Photoelectric tracker virtualization method of unmanned ship operating system

Abstract

The invention provides a photoelectric tracker virtualization method of an unmanned ship operating system. The photoelectric tracker virtualization method of the unmanned ship operating system comprises the steps that a virtual load sends a registration request to the unmanned ship operating system; and an identifier is allocated to the virtual load, and the identifier is used for identifying the virtual load. And the virtual load sends a connection request and task resource information to a resource management center. And after receiving the connection request, the resource management center performs resource scheduling and task allocation according to the task resource information, sends a corresponding task instruction to the virtual load, and realizes subsequent photoelectric tracking. A resource management center and a virtual load system are established in a virtualization mode, the adaptability of the photoelectric tracker is greatly improved, and the technical problem that a high-adaptability photoelectric tracker adaptation method is lacked in the prior art is solved.

Description

Technical Field This invention relates to the field of unmanned surface vessels (USVs), and in particular to a method for virtualizing an optoelectronic tracker in an USV operating system. Background Technology With the widespread application of unmanned surface vessels (USVs) in marine surveying, rescue, and scientific research, electro-optical trackers play a crucial role as key payloads. However, the actual deployment of electro-optical trackers on USVs faces various challenges, with their timeliness and flexibility being greatly restricted. To provide better adaptability in image processing and ranging of electro-optical trackers, there is an urgent need for a virtualization method for electro-optical trackers within the USV operating system. This would help solve the technical problem of the lack of a highly adaptable electro-optical tracker adaptation method in existing technologies. Summary of the Invention In one embodiment, the present invention provides a method for virtualizing an electro-optical tracker in an unmanned surface vessel operating system. By establishing a resource management center and a virtual payload system through virtualization, the adaptability of the electro-optical tracker is greatly improved, which helps to solve the technical problem of the lack of a highly adaptable electro-optical tracker adaptation method in the prior art.

[0001] The photoelectric tracking virtualization method of the unmanned surface vessel operating system includes: The virtual payload sends a registration request to the unmanned surface vessel operating system; Assign an identifier to the virtual payload, wherein the identifier is used to identify the virtual payload; The virtual payload sends a connection request and task resource information to the resource management center; After receiving the connection request, the resource management center performs resource scheduling and task allocation based on the task resource information, sends the corresponding task instructions to the virtual payload, and implements subsequent photoelectric tracking.

[0002] In one embodiment, the task resource information includes the required resources and task type of the virtual payload, as well as its priority.

[0003] In one embodiment, after the resource management center receives the connection request, performs resource scheduling and task allocation based on the task resource information, and sends the corresponding task instructions to the virtual payload, the method further includes: The virtual payload acquires data from sensors; Image and distance measurement information are calculated based on the data.

[0004] In one embodiment, the virtual payload sends the data to an unmanned surface vessel node or other virtual payload.

[0005] In one embodiment, after the resource management center receives the connection request, performs resource scheduling and task allocation based on the task resource information, and sends the corresponding task instructions to the virtual payload, the method further includes: After executing the task instructions, the virtual payload will send the task results back to the resource management center or the unmanned surface vessel node.

[0006] In one embodiment, the image and ranging information includes optical images and infrared images, as well as laser ranging data.

[0007] In one embodiment, the task instructions include target detection, tracking, and identification tasks.

[0008] In one embodiment, the task results include the target's location, status, and identification results.

[0009] In one embodiment, calculating the image and ranging information based on the data includes: Construct a data simulation model of the sensor; Based on the data, image and distance measurement information are calculated. Comparison data was collected using an optical tracker; The comparison results are obtained by comparing the comparison data with the image and ranging information. The data simulation model is corrected based on the comparison results.

[0010] In one embodiment, shortest job first, first-come-first-served, and priority scheduling algorithms are used for resource scheduling. Attached Figure Description Figure 1 This is a schematic diagram of the virtualization method framework for an unmanned surface vessel-borne photoelectric tracker in another embodiment of the present invention; Figure 2 This is a schematic diagram of a task scheduling framework in another embodiment of the present invention; Figure 3 This is a schematic diagram of the virtual photoelectric tracker payload access process framework in another embodiment of the present invention. Detailed Implementation Virtualization of virtual machines is an important technical means to build, debug, and maintain unmanned surface vessel (USV) systems more flexibly, and to enhance the system's adaptability and maintainability. This invention takes this as its guiding principle and virtualizes the photoelectric tracker of the USV operating system, which greatly improves the adaptability of the photoelectric tracker for deployment on USVs.

[0011] Virtualization technologies for unmanned surface vessel (USV) electro-optical trackers mainly include full virtualization based on virtual machines, lightweight virtualization based on Linux containers, service-oriented virtualization based on microservice architecture, and hardware-accelerated virtualization based on GPUs / FPGAs. Each method has its unique advantages and limitations. To achieve the best virtualization effect, it is necessary to conduct overall planning and design based on the performance, cost, and scalability requirements of the USV system, selecting the most suitable virtualization scheme or using a combination of multiple virtualization technologies to leverage the strengths of each. Overall, virtualization can significantly improve resource utilization, reduce system costs, and enhance scalability and reliability. By decoupling the hardware and software of the electro-optical tracker system through virtualization, the USV system can be built, debugged, and maintained more flexibly, enhancing system adaptability and maintainability.

[0012] By introducing virtualization technology to achieve dynamic management and optimization of payloads, unmanned surface vessel (USV) swarms can achieve more efficient and flexible application capabilities. This paper focuses on the design and implementation of this virtualization method, as well as the payload access process of the virtual electro-optical tracker, aiming to provide useful exploration and reference for the further development of USV technology.

[0013] In the ongoing development of unmanned surface vessel (USV) operating systems, electro-optical trackers play a crucial role as key payloads. However, the practical deployment of electro-optical trackers on USVs presents challenges. Therefore, we will first focus on introducing an innovative virtualization method for electro-optical trackers within USV operating systems. By introducing virtualization technology, we can achieve dynamic management and optimization of the payload, bringing more efficient and flexible application capabilities to USV swarms. Next, we will discuss the virtual electro-optical tracker payload access process, detailing the initialization, data transmission, and task execution steps, as well as the advantages and practical applications of the virtualization method in electro-optical tracker access. Through these two parts, we hope to provide valuable exploration and inspiration for the further development of electro-optical tracker virtualization technology in USV operating systems.

[0014] Figure 1 This is a schematic diagram of the virtualization method framework for an unmanned surface vessel-borne photoelectric tracker in another embodiment of the present invention; Figure 2 This is a schematic diagram of a task scheduling framework in another embodiment of the present invention; Figure 3 This is a schematic diagram of the virtual photoelectric tracker payload access process framework in another embodiment of the present invention.

[0015] like Figures 1 to 3 As shown, in one embodiment, the present invention provides a method for virtualizing the photoelectric tracker in an unmanned surface vessel (USV) operating system, the method comprising: S101, the virtual payload sends a registration request to the unmanned surface vessel operating system.

[0016] This step provides a specific procedure for a virtual payload to send a registration request to the unmanned surface vessel operating system.

[0017] S102, assign an identifier to the virtual payload, wherein the identifier is used to identify the virtual payload.

[0018] This step provides a specific procedure for assigning the virtual payload identifier.

[0019] S103, the virtual payload sends a connection request and task resource information to the resource management center.

[0020] This step provides a specific procedure for the virtual payload to send a connection request and task resource information to the resource management center.

[0021] S104, after receiving the connection request, the resource management center performs resource scheduling and task allocation according to the task resource information, sends the corresponding task instructions to the virtual payload, and realizes subsequent photoelectric tracking.

[0022] This step provides a specific procedure for the resource management center to receive the connection request, perform resource scheduling and task allocation based on the task resource information, send the corresponding task instructions to the virtual payload, and implement subsequent photoelectric tracking.

[0023] This embodiment provides a specific implementation method for the photoelectric tracking virtualization method of an unmanned surface vessel operating system.

[0024] In the virtualization method for electro-optical trackers within an unmanned surface vessel (USV) operating system, we designed a flexible and efficient virtualization framework. This framework includes key modules such as a virtualization manager, sensor data virtualization, and task scheduling and resource optimization. The virtualization manager is responsible for the creation, management, and destruction of virtual payloads, enabling dynamic resource scheduling and isolation. The sensor data virtualization module simulates sensor operation, generating the data required by the virtual payloads. The task scheduling and resource optimization module employs intelligent algorithms to rationally allocate tasks and efficiently utilize cluster resources. Through this approach, the electro-optical tracker payload is effectively managed and optimized within the USV operating system, improving system performance and execution efficiency. This brings more efficient and flexible application capabilities to USV swarms in fields such as marine surveying, scientific research, and rescue operations, further promoting the development of USV technology.

[0025] The virtual electro-optical tracker payload access process is a key technical solution in the unmanned surface vessel (USV) operating system's electro-optical tracker virtualization method, comprising three key steps: initialization, data transmission, and task execution. In the initialization phase, we focus on payload registration and configuration to ensure the virtual payload correctly establishes a connection with the system. Next, in the data transmission phase, we describe in detail the acquisition and transmission of sensor data, as well as the feedback of task results, ensuring the real-time performance and stability of data transmission. Finally, in the task execution phase, we introduce the process of the virtual payload executing tasks, including task reception and execution, and result feedback, focusing on resource scheduling and task priority optimization to ensure efficient task completion on the virtual payload. The comprehensive implementation of these technical solutions will provide strong support for the application of electro-optical tracker virtualization technology, promoting continuous innovation and progress in USV technology.

[0026] Initialization phase The initialization phase in the virtual electro-optical tracker payload access process is a crucial step in establishing a connection between the virtual payload and the unmanned surface vessel (USV) nodes. In this technical solution, we designed an efficient initialization process, including payload registration and configuration, and establishing a connection with the resource management center. Through these steps, the virtual payload can be accurately registered and identified, establishing a connection with the USV swarm. Simultaneously, we focus on data exchange and protocols during the initialization process to ensure reliable access of the virtual payload within the USV operating system. This solution lays a solid foundation for the effective deployment of virtual electro-optical tracker payloads in USV swarms and promotes the further application and development of electro-optical tracker virtualization technology.

[0027] Data transmission phase The data transmission phase is a crucial step in the virtualization method of the electro-optical tracker in the unmanned surface vessel (USV) operating system. In this technical solution, we focus on the data transmission process between the virtual payload and USV nodes, including sensor data acquisition and transmission, as well as the feedback of mission results. An efficient data transmission scheme ensures real-time data exchange and stable communication between the virtual payload and the USV swarm, providing a reliable foundation for mission execution and resource collaboration. We will describe in detail the real-time requirements and data transmission protocols, providing valuable exploration and guidance for the practical application of electro-optical tracker virtualization technology.

[0028] Task execution phase The task execution phase is a crucial link in the virtualization method of the electro-optical tracker in the unmanned surface vessel (USV) operating system. We focus on exploring the specific technical solutions for virtual payload task execution. In this solution, we employ an intelligent scheduling algorithm to rationally allocate tasks to the virtual payload based on task type, priority, and USV node resource status. Simultaneously, we emphasize the optimization of resource scheduling and task prioritization to ensure efficient task completion on the virtual payload. Through these specific implementation measures, we hope to provide useful guidance and support for the application of USV operating system electro-optical tracker virtualization technology, promoting continuous innovation and progress in USV technology.

[0029] This technical solution aims to virtualize the onboard electro-optical tracker of an unmanned surface vessel (USV) and designs a virtual electro-optical tracker payload access process based on this. The virtualization method focuses on virtualization framework design, sensor data virtualization, and task scheduling and resource optimization. The virtualization framework ensures dynamic management and isolation of the payload, while sensor data virtualization generates optical images, infrared images, and laser ranging data, guaranteeing the accuracy of the virtual payload in task execution. Task scheduling and resource optimization employ intelligent algorithms to rationally allocate tasks and resources, improving the overall system performance. The virtual electro-optical tracker payload access process includes an initialization phase, a data transmission phase, and a task execution phase, achieving efficient data exchange and stable communication between the payload and USV nodes, providing a reliable foundation for task execution and resource collaboration. This technical solution provides comprehensive guidance and support for the application of electro-optical tracker virtualization technology in USV operating systems, promoting continuous innovation and progress in USV technology.

[0030] In one embodiment, the task resource information includes the required resources and task type of the virtual payload, as well as its priority.

[0031] This embodiment provides a specific implementation method for the aforementioned task resource information.

[0032] In one embodiment, after the resource management center receives the connection request, performs resource scheduling and task allocation based on the task resource information, and sends the corresponding task instructions to the virtual payload, the method further includes: S201, the virtual payload acquires data from the sensor.

[0033] This step provides a specific procedure for the virtual payload to acquire data from the sensor.

[0034] S202, calculate the image and ranging information based on the data.

[0035] This step provides a specific procedure for calculating image and ranging information based on the data.

[0036] This embodiment provides a specific implementation for acquiring data from sensors and performing calculations. Sensor data virtualization is a crucial step in the virtualization method of the electro-optical tracker in an unmanned surface vessel (USV) operating system. This technical solution aims to generate optical images, infrared images, and laser ranging data through simulation technology to achieve simulation and modeling of the virtual payload in a computer. Through the sensor data generated by virtualization, the USV operating system can efficiently execute tasks on the virtual payload and acquire data similar to that of the actual electro-optical tracker in real time, thereby providing higher accuracy and reliability support for the application of USVs in complex marine environments.

[0037] In one embodiment, the virtual payload sends the data to an unmanned surface vessel node or other virtual payload.

[0038] This embodiment provides a specific implementation method for sending the data to an unmanned surface vessel (USV) node or other virtual payloads. In the USV operating system, task scheduling and resource optimization are crucial for ensuring efficient task execution. To achieve optimized management of the virtualized photoelectric tracker, this paper proposes an innovative task scheduling and resource optimization technology. Through task allocation strategies and resource scheduling algorithms, tasks are rationally allocated to virtual payloads based on task type, priority, and USV node resource status, achieving dynamic optimization of resource allocation. Simultaneously, the task execution monitoring module can monitor the execution status of virtual payloads in real time, adjusting the task execution strategy and resource allocation promptly based on actual execution results and task feedback to achieve optimal task execution performance. Through this technology, the USV operating system can efficiently manage and schedule virtual payloads, optimize resource allocation and task execution, and improve the overall performance and reliability of the system.

[0039] In one embodiment, after the resource management center receives the connection request, performs resource scheduling and task allocation based on the task resource information, and sends the corresponding task instructions to the virtual payload, the method further includes: After executing the task instructions, the virtual payload will send the task results back to the resource management center or the unmanned surface vessel node.

[0040] This embodiment provides a specific implementation method for the virtual payload to send the task results back to the resource management center or the unmanned surface vessel node after executing the task instructions.

[0041] In one embodiment, the image and ranging information includes optical images and infrared images, as well as laser ranging data.

[0042] This embodiment provides a specific implementation of the image and ranging information.

[0043] In one embodiment, the task instructions include target detection, tracking, and identification tasks.

[0044] This embodiment provides a specific implementation of the task instruction.

[0045] In one embodiment, the task results include the target's location, status, and identification results.

[0046] This embodiment provides a specific implementation method for the task result.

[0047] In one embodiment, calculating the image and ranging information based on the data includes: S301, Construct the data simulation model of the sensor.

[0048] This step provides a specific implementation method for constructing a data simulation model of the sensor.

[0049] S302, based on the data, calculate the image and distance measurement information.

[0050] This step provides a specific procedure for calculating image and ranging information based on the data.

[0051] S303 collects and compares data using a photoelectric tracker.

[0052] This step provides a specific procedure for collecting and comparing data using a photoelectric tracker.

[0053] S304, compare the comparison data with the image and ranging information to obtain the comparison result.

[0054] This step provides a specific procedure for obtaining a comparison result by comparing the comparison data with the image and ranging information.

[0055] S305, Correct the data simulation model based on the comparison results.

[0056] This step provides a specific procedure for correcting the data simulation model based on the comparison results.

[0057] This embodiment provides a specific implementation method for calculating image and ranging information based on the data.

[0058] In one embodiment, shortest job first, first-come-first-served, and priority scheduling algorithms are used for resource scheduling.

[0059] This embodiment provides a specific implementation method for resource scheduling using different algorithms.

[0060] Beneficial effects: First, the optimization of the virtual electro-optical tracker payload access process enhances the flexibility and scalability of electro-optical tracker virtualization within the unmanned surface vessel (USV) operating system. Through the design of the virtualization framework, USVs can dynamically create and manage virtual payloads according to mission requirements, enabling flexible resource allocation and optimized scheduling. This allows the USV system to better adapt to different mission and environmental needs, improving its adaptability and application scope.

[0061] Secondly, optimization during the mission execution phase enables the virtual payload to perform tasks efficiently and transmit results promptly. Through sensor data virtualization and intelligent task scheduling and resource optimization, the virtual payload can simulate the generation and processing of sensor data, reducing the resource consumption of the actual photoelectric tracker and improving mission execution efficiency. This allows the unmanned surface vessel swarm to better utilize resources, improving the overall resource utilization and performance of the system.

[0062] In summary, this technical solution brings multiple benefits to the application of photoelectric tracker virtualization technology in unmanned surface vessel (USV) operating systems. By optimizing the virtual photoelectric tracker payload access process and mission execution phase, the USV system achieves greater flexibility, scalability, and resource utilization. This will enable USVs to have more efficient and intelligent application capabilities in fields such as marine surveying, scientific research, and rescue operations, promoting continuous innovation and progress in USV technology.

[0063] The above methods will be explained in detail below: Virtualization method of unmanned surface vessel-borne electro-optical tracking device In unmanned surface vessel (USV) operating systems, electro-optical trackers play a crucial role as important payloads in marine surveying, rescue, and scientific research. However, the practical deployment of electro-optical trackers on USVs faces a series of challenges. To address these issues, this paper proposes an innovative virtualization method for USV-borne electro-optical trackers, achieving dynamic management and optimization of the payload through the introduction of virtualization technology. In the specific implementation plan, a virtualization framework is established to realize the virtual creation and management of the electro-optical tracker; simultaneously, sensor data virtualization technology is used to generate optical images, infrared images, and laser ranging data, and resource scheduling and optimization are used to achieve rational task allocation. Through these innovative technical solutions, the USV operating system will achieve more efficient and flexible electro-optical tracker payload management, bringing greater application potential to USV swarms in marine surveying, scientific research, and rescue operations. Detailed USV-borne electro-optical tracker virtualization method: Virtualization framework design Virtualization Manager: A virtualization manager module is designed to be responsible for the virtualization creation and management of the electro-optical tracker payload. When the unmanned surface vessel's operating system starts, the virtualization manager will dynamically create virtual payload instances as needed and allocate resources to each virtual payload. This module is also responsible for monitoring the operational status of the virtual payloads and adjusting their operational priority and resource allocation as needed.

[0064] Sensor data virtualization: This technology enables the generation of optical images, infrared images, and laser ranging data through simulation. Within the virtual payload, sensor data similar to that of an actual photoelectric tracker is dynamically generated based on the type and requirements of the task, maintaining the authenticity and accuracy of the data.

[0065] Resource Scheduling and Optimization: The virtualization manager employs resource scheduling algorithms for optimized resource allocation. Based on the task's computational requirements and the unmanned surface vessel (USV) node's computing power, the task execution priority and execution time of the virtual payload are dynamically adjusted to ensure optimal resource allocation for the task.

[0066] Task Execution Monitoring: The system monitors the execution status of virtual payloads in real time, acquiring results and operational status through data transmission and feedback mechanisms. Based on actual execution results and task feedback, the virtualization manager adjusts task execution strategies and resource allocation promptly to achieve optimal task execution performance.

[0067] Through the implementation of the virtualization framework, the unmanned surface vessel (USV) operating system can efficiently manage and schedule virtual payloads, optimize resource allocation and task execution, and improve the overall performance and reliability of the system. At the same time, the virtualization framework also brings greater flexibility and innovation to the application of USVs in fields such as marine surveying, scientific research, and rescue operations.

[0068] Sensor data virtualization Sensor Data Simulation Model Design: First, a sensor data simulation model is designed, including simulation algorithms for optical images, infrared images, and laser ranging data. Based on the working principle and sensor characteristics of the actual photoelectric tracker, a data simulation model is established to ensure that the virtual data has similar characteristics and statistical properties to the actual data.

[0069] Sensor Data Generation: Based on the sensor data simulation model, the virtualization manager generates sensor data. The virtualization manager dynamically generates sensor data similar to that of the actual photoelectric tracker, according to the task type and requirements. For example, by simulating the imaging process of an optical sensor and the thermal imaging process of an infrared sensor, virtual optical and infrared images are generated. Simultaneously, by simulating the ranging principle of a laser rangefinder, virtual laser ranging data is generated.

[0070] Data accuracy verification: The generated virtual sensor data needs to undergo accuracy verification to ensure consistency with the data collected by the actual photoelectric tracker. Through comparison and comparative analysis with the actual photoelectric tracker, the parameters and algorithms of the data simulation model are optimized to further improve the accuracy and reliability of the virtual data.

[0071] Data Transmission and Feedback: The virtualization manager transmits the generated sensor data to the virtual payload for task execution. After completing the task, the virtual payload sends the results and data back to the virtualization manager. Through this data transmission and feedback mechanism, the virtualization manager adjusts the task execution strategy and resource allocation in a timely manner to achieve optimal task performance.

[0072] Through the specific implementation of sensor data virtualization, the unmanned surface vessel (USV) operating system can efficiently simulate and model the operation of photoelectric trackers, providing a virtual payload simulation environment in a computer, thus bringing higher reliability and adaptability to the application of USVs in fields such as marine surveying, scientific research, and rescue operations.

[0073] Task scheduling and resource optimization Task allocation strategy design: First, the resource management center collects information such as task type, priority, and execution deadline. Then, based on task characteristics and the resource status of the unmanned surface vessel (USV) nodes, a task allocation strategy is formulated. High-priority tasks may be preferentially assigned to nodes with higher performance to ensure timely response and emergency execution. Simultaneously, tasks are allocated to appropriate virtual payloads based on their execution time and resource requirements.

[0074] Resource scheduling algorithm implementation: In the resource management center, a resource scheduling algorithm is used for dynamic optimization and allocation of resources. This algorithm may employ Shortest Job First (SJF), First-Come, First-Served (FCFS), priority scheduling, and other algorithms. Based on the computational requirements of the task and the computational capabilities of the unmanned surface vessel (USV) nodes, the task execution priority and execution time of the virtual payload are dynamically adjusted to ensure optimal resource allocation for the task.

[0075] Task Execution Monitoring and Feedback: During the task execution phase, the resource management center monitors the virtual payload's task execution status in real time. Through sensor data virtualization and task execution simulation, the center obtains the virtual payload's task execution status and effectiveness. Based on the actual execution results and task feedback, the center promptly adjusts the task execution strategy and resource allocation to achieve optimal task execution results.

[0076] Through task scheduling and resource optimization schemes, the unmanned surface vessel (USV) operating system can intelligently schedule tasks and resources within a cluster, maximizing task execution efficiency and overall system performance. The specific task scheduling process is as follows: Figure 2As shown, this optimization scheme brings greater flexibility and reliability to the application of unmanned surface vessels in fields such as marine surveying, rescue operations, and scientific research.

[0077] Virtual photoelectric tracker payload access process The virtual electro-optical tracker payload access process is a crucial step in the electro-optical tracker virtualization method of the unmanned surface vessel (USV) operating system. This section will discuss the specific implementation plan of this process in detail, including the initialization phase, data transmission phase, and task execution phase. In the initialization phase, we will focus on the registration and configuration of the virtual payload to ensure it can accurately identify and access the USV swarm. Next, in the data transmission phase, we will describe in detail the sensor data acquisition and transmission process, as well as the return of task results, ensuring the real-time performance and stability of data transmission. Finally, in the task execution phase, we will focus on the process of the virtual payload executing tasks, including task reception and execution, and result return, with a focus on optimizing resource scheduling and task priority to ensure efficient task completion on the virtual payload.

[0078] Initialization phase Payload Registration and Configuration: During the initialization phase, virtual payloads need to be registered and configured. First, the virtual payload sends a registration request to the unmanned surface vessel operating system, indicating its existence and the functions it provides. After receiving the registration request, the system assigns a unique identifier to the virtual payload for identification within the cluster.

[0079] Establishing a connection with the resource management center: Next, the virtual payload needs to establish a connection with the resource management center to obtain resource scheduling and optimization instructions. During the connection establishment process, the virtual payload sends a connection request to the resource management center, providing its own information, including required resources, task type, and priority. After receiving the request, the resource management center performs resource scheduling and task allocation based on the cluster resource status and task requirements, and sends the corresponding instructions back to the virtual payload.

[0080] Data Exchange and Protocols: During the initialization phase, the virtual payload exchanges data and protocols with the unmanned surface vessel (USV) operating system to ensure accurate system integration. This includes transmitting payload configuration information, task requirements, and resource scheduling instructions. The stability and accuracy of data exchange and protocols are crucial factors in ensuring the smooth integration of virtual payloads into the USV swarm.

[0081] Through these specific implementation steps, the virtual electro-optical tracker payload was successfully registered and connected to the unmanned surface vessel (USV) operating system during the initialization phase. The exchange of instructions from the resource management center and data between the virtual payload ensured efficient collaboration and resource optimization between the virtual payload and the system. The successful implementation of this process laid the foundation for subsequent data transmission and mission execution phases, providing valuable exploration and guidance for the application of electro-optical tracker virtualization methods in USV operating systems.

[0082] Data transmission phase The data transmission phase is a crucial part of the virtualization method for the electro-optical tracker in the unmanned surface vessel (USV) operating system. In this phase, we focus on the acquisition and transmission of sensor data, as well as the feedback of mission results, ensuring the real-time performance and stability of the data.

[0083] Sensor Data Acquisition: Before the data transmission phase begins, the virtual payload first needs to acquire data from the sensors. By simulating the working principles and characteristics of the sensors, we can generate the optical images, infrared images, and laser ranging data required by the virtual payload. This virtually generated data will become an important input for the virtual payload during mission execution.

[0084] Real-time data transmission requirements: During the data transmission phase, the virtual payload needs to transmit the acquired data to the unmanned surface vessel (USV) node or other payloads in real time. Real-time data transmission is crucial to ensure that the USV operating system can accurately acquire and process the latest data to support mission execution and decision-making.

[0085] Data transmission protocol design: In practical transmission, we need to design data transmission protocols to specify data format, transmission method, and error handling mechanisms. Such protocols can ensure the stability and reliability of data transmission, prevent data loss or corruption, and guarantee the smooth completion of the transmission process.

[0086] Mission Result Transmission: After mission completion, the virtual payload needs to transmit the mission results back to the resource management center or unmanned surface vessel node. Mission results may include target detection, tracking, and identification results. The transmitted results will be used for resource scheduling and decision-making to optimize and adjust mission execution.

[0087] Through the specific implementation steps described above, the data transmission phase ensures efficient data exchange and stable communication between the virtual payload and the unmanned surface vessel (USV) operating system. This provides reliable support for the application and development of photoelectric tracker virtualization technology, promoting the continuous progress and innovation of USV technology.

[0088] 2.3 Task Execution Phase During the mission execution phase, the virtual electro-optical tracker payload needs to perform specific tasks and transmit the results back to the resource management center or the unmanned surface vessel node. The following are the detailed implementation steps for the mission execution phase: Task Reception and Distribution: Virtual payloads send task requests to the resource management center. The resource management center distributes tasks to suitable virtual payloads based on the cluster's task requirements and resource status. Task distribution may include different types of target detection, tracking, and identification tasks, which are sorted according to task priority.

[0089] Task Execution: The virtual payload begins executing the task based on the received task information. By simulating the processing of sensor data, the virtual payload generates optical images, infrared images, and laser ranging data, and detects, tracks, and identifies targets. Task execution may involve complex calculations and algorithms, ensuring that the virtual payload completes the task efficiently within the unmanned surface vessel swarm.

[0090] Mission Result Transmission: After mission completion, the virtual payload transmits the mission results back to the resource management center or unmanned surface vessel node. The results may include information such as the target's location, status, and identification results. The transmitted results will be used for resource scheduling and mission optimization, enabling real-time monitoring and feedback of mission execution.

[0091] Resource Scheduling and Task Optimization: During the task execution phase, the virtual payload performs resource scheduling and task optimization based on the cluster's resource status and task requirements. Through intelligent scheduling algorithms, the virtual payload can rationally allocate computing and communication resources, ensuring efficient task execution on the virtual payload. Optimization of task priorities ensures that important tasks are processed promptly, improving the overall system performance.

[0092] Through the detailed implementation steps described above, the mission execution phase ensures that the virtual electro-optical tracker payload efficiently performs its tasks within the unmanned surface vessel's operating system, and optimizes the transmission of mission results and resource scheduling. The framework for the virtual electro-optical tracker payload access process is as follows: Figure 3 As shown in the diagram, these specific implementation schemes provide valuable guidance and support for the application of photoelectric tracking virtualization technology, promoting continuous innovation and progress in unmanned surface vessel (USV) technology.

Claims

1. A method for virtualizing an electro-optical tracker in an unmanned surface vessel operating system, characterized in that, The photoelectric tracking virtualization method of the unmanned surface vessel operating system includes: The virtual payload sends a registration request to the unmanned surface vessel operating system; Assign an identifier to the virtual payload, wherein the identifier is used to identify the virtual payload; The virtual payload sends a connection request and task resource information to the resource management center; After receiving the connection request, the resource management center performs resource scheduling and task allocation based on the task resource information, sends the corresponding task instructions to the virtual payload, and implements subsequent photoelectric tracking.

2. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 1, characterized in that, The task resource information includes the resources required for the virtual payload, the task type, and the priority.

3. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 2, characterized in that, After receiving the connection request from the resource management center, performing resource scheduling and task allocation based on the task resource information, and sending the corresponding task instructions to the virtual payload, the method further includes: The virtual payload acquires data from sensors; Image and distance measurement information are calculated based on the data.

4. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 3, characterized in that, The virtual payload sends the data to unmanned surface vessel nodes or other virtual payloads.

5. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 4, characterized in that, After receiving the connection request from the resource management center, performing resource scheduling and task allocation based on the task resource information, and sending the corresponding task instructions to the virtual payload, the method further includes: After executing the task instructions, the virtual payload will send the task results back to the resource management center or the unmanned surface vessel node.

6. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 5, characterized in that, The image and ranging information includes optical images and infrared images, as well as laser ranging data.

7. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 6, characterized in that, The task instructions include target detection, tracking, and identification tasks.

8. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 7, characterized in that, The task results include the target's location, status, and identification results.

9. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 8, characterized in that, The calculation of image and ranging information based on the data includes: Construct a data simulation model of the sensor; Based on the data, image and distance measurement information are calculated. Comparison data was collected using an optical tracker; The comparison results are obtained by comparing the comparison data with the image and ranging information. The data simulation model is corrected based on the comparison results.

10. The method for virtualizing the photoelectric tracker in the unmanned surface vessel operating system according to claim 9, characterized in that, Resource scheduling is performed using shortest job first, first-come-first-served, and priority scheduling algorithms.

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