Multi-dimensional performance evaluation method and simulation system for large-scale distributed space system

Abstract

The invention discloses a multi-dimensional effectiveness evaluation method and simulation system for a large-scale distributed space system, which are realized based on a low earth orbit satellite and comprise an end node and a calculation satellite node, the method specifically comprises the following steps: S1, constructing a distributed space system efficiency evaluation index system, wherein the evaluation index system comprises heterogeneous task supportability, massive task supportability, task real-time performance, resource utilization rate, load balance and application business indexes; s2, constructing a relative matrix based on an analytic hierarchy process; s3, obtaining a comprehensive score through a multi-dimensional fuzzy evaluation method; and S4, designing a distributed space system simulation system architecture and a function module. Through kinematics simulation of satellite nodes, configuration and detection of node resources, design of various simulation scenes and schemes, scene multi-index evaluation and the like, the performance evaluation and optimization requirements of a distributed space system in a complex application scene can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite networks and performance evaluation, and invents a multi-dimensional performance evaluation method and simulation system for a large-scale distributed space system. Background Art

[0002] With the rapid development of satellite technology, large-scale distributed satellite space systems have been widely used in global communications, remote sensing, navigation and positioning, and other fields. It is mainly based on low-orbit small satellites, connecting a variety of space-based heterogeneous nodes to build a large-scale network, achieving seamless global coverage and efficient communication, and becoming an important direction for the development of future space networks. In the research and application of distributed space systems, it is crucial to accurately evaluate their effectiveness. However, existing related technologies have many limitations. The existing technologies most relevant to the present invention are as follows: A Satellite Effectiveness Evaluation Method (CN202010486519.0): This method provides a performance evaluation scheme based on environmental fuzzy factors within cognitive radio-based satellite communication system performance evaluation technology. In cognitive satellite communication system performance evaluation, this scheme innovatively incorporates environmental fuzzy factors into the AHP and fuzzy comprehensive evaluation method to construct an environmental parameter system. First, an indicator parameter system is constructed based on the AHP, and then the environmental parameter system is constructed based on the surrounding environment and environmental fuzzy factors. The fuzzy operators in the fuzzy comprehensive evaluation method compensate for the subjective weighting issues of the AHP, and the AHP simplifies the tedious data preprocessing steps of the fuzzy comprehensive evaluation method. Furthermore, the newly added environmental parameter system not only provides a more realistic assessment of satellite communication system performance but also enables dynamic adjustment of system input parameters, thereby functioning as a feedback system and improving overall system performance.

[0003] Distributed Computing Data Flow Scheduling and Routing Method for Low-Earth Orbit Satellite Networks (CN202210793602.1): This method, applicable to the field of information and communication technology improvement, provides a distributed computing data flow scheduling and routing method for low-earth orbit satellite networks. The method includes: S1. Building a satellite distributed computing framework based on SDN and using satellite simulation software to simulate the orbit of the giant satellite to obtain a satellite network model; S2. Using the obtained satellite network model, using Mininet to establish a virtualized network that simulates the satellite network topology; S3. Configuring a routing algorithm using an SDN controller; and S4. Configuring the distributed computing framework Hadoop on host nodes in the satellite network and deploying actual computing tasks to achieve packet-level simulation of satellite distributed computing. The low-earth orbit satellite model is obtained through simulation of the giant satellite orbit. Combining the advantages of OpenFlow flow tables and the topology-aware, centralized control of the SDN controller, a flood-free routing mechanism is proposed to improve the efficiency of satellite network information forwarding.

[0004] Satellite Effectiveness Evaluation Method, Apparatus, and Electronic Equipment (CN202111514065.4): Provided are a satellite effectiveness evaluation method, apparatus, and electronic equipment. The method comprises: determining satellite effectiveness indicators based on satellite model; classifying the satellite effectiveness indicators according to a system effectiveness analysis method based on preset indicator categories; obtaining a dataset based on the classified satellite effectiveness indicators; and processing the dataset using the Analytic Hierarchy Process (AHP) to obtain an effectiveness evaluation result. This application constructs a classification model for satellite effectiveness indicators using the system effectiveness analysis method, refining the satellite effectiveness evaluation into a set of satellite effectiveness indicators, thereby improving the accuracy of the satellite effectiveness evaluation results. Furthermore, the feedback source for the satellite effectiveness indicator data in the system effectiveness analysis method is based on actual usage, avoiding overly idealized models. The consistency check in the AHP significantly reduces the error of the single-expert scoring mechanism.

[0005] The performance evaluation method proposed in this paper leverages the modular architecture and full-process integration capabilities of large-scale distributed spatial system simulation software, integrating evaluation simulation with simulation verification. Compared to the isolated evaluation model of the traditional analytic hierarchy process, this method achieves a closed-loop verification process: "metric definition - weight setting - scenario simulation." By linking data between the simulation and evaluation modules and leveraging real-time monitoring using Prometheus and Grafana within the simulation system, it is possible to quickly locate the source of problems and verify the accuracy of evaluation data. For example, node resource utilization metrics are correlated with the CPU usage of virtualized nodes in real time, indicating that "low resource utilization" is caused by limited link bandwidth in the simulation scenario.

[0006] In addition, in order to address the problem that traditional evaluation methods have a single scenario method and poor evaluation effect, the present invention supports multi-scenario and multi-method comparison, and sets differentiated performance evaluation matrices and weight matrices for multiple scenarios. For example, the real-time weight of the task is appropriately strengthened in the on-orbit micro-cloud migration of satellites, and the weight of application layer indicators is strengthened in space-based data processing, so as to maximize the accuracy and reliability of the evaluation. Summary of the Invention

[0007] In response to the problems existing in the prior art, the purpose of the present invention is to provide a multi-dimensional performance evaluation method and simulation system for large-scale distributed space systems. The simulation system covers a wide range of technical applications, including but not limited to kinematic simulation of satellite nodes, configuration and detection of node resources, design of multiple simulation scenarios and schemes, and multi-index evaluation of scenarios, etc., which can meet the performance evaluation and optimization needs of distributed space systems in complex application scenarios.

[0008] To achieve the above objectives, the present invention provides a multi-dimensional performance evaluation method for a large-scale distributed space system, wherein the distributed space system is implemented based on low-Earth orbit satellites. The method specifically comprises the following steps: S1. Build a distributed space system performance evaluation index system: This evaluation index system includes heterogeneous task support, massive task support, task real-time performance, resource utilization, load balancing, and application business indicators; S2. Construct a relative matrix based on the analytic hierarchy process; S3. Multi-dimensional fuzzy evaluation method to obtain comprehensive scores; S4. Design the distributed space system simulation system architecture and functional modules.

[0009] Furthermore, step S2 specifically includes the following steps: S2.1: For the distributed space system performance evaluation index system, the relative importance of each element of the index is compared and scored in pairs; this is used to construct a judgment matrix ; S2.2: Calculate eigenvectors; S2.3: Consistency check.

[0010] Furthermore, the pairwise scoring standard is a 1-9 scale; 1 represents equal or slightly equal, 3 represents the former is slightly more important than the latter, 5 represents obviously important, 7 represents extremely important, 9 represents strongly important, and 2, 4, 6, and 8 represent intermediate values.

[0011] Furthermore, the eigenvector is calculated as follows: multiply the elements of each row of the matrix and take the nth root to obtain the eigenvector of the i-th index : The feature vector Normalize it to get the weight vector ; Calculate the judgment matrix The eigenvalue of .

[0012] Furthermore, the consistency check is intended to determine whether the weight values derived from the judgment matrix A are reasonable.

[0013] Furthermore, the consistency check is based on the consistency index CI. The closer the index value is to 0, the higher the reliability of the obtained weight value.

[0014] Furthermore, during the specific verification process, if the consistency check passes, a result of a consistency ratio CR < 0.1 will be obtained, which indicates that the weight derived at this time is within a reasonable range.

[0015] Furthermore, the calculation formula of the consistency index is as follows: ; RI is obtained by looking up the table, and CI is calculated according to the following formula: .

[0016] Furthermore, in step S3, each indicator is normalized, and each indicator value is transformed into a certain interval so that it meets the normalization and dimensionless conditions, and its fuzzy membership is determined; and a corresponding fuzzy membership function is established for each indicator.

[0017] On the other hand, the present invention provides a large-scale distributed space system multi-dimensional performance evaluation simulation system, which is implemented according to the above method.

[0018] The beneficial effects of the present invention are as follows: The performance evaluation index system established by the present invention starts from the various application requirements of distributed space systems, comprehensively covers multiple levels such as business, resources, and performance, can better reflect the characteristics of performance oriented to tasks and needs, and is closely integrated with the business direction and needs of the application party. The designed multi-dimensional fuzzy evaluation method comprehensively considers multiple factors and can scientifically and objectively evaluate the degree to which the distributed space system meets user needs, with high accuracy and operability. The developed simulation software combines the advantages of discrete event simulation and virtualization simulation, supports the simulation of a large number of distributed space system nodes within limited simulation resources, and provides important tools and technical support for the research, design optimization, operation management, etc. of distributed space systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the architecture design of the multi-dimensional performance evaluation method and simulation system for large-scale distributed space systems according to the present invention; Figure 2 It is a multi-index comprehensive evaluation flow chart according to the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] 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.

[0023] The following combination Figure 1-Figure 2 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.

[0024] The technical problem solved by the present invention is to provide a multi-dimensional performance evaluation method and simulation system for large-scale distributed space systems, so as to meet the needs of quantitative performance evaluation and comparative analysis of multiple satellite nodes and multiple mission scenarios in distributed space systems, and provide technical support for the design and optimization of distributed space systems. The technical solution of the present invention is: a multi-dimensional performance evaluation method for large-scale distributed space systems, specifically comprising the following steps: S1. Construct a distributed space system performance evaluation index system: This evaluation index system covers multiple key dimensions to comprehensively measure the performance of distributed space systems. It includes heterogeneous task support, which reflects the system's compatibility and efficient execution capabilities for different types of tasks (such as bandwidth-sensitive, computing resource-sensitive, and orbital resource-sensitive businesses), and requires the system to have flexible task scheduling and resource allocation mechanisms; massive task support, which reflects the system's ability to handle a large number of concurrent tasks, involving efficient resource allocation algorithms, powerful parallel processing capabilities, and scalable architectures; task real-time performance, which focuses on the system's ability to respond and complete tasks within a specified time, and is crucial for critical applications; resource utilization, which measures the system's effective utilization of nodes, links, and orbital resources, and requires the adoption of reasonable resource allocation strategies; load balancing, which ensures that the system's workload is evenly distributed, improves concurrent processing capabilities and system availability; and application business indicators, which are directly related to specific application scenarios and user needs, and reflect the actual application effects of the system. The performance evaluation module multi-index comprehensive evaluation flow chart is as follows: Figure 2 shown.

[0025] The input layer is based on the various indicator parameters output from the system simulation results, including heterogeneous task support, massive task support, task implementation, resource utilization, load balancing, and application business indicators.

[0026] S2. Construct a relative matrix based on the analytic hierarchy process: specifically including: S2.1: For the distributed space system performance evaluation index system, the relative importance of each element of the index is compared pairwise through expert scoring. The pairwise scoring standard is 1-9. 1 represents equal or slightly equal, 3 represents the former is slightly more important than the latter, 5 represents obviously important, 7 represents extremely important, 9 represents strongly important, 2, 4, 6, and 8 represent intermediate values, and the reciprocal of 1-9 represents the importance of swapping the order. This is used to construct a judgment matrix. For example, in a specific embodiment, the judgment matrix is as follows:

[0027] S2.2: Calculate the eigenvector. By multiplying and taking the nth root, we can get the eigenvector of the i-th index :

[0028] In a specific embodiment, first find the product of the elements in each row of the matrix, and then find the nth root. (n = 6): = 1.817 = 1.781 = 1.414 = 0.890 = 0.890 = 0.275 The feature vector Normalize it to get the weight vector :

[0029] in, for The corresponding eigenvector; The normalization process is as follows: = 7.070 ≈ 0.257 ≈ 0.252 ≈ 0.200 ≈ 0.126 ≈ 0.126 ≈ 0.038 ; Calculate the judgment matrix The eigenvalue of :

[0030] calculate =[1.646,1.620 1.269 0.806 0.864 0.243] =38.700 =6.450.

[0031] S2.3: Consistency Check. This check determines whether the weights derived from the judgment matrix A are reasonable. The key to this determination is the consistency index (CI). The closer this index is to 0, the more reliable the weights are.

[0032] During the specific verification process, if the consistency check passes, the consistency ratio CR < 0.1 will be obtained, which indicates that the weight derived at this time is relatively reasonable. The calculation formula of the consistency index is as follows:

[0033] RI is obtained by looking up the table, and CI is calculated according to the following formula.

[0034]

[0035] Calculated CI=0.090.

[0036] The RI table is as follows. From the table, we get RI=1.26 (n=6).

[0037] n 1 2 3 4 5 6 7 8 9 10 11 12 RI 0.00 0.00 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 The corresponding CR is 0.072, and the consistency ratio CR is less than 0.1. This shows that the weights we derived are reasonable.

[0038] S3. Multi-dimensional fuzzy evaluation method to obtain comprehensive scores: In human factors engineering assessment, the evaluation criteria, methods and units of each indicator are different, lack commensurability, and are difficult to compare and evaluate. Therefore, the present invention normalizes each indicator and transforms the value of each indicator into a certain interval so that it meets the normalization and dimensionless conditions, and then its fuzzy membership can be determined. For each indicator, a corresponding fuzzy membership function is established based on its characteristics and actual application requirements. For example, for the task real-time indicator, the corresponding membership is set according to different task response time ranges. The relative matrix weights obtained by the hierarchical analysis method are combined with the calculation results of the fuzzy membership function to obtain a comprehensive score.

[0039] For the quantitative indicators in the index system, this paper adopts a "bigger is better" approach, using a threshold method to determine the linear function. The dimensionless index value is obtained by comparing the actual index value with the threshold. The weight is determined using the Analytic Hierarchy Process (AHP), which generally represents the "importance" of the indicator. After multiplying the evaluation value of the "bigger is better" indicator by the weight, the larger the indicator, the more significant the positive contribution to the overall score. This conforms to the evaluation logic of "important indicators require key optimization." Therefore, this paper adopts the "bigger is better" approach, specifically using the following calculation formula: ; in, Represents the original index in the method of the present invention, The lower threshold represents the lowest acceptable level of the indicator. , then the contribution of this indicator to the system efficiency is considered to be 0. The upper threshold of the i-th indicator indicates the value when the indicator reaches the ideal state. Meet or exceed , it is considered that the contribution of this indicator to the system performance reaches the full score of 1. is the evaluation value of the i-th indicator after transformation, and its value range is [0, 1].

[0040] The task completion time obtained from the simulation results and the calculated load balancing rate are used as input layer parameters and converted through the fuzzy membership function to obtain the scores of each indicator.

[0041] ; Multiply each score by the weight vector to get the comprehensive score: ; The evaluation score is a real number ranging from 0 to 1. The closer the score is to 0, the worse the effect is and it is not recommended. The closer the score is to 1, the better the effect is and it is recommended.

[0042] S4. Store the evaluation results in the MySQL database and output them to the front-end simulation interface.

[0043] According to the large-scale distributed space system multi-dimensional performance evaluation simulation system of the present invention, the distributed space system simulation system architecture and functional modules are designed. Figure 1 As shown in the figure, the architecture adopts the integration of discrete event simulation and virtualized simulation. The architecture is divided into three layers: virtual node and resource simulation layer, simulation scenario and performance evaluation layer, and application and data layer: Virtual Node and Resource Simulation Layer: This layer provides the basic operating environment for virtualized nodes and underlying support for configuring node communication, computing, and storage resources. The virtual node and resource simulation layer provides node resource status information to the simulation scenario and performance evaluation layer, executes the returned network decisions, and provides the software and hardware environment for business operations for the application and data layers. This is achieved by establishing a Docker container corresponding to the satellite node and simulating the software operating environment by configuring the container's hard disk space, memory, CPU, and GPU. Furthermore, the node's communication bandwidth, latency, and packet loss rate are configured to simulate virtual node resources.

[0044] Simulation Scenario and Performance Evaluation Layer: This layer is divided into two sub-layers: the scenario simulation layer and the performance evaluation layer. The scenario simulation layer first models and simulates the basic characteristics of the three system nodes: the ground station, satellite, and end node, including the node's kinematic parameters and basic resource modeling. Based on this, the scenario simulation layer combines resource information provided by the virtual node and resource simulation layers to organize the infrastructure nodes into a space network. The simulation model and behavior of the network include the selection of edge computing nodes, the motion of satellite orbit changes, and the data transmission process for intersatellite communications. The simulation scenario layer receives device and resource status information from the virtual node and resource simulation layers and returns network decision instructions. It also receives business requirements and execution status information from the application and data layers and returns task execution instructions. Parameters from these task execution processes and results are further fed back to the performance evaluation layer, which comprehensively assesses factors such as the strategy's compatibility with heterogeneous tasks, support for massive tasks, task real-time performance, resource utilization, load balancing, and application business metrics. Specifically, scenario simulation is performed using the satellite micro-cloud simulation platform developed by the present invention. This includes four scenarios: on-orbit micro-cloud migration, massive data transmission back to the ground, space-based native data processing, and high-orbit interception. The performance evaluation layer is based on the performance evaluation method of the present invention.

[0045] Application and Data Layer: This layer encapsulates space-based tasks in containers and deploys and computes them in virtual nodes based on the decisions made by the simulation scenario and performance evaluation layer. This layer allows for observation of the execution performance of space-based tasks. This layer operates within the virtual node and resource simulation layer environment, receiving task execution instructions from the simulation scenario and performance evaluation layer and returning task execution status. Specifically, remote sensing satellite image recognition is implemented as a space-based distributed task, with the image recognition program running in the Docker node corresponding to the satellite node.

[0046] In order to verify the effectiveness of the distributed space system simulation system, the present invention takes the on-orbit dynamic edge computing scenario as an example to explain the specific implementation method of the performance evaluation method.

[0047] Due to the unique advantage of ubiquitous access, space-based distributed systems can provide crucial edge computing services in remote areas where terrestrial network coverage is insufficient, congested, or nonexistent. This service is particularly important for scenarios such as emergency communications, ocean voyages, and polar scientific research, significantly expanding the boundaries of information services and improving operational efficiency and safety in these challenging environments. Low Earth Orbit (LEO) satellites, the core components of space-based distributed systems, are constantly in high-speed motion. This not only requires high flexibility and adaptability, but also subjects the entire space network topology, including the satellite-ground network, to continuous and rapid change. Therefore, strategies for building on-orbit edge clouds must consider the motion characteristics of satellites and design dynamic resource allocation algorithms. In practical applications, maintaining service continuity and stability becomes a key issue when served end nodes (such as mobile devices on the ground, offshore platforms, or aircraft) leave the current edge cloud coverage area.

[0048] The distributed space system according to the present invention includes two types of nodes: end nodes and computing satellite nodes. When the computing demand generated by the end node reaches the satellite node, the satellite node becomes the task initiation node. If the computing power and storage resources of the satellite node itself cannot meet the task requirements, the system will use the satellite node as the master node and form an on-orbit edge cloud with the surrounding nodes to complete the computing task. Due to the high-speed movement of LEO satellites and end nodes, the end node will move relative to the edge cloud. Therefore, after the end node leaves the coverage of the edge cloud, the current edge cloud master node will maintain contact with the end node through service migration or inter-satellite data routing.

[0049] Currently, research on dynamic edge computing in space networks mostly focuses on stationary end nodes on the ground or those with relatively slow movement speeds, such as smartphones, IoT devices, etc. However, the end nodes in the present invention are high-speed mobile end nodes, which can be airplanes, high-speed trains, or driverless cars. The relative motion between such high-speed mobile end nodes and satellite nodes is extremely violent, resulting in frequent and drastic changes in the topology of the satellite-ground network, which brings unprecedented challenges to the provision of edge computing services. The problem of continuous connection between the edge cloud and the end nodes, especially for delay-sensitive services such as real-time video processing, online games, telemedicine, etc., the construction location of the edge cloud, the migration strategy, and the routing selection of the data will directly determine the total delay of the entire process from the node issuing a computing request to receiving the processing result. This will affect the quality of service.

[0050] The present invention uses inter-satellite service migration to adapt to high-speed mobile end nodes. The specific implementation method of inter-satellite service migration is as follows: When an end node issues a computing request, in order to reduce the access delay between the end node and the edge cloud, the end node broadcasts to the space node, and the computing satellite node closest to the end node in the distributed space system responds to the computing needs of the end node. If the computing power of the nearest computing satellite node is insufficient to handle the computing demand, the computing satellite node will serve as the master node and form an on-orbit edge cloud with the surrounding computing satellite nodes. In order to reduce the time it takes to establish the edge cloud and the communication cost between the master and slave nodes, nodes with a smaller number of hops from the master node are given priority to enter the cloud. The specific selection method is to use a greedy algorithm to select the computing satellite node with the smallest number of hops from the master node from the set of idle computing satellite nodes to enter the cloud, and then check whether the computing power and storage requirements of the current edge cloud meet the requirements, and repeat this process until the requirements are met.

[0051] When the end node leaves or is about to leave the edge cloud coverage, the service migration mechanism of the distributed space system will be triggered. Intersatellite service migration can be divided into two steps: edge cloud service information data transmission and new edge cloud node networking. At the moment of service migration, user services will be temporarily interrupted. In order to improve the user experience, the service migration time should be reduced as much as possible. Therefore, the present invention uses the two data communication delays, transmission delay and propagation delay, as weights when designing the transmission path. The selection algorithm for the new edge cloud node is designed based on the edge cloud establishment strategy. The service migration strategy of the present invention can make the end node always located under the coverage of the edge cloud, with lower access delay. During the service process, the satellite nodes continue to relay work, with better load balancing.

[0052] The specific operation process of the simulation system is as follows: ① After opening the distributed space system multi-dimensional performance evaluation simulation software, the user first clicks [File] in the menu bar to create a new simulation scene or open a .sc file to import a simulation scene; ② After completing the scene creation, click the menu bar [Object] to insert and configure the distributed space node simulation object.

[0053] ③ After completing the configuration of the distributed space node simulation object, click the task you want to simulate, the selected strategy and configure the parameters in the task configuration and strategy selection display section.

[0054] ④ After completing the task configuration, the simulation completes initialization, and the simulation log prompts "Simulation initialization is complete, simulation can start", and the 3D display module displays the status of the initialized distributed space system.

[0055] ⑤ Click the [Start Simulation] button on the menu bar to start the simulation. The simulation log module records the real-time status of task execution and key information. The 3D display module visualizes information such as the computing cluster formation and data transmission path. During the simulation, Prometheus + Grafana can monitor the operation of satellite nodes in real time. After the simulation, the simulation log and performance evaluation module evaluate the performance indicators.

[0056] 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, which 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, etc.

[0057] Throughout this specification, reference to terms such as "embodiment" and "example" indicates 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 in this specification, as well as features therein, without creating any inconsistency.

[0058] 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 multi-dimensional performance evaluation method for large-scale distributed space systems, characterized by: The distributed space system is implemented based on low earth orbit satellites; the distributed space system includes end nodes and computing satellite nodes, and the method specifically includes the following steps: S1. Build a distributed space system performance evaluation index system: This evaluation index system includes heterogeneous task support, massive task support, task real-time performance, resource utilization, load balancing, and application business indicators; S2. Construct a relative matrix based on the analytic hierarchy process; S3. Multi-dimensional fuzzy evaluation method to obtain comprehensive scores; S4. Store the evaluation results in the MySQL database and output them to the front-end simulation interface.

2. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 1, characterized in that: Step S2 specifically includes the following steps: S2.1: For the distributed space system performance evaluation index system, the relative importance of each element of the index is compared and scored in pairs; this is used to construct a judgment matrix ; S2.2: Calculate eigenvectors; S2.3: Consistency check.

3. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 2, characterized in that: The pairwise scoring standard is 1-9 scale; 1 means equal, 3 means the former is slightly more important than the latter, 5 means obviously important, 7 means extremely important, 9 means strongly important, and 2, 4, 6, and 8 represent intermediate values.

4. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 2, characterized in that: The way to calculate the eigenvector is as follows: multiply the elements of each row of the matrix and take the nth root to get the eigenvector of the i-th index : The feature vector Normalize it to get the weight vector ; Calculate the judgment matrix The eigenvalue of .

5. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 2, characterized in that: The consistency check is to determine whether the weight values derived from the judgment matrix A are reasonable.

6. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 5, characterized in that: The consistency check is based on the consistency index CI. The closer the index value is to 0, the higher the reliability of the obtained weight value.

7. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 6, characterized in that: During the specific verification process, if the consistency check passes, a consistency ratio CR < 0.1 will be obtained, which indicates that the weight derived at this time is within a reasonable range.

8. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 7, characterized in that: The calculation formula of consistency index is as follows: ； RI is obtained by looking up the table, and CI is calculated according to the following formula: 。 9. The multi-dimensional performance evaluation method for a large-scale distributed space system according to claim 8, characterized in that: In step S3, each indicator is normalized, and each indicator value is transformed into a certain interval so that it meets the normalization and dimensionless conditions, and its fuzzy membership is determined; and a corresponding fuzzy membership function is established for each indicator.

10. A large-scale distributed space system multi-dimensional performance simulation system, characterized by: The simulation system is used to implement the large-scale distributed space system multi-dimensional performance evaluation method according to any one of claims 1 to 9, wherein the distributed space system includes end nodes and computing satellite nodes, and the end nodes are high-speed moving nodes.

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