Semi-physical simulation system and method for low-orbit star group edge microclouds

Abstract

The invention discloses a semi-physical simulation system and method for low-orbit satellite group edge microclouds. The system comprises a satellite edge calculation principle prototype, a satellite edge calculation digital prototype and a satellite edge calculation semi-physical simulation platform. Wherein the satellite edge computing prototype adopts a cloud native technical architecture, and provides a hardware platform and a software drive support for the space-based edge microcloud; the satellite edge calculation digital prototype is a digital twin system of a principle prototype and provides operation environment support for space-based edge microcloud application software. The satellite edge computing semi-physical simulation platform comprises a hardware part and a software part, wherein the hardware part comprises a simulation server, a display control assembly and a virtual-real interconnection interface assembly; and the satellite edge calculation semi-physical simulation platform adopts a distributed simulation mode to complete an overall simulation task. According to the system and the method, the accuracy of large-scale low-orbit satellite group edge micro-cloud system performance simulation verification can be improved, and the simulation cost is saved.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to a semi-physical simulation system and method for a micro-cloud at the edge of a low-orbit constellation. Background Art

[0002] In recent years, edge computing technology has become a hot topic in terrestrial 5G / 6G mobile communication network research. Open edge computing architectures and software and hardware development platforms have matured, and the cloud-edge collaborative model of "edge computing + cloud service center" has been applied to intelligent digital information systems such as smart driving, smart factories, and smart cities. To improve the computing and communication capabilities of space-based systems, a growing number of researchers are considering applying edge computing directly to low-orbit satellites. By integrating distributed satellite cluster computing power through multi-satellite networking, they are building a space-based computing network, or space-based edge micro-cloud, that collaborates with ground-based cloud computing centers to complete real-time on-orbit processing and efficient distribution of space-based information, providing faster and higher-quality services to various user terminals on land, sea, and air.

[0003] Unlike ground-based edge micro-clouds, space-based edge micro-clouds based on low-orbit satellites are characterized by large temporal and spatial scales, high dynamism, and resource constraints. The high-speed movement of low-orbit satellites not only causes the connection relationships within the edge micro-cloud to vary over time, but also leads to dynamic changes in the connection between the edge micro-cloud and ground-based cloud computing centers and user terminal nodes. Nodes within the edge micro-cloud require frequent dynamic updates, making service continuity a crucial consideration in the construction of space-based edge micro-clouds. Furthermore, satellite edge nodes are small in size and have limited resources such as computing, storage, and energy consumption. Therefore, collaboration between edge nodes and between cloud and edge nodes is essential to improve task completion efficiency and resource utilization efficiency.

[0004] To address these issues, some research efforts have focused on designing space-based edge micro-cloud architectures, studying efficient service migration and collaborative intelligent computing solutions, and verifying the performance of space-based edge micro-cloud systems using digital simulation software. However, the virtual digital models employed in these simulations cannot fully simulate real physical models and can only provide approximate results, making their reliability difficult to verify. On the other hand, a few studies have developed satellite edge computing prototypes and established ground-based test systems to test and verify the performance of space-based edge micro-cloud systems. While these test and verification systems can generate more realistic and reliable experimental data, they are costly, time-consuming, and risky to construct. Furthermore, conducting full-scale physical testing of edge micro-clouds for large-scale LEO satellite constellations is unrealistic. Semi-physical simulation methods combine the advantages of digital simulation and physical testing. By replacing some functions or modules with physical hardware based on digital simulation, they enable coordinated simulation of digital and physical models, enabling realistic and accurate performance verification of large-scale LEO satellite constellations at a lower cost. Summary of the Invention

[0005] In response to the problems existing in the prior art, the purpose of the present invention is to provide a semi-physical simulation system and method for micro-clouds at the edge of a low-orbit constellation, which can improve the accuracy of performance simulation verification of micro-cloud systems at the edge of a large-scale low-orbit satellite constellation and save simulation costs.

[0006] To achieve the above-mentioned purpose, the present invention provides a semi-physical simulation system for edge micro-clouds of low-orbit constellations, the system comprising a satellite edge computing principle prototype, a satellite edge computing digital prototype and a satellite edge computing semi-physical simulation platform; wherein, The hardware of the satellite edge computing prototype includes a computing module, a storage module, an interconnection module and a power module. Among them, the computing module adopts a master-slave scalable solution of "computing core board" + "computing accelerator board". The "computing core board" is responsible for running the core functions of the star cluster edge micro-cloud and the resource scheduling of the "computing accelerator board". It uses domestic ARM+GPU architecture chips that support the containerized micro-service ecosystem, with a computing power of up to 10Tops, ensuring that the prototype runs safely, reliably and autonomously. The "computing accelerator board" uses domestic industrial-grade ARM+GPU architecture chips of the same model, with a board-level computing power of up to 10Tops. It can be expanded to 50Tops or more on demand in a plug-and-play manner according to the computing power requirements of on-board AI processing to meet subsequent system expansion and upgrade requirements. The prototype software adopts a cloud-native technology architecture, including an edge micro-cloud infrastructure layer, an edge micro-cloud platform service layer and an edge micro-cloud software service layer. It is compatible with a lightweight machine learning framework and has the ability to deploy applications, algorithm models, and other applications in real time at the container and micro-service levels on orbit. The prototype provides hardware platform and software driver support for the space-based edge micro-cloud. The satellite edge computing digital prototype is a digital twin system of the principle prototype, including an infrastructure resource layer, a control and management simulation layer, and a software operation layer. Among them, the infrastructure resource layer uses KVM virtual machine technology to implement basic resource configuration functions such as CPU, GPU, and memory; the control and management simulation layer uses the Quagga tool to configure the routing protocol of the digital prototype, and uses the Traffic Control tool to configure the bandwidth, delay, and packet loss rate of the simulated digital prototype network; the software operation layer deploys and runs the satellite edge computing application software by using Docker container technology. The digital prototype is essentially a digital virtual model of the satellite edge computing principle prototype, providing operating environment support for the space-based edge micro-cloud application software; The satellite edge computing semi-physical simulation platform includes hardware and software parts. The hardware part includes a simulation server, display and control components, and virtual-real interconnection interface components. The software part consists of a satellite constellation operating environment simulation unit, a satellite networking simulation unit, an edge micro-cloud simulation support unit, and an application scenario simulation control unit. Among them, the edge micro-cloud simulation support unit uses Docker container technology and Kubernetes container management tools to realize functions such as low-orbit constellation edge micro-cloud establishment, edge micro-cloud service migration, and edge micro-cloud collaborative computing. The satellite edge computing semi-physical simulation platform adopts a distributed simulation method, dividing the overall simulation task into multiple modules according to the component functional characteristics. A single module independently performs local functional simulation, and cross-module information exchange is carried out through the underlying support module, thereby completing the overall simulation task.

[0007] Furthermore, the satellite edge computing digital prototype runs in the simulation server of the satellite edge computing semi-physical simulation platform. The satellite edge computing semi-physical simulation platform provides physical resources and basic operating environment for the operation of the satellite edge computing digital prototype, and provides a logical soft bus interface for the satellite edge computing digital prototype.

[0008] Furthermore, the satellite edge computing principle prototype and the satellite edge computing semi-physical simulation platform are connected through a virtual-reality interconnection interface component to realize information interaction between the satellite edge computing principle prototype and the satellite edge computing digital prototype.

[0009] In another aspect, the present invention provides a semi-physical simulation method for micro clouds at the edge of a low-orbit constellation, the simulation method comprising the following steps: S1. Mission scenario modeling: Through mission requirement analysis, carry out constellation configuration design and constellation model construction, and create real-time or offline driven 3D simulation scenario models according to the mission scenario; S2. Simulation environment configuration: Configure parameters according to the mission scenario, create a satellite edge computing digital prototype, configure basic resources for the digital prototype, and simulate the satellite ground and inter-satellite network environment; S3, Edge Micro-Cloud Construction: Based on the mission requirements, the main satellite node (based on the available computing and storage resources of neighboring satellite nodes within a 5-hop range) selects the minimum number of satellite nodes that can meet the mission requirements to build a space-based edge micro-cloud. This enables the sharing and flexible scheduling of computing, storage, and communication resources within the edge micro-cloud, and provides operation and maintenance support for various mission application software. S4. System capability verification: Based on the mission requirement information, the mission image is deployed in each node of the space-based edge micro-cloud, the edge micro-cloud resources are scheduled and the mission application software is run. After the mission is completed, the mission performance evaluation is carried out.

[0010] Furthermore, step S1 is completed by the satellite edge computing semi-physical simulation platform, including: S1.1 Constellation configuration design: Constellation configuration parameters include satellite orbit altitude, orbit inclination, total number of satellites, number of orbital planes, and phase factor; S1.2 Constellation Model Construction: The satellite edge computing hardware-in-the-loop simulation platform conducts satellite orbit dynamics simulation and visualization simulation based on constellation configuration parameters, and analyzes and obtains the position and attitude information of all satellite nodes; S1.3 Simulation scenario construction: refers to the simulation platform modeling other elements in the task scenario and configuring their kinematic parameters or their own characteristic parameters.

[0011] Furthermore, step S2 is completed by the satellite edge computing digital prototype and semi-physical simulation platform; including: S2.1 Digital Prototype Creation: The satellite edge computing hardware-in-the-loop simulation platform uses a virtual machine for each satellite node to build a satellite edge computing digital prototype. Each digital prototype is assigned an independent IP address. S2.2 Basic Resource Configuration: The computing resources, storage resources, and network resources of each digital prototype are configurable. Computing resources include the number and performance parameters of CPUs and GPUs, storage resources include storage space size parameters, and network resources include satellite-to-ground and inter-satellite link communication bandwidth parameters. S2.3 Network environment simulation: The simulation platform simulates the impact of satellite-to-ground and inter-satellite network environments based on the location information of satellite-to-ground and inter-satellite network nodes and channel model parameters, and analyzes the satellite network transmission status. Channel model parameters include free space path loss exponent, large-scale and small-scale fading parameters, rain attenuation and atmospheric loss parameters.

[0012] Furthermore, step S3 is completed by the satellite edge computing prototype and the semi-physical simulation platform; including: S3.1 Micro-cloud Node Selection: The satellite edge computing hardware-in-the-loop simulation platform selects some satellite nodes as needed to form a space-based edge micro-cloud system based on the computing power, storage resources, and network resources required for mission execution, according to the location information and resource usage of satellite nodes and the satellite constellation network topology. S3.2 Principle Prototype Startup: The satellite edge computing principle prototype is powered on and the cloud-native software operating environment is initialized, and cloud-native application deployment and management tools are run normally. S3.3 Prototype virtual-reality mapping: The satellite edge computing semi-physical simulation platform uses virtual-reality interconnection interface components to connect the satellite edge computing principle prototype with the digital prototypes of some satellite nodes in the space-based edge micro-cloud, and realizes the virtual-reality mapping of time and space between satellite physical nodes and satellite virtual nodes through data transmission.

[0013] Furthermore, step S4 is completed by the satellite edge computing hardware-in-the-loop simulation platform, the digital prototype, and the principle prototype, including: S4.1 Mission Image Deployment: The satellite edge computing hardware-in-the-loop simulation platform encapsulates the mission application software in the form of a Docker container, which is then packaged into an image and uploaded to all satellite nodes of the space-based edge micro-cloud via the satellite network. The space-based edge micro-cloud then loads the image and starts the mission application software. S4.2 Task Execution Scheduling: The space-based edge micro-cloud system performs task orchestration and resource scheduling on demand based on task data. Multiple satellites efficiently utilize their own computing, storage, and network resources to collaboratively complete task requirements and return task execution results. S4.3 Mission effectiveness evaluation: The satellite edge computing semi-physical simulation platform conducts comprehensive analysis and evaluation of mission effectiveness, and displays the mission effectiveness evaluation results on the front end of the simulation platform.

[0014] Furthermore, in step S3.2, the cloud-native application deployment and management tools include Docker containers and Kubernetes.

[0015] Furthermore, in step S4.3, the evaluation indicators are task execution results, task execution time, and task execution consumed resources.

[0016] Beneficial effects: The semi-physical simulation system and method for satellite edge computing proposed in the present invention focuses on using semi-physical simulation technology to verify satellite edge computing functions. Most of the semi-physical simulation technologies known in the prior art can only verify satellite communications, remote sensing, and attitude and orbit control functions. Currently, there is no technology for semi-physical simulation verification of satellite edge computing functions. In order to verify the satellite edge computing function, the principle prototype in the present invention needs to be installed with a high-performance computing module, adopt a cloud-native technology architecture, adapt to a lightweight machine learning framework, and have the ability to deploy applications, algorithm models, and other container-level and microservice-level real-time on-orbit, providing a hardware platform and software driver support for the space-based edge micro-cloud; the digital prototype needs to include an infrastructure resource layer, a control and management simulation layer, and a software operation layer to provide an operating environment support for the space-based edge micro-cloud application software; the semi-physical simulation platform adds an edge micro-cloud simulation support unit, adopts Docker container technology and Kubernetes container management tools, and realizes functions such as low-orbit constellation edge micro-cloud establishment, edge micro-cloud service migration, and edge micro-cloud collaborative computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the architecture of the hardware-in-the-loop simulation system for micro-clouds at the edge of a low-orbit constellation according to the present invention; Figure 2 Schematic diagram of the hardware-in-the-loop simulation method for micro clouds at the edge of a low-orbit constellation according to the present invention; Figure 3This is a flow chart of a method according to embodiment 1 of the present invention; Figure 4 This is a flow chart of the method of embodiment 2 of the present invention; Figure 5 This is a flow chart of the method of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0020] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0021] The following is combined with Figure 1-Figure 5 The specific embodiments of the present invention are described in detail. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0022] The present invention discloses a semi-physical simulation system and method for low-orbit satellite cluster edge micro-clouds. First, a semi-physical simulation system for low-orbit satellite cluster edge micro-clouds is proposed, which mainly includes a satellite edge computing principle prototype, a satellite edge computing digital prototype, and a satellite edge computing semi-physical simulation platform. Second, a semi-physical simulation method for low-orbit satellite cluster edge micro-clouds is proposed, which includes four parts: mission scenario modeling, simulation environment configuration, edge micro-cloud construction, and system capability verification. Finally, three semi-physical simulation verification schemes for mission effectiveness and key technologies for low-orbit satellite cluster edge micro-clouds are proposed. Based on the above-mentioned semi-physical simulation system and simulation method, simulation evaluation is performed to evaluate the effectiveness of low-orbit satellite cluster Earth observation missions, the migration time of low-orbit satellite cluster cloud-edge services, and the effectiveness of multi-satellite collaborative computing.

[0023] The present invention proposes a semi-physical simulation system for micro-clouds at the edge of a low-orbit constellation. The system consists of the following components: Figure 1 As shown in Figure 1, the simulation system consists of a satellite edge computing prototype, a satellite edge computing digital prototype, and a satellite edge computing hardware-in-the-loop simulation platform. The satellite edge computing prototype comprises a computing module, storage module, interconnect module, power module, and corresponding structural components. It utilizes a cloud-native technology architecture and is compatible with a lightweight machine learning framework. It enables on-orbit real-time deployment of applications, algorithm models, and other systems at the container and microservice levels, providing the hardware platform and software driver support for the space-based edge micro-cloud. The satellite edge computing digital prototype is a digital twin of the prototype, consisting of an infrastructure resource layer, a control and management simulation layer, and a software runtime layer. Using digital twin and artificial intelligence technologies, it creates a digital virtual model of the satellite edge computing prototype, providing an operating environment support for space-based edge micro-cloud application software. The satellite edge computing hardware-in-the-loop simulation platform includes both hardware and software components. The hardware component comprises a simulation server, display and control components, and virtual-to-real interconnection interface components. The software component comprises a satellite constellation operating environment simulation unit, a satellite networking simulation unit, an edge micro-cloud simulation support unit, and an application scenario simulation control unit. The satellite edge computing semi-physical simulation platform adopts a distributed simulation method, dividing the overall simulation task into multiple modules according to the component functional characteristics. Each module independently performs local functional simulation, and cross-module information interaction is carried out through the underlying support module, thereby completing the overall simulation task.

[0024] There are both connection relationships and attachment or dependency relationships between the satellite edge computing principle prototype, satellite edge computing digital prototype and satellite edge computing semi-physical simulation platform. Among them, the satellite edge computing digital prototype runs in the simulation server of the satellite edge computing semi-physical simulation platform. The satellite edge computing semi-physical simulation platform provides physical resources and basic operating environment for the operation of the digital prototype, and provides a logical soft bus interface for the satellite edge computing digital prototype; the satellite edge computing principle prototype and the satellite edge computing semi-physical simulation platform are connected through virtual-real interconnection interface components to realize information interaction between the satellite edge computing principle prototype and the satellite edge computing digital prototype; the satellite edge computing principle prototype, the satellite edge computing digital prototype and the satellite edge computing semi-physical simulation platform jointly construct a semi-physical simulation system for the edge micro-cloud of the low-orbit constellation. Users can set simulation parameters, scenario parameters and business parameters according to task requirements to realize service path planning and calculation output. During the simulation process, statistical indicators are sent to the visual demonstration page in real time, and performance evaluation of software deployment, task coordination, service migration, etc. is performed, providing technical support for important links such as the design and development, test evaluation, collaborative interconnection, and test verification of the space-based edge micro-cloud system.

[0025] The hardware of the satellite edge computing prototype includes a computing module, a storage module, an interconnection module and a power module. Among them, the computing module adopts a master-slave scalable solution of "computing core board" + "computing accelerator board". The "computing core board" is responsible for running the core functions of the star cluster edge micro-cloud and the resource scheduling of the "computing accelerator board". It uses domestic ARM+GPU architecture chips that support the containerized micro-service ecosystem, with a computing power of up to 10Tops, ensuring that the prototype runs safely, reliably and autonomously. The "computing accelerator board" uses domestic industrial-grade ARM+GPU architecture chips of the same model, with a board-level computing power of up to 10Tops. It can be expanded to 50Tops or more on demand in a plug-and-play manner according to the computing power requirements of on-board AI processing to meet subsequent system expansion and upgrade requirements. The prototype software adopts a cloud-native technology architecture, including an edge micro-cloud infrastructure layer, an edge micro-cloud platform service layer and an edge micro-cloud software service layer. It is compatible with a lightweight machine learning framework and has the ability to deploy applications, algorithm models, and other applications in real time at the container and micro-service levels on orbit. The prototype provides hardware platform and software driver support for the space-based edge micro-cloud. The satellite edge computing digital prototype is a digital twin of the prototype, comprising an infrastructure resource layer, a control and management simulation layer, and a software runtime layer. The infrastructure resource layer uses KVM virtual machine technology to configure basic resources such as CPU, GPU, and memory. The control and management simulation layer uses the Quagga tool to configure the digital prototype's routing protocol and the Traffic Control tool to configure the bandwidth, latency, and packet loss rate of the simulated digital prototype network. The software runtime layer uses Docker container technology to deploy and run satellite edge computing application software. The digital prototype is essentially a digital virtual model of the satellite edge computing prototype, providing an operating environment support for space-based edge micro-cloud application software.

[0026] The satellite edge computing semi-physical simulation platform includes hardware and software parts. The hardware part includes a simulation server, display and control components, and virtual-real interconnection interface components. The software part consists of a satellite constellation operating environment simulation unit, a satellite networking simulation unit, an edge micro-cloud simulation support unit, and an application scenario simulation control unit. Among them, the edge micro-cloud simulation support unit uses Docker container technology and Kubernetes container management tools to realize functions such as low-orbit constellation edge micro-cloud establishment, edge micro-cloud service migration, and edge micro-cloud collaborative computing. The satellite edge computing semi-physical simulation platform adopts a distributed simulation method, dividing the overall simulation task into multiple modules according to the component functional characteristics. A single module independently performs local functional simulation, and cross-module information exchange is carried out through the underlying support module, thereby completing the overall simulation task.

[0027] On the other hand, the present invention proposes a semi-physical simulation method for micro clouds at the edge of a low-orbit constellation. The simulation process is as follows: Figure 2 The simulation method includes the following steps: S1. Mission Scenario Modeling: Based on mission requirements analysis, constellation configuration design and constellation model construction are conducted. A 3D simulation scenario model capable of real-time and offline operation is created based on the mission scenario. This function is performed by the satellite edge computing hardware-in-the-loop simulation platform and mainly includes three parts: constellation configuration design, constellation model construction, and simulation scenario construction. Constellation configuration design involves the simulation platform designing an appropriate satellite constellation configuration based on mission requirements (such as regional coverage and revisit period). Constellation configuration parameters include satellite orbit altitude, orbital inclination, total number of satellites, number of orbital planes, and phase factor. Constellation model construction involves the simulation platform performing satellite orbit dynamics simulation and visualization based on the constellation configuration parameters, analyzing and obtaining the position and attitude information of all satellite nodes. Simulation scenario construction involves the simulation platform modeling other elements in the mission scenario (such as user terminals and satellite ground stations), and configuring their kinematic parameters and their own characteristic parameters. The satellite edge computing hardware-in-the-loop simulation platform can obtain relevant configuration parameters by reading the mission scenario configuration file or manually setting the mission scenario parameters to create the simulation scenario and write it to the simulation database.

[0028] S2. Simulation Environment Configuration: Configure parameters based on the mission scenario, create a satellite edge computing digital prototype, configure basic resources for the digital prototype, and simulate the satellite-to-ground and intersatellite network environments. This functionality is performed by the satellite edge computing digital prototype and the hardware-in-the-loop simulation platform. The hardware-in-the-loop simulation platform performs digital prototype creation and network environment simulation, while the digital prototype performs basic resource configuration. Digital prototype creation involves the simulation platform using a virtual machine to build a satellite edge computing digital prototype for each satellite node. Each digital prototype is assigned a unique IP address. Basic resource configuration involves configuring the computing, storage, and network resources for each digital prototype. Computing resources include parameters such as the number and performance of CPUs and GPUs; storage resources include parameters such as storage space size; and network resources include parameters such as satellite-to-ground and intersatellite link communication bandwidth. Network environment simulation involves the simulation platform simulating the impact of satellite-to-ground and intersatellite network environments based on the location information of satellite-to-ground and intersatellite network nodes and channel model parameters, analyzing and analyzing satellite network transmission conditions. In view of the different frequency bands, bandwidths, and application scenarios of different types of communication links, channel model parameters can be configured on demand, including free space path loss exponent, large-scale and small-scale fading parameters, rain attenuation and atmospheric loss parameters, etc.

[0029] S3. Edge Micro-Cloud Construction: Based on mission requirements, the primary satellite node (the one that receives the mission requirements) selects the minimum number of satellite nodes required to complete the mission, based on the available computing and storage resources of neighboring satellite nodes within a 5-hop radius. This selects and flexibly schedules computing, storage, and communication resources within the edge micro-cloud, and provides operation and maintenance support for various mission application software. This functionality is implemented by a satellite edge computing prototype and a hardware-in-the-loop simulation platform. The hardware-in-the-loop simulation platform performs micro-cloud node selection and prototype virtual-to-real mapping, while the prototype itself is activated. Micro-cloud node selection involves the simulation platform selecting satellite nodes as needed to form the space-based edge micro-cloud system, based on the computing, storage, and network resources required for mission execution, along with their location information, resource usage, and the satellite constellation network topology. The prototype startup refers to the satellite edge computing prototype being powered on and completing the initialization of the cloud-native software operating environment, normally running cloud-native application deployment and management tools such as Docker containers and Kubernetes, and providing operating support for container-level and microservice-level software such as applications and algorithm models; the prototype virtual-reality mapping refers to the simulation platform using virtual-reality interconnection interface components to connect the prototype with the digital prototypes of some satellite nodes in the space-based edge micro-cloud, and realize the virtual-reality mapping of time and space between the satellite physical nodes and the satellite virtual nodes through Gigabit Ethernet data transmission.

[0030] S4. System Capability Verification: Based on mission requirements, the mission image is deployed on each node of the space-based edge micro-cloud. Edge micro-cloud resources are scheduled and the mission application software runs. After the mission is completed, a mission performance evaluation is conducted. This function is jointly performed by the satellite edge computing hardware-in-the-loop simulation platform, a digital prototype, and a prototype. It mainly includes three parts: mission image deployment, mission execution scheduling, and mission performance evaluation. Mission image deployment involves the simulation platform packaging the mission application software as a Docker container. The image is then uploaded to all satellite nodes of the space-based edge micro-cloud via the satellite network. The space-based edge micro-cloud loads the image and starts the mission application software. Mission execution scheduling involves the space-based edge micro-cloud system orchestrating tasks and scheduling resources based on mission data. Multiple satellites efficiently utilize their own computing, storage, and network resources to collaboratively complete mission requirements and return mission execution results. Mission performance evaluation involves the simulation platform conducting a comprehensive analysis and evaluation of mission performance based on metrics such as mission execution results, execution time, and resource consumption. The results are then displayed on the simulation platform front-end.

[0031] The above-mentioned semi-physical simulation system and method can not only realize the performance evaluation of important tasks such as multi-satellite collaborative earth observation of low-orbit remote sensing constellations, multi-satellite collaborative positioning of low-orbit navigation constellations, and multi-satellite collaborative electromagnetic spectrum monitoring of low-orbit electromagnetic perception constellations, but also support the simulation verification of key technologies such as space-based edge micro-cloud service migration and collaborative computing. It has high reliability and low cost, and is very suitable for the performance simulation verification of edge micro-cloud systems of large-scale low-orbit satellite constellations. Example

[0032] Example 1 describes in detail a method for semi-physical simulation of earth observation mission performance for low-orbit star cluster edge micro-clouds implemented by a semi-physical simulation system for low-orbit star cluster edge micro-clouds according to the present invention. The simulation process is as follows: Figure 3 The specific steps are as follows: S101. The user enters Earth observation mission requirements (observation area latitude and longitude, observation interval, and observation resolution) into the front-end interface of the hardware-in-the-loop simulation system, sets satellite remote sensing payload capacity parameters, runs the low-orbit satellite constellation configuration design function, and outputs constellation configuration parameters, including satellite orbit altitude, orbit inclination, total number of satellites, number of orbital planes, and phase factor. S102. The simulation system runs the constellation model building function to complete the satellite orbit dynamics simulation. The front-end displays a three-dimensional view of the satellite constellation operation, and the back-end stores the position and attitude information of all satellite nodes. S103. Create digital models of ground observation targets, satellite ground stations, ground data centers, etc., and write the simulation scene model into the simulation database; S104. Run the simulation scenario model, create a corresponding satellite edge computing digital prototype for all satellite nodes, and assign independent IP addresses; S105. Configure all digital prototype computing resources, storage resources and network resource related parameters; S106. Configure satellite-to-ground and inter-satellite communication network channel model parameters, including free space path loss exponent, large-scale and small-scale fading parameters, rain attenuation and atmospheric loss parameters; S107. Based on the requirements of the Earth observation mission, set the amount of satellite remote sensing image data, run the micro-cloud node selection function, and output the space-based edge micro-cloud construction strategy, including the number of edge micro-cloud satellite nodes, satellite node locations, edge micro-cloud network topology, and other information; S108. Power on the satellite edge computing prototype, load software such as Docker and Kubernetes, and initialize the cloud-native software operating environment. S109. Run the prototype virtual-reality mapping function to connect the principle prototype with the digital prototypes of some satellite nodes in the space-based edge micro-cloud to complete the virtual-reality mapping of time and space; S110. Run the task image deployment function to encapsulate the remote sensing image recognition software in the form of a Docker container, then package it into an image and upload it to all satellite nodes of the Tianji Edge Micro Cloud. The Tianji Edge Micro Cloud completes the image loading and starts the remote sensing image recognition software. S111. Run the task execution scheduling function, load and decompose the remote sensing image data files according to the amount of satellite remote sensing image data, and the space-based edge micro-cloud system schedules the computing, storage, and communication resources of the satellite nodes to perform the remote sensing image recognition task, and output and display the remote sensing image recognition results on the front end; S112. Run the mission effectiveness evaluation function, and output and display the earth observation mission effectiveness evaluation results on the front end based on indicators such as remote sensing image recognition results, remote sensing image recognition time, and remote sensing image recognition resource consumption. Example

[0033] Example 2 describes in detail a semi-physical simulation method for cloud-edge service migration time for low-orbit constellation edge micro-clouds implemented by the semi-physical simulation system for low-orbit constellation edge micro-clouds of the present invention. The simulation process is as follows: Figure 4 The specific steps are as follows: S201. The user builds a satellite constellation model through a hardware-in-the-loop simulation system, configures the simulation environment, builds a space-based edge micro-cloud, and sets the mission application software image size and mission start time. S202 arrives at the task start time, obtains edge micro cloud node location information and network topology information, and ground data center location information; S203. Select the cloud-edge service migration strategy. The ground data center uploads the mission application software image to all satellite nodes of the space-based edge micro cloud through the inter-satellite and satellite-to-ground communication networks according to the selected strategy. S204. After receiving the image, the edge micro-cloud node begins to deploy the image and start the task application software; S205. All satellites on the edge micro-cloud start the mission application software, have edge application service capabilities, and record the end time of cloud-edge migration; S206. The cloud-edge service migration time can be obtained based on the task start time and the cloud-edge migration end time. Example

[0034] Example 3 describes in detail a semi-physical simulation method for multi-satellite collaborative computing efficiency for low-orbit star cluster edge micro-clouds implemented by the semi-physical simulation system for low-orbit star cluster edge micro-clouds of the present invention. The simulation process is as follows: Figure 5 The specific steps are as follows: S301. The user builds a satellite constellation model through a semi-physical simulation system, configures the simulation environment, establishes a space-based edge micro-cloud, and completes the deployment of the mission application software image; S302. Load task data, obtain edge micro-cloud node computing, storage, communication resource information and network topology information, and record the calculation start time; S303. Select a multi-satellite collaborative computing strategy. The edge micro-cloud divides the task data according to the selected strategy and distributes it to the corresponding satellite edge nodes; S304. The mission application software deployed on the satellite edge node analyzes the mission data and returns the mission completion result; S305. The edge micro-cloud summarizes the task completion results and records the calculation end time; S306. Based on the calculation start time and calculation end time, the multi-satellite collaborative computing time can be obtained, and then the multi-satellite collaborative computing efficiency can be evaluated based on the task completion result.

[0035] Any process or method described in the flowchart of the present invention or in other ways herein can be understood as representing a module, segment, or portion of code including one or more executable instructions for implementing specific logical functions or process steps, and can be implemented in any computer-readable medium for use by an instruction execution system, device, or apparatus. The computer-readable medium can be any medium that stores, communicates, propagates, or transmits a program for use by an execution system, device, or apparatus, including read-only memory, magnetic disk, or optical disk.

[0036] Throughout this specification, references to terms such as "embodiment" and "example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, those skilled in the art may combine or integrate different embodiments or examples described herein, and the features thereof, without creating any inconsistency.

[0037] Although the above content has shown and described the embodiments of the present invention, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can perform update operations such as changes, modifications, replacements and variations on the above embodiments within the scope of the present invention.

Claims

1. A semi-physical simulation system for micro-clouds at the edge of a low-orbit constellation, characterized in that: The system includes a satellite edge computing principle prototype, a satellite edge computing digital prototype and a satellite edge computing semi-physical simulation platform; wherein, The hardware of the satellite edge computing prototype includes a computing module, a storage module, an interconnection module and a power module, and the software includes an edge micro-cloud infrastructure layer, an edge micro-cloud platform service layer and an edge micro-cloud software service layer, providing a hardware platform and software driver support for the space-based edge micro-cloud; The satellite edge computing digital prototype is a digital twin system of the principle prototype, including an infrastructure resource layer, a control management simulation layer, and a software operation layer, which creates a digital virtual model of the satellite edge computing principle prototype and provides an operating environment support for the space-based edge micro-cloud application software; The satellite edge computing semi-physical simulation platform includes a hardware part and a software part. The hardware part includes a simulation server, a display and control component, and a virtual-reality interconnection interface component; the software part includes a satellite constellation operating environment simulation unit, a satellite networking simulation unit, an edge micro-cloud simulation support unit, and an application scenario simulation control unit; the satellite edge computing semi-physical simulation platform adopts a distributed simulation method, divides the overall simulation task into multiple modules according to the component functional characteristics, and a single module independently performs local function simulation, and performs cross-module information interaction through the underlying support module, thereby completing the overall simulation task.

2. The semi-physical simulation system for edge micro-clouds of low-orbit star clusters according to claim 1 is characterized in that: The satellite edge computing digital prototype runs in the simulation server of the satellite edge computing semi-physical simulation platform. The satellite edge computing semi-physical simulation platform provides physical resources and basic operating environment for the operation of the satellite edge computing digital prototype, and provides a logical soft bus interface for the satellite edge computing digital prototype.

3. The semi-physical simulation system for edge micro-clouds of low-orbit star clusters according to claim 2 is characterized in that: The satellite edge computing principle prototype and the satellite edge computing semi-physical simulation platform are connected through a virtual-real interconnection interface component to realize information interaction between the satellite edge computing principle prototype and the satellite edge computing digital prototype.

4. A semi-physical simulation method for micro-clouds at the edge of a low-orbit constellation, characterized in that: The method is implemented based on the semi-physical simulation system for low-orbit star cluster edge micro-clouds according to any one of claims 1 to 3, and the simulation method comprises the following steps: S1. Mission scenario modeling: Through mission requirement analysis, carry out constellation configuration design and constellation model construction, and create real-time or offline driven 3D simulation scenario models according to mission scenarios; S2. Simulation environment configuration: Configure parameters according to the mission scenario, create a satellite edge computing digital prototype, configure basic resources for the digital prototype, and simulate the satellite ground and inter-satellite network environment; S3, edge micro-cloud construction: According to the mission requirement information, the main satellite node selects the minimum number of satellite nodes to complete the mission requirements based on the computing resources and storage resources available to the neighboring satellite nodes within its 5-hop range to build a space-based edge micro-cloud, realize the sharing and scheduling of computing, storage and communication resources within the edge micro-cloud, and provide operation and maintenance support for various mission application software; S4. System capability verification: According to the mission requirement information, the mission image is deployed in each node of the space-based edge micro-cloud, the edge micro-cloud resources are scheduled and the mission application software is run. After the mission is completed, the mission performance evaluation is carried out.

5. The method for semi-physical simulation of micro-clouds at the edge of a low-orbit constellation according to claim 4, characterized in that: Step S1 is completed by the satellite edge computing semi-physical simulation platform, including: S1.1 Constellation configuration design: Constellation configuration parameters include satellite orbit altitude, orbit inclination, total number of satellites, number of orbital planes and phase factor; S1.2 Cluster model construction: The satellite edge computing semi-physical simulation platform conducts satellite orbit dynamics simulation and visualization simulation based on the cluster configuration parameters, and analyzes and obtains the position and attitude information of all satellite nodes; S1.3 Simulation scenario construction: refers to the simulation platform modeling other elements in the task scenario and configuring their kinematic parameters or their own characteristic parameters.

6. The method for semi-physical simulation of micro-clouds at the edge of a low-orbit constellation according to claim 4, characterized in that: Step S2 is completed by the satellite edge computing digital prototype and semi-physical simulation platform; including: S2.1 Digital prototype creation: The satellite edge computing semi-physical simulation platform uses a virtual machine for each satellite node to implement the construction of the satellite edge computing digital prototype. Each digital prototype will be assigned an independent IP address; S2.2 Basic resource configuration: The computing resources, storage resources and network resources of each digital prototype can be configured, where computing resources include the number and performance parameters of CPUs and GPUs, storage resources include storage space size parameters, and network resources include satellite-to-ground and inter-satellite link communication bandwidth parameters; S2.3 Network environment simulation: The simulation platform simulates the impact of satellite-to-ground and inter-satellite network environments based on the location information of satellite-to-ground and inter-satellite network nodes and channel model parameters, and analyzes the satellite network transmission conditions; channel model parameters include free space path loss index, large-scale and small-scale fading parameters, rain attenuation and atmospheric loss parameters.

7. The method for semi-physical simulation of micro-clouds at the edge of a low-orbit constellation according to claim 4, characterized in that: Step S3 is completed by the satellite edge computing prototype and semi-physical simulation platform; including: S3.1 Micro-cloud node selection: The satellite edge computing semi-physical simulation platform selects some satellite nodes as needed to form a space-based edge micro-cloud system based on the computing resources, storage resources and network resources required for task execution, according to the location information and resource usage of satellite nodes and the network topology of satellite constellations; S3.2 Principle prototype startup: The satellite edge computing principle prototype is powered on and the cloud native software operating environment is initialized, and cloud native application deployment and management tools are run normally; S3.3 Prototype virtual-reality mapping: The satellite edge computing semi-physical simulation platform uses the virtual-reality interconnection interface component to connect the satellite edge computing principle prototype with the digital prototypes of some satellite nodes in the space-based edge micro-cloud, and realizes the virtual-reality mapping of time and space between satellite physical nodes and satellite virtual nodes through data transmission.

8. The method for semi-physical simulation of micro-clouds at the edge of a low-orbit star cluster according to claim 4, characterized in that: Step S4 is completed by the satellite edge computing semi-physical simulation platform, digital prototype and principle prototype, including: S4.1 Mission image deployment: The satellite edge computing semi-physical simulation platform encapsulates the mission application software in the form of a Docker container, and then packages it into an image and uploads it to all satellite nodes of the space-based edge micro-cloud through the satellite network. The space-based edge micro-cloud completes the image loading and starts the mission application software; S4.2 Task execution scheduling: The space-based edge micro-cloud system performs task scheduling and resource scheduling on demand based on task data. Multiple satellites efficiently use their own computing, storage, and network resources to collaboratively complete task requirements and return task execution results. S4.3 Mission effectiveness evaluation: The satellite edge computing semi-physical simulation platform conducts comprehensive analysis and evaluation of mission effectiveness, and displays the mission effectiveness evaluation results on the front end of the simulation platform.

9. The method for semi-physical simulation of micro-clouds at the edge of a low-orbit constellation according to claim 7, characterized in that: In step S3.2, the cloud-native application deployment and management tools include Docker containers and Kubernetes.

10. The method for semi-physical simulation of micro-clouds at the edge of a low-orbit constellation according to claim 8, characterized in that: In step S4.3, the evaluation indicators are task execution results, task execution time and task execution consumed resources.

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