Space-based transmission network resource configuration method and device, equipment and storage medium

Abstract

The application discloses a space-based transmission network resource configuration method and device, equipment and storage medium, including: constructing an OSA-CBM-based space-based transmission network health management architecture, and determining edge sites and hub sites in the space-based transmission network health management architecture; determining the maximum mutual information coefficient between a plurality of node service indicators of the edge sites, determining the node correlation strength between a plurality of network nodes of the edge sites according to the maximum mutual information coefficient, and then dynamically configuring network node resources of the edge sites according to the node correlation strength; and dividing a plurality of network nodes of the hub sites into a plurality of node communities through a community clustering algorithm based on the shortest path feature, and dynamically configuring network node resources of the hub sites according to the node communities. The application improves the data transmission efficiency and network stability of the space-based transmission network, and can be widely applied to the space-ground integration technical field.

Description

Technical Field

[0001] This invention relates to the field of space-ground integrated technology, and in particular to a method, apparatus, equipment and storage medium for configuring space-based transmission network resources. Background Technology

[0002] With the rapid development of space-based transmission network equipment and technology, the integration of equipment is increasing, the network scale is expanding, and the complexity is also rising. Simultaneously, addressing the requirements for reducing equipment failure rates and achieving automation, intelligence, and integration in space-based transmission network management has increased the demands on health management technologies. Therefore, the application of intelligent space-based transmission networks is urgently needed. Traditional evaluation index systems consist of index values ​​and correlations, with correlations describing causal relationships. In contrast, in dynamic networked index systems, both indicators and correlations are derived from actual space-based transmission network data, and correlations merely describe the correlation between indicators. The intelligent space-based transmission network employs two main methods: First, it performs time evolution analysis on indicators, analyzing the characteristic parameters of various indicators at different levels as they evolve over time, thus providing a reference for discovering the evolutionary patterns of the system at different stages. Second, it conducts correlation analysis on multiple indicators. Since the correlation between indicators is unknown beforehand, it may be linear or nonlinear, and may also include time delays, and it changes continuously over time. Therefore, it is assumed that all indicators are correlated. By selecting an appropriate number of windows and correlation analysis methods, moving window correlation analysis is performed on the time series of all indicators to obtain the evolutionary patterns of the correlation between any two indicators. Finally, a fully connected, time-evolving dynamic indicator network is obtained, in which the indicator values ​​and correlations continuously evolve over time.

[0003] In existing technologies, space-based transmission networks suffer from unreasonable configuration of network node resources, which affects the data transmission efficiency and network stability of the space-based transmission network.

[0004] Terminology Explanation:

[0005] Open System Architecture for Condition Based Maintenance (OSA-CBM): An internationally recognized health management system architecture, proposed based on the logical relationships between various health management functions, including a data acquisition layer, a data processing layer, a status monitoring layer, a health assessment layer, a prediction layer, a decision support layer, and a presentation layer.

[0006] Maximum Mutual Information (MMI) is a mutual information-based metric used to find optimal association patterns. Its basic principle is to find a projection that preserves the association information in the original data to the greatest extent possible. Specifically, MMI calculates the mutual information between all possible projections and selects the projection with the highest mutual information as the optimal projection. Summary of the Invention

[0007] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0008] Therefore, one objective of this invention is to provide a method for configuring resources in a space-based transmission network, which improves the data transmission efficiency and network stability of the space-based transmission network.

[0009] Another objective of this invention is to provide a space-based transmission network resource configuration device.

[0010] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include:

[0011] On one hand, embodiments of the present invention provide a method for configuring resources in a space-based transmission network, comprising the following steps:

[0012] Construct a space-based transmission network health management architecture based on OSA-CBM, and identify the edge sites and hub sites in the space-based transmission network health management architecture;

[0013] The maximum mutual information coefficient among multiple node service indicators of the edge site is determined, the node association strength among multiple network nodes of the edge site is determined based on the maximum mutual information coefficient, and then the network node resources of the edge site are dynamically configured based on the node association strength.

[0014] The hub site's multiple network nodes are divided into multiple node communities using a community clustering algorithm based on the shortest path feature, and the hub site's network node resources are dynamically configured according to the node communities.

[0015] Furthermore, in one embodiment of the present invention, the construction of an OSA-CBM-based space-based transmission network health management architecture, and the determination of edge sites and hub sites in the space-based transmission network health management architecture, specifically includes:

[0016] Based on OSA-CBM, the space-based transmission network is divided into multiple layers, and the health management functions and service types provided by each layer are determined to obtain the health management architecture of the space-based transmission network.

[0017] Based on the health management function, the network devices at each of the aforementioned levels are divided into edge sites and hub sites;

[0018] The health management functions of the edge stations include data collection, data processing, status detection, and automatic health assessment, while the health management functions of the hub stations include comprehensive health assessment, fault prediction, and decision support processing.

[0019] Furthermore, in one embodiment of the present invention, determining the maximum mutual information coefficient among multiple node service indicators of the edge site specifically includes:

[0020] Determine the two-dimensional coordinate plane of the two node business indicators, and generate a scatter plot on the two-dimensional coordinate plane based on the data of the two node business indicators;

[0021] The two-dimensional coordinate plane is divided into grids, and the joint distribution probability of the two node service indicators in each grid and the edge distribution probability of each of the two node service indicators are calculated.

[0022] The mutual information value between the two node business indicators under the corresponding grid division is determined based on the joint distribution probability and the edge distribution probability.

[0023] Determine the maximum mutual information value under multiple grid divisions with the same number of rows and columns, and determine the mutual information feature value under multiple grid divisions with the same number of rows and columns based on the maximum mutual information value and the minimum value among the number of rows and columns;

[0024] The feature value matrices of the two node business indicators are generated based on the mutual information feature values ​​corresponding to different numbers of rows and columns, and the maximum mutual information coefficient of the two node business indicators is determined based on the largest mutual information feature value in the feature value matrix.

[0025] Furthermore, in one embodiment of the present invention, the step of determining the node association strength among multiple network nodes of the edge site based on the maximum mutual information coefficient, and then dynamically configuring network node resources of the edge site based on the node association strength, specifically includes:

[0026] Determine the network nodes corresponding to each of the aforementioned node service metrics;

[0027] The node association strength between the two network nodes is determined based on the maximum mutual information coefficient between the two node service indicators.

[0028] The average association strength between each network node of the edge site and other network nodes is determined based on the node association strength.

[0029] The critical network nodes and ordinary network nodes of the edge site are determined based on the average association strength, and then the network node resources of the edge site are dynamically configured based on the critical network nodes and the ordinary network nodes.

[0030] Furthermore, in one embodiment of the present invention, the step of dividing the multiple network nodes of the hub station into multiple node communities using a community clustering algorithm based on shortest path features specifically includes:

[0031] Determine the set of network nodes of the hub station, as well as the number and length of the shortest paths between multiple network nodes;

[0032] The node influence coefficient of each network node is determined based on the number of shortest paths.

[0033] The node similarity between two network nodes is determined based on the shortest path length, and the partitioning threshold is determined based on the mean of the node similarity between all network nodes.

[0034] The network node with the highest influence coefficient is selected from the network node set as the current community center node, and the similarity between the other network nodes in the network node set and the current community center node is determined.

[0035] Other network nodes with a node similarity greater than or equal to the partitioning threshold are removed from the network node set and combined with the current community center node to form a node community. Then, the network node with the largest node influence coefficient is selected from the network node set as the current community center node, until the network node set is empty.

[0036] Furthermore, in one embodiment of the present invention, the node influence coefficient is calculated using the following formula:

[0037]

[0038] Among them, B i p represents the node influence coefficient of node i. jk This represents the number of shortest paths between node j and node k. This represents the number of shortest paths between node j and node k that pass through node i, and n represents the total number of nodes in the network.

[0039] The node similarity is calculated using the following formula:

[0040]

[0041] Among them, S (j,k) d represents the node similarity between node j and node k. jid represents the distance between node j and node i. ki This represents the distance between node k and node i;

[0042] The partitioning threshold is calculated using the following formula:

[0043]

[0044] Where λ represents the dividing threshold.

[0045] Furthermore, in some optional embodiments, the step of dynamically configuring network node resources for the hub site based on the node community specifically includes:

[0046] Determine the average node influence coefficient of each of the aforementioned node communities;

[0047] The node communities are divided into key node communities and ordinary node communities based on the average value of the node influence coefficient.

[0048] The network node resources of the hub site are dynamically configured based on the key node community and the ordinary node community.

[0049] On the other hand, embodiments of the present invention provide a space-based transmission network resource allocation device, comprising:

[0050] A health management architecture construction module is used to construct a space-based transmission network health management architecture based on OSA-CBM, and to determine the edge sites and hub sites in the space-based transmission network health management architecture.

[0051] The edge site resource configuration module is used to determine the maximum mutual information coefficient among multiple node service indicators of the edge site, determine the node association strength among multiple network nodes of the edge site based on the maximum mutual information coefficient, and then dynamically configure the network node resources of the edge site based on the node association strength.

[0052] The hub site resource configuration module is used to divide multiple network nodes of the hub site into multiple node communities using a community clustering algorithm based on the shortest path feature, and to dynamically configure network node resources of the hub site according to the node communities.

[0053] On the other hand, embodiments of the present invention provide an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing communication between the processor and the memory. When the program is executed by the processor, it implements the space-based transmission network resource configuration method as described above.

[0054] On the other hand, embodiments of the present invention also provide a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the space-based transmission network resource configuration method as described above.

[0055] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:

[0056] This invention constructs a space-based transmission network health management architecture based on OSA-CBM, identifies edge sites and hub sites within this architecture, determines the maximum mutual information coefficient among multiple node service indicators at the edge sites, and determines the node association strength among multiple network nodes at the edge sites based on the maximum mutual information coefficient. Then, it dynamically allocates network node resources at the edge sites based on the node association strength. Finally, it divides multiple network nodes at the hub sites into multiple node communities using a community clustering algorithm based on shortest path characteristics, and dynamically allocates network node resources at the hub sites based on these communities. This invention, by constructing a space-based transmission network health management architecture based on OSA-CBM, determining the node association strength among network nodes at the edge sites based on the maximum mutual information coefficient among node service indicators, dynamically allocating network node resources at the edge sites based on the node association strength, and dividing the hub sites into node communities using a community clustering algorithm based on shortest path characteristics, improves the efficiency and accuracy of network node resource allocation, thereby enhancing the data transmission efficiency and network stability of the space-based transmission network. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating the steps of a space-based transmission network resource allocation method provided in an embodiment of the present invention;

[0059] Figure 2 A flowchart of step S101 provided in an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the space-based transmission network health management architecture provided in an embodiment of the present invention;

[0061] Figure 4 A flowchart of step S102 provided in an embodiment of the present invention;

[0062] Figure 5 Another flowchart of step S102 provided in an embodiment of the present invention;

[0063] Figure 6 A flowchart of step S103 provided in an embodiment of the present invention;

[0064] Figure 7 Another flowchart of step S103 provided in an embodiment of the present invention;

[0065] Figure 8 This is a schematic diagram of the structure of the space-based transmission network resource configuration device provided in an embodiment of the present invention;

[0066] Figure 9 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention;

[0067] Figure 10 This is a schematic diagram of the structure of the storage medium provided in an embodiment of the present invention. Detailed Implementation

[0068] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. It should be noted that although functional modules are divided in the system schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system schematic diagram or the order in the flowchart. The step numbers in the following embodiments are only set for ease of explanation and do not limit the order between steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0069] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0070] The space-based transmission network resource configuration method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, set-top box, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the space-based transmission network resource configuration method, but is not limited to the above forms.

[0071] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0072] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards of the relevant countries and regions. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data for the proper functioning of the embodiments of this application obtained.

[0073] like Figure 1 The diagram shown is a flowchart of a space-based transmission network resource allocation method provided in an embodiment of the present invention. (Refer to...) Figure 1 This invention provides a method for configuring resources in a space-based transmission network, specifically including the following steps:

[0074] S101. Construct a space-based transmission network health management architecture based on OSA-CBM, and determine the edge sites and hub sites in the space-based transmission network health management architecture.

[0075] S102. Determine the maximum mutual information coefficient among multiple node service indicators of the edge site, determine the node association strength among multiple network nodes of the edge site based on the maximum mutual information coefficient, and then dynamically configure network node resources of the edge site based on the node association strength.

[0076] S103. The network nodes of the hub station are divided into multiple node communities by a community clustering algorithm based on the shortest path feature, and the network node resources of the hub station are dynamically configured according to the node communities.

[0077] Specifically, this invention constructs a space-based transmission network health management architecture based on OSA-CBM. This design method is proposed based on the logical relationships between various health management functions, and includes a data acquisition layer, data processing layer, status monitoring layer, health assessment layer, prediction layer, decision support layer, and representation layer. It optimizes the OSA-CBM-based health management architecture by constructing an intelligent, task-oriented, model-based, hierarchical, and distributed layered converged space-based transmission network health management architecture. For edge sites with relatively few devices, there are high requirements for the integration and automation capabilities of health management between network nodes. This invention employs a maximized mutual information algorithm to intelligently analyze the correlation relationships between service node indicators, thereby obtaining all associated nodes with varying degrees of correlation in service indicators among the network nodes of the edge site. The number of nodes is dynamically configured based on the correlation strength to maintain the optimal number of nodes for data transmission applications. For hub sites with relatively complex equipment configurations, a hierarchical health management system is adopted for dynamic equipment configuration of network nodes. Each level of node constructs a health management system that includes basic information, environmental information, test information, historical information, expert system, fault prediction model, fault diagnosis model, status assessment model, and decision support model. It needs to have the ability to independently judge and handle problems within the management scope of the corresponding level. This embodiment of the invention uses a community algorithm based on the shortest path feature to cluster community nodes, thereby dynamically dividing the complex hub sites in the space-based transmission network, greatly increasing the flexibility of configuring network node resources in application scenarios, improving the intelligence of the space-based transmission network, and reducing the waste caused by unreasonable resource allocation.

[0078] This invention constructs a space-based transmission network health management architecture based on OSA-CBM. It determines the node association strength between network nodes at edge sites based on the maximum mutual information coefficient between node service indicators, dynamically allocates network node resources to edge sites based on the node association strength, and divides node communities of hub sites using a community clustering algorithm based on shortest path characteristics. It then dynamically allocates network node resources to hub sites based on these communities, improving the efficiency and accuracy of network node resource allocation, thereby enhancing the data transmission efficiency and network stability of the space-based transmission network.

[0079] like Figure 2 The diagram shown is a flowchart of step S101 provided in an embodiment of the present invention. (Refer to...) Figure 2 As an optional implementation, an OSA-CBM-based space-based transmission network health management architecture is constructed, and the edge sites and hub sites in the space-based transmission network health management architecture are identified, specifically including:

[0080] S1011. Based on OSA-CBM, the space-based transmission network is divided into multiple layers, and the health management functions and service types provided by each layer are determined to obtain the health management architecture of the space-based transmission network.

[0081] S1012. Based on the health management function, network devices at each level are divided into edge sites and hub sites;

[0082] The health management functions of edge sites include data collection, data processing, status detection, and automatic health assessment, while the health management functions of hub sites include comprehensive health assessment, fault prediction, and decision support processing.

[0083] Specifically, space-based transmission networks are characterized by decentralized deployment and hierarchical management and maintenance models. Following the OSA-CBM architecture design method, an intelligent, task-oriented, model-based, hierarchical, and distributed layered converged space-based transmission network health management architecture is constructed. For example... Figure 3 The diagram shows a schematic of the health management architecture of the space-based transmission network provided in an embodiment of the present invention. It includes layers such as board level, device level, node level, network level, and system level. Different layers are equipped with different health management functions and service types. For example, the health management functions of the bottom board level include data acquisition and data processing, and the service type provided is information acquisition service. The health management functions of the top system level include data acquisition, data processing, status detection, health assessment, predictive analysis, decision support, and human-computer interaction, and the service types provided are application management service and station equipment management service.

[0084] In space-based transmission application systems, equipment is distributed across various ground stations and satellite nodes. The technical skill levels of maintenance personnel at each ground station vary, and their focus also differs. Satellite node status maintenance is performed by the ground control system based on received satellite downlink telemetry information. At vehicle-mounted, shipborne, and fixed stations with relatively fewer devices, maintenance personnel are fewer and more mobile. The focus is primarily on the operational status of the equipment and the connectivity of services. These edge stations require high levels of integration and automation in their health management, providing simple, concise, and clear instructions. Health management emphasizes data collection, processing, status monitoring, automatic health assessment, and presentation. Simultaneously, the collected information is reported to hub stations with stronger management capabilities for comprehensive health assessments, fault prediction, and decision support processing.

[0085] like Figure 4 The diagram shown is a flowchart of step S102 provided in an embodiment of the present invention. (Refer to...) Figure 4 As an optional implementation, the maximum mutual information coefficient among multiple node service metrics of the edge site is determined, specifically including:

[0086] S1021. Determine the two-dimensional coordinate plane of the business indicators of the two nodes, and generate a scatter plot on the two-dimensional coordinate plane based on the data of the business indicators of the two nodes.

[0087] S1022. Divide the two-dimensional coordinate plane into grids and calculate the joint distribution probability of the business indicators of two nodes in each grid and the marginal distribution probability of the business indicators of two nodes.

[0088] S1023. Determine the mutual information value between the business indicators of two nodes under the corresponding grid division based on the joint distribution probability and the marginal distribution probability:

[0089] S1024. Determine the maximum mutual information value under multiple grid divisions with the same number of rows and columns, and determine the mutual information characteristic value under multiple grid divisions with the same number of rows and columns based on the maximum mutual information value and the minimum value among the number of rows and columns.

[0090] S1025. Generate an eigenvalue matrix of the business indicators of the two nodes based on the mutual information eigenvalues ​​corresponding to different numbers of rows and columns, and determine the maximum mutual information coefficient of the business indicators of the two nodes based on the largest mutual information eigenvalue in the eigenvalue matrix.

[0091] Specifically, the basic idea of ​​the maximum mutual information algorithm is: if there is a correlation between the index pairs so that all data points are distributed in the grid cells, by continuously increasing the resolution, the mutual information values ​​of all grids under each grid division are compared, and then the mutual information values ​​are standardized. The largest standardized mutual information value is the maximum mutual information coefficient.

[0092] If there is a correlation between two node business metrics, then there exists an "optimal" grid division on the scatter plot of this metric pair, such that most of the data points of the metric pair are concentrated in a few cells of that grid. Therefore, for dividing the metric pair (X... i ;Y j In a coordinate plane, a grid g with X rows and Y columns is used. The probability density p(x,y) of a cell is defined as the proportion of the number of sample points in that cell to the total number of samples for that index pair, i.e., the probability density of the index pair (X,Y). i ;Y j The joint probability distribution of X is defined as the probability p(x) of a cell in the cell index X. i The sample size accounts for a percentage of the index X i The proportion of the total sample size, i.e., indicator X. ii The marginal distribution probability is defined as the probability p(y) of a cell as the index Y in that cell. j The sample size accounts for a certain percentage of the index Y j The proportion of the total sample size, i.e., indicator Y. j The marginal distribution probability. To measure the degree of concentration of the index on the sample, the mutual information value of the grid g is defined as follows:

[0093]

[0094] Wherein I(X) i ;X j This means that under the condition of grid g partitioning, the index pair (X) i ;Y j The mutual information value of ).

[0095] Since the mesh does not need to be of uniform width, there are multiple ways to partition a mesh with the same number of rows X and columns Y, which can be represented as G{g1, g2, ...}. Therefore, the mutual information characteristic value under multiple mesh partitions with the same number of rows X and columns Y is defined as:

[0096]

[0097] Where max(I) G ) represents the maximum mutual information value under the grid division of the number of rows X and the number of columns Y, which is the most suitable grid division method of the number of rows X and the number of columns Y. log(min(x,y)) represents the logarithm of the minimum value among the number of rows and the number of columns, with base 2.

[0098] Based on the mutual information eigenvalues ​​corresponding to different numbers of rows and columns, generate eigenvalue matrices for the business indicators of the two nodes. Then, determine the maximum mutual information coefficient of the two node business indicators based on the largest mutual information eigenvalue in the eigenvalue matrices, as follows:

[0099] MIC = max{MI(X, Y)}

[0100] Where MIC∈[0;1], the closer the value is to 1, the stronger the correlation between the indicators.

[0101] like Figure 5 The diagram shown is another flowchart of step S102 provided in an embodiment of the present invention. (Refer to...) Figure 5 As an optional implementation, the node association strength between multiple network nodes of the edge site is determined based on the maximum mutual information coefficient, and then the network node resources of the edge site are dynamically configured according to the node association strength. Specifically, this includes:

[0102] S1026. Determine the network nodes corresponding to the service indicators of each node;

[0103] S1027. Determine the node association strength between the two network nodes based on the maximum mutual information coefficient between the two node business indicators.

[0104] S1028. Determine the average association strength between each network node of the edge site and other network nodes based on the node association strength.

[0105] S1029. Determine the key network nodes and ordinary network nodes of the edge site based on the average association strength, and then dynamically configure the network node resources of the edge site based on the key network nodes and ordinary network nodes.

[0106] Specifically, the node association strength between two corresponding network nodes is determined based on the maximum mutual information coefficient between node business indicators. Then, the average value of the node association strength between each network node and other network nodes is calculated. This average value can represent the importance of the corresponding network node. Thus, all network nodes of the edge site can be divided into key network nodes and ordinary network nodes based on the average value of association strength. Therefore, different levels of resource allocation can be carried out based on the division of key network nodes and ordinary network nodes, realizing dynamic allocation of network node resources of the edge site.

[0107] like Figure 6 The diagram shown is a flowchart of step S103 provided in an embodiment of the present invention. (Refer to...) Figure 6 As an optional implementation, a community clustering algorithm based on shortest path features is used to divide multiple network nodes of a hub station into multiple node communities, specifically including:

[0108] S1031. Determine the set of network nodes of the hub station and the number and length of the shortest paths between multiple network nodes;

[0109] S1032. Determine the node influence coefficient of each network node based on the number of shortest paths;

[0110] S1033. Determine the node similarity between two network nodes based on the shortest path length, and determine the splitting threshold based on the mean of the node similarity between all network nodes.

[0111] S1034. Select the network node with the largest node influence coefficient from the network node set as the current community center node, and determine the node similarity between other network nodes in the network node set and the current community center node.

[0112] S1035. Remove other network nodes with a node similarity greater than or equal to the partitioning threshold from the network node set, and form a node community with the current community center node. Then, return the network node with the largest node influence coefficient from the network node set as the current community center node, until the network node set is empty.

[0113] As an optional implementation, the node influence coefficient is calculated using the following formula:

[0114]

[0115] Among them, B i p represents the node influence coefficient of node i. jk This represents the number of shortest paths between node j and node k. This represents the number of shortest paths between node j and node k that pass through node i, and n represents the total number of nodes in the network.

[0116] Node similarity is calculated using the following formula:

[0117]

[0118] Among them, S (j，k) d represents the node similarity between node j and node k. ji d represents the distance between node j and node i. ki This represents the distance between node k and node i;

[0119] The segmentation threshold is calculated using the following formula:

[0120]

[0121] Where λ represents the dividing threshold.

[0122] Specifically, the set of network nodes of the hub station and the number and length of the shortest paths between multiple network nodes are determined. The shortest paths can be solved by existing algorithms, which will not be elaborated here.

[0123] The number of shortest paths can be used to calculate the node influence coefficient of each network node, as follows:

[0124]

[0125] Among them, the denominator The numerator represents the maximum number of shortest paths that may pass through node i among all pairs of network nodes. This represents the number of shortest paths that actually pass through node i in each pair of nodes within the network node set.

[0126] The shortest path length can be used to calculate the node similarity between two network nodes, as follows:

[0127]

[0128] Among them, S (j，k) The range of its value is (0, 1).

[0129] The thresholds for defining community structure are as follows:

[0130]

[0131] in, This represents the total number of node pairs.

[0132] Community node clustering based on shortest path features describes the importance and influence of a node in the network topology by using the number of shortest paths from any pair of nodes that pass through a particular node. This yields an influence coefficient for each node, which is then sorted from highest to lowest. The similarity of node pairs is determined by comparing the difference in shortest path lengths from any two nodes to any other node in the network. The average similarity of all node pairs is used as the threshold for clustering. The specific process is as follows:

[0133] 1) Calculate the node influence coefficient of each node in the network and store it in array B in descending order;

[0134] 2) Calculate the node similarity of all node pairs and store them in the similarity matrix s.

[0135] 3) Determine the threshold for dividing the community structure;

[0136] 4) Take the node corresponding to the largest element in array B as the community center node;

[0137] 5) According to the partitioning rules, compare the similarity value between the node and the community center node with the partitioning threshold, cluster the nodes that are greater than or equal to the threshold, and delete the partitioned nodes from the node set.

[0138] 6) Determine if the network node set is not empty. If it is not empty, repeat steps 4) to 6). Otherwise, the clustering is complete, resulting in multiple node communities.

[0139] like Figure 7 The diagram shown is another flowchart of step S103 provided in an embodiment of the present invention. (Refer to...) Figure 7 As an optional implementation, network node resources for hub sites are dynamically configured based on node communities, specifically including:

[0140] S1036. Determine the average node influence coefficient of each node community;

[0141] S1037. Based on the average node influence coefficient, node communities are divided into key node communities and ordinary node communities;

[0142] S1038. Dynamically allocate network node resources for hub sites based on critical node communities and ordinary node communities.

[0143] Specifically, the average value of the node influence coefficient of each network node in each node community is determined. This average value can represent the importance of the corresponding node community. Thus, all node communities of the hub site can be divided into key node communities and ordinary node communities based on the average value of the node influence coefficient. Different levels of resource allocation can then be carried out based on the division of key node communities and ordinary node communities, thereby realizing the dynamic allocation of network node resources of edge sites.

[0144] The method steps of the embodiments of the present invention have been described above. It can be understood that the embodiments of the present invention construct a health management architecture for space-based transmission networks based on OSA-CBM, determine the node association strength between network nodes at edge sites based on the maximum mutual information coefficient between node service indicators, dynamically allocate network node resources for edge sites based on the node association strength, and divide the node communities of hub sites into groups using a community clustering algorithm based on shortest path characteristics. Dynamically allocating network node resources for hub sites based on node communities improves the efficiency and accuracy of network node resource allocation, thereby improving the data transmission efficiency and network stability of the space-based transmission network.

[0145] like Figure 8 The diagram shown is a structural schematic of a space-based transmission network resource configuration device provided in an embodiment of the present invention. (Refer to...) Figure 8This invention provides a space-based transmission network resource configuration device, comprising:

[0146] The health management architecture construction module is used to build a health management architecture for space-based transmission networks based on OSA-CBM, and to identify edge sites and hub sites in the health management architecture for space-based transmission networks.

[0147] The edge site resource configuration module is used to determine the maximum mutual information coefficient between multiple node business indicators of the edge site, determine the node association strength between multiple network nodes of the edge site based on the maximum mutual information coefficient, and then dynamically configure the network node resources of the edge site based on the node association strength.

[0148] The hub site resource configuration module is used to divide multiple network nodes of the hub site into multiple node communities using a community clustering algorithm based on the shortest path feature, and to dynamically configure network node resources of the hub site according to the node communities.

[0149] The content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0150] This invention also provides an electronic device, comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned space-based transmission network resource configuration method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0151] like Figure 9 The diagram shown is a hardware structure schematic of an electronic device provided in an embodiment of the present invention. (Refer to...) Figure 9 This invention provides an electronic device, comprising:

[0152] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0153] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the space-based transmission network resource configuration method of the embodiments of this invention.

[0154] The input / output interface 903 is used to implement information input and output;

[0155] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0156] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0157] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0158] like Figure 10 The diagram shown is a structural schematic of the storage medium provided in an embodiment of the present invention. (Refer to...) Figure 10 The present invention also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs 1001, which can be executed by one or more processors to implement the above-described space-based transmission network resource configuration method.

[0159] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0160] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform... Figure 1 The method shown.

[0161] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0162] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0163] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0165] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or, if necessary, processing in other suitable ways, and then stored in computer memory.

[0166] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0167] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0168] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0169] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for allocating resources in a space-based transmission network, characterized in that, Includes the following steps: Construct a space-based transmission network health management architecture based on OSA-CBM, and identify the edge sites and hub sites in the space-based transmission network health management architecture; The maximum mutual information coefficient among multiple node service indicators of the edge site is determined, the node association strength among multiple network nodes of the edge site is determined based on the maximum mutual information coefficient, and then the network node resources of the edge site are dynamically configured based on the node association strength. The hub station's multiple network nodes are divided into multiple node communities using a community clustering algorithm based on the shortest path feature, and the hub station's network node resources are dynamically configured according to the node communities. The construction of the OSA-CBM-based space-based transmission network health management architecture, and the identification of edge sites and hub sites within the architecture, specifically includes: Based on OSA-CBM, the space-based transmission network is divided into multiple layers, and the health management functions and service types provided by each layer are determined to obtain the health management architecture of the space-based transmission network. Based on the health management function, the network devices at each of the aforementioned levels are divided into edge sites and hub sites; The health management functions of the edge stations include data collection, data processing, status detection, and automatic health assessment, while the health management functions of the hub stations include comprehensive health assessment, fault prediction, and decision support processing. Determining the maximum mutual information coefficient among multiple node service metrics of the edge site specifically includes: Determine the two-dimensional coordinate plane of the two node business indicators, and generate a scatter plot on the two-dimensional coordinate plane based on the data of the two node business indicators; The two-dimensional coordinate plane is divided into grids, and the joint distribution probability of the two node service indicators in each grid and the edge distribution probability of each of the two node service indicators are calculated. The mutual information value between the two node business indicators under the corresponding grid division is determined based on the joint distribution probability and the edge distribution probability. Determine the maximum mutual information value under multiple grid divisions with the same number of rows and columns, and determine the mutual information feature value under multiple grid divisions with the same number of rows and columns based on the maximum mutual information value and the minimum value among the number of rows and columns; Generate feature value matrices for two node business indicators based on the mutual information feature values ​​corresponding to different numbers of rows and columns, and determine the maximum mutual information coefficient of the two node business indicators based on the largest mutual information feature value in the feature value matrix; The step of determining the node association strength among multiple network nodes of the edge site based on the maximum mutual information coefficient, and then dynamically configuring network node resources of the edge site based on the node association strength, specifically includes: Determine the network nodes corresponding to each of the aforementioned node service metrics; The node association strength between the two network nodes is determined based on the maximum mutual information coefficient between the two node service indicators. The average association strength between each network node of the edge site and other network nodes is determined based on the node association strength. The critical network nodes and ordinary network nodes of the edge site are determined based on the average association strength, and then the network node resources of the edge site are dynamically configured based on the critical network nodes and the ordinary network nodes.

2. The method for allocating resources in a space-based transmission network according to claim 1, characterized in that, The process of dividing multiple network nodes of the hub station into multiple node communities using a community clustering algorithm based on shortest path features specifically includes: Determine the set of network nodes of the hub station, as well as the number and length of the shortest paths between multiple network nodes; The node influence coefficient of each network node is determined based on the number of shortest paths. The node similarity between two network nodes is determined based on the shortest path length, and the partitioning threshold is determined based on the mean of the node similarity between all network nodes. The network node with the highest influence coefficient is selected from the network node set as the current community center node, and the similarity between the other network nodes in the network node set and the current community center node is determined. Other network nodes with a node similarity greater than or equal to the partitioning threshold are removed from the network node set and combined with the current community center node to form a node community. Then, the network node with the largest node influence coefficient is selected from the network node set as the current community center node, until the network node set is empty.

3. The method for allocating resources in a space-based transmission network according to claim 2, characterized in that, The node influence coefficient is calculated using the following formula: in, This represents the node influence coefficient of node i. This represents the number of shortest paths between node j and node k. This represents the number of shortest paths between node j and node k that pass through node i, and n represents the total number of nodes in the network. The node similarity is calculated using the following formula: in, This represents the node similarity between node j and node k. This represents the distance between node j and node i. This represents the distance between node k and node i; The partitioning threshold is calculated using the following formula: Where λ represents the dividing threshold.

4. The method for allocating resources in a space-based transmission network according to claim 2, characterized in that, The dynamic allocation of network node resources for the hub site based on the node community specifically includes: Determine the average node influence coefficient of each of the aforementioned node communities; The node communities are divided into key node communities and ordinary node communities based on the average value of the node influence coefficient. The network node resources of the hub site are dynamically configured based on the key node community and the ordinary node community.

5. A space-based transmission network resource allocation device, characterized in that, The method for configuring space-based transmission network resources as described in any one of claims 1 to 4 includes: A health management architecture construction module is used to construct a space-based transmission network health management architecture based on OSA-CBM, and to determine the edge sites and hub sites in the space-based transmission network health management architecture. The edge site resource configuration module is used to determine the maximum mutual information coefficient among multiple node service indicators of the edge site, determine the node association strength among multiple network nodes of the edge site based on the maximum mutual information coefficient, and then dynamically configure the network node resources of the edge site based on the node association strength. The hub site resource configuration module is used to divide multiple network nodes of the hub site into multiple node communities using a community clustering algorithm based on the shortest path feature, and to dynamically configure network node resources of the hub site according to the node communities.

6. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the steps of the space-based transmission network resource configuration method as described in any one of claims 1 to 4.

7. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the space-based transmission network resource configuration method as described in any one of claims 1 to 4.

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