low real-time requirements. For jobs with low real-time requirements, there are primary scheduling and secondary scheduling, such as task schedules, scheduling orders, public configuration parameters, etc., which are primary scheduling. The operation control system completes the generation, sending and execution status monitoring of scheduling orders, and has the main console scheduling command management function. According to the definition of the system's external interface, hierarchical scheduling control is adopted for data reception and command control system, payload antenna dynamic measurement and correction system, observation simulation system, quality assessment system, data archiving and management system, data archiving and management system, etc. Hierarchical scheduling control completes task planning according to task requirements, generates payload task schedules, observation schedules, and broadcast schedules on a daily basis, and sends various schedules to related technical systems, and issues daily task schedules to issue scheduling commands. The secondary monitoring of each technical system analyzes the task schedule, confirms that the reception is normal, and reports the response to the scheduling order. The secondary monitoring of each technical system generates the operation plan of this system based on the attribute description of the task in the task schedule, and schedules the automatic operation of the system's operations according to the operation plan, and schedules to complete their respective business functions. The secondary monitoring of each technical system timely reports the task execution status and conducts full-process monitoring of each payload observation task. For the task schedule being executed, due to changes in business needs and user needs, the order of task scheduling needs to be changed, or the already scheduled tasks need to be added or reduced in the task schedule being executed, so the task schedule needs to be regenerated. In this case, the task schedule needs to be modified manually, and after completion, the updated task schedule is sent to the relevant technical system, and a task schedule update scheduling command is issued at the same time to notify each system to update the new task schedule at the specified time. Each technical system will report the fault information during the operation process and conduct alarm monitoring on the operation status of the entire business process.

[0031] S104: Determine whether the task and operation execution results output by the main agent reach the preset results. If so, execute step S106; if not, execute step S108.

[0032] In one or more embodiments of the present specification, the server may determine whether the task or job execution results output by the main agent reach the preset results. If so, step S106 is executed; if not, step S108 is executed.

[0033] For the integrated dispatching part of the ground system, the main and standby coordinated dispatching control method is adopted, such as Figure 2 Schematic diagram of the main and standby coordinated operation of the ground system Figure 3The business flow chart of multi-agent collaborative scheduling control, the main agent and the backup agent work at the same time, and only the host outputs the status synchronization of the backup agent to the outside, that is, the main agent processes the business mainly, and the backup agent works as a supplement. When the host of a certain system in the ground system works abnormally or interrupts the service, the backup agent takes over the business service of the main agent. Without manual intervention, the two work together to automatically ensure that the system can continue to provide services. For multi-agent collaborative scheduling control, specifically, the server can input the remote sensing data (L0) and the task requirements into the positioning and registration system in the main agent to obtain the first task scheduling data (L1A). The first task scheduling data is input into the calibration processing system to obtain the second task scheduling data (L1). The second task scheduling data is input into the product generation system to obtain the task and operation execution results (L2). Similarly, the server can also input the remote sensing data (L0) and the task requirements into the positioning and registration system in the backup agent to obtain the first task scheduling data (L1A). The first task scheduling data is input into the calibration processing system to obtain the second task scheduling data (L1). The second task scheduling data is input into the product generation system to obtain the task and job execution results (L2).

[0034] Afterwards, taking the main agent as an example, the server can determine the state vector array of remote sensing data (L0), first task scheduling data (L1A), second task scheduling data (L1), and task and job execution results (L2).

[0035] The representation of the state vector array is: ; Among them, L0 represents remote sensing data, L1A represents the first task scheduling data, L1 represents the second task scheduling data, and L2 represents the task and job execution results. Indicates L0 data integrity flag, Indicates the timeliness of L0 data. Indicates the L1A data quality inspection results, Indicates the timeliness of L1A data. Indicates the L1 data quality inspection results, Indicates the timeliness of L1 data. Indicates the L2 data quality inspection results, Indicates the timeliness of data.

[0036] The server then determines the reinforcement value of the main agent based on the state vector array and the action information of the main agent. The maximum expected value of the main agent is determined based on the reinforcement value of the main agent, the state vector array, and the preset strategy. Finally, it can be determined whether the maximum expected value reaches the preset expected result.

[0037] The calculation formula of the main agent's reinforcement value is: in, represents the i-th agent, represents the current state of the agent, represents the current action of the agent, is the preset learning rate, r is the preset reward value, γ is the preset discount factor, represents the next state of the agent, Represents the next action of the agent.

[0038] The calculation formula for the maximum expected value of the main agent is: Among them, A is the joint action space, is the probability of taking the joint action α in state s.

[0039] The constraint formula for the maximum expected value of the main agent is: It is worth noting that in this multi-agent framework, each state transition is the result of the joint actions of all agents. The reward return of each agent depends not only on its own strategy choice, but also on the joint actions of other agents. Therefore, the rewards obtained by different agents may be different, and each agent has its own state value function. Specifically, given a specific strategy, the value function Vi of agent i in state s is defined as Among them, a=(a1, a2,…, aN) is the joint action, π is the strategy followed by the agent, indicating that the action of each agent is a decision made based on its own strategy and environment state. The size of the discount factor γ reflects the importance of future rewards and current rewards. When γ is 0, it means that the agent only considers the current reward. When γ is 1, it means that the reward at each moment in the future is equally important as the current reward. The final result is the present value of the total return that can be expected to be achieved from the initial condition s when operating according to the above rules.

[0040] S106: Input the task and operation execution results output by the master agent into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the master agent.

[0041] In one or more embodiments of the present specification, if the task and operation execution results output by the main intelligent agent reach the preset results, the server will input the task and operation execution results output by the main intelligent agent into the atmospheric remote sensing data service system, so that the atmospheric remote sensing data service system can perform task scheduling for remote sensing data processing according to the task and operation execution results output by the main intelligent agent. At the same time, the atmospheric remote sensing data service system will also transmit the task and operation execution results to the data archiving and management system for archiving.

[0042] S108: Inputting the task and operation execution results output by the standby agent into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the standby agent.

[0043] In one or more embodiments of the present specification, if the task and operation execution results output by the main agent do not reach the preset results, the server inputs the task and operation execution results output by the standby agent into the atmospheric remote sensing data service system, so that the atmospheric remote sensing data service system performs task scheduling for remote sensing data processing according to the task and operation execution results output by the standby agent.

[0044] It is worth noting that if the task and operation execution results output by the main intelligent agent do not reach the preset results, if it is necessary to enable the task and operation execution results output by the backup intelligent agent, the big data multimodal adaptive health management subsystem of the operation control system will evaluate the L1A, L1, and L2 data quality of the main intelligent agent and the backup intelligent agent and output the results. The main and backup L1A / L1 / L2 data service decision subsystem makes decisions on the operation quality and data quality inspection results of the big data multimodal adaptive health management subsystem. After the decision, the L1A / L1 / L2 data of the main intelligent agent or the backup intelligent agent is transmitted to the atmospheric remote sensing data service system, and the atmospheric remote sensing data service system transmits each data (L1A / L1 / L2) to the data archiving and management system for archiving.

[0045] The above is a satellite ground system multi-agent collaborative scheduling control method provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding satellite ground system multi-agent collaborative scheduling control device, such as Figure 4 shown.

[0046] An acquisition module 400 is used to acquire remote sensing data transmitted by satellites and task requirements corresponding to the remote sensing data; Output module 402, used to input the remote sensing data and the task requirements into the main agent and the backup agent respectively, and obtain the task and operation execution results output by the main agent and the task and operation execution results output by the backup agent; The judgment module 404 is used to judge whether the task and job execution results output by the main intelligent agent reach the preset results; if so, the task and job execution results output by the main intelligent agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and job execution results output by the main intelligent agent; if not, the task and job execution results output by the backup intelligent agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and job execution results output by the backup intelligent agent.

[0047] Optionally, the main intelligent agent includes a positioning and registration system, a calibration processing system, and a product generation system; The output module 402 is used to input the remote sensing data and the task requirements into the positioning and registration system in the main intelligent body to obtain first task scheduling data, input the first task scheduling data into the calibration processing system to obtain second task scheduling data, input the second task scheduling data into the product generation system to obtain task and job execution results.

[0048] Optionally, the output module 402 is used to determine the state vector array of the remote sensing data, the first task scheduling data, the second task scheduling data, and the task and job execution results, determine the reinforcement value of the main intelligent agent based on the state vector array and the action information of the main intelligent agent, determine the maximum expected value of the main intelligent agent based on the reinforcement value of the main intelligent agent, the state vector array and a preset strategy, and judge whether the maximum expected value reaches the preset expected result.

[0049] Optionally, the state vector array in the output module 402 is represented as follows: Among them, L0 represents remote sensing data, L1A represents the first task scheduling data, L1 represents the second task scheduling data, and L2 represents the task and job execution results. Indicates L0 data integrity flag, Indicates the timeliness of L0 data. Indicates the L1A data quality inspection results, Indicates the timeliness of L1A data. Indicates the L1 data quality inspection results, Indicates the timeliness of L1 data. Indicates the L2 data quality inspection results, Indicates the timeliness of data.

[0050] Optionally, the calculation formula of the enhancement value of the main agent in the output module 402 is: in, represents the i-th agent, represents the current state of the agent, represents the current action of the agent, is the preset learning rate, r is the preset reward value, γ is the preset discount factor, represents the next state of the agent, Represents the next action of the agent.

[0051] Optionally, the calculation formula of the maximum expected value of the main agent in the output module 402 is: Among them, A is the joint action space, is the probability of taking the joint action α in state s.

[0052] Optionally, the constraint formula of the maximum expected value of the master agent in the output module 402 is: This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A satellite ground system multi-agent collaborative scheduling control method is provided.

[0053] This manual also provides Figure 5 The schematic structure diagram of the electronic device shown in FIG. Figure 5 As shown, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The satellite ground system multi-agent collaborative scheduling control method.

[0054] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0055] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with hardware entity modules. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0056] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0057] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0058] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0059] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0063] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0064] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0065] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0066] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0067] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0069] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0070] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the technology and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A satellite ground system multi-agent collaborative scheduling control method, characterized in that: include: Acquire remote sensing data transmitted by satellite and task requirements corresponding to the remote sensing data; Input the remote sensing data and the task requirements into the main intelligent agent and the backup intelligent agent respectively, and obtain the task and operation execution results output by the main intelligent agent and the task and operation execution results output by the backup intelligent agent; Determine whether the task and operation execution results output by the master agent meet the preset results; If yes, the task and operation execution results output by the master agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the master agent; If not, the task and operation execution results output by the standby agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and operation execution results output by the standby agent.

2. The satellite ground system multi-agent collaborative scheduling control method according to claim 1, characterized in that: The main intelligent body includes the positioning and registration system, the calibration processing system and the product generation system; Inputting the remote sensing data and the task requirements into the main agent, and obtaining the task and operation execution results output by the main agent, specifically including: Inputting the remote sensing data and the task requirements into the positioning and registration system in the master agent to obtain first task scheduling data; Inputting the first task scheduling data into the calibration processing system to obtain second task scheduling data; The second task scheduling data is input into the product generation system to obtain task and operation execution results.

3. The satellite ground system multi-agent collaborative scheduling control method according to claim 2, characterized in that: Determining whether the task and operation execution results output by the master agent meet the preset results specifically includes: Determine a state vector array of the remote sensing data, the first task scheduling data, the second task scheduling data, and the task and job execution results; Determine the reinforcement value of the main agent according to the state vector array and the action information of the main agent; Determine the maximum expected value of the master agent according to the reinforcement value of the master agent, the state vector array and a preset strategy; Determine whether the maximum expected value reaches a preset expected result.

4. The satellite ground system multi-agent collaborative scheduling control method according to claim 3, characterized in that: The representation of the state vector array is: ； Among them, L0 represents remote sensing data, L1A represents the first task scheduling data, L1 represents the second task scheduling data, and L2 represents the task and job execution results. Indicates L0 data integrity flag, Indicates the timeliness of L0 data. Indicates the L1A data quality inspection results, Indicates the timeliness of L1A data. Indicates the L1 data quality inspection results, Indicates the timeliness of L1 data. Indicates the L2 data quality inspection results, Indicates the timeliness of data.

5. The satellite ground system multi-agent collaborative scheduling control method according to claim 4, characterized in that: The calculation formula of the enhancement value of the main agent is: ； in, represents the i-th agent, represents the current state of the agent, represents the current action of the agent, is the preset learning rate, r is the preset reward value, γ is the preset discount factor, represents the next state of the agent, Represents the next action of the agent.

6. The satellite ground system multi-agent collaborative scheduling control method according to claim 5, characterized in that: The calculation formula of the maximum expected value of the master agent is: ； Among them, A is the joint action space, is the probability of taking the joint action α in state s.

7. The satellite ground system multi-agent collaborative scheduling control method according to claim 6, characterized in that: The constraint formula of the maximum expected value of the master agent is: 。 8. A satellite ground system multi-agent collaborative scheduling control device, characterized in that: include: An acquisition module, used to acquire remote sensing data transmitted by satellite and task requirements corresponding to the remote sensing data; An output module, used to input the remote sensing data and the task requirements into the main intelligent agent and the backup intelligent agent respectively, and obtain the task and operation execution results output by the main intelligent agent and the task and operation execution results output by the backup intelligent agent; A judgment module is used to judge whether the task and operation execution results output by the main agent meet the preset results; If so, the task and job execution results output by the main intelligent agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and job execution results output by the main intelligent agent; if not, the task and job execution results output by the backup intelligent agent are input into the remote sensing data service system, so that the remote sensing data service system processes the remote sensing data according to the task and job execution results output by the backup intelligent agent.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

Images