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

Abstract

The invention discloses a space-based transmission network resource allocation method, device and equipment and a storage medium, and the method comprises the steps: 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 a maximum mutual information coefficient among a plurality of node service indexes of the edge site, determining node association strength among a plurality of network nodes of the edge site according to the maximum mutual information coefficient, and further performing network node resource dynamic configuration on the edge site according to the node association strength; a plurality of network nodes of the hub site are divided into a plurality of node communities through a social clustering algorithm based on shortest path characteristics, and network node resource dynamic configuration is carried out on the hub site according to the node communities. According to the invention, the data transmission efficiency and network stability of the space-based transmission network are improved, and the method can be widely applied to the technical field of space-ground integration.

Description

Technical Field

[0001] The present invention relates to the field of space-ground integration technology, and in particular to a space-based transmission network resource configuration method, device, equipment and storage medium. Background Art

[0002] With the rapid development of space-based transmission network equipment and technology, the integration of equipment is getting higher and higher, the network scale is getting larger and larger, and the complexity is getting higher and higher. At the same time, the requirements for reducing equipment failure rate and the automation, intelligence, and integration of space-based transmission networks have increased the application requirements of health management technology in space-based transmission networks. Therefore, the application of smart space-based transmission networks is imminent. The traditional evaluation index system consists of indicator values ​​and correlation relationships. The correlation relationship describes the cause-effect relationship, while the indicators and correlation relationships in the dynamic networked indicator system are derived from the actual space-based transmission network data, and the correlation relationship only describes the correlation between indicators. The smart space-based transmission network first conducts time evolution analysis on indicators, analyzes the characteristic parameters of various types and levels of indicators evolving over time, and then provides a reference for discovering the evolution law of the system at different stages; second, it conducts correlation analysis on multiple indicators. Because the correlation between indicators is unknown in advance, it may be linear or nonlinear, and may also include time delays, and it is constantly changing over time. Therefore, it is assumed that all indicators are correlated, and an appropriate number of windows and correlation analysis methods are selected to perform moving window correlation analysis on all indicator time series to obtain the evolution law of the correlation between any two indicators. Finally, a fully connected dynamic indicator network that evolves over time is obtained, in which the indicator values ​​and correlation relationships are constantly evolving over time.

[0003] In the prior art, space-based transmission networks suffer from irrational allocation of network node resources, which affects the data transmission efficiency and network stability of space-based transmission networks.

[0004] Terminology explanation:

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

[0006] Maximum Mutual Information (MMI): A mutual information-based metric used to find the optimal association pattern. Its basic principle is to find a projection under which the association information in the original data can be preserved to the greatest extent. Specifically, MMI calculates the mutual information between all possible projections and selects the projection with the maximum mutual information as the optimal projection. Summary of the invention

[0007] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0008] To this end, an object of an embodiment of the present invention is to provide a method for configuring space-based transmission network resources, which improves the data transmission efficiency and network stability of the space-based transmission network.

[0009] Another object of an embodiment of the present invention is to provide a space-based transmission network resource configuration device.

[0010] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

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

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

[0013] Determine a maximum mutual information coefficient between multiple node service indicators of the edge site, determine a node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configure network node resources of the edge site according to the node association strength;

[0014] The multiple network nodes of the hub site 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 site are dynamically configured according to the node communities.

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

[0016] Divide the space-based transmission network into multiple levels based on OSA-CBM, and determine the health management function and service type provided by each level to obtain the health management architecture of the space-based transmission network;

[0017] Dividing the network devices at each level into the edge sites and the hub sites according to the health management function;

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

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

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

[0021] Dividing the two-dimensional coordinate plane into grids, and calculating the joint distribution probability of the two node service indicators in each grid and the respective marginal distribution probability of the two node service indicators;

[0022] Determine the mutual information value between the two node service indicators under the corresponding grid division according to 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 eigenvalue under multiple grid divisions with the same number of rows and columns according to the maximum mutual information value and the minimum value of the number of rows and the number of columns;

[0024] An eigenvalue matrix of two node service indicators is generated according to the mutual information eigenvalues ​​corresponding to different numbers of rows and columns, and the maximum mutual information coefficient of the two node service indicators is determined according to the largest mutual information eigenvalue in the eigenvalue matrix.

[0025] Further, in one embodiment of the present invention, determining the node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configuring network node resources of the edge site according to the node association strength, specifically includes:

[0026] Determine the network node corresponding to each of the node service indicators;

[0027] Determine the node association strength between the corresponding two network nodes according to the maximum mutual information coefficient between the two node service indicators;

[0028] Determine an average value of association strength between each network node of the edge site and other network nodes according to the node association strength;

[0029] The key network nodes and common network nodes of the edge site are determined according to the average value of the association strength, and then the network node resources of the edge site are dynamically configured according to the key network nodes and the common network nodes.

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

[0031] Determine a set of network nodes at the hub site and the number and length of shortest paths between a plurality of network nodes;

[0032] Determining a node influence coefficient of each network node according to the number of shortest paths;

[0033] Determine the node similarity between two network nodes according to the shortest path length, and determine the partition threshold according to the average of the node similarities between all network nodes;

[0034] Taking out the network node with the largest node influence coefficient from the network node set as the current community center node, and determining the node similarity between other network nodes in the network node set and the current community center node;

[0035] Other network nodes whose node similarity is greater than or equal to the division threshold are taken out from the network node set, and form a node community with the current community center node, and then the network node with the largest node influence coefficient is returned 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 by the following formula:

[0037]

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

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

[0040]

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

[0042] The division threshold is calculated by the following formula:

[0043]

[0044] Among them, λ represents the partition threshold.

[0045] Further, in some optional embodiments, the dynamically configuring network node resources of the hub site according to the node community specifically includes:

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

[0047] Dividing the node community into a key node community and a common node community according to the average value of the node influence coefficient;

[0048] The network node resources of the hub site are dynamically configured according to the key node community and the common node community.

[0049] On the other hand, an embodiment of the present invention provides a space-based transmission network resource configuration device, including:

[0050] A health management architecture building module, used to build a health management architecture of a space-based transmission network based on OSA-CBM, and determine edge sites and hub sites in the health management architecture of the space-based transmission network;

[0051] An edge site resource configuration module, used to determine the maximum mutual information coefficient between multiple node service indicators of the edge site, determine the node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configure network node resources for the edge site according to the node association strength;

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

[0053] On the other hand, an embodiment of the present invention 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 realizing connection and communication between the processor and the memory, wherein the program, when executed by the processor, realizes the space-based transmission network resource configuration method as described above.

[0054] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs 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 partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0056] The embodiment of the present invention constructs a space-based transmission network health management architecture based on OSA-CBM, determines the edge site and the hub site in the space-based transmission network health management architecture, determines the maximum mutual information coefficient between multiple node business indicators of the edge site, determines the node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configures network node resources for the edge site according to the node association strength, divides multiple network nodes of the hub site into multiple node communities through a community clustering algorithm based on the shortest path feature, and dynamically configures network node resources for the hub site according to the node community. The embodiment of the present invention constructs a space-based transmission network health management architecture based on OSA-CBM, determines the node association strength between network nodes of the edge site according to the maximum mutual information coefficient between node business indicators, dynamically configures network node resources for the edge site according to the node association strength, divides the node community of the hub site through a community clustering algorithm based on the shortest path feature, and dynamically configures network node resources for the hub site according to the node community, thereby improving the efficiency and accuracy of network node resource configuration, thereby improving the data transmission efficiency and network stability of the space-based transmission network. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 A flow chart of the steps of a method for configuring space-based transmission network resources provided by an embodiment of the present invention;

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

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

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

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

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

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

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

[0066] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention;

[0067] Fig.10 A schematic diagram of the structure of a storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limitations on the present application. It should be noted that, although the functional module division is performed in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order from the module division in the system schematic diagram or the flow chart. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0069] In the description of the present invention, the meaning of "a plurality" is two or more. If there is a description of the first or the second, it is only for the purpose of distinguishing the technical features, and it cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this document are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0070] The space-based transmission network resource configuration method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a set-top box, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, 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] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present 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. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0072] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0073] like Figure 1 FIG. 1 is a flow chart showing a method for configuring space-based transmission network resources according to an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a method for configuring space-based transmission network resources, which specifically includes the following steps:

[0074] S101. Build 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, determining a maximum mutual information coefficient between multiple node service indicators of an edge site, determining a node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configuring network node resources for the edge site according to the node association strength;

[0076] S103. Divide the multiple network nodes of the hub site into multiple node communities through a community clustering algorithm based on the shortest path feature, and dynamically configure network node resources of the hub site according to the node communities.

[0077] Specifically, an embodiment of the present invention constructs a health management architecture for a space-based transmission network based on OSA-CBM. The design method is proposed based on the logical relationship between various health management functions, and includes 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. The health management architecture based on OSA-CBM is optimized by constructing an intelligent, task-based, modeled, hierarchical, and distributed hierarchical and integrated space-based transmission network health management architecture. For edge sites with relatively few equipment configurations, there are high requirements for the integration and automation capabilities of health management between network nodes and nodes. The embodiment of the present invention uses a maximum mutual information algorithm to intelligently analyze the correlation between business node indicators, thereby obtaining all associated nodes with business indicator correlation and different strengths between network nodes at edge sites. The number of nodes is dynamically configured according to the association strength to maintain the optimal number of nodes for data transmission applications; for hub sites with relatively complex equipment configurations, a hierarchical and graded health management system is used to dynamically configure the equipment of network nodes. Nodes at all levels construct health management systems at all levels that include basic information, environmental information, test information history information, expert systems, fault prediction models, fault diagnosis models, status assessment models, and decision support models. They need to have the ability to independently judge and independently handle problems within the management scope of the corresponding level. The embodiment of the present 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] The embodiment of the present invention constructs a space-based transmission network health management architecture based on OSA-CBM, determines the node association strength between network nodes of edge sites according to the maximum mutual information coefficient between node business indicators, dynamically configures network node resources for edge sites according to the node association strength, divides the node communities of hub sites through a community clustering algorithm based on shortest path characteristics, and dynamically configures network node resources for hub sites according to the node communities, thereby improving the efficiency and accuracy of network node resource configuration, thereby improving the data transmission efficiency and network stability of the space-based transmission network.

[0079] like Figure 2 FIG. 1 is a flowchart of step S101 provided in an embodiment of the present invention, referring to FIG. Figure 2 As an optional implementation method, a space-based transmission network health management architecture based on OSA-CBM is constructed, and edge sites and hub sites in the space-based transmission network health management architecture are determined, which specifically include:

[0080] S1011. Divide the space-based transmission network into multiple levels based on OSA-CBM, determine the health management functions and service types provided by each level, and obtain the health management architecture of the space-based transmission network;

[0081] S1012. Divide network devices at each level into edge sites and hub sites according to health management functions;

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

[0083] Specifically, the space-based transmission network has the characteristics of decentralized deployment and hierarchical management and maintenance mode. According to the OSA-CBM architecture design method, an intelligent, task-oriented, model-based, hierarchical, distributed and hierarchical integrated space-based transmission network health management architecture is constructed. Figure 3 As shown is a schematic diagram of the health management architecture of the space-based transmission network provided by an embodiment of the present invention, including board level, device level, node level, network level and system level, and different levels are provided with different health management functions and service types. For example, the health management functions of the bottom board level include data collection and data processing, and the service type provided is information collection service. The health management functions of the top system level include data collection, 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 the space-based transmission application system, the equipment is dispersedly deployed at various levels of ground stations and satellite nodes. The technical levels of the equipment maintenance personnel configured at each earth station are different, and the focus of the maintenance personnel at each station is also different. The satellite node status maintenance is maintained by the ground operation and control system based on the received satellite downlink telemetry information. At vehicle-mounted stations, ship-mounted stations, and fixed stations with relatively few equipment configurations, the maintenance personnel are relatively weak and the personnel mobility is relatively large. The focus is mainly on the working status of the equipment and the connectivity status of the business. The health management integration and automation capabilities of edge sites at this level should be high, the operations provided should be as simple as possible, the information should be as concise as possible, and the guidance should be clear. Health management focuses on data collection, data processing, status monitoring, automatic health assessment and presentation. At the same time, the collected information is reported to the hub site with strong management capabilities for comprehensive health assessment, fault prediction, decision support processing, etc.

[0085] like Figure 4 FIG. 1 is a flow chart of step S102 provided in an embodiment of the present invention, referring to FIG. Figure 4 As an optional implementation, determining the maximum mutual information coefficient between multiple node service indicators of the edge site specifically includes:

[0086] S1021, determining a two-dimensional coordinate plane of the business indicators of the two nodes, and generating a scatter plot on the two-dimensional coordinate plane according to the data pairs of the business indicators of the two nodes;

[0087] S1022, dividing the two-dimensional coordinate plane into grids, and calculating the joint distribution probability of the business indicators of the two nodes in each grid and the marginal distribution probability of the business indicators of the two nodes;

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

[0089] S1024, determining the maximum mutual information value under multiple grid divisions with the same number of rows and columns, and determining the mutual information eigenvalue under multiple grid divisions with the same number of rows and columns according to the maximum mutual information value and the minimum value of the number of rows and columns;

[0090] S1025. Generate an eigenvalue matrix of the two node business indicators according to the mutual information eigenvalues ​​corresponding to different numbers of rows and columns, and determine the maximum mutual information coefficient of the two node business indicators according to 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 indicator pairs, all the data points are distributed in the cells of the grid division. 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 mutual information standardized value is the maximum mutual information coefficient.

[0092] If there is a correlation between the business indicators of two nodes, then there is a "most suitable" grid division on the scatter plot of this indicator pair, so that most of the data points of the indicator pair are concentrated in several cells of the grid. i ; Y j ) coordinate plane is a grid g with X rows and Y columns. The probability density p(x,y) of a cell is defined as the ratio of the number of sample points in the grid to the total number of samples for this indicator pair, that is, the indicator pair (X i ; Y j ), and define the probability p(x) of a cell as the index X in the cell i The number of samples accounts for the index X i The proportion of the total number of samples, that is, the indicator X ii The marginal distribution probability of the cell is defined as the probability p(y) of the cell as the index Y in the cell. j The number of samples accounts for the indicator Y j The proportion of the total number of samples, that is, the indicator Y j In order to measure the concentration of indicators on samples, the mutual information value of grid g is defined as follows:

[0093]

[0094] Among them, I(X i ;X j ) means that the index pair (X i ; Y j )’s mutual information value.

[0095] Since the grid does not have to be divided into equal widths, there are multiple ways to divide the grid with the same number of rows X and columns Y, which can be expressed as G{g1, g2, ...}. Therefore, the mutual information eigenvalue under multiple grid divisions with the number of rows X and columns Y is defined as:

[0096]

[0097] Among them, 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 corresponds to the most appropriate 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 of the number of rows and columns with base 2.

[0098] The eigenvalue matrix of the two node business indicators is generated according to the mutual information eigenvalues ​​corresponding to different numbers of rows and columns, and the maximum mutual information coefficient of the two node business indicators is determined according to the largest mutual information eigenvalue in the eigenvalue matrix as follows:

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

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

[0101] like Figure 5 FIG. 1 is another step flow chart of step S102 provided in an embodiment of the present invention, referring to FIG. Figure 5 As an optional implementation, the node association strength between multiple network nodes of the edge site is determined according to the maximum mutual information coefficient, and then the network node resources of the edge site are dynamically configured according to the node association strength, which specifically includes:

[0102] S1026. Determine the network node corresponding to each node service indicator;

[0103] S1027, determining the node association strength between the corresponding two network nodes according to the maximum mutual information coefficient between the service indicators of the two nodes;

[0104] S1028. Determine an average value of association strength between each network node of the edge site and other network nodes according to the node association strength;

[0105] S1029: Determine the key network nodes and common network nodes of the edge site according to the average value of the association strength, and then dynamically configure network node resources for the edge site according to the key network nodes and the common network nodes.

[0106] Specifically, the node association strength between the corresponding two network nodes is determined according to the maximum mutual information coefficient between the node business indicators, and then the average value of the node association strength between each network node and other network nodes is calculated. The average value can represent the importance of the corresponding network node, so that all network nodes of the edge site can be divided into key network nodes and ordinary network nodes according to the average value of the association strength, so that different levels of resource allocation can be performed according to the division of key network nodes and ordinary network nodes, thereby realizing dynamic configuration of network node resources at the edge site.

[0107] like Figure 6 FIG. 1 is a flow chart of step S103 provided in an embodiment of the present invention, referring to FIG. Figure 6 As an optional implementation, multiple network nodes of the hub site are divided into multiple node communities by a community clustering algorithm based on the shortest path feature, which specifically includes:

[0108] S1031, determining a set of network nodes at a hub site and the number and length of the shortest paths between a plurality of network nodes;

[0109] S1032, determining the node influence coefficient of each network node according to the number of shortest paths;

[0110] S1033, determining the node similarity between two network nodes according to the shortest path length, and determining the partition threshold according to the average of the node similarities between all network nodes;

[0111] S1034, taking out the network node with the largest node influence coefficient from the network node set as the current community center node, and determining the node similarity between other network nodes in the network node set and the current community center node;

[0112] S1035. Take out other network nodes whose node similarity is greater than or equal to the division threshold from the network node set, and form a node community with the current community center node, and 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 by the following formula:

[0114]

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

[0116] The node similarity is calculated by the following formula:

[0117]

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

[0119] The partition threshold is calculated by the following formula:

[0120]

[0121] Among them, λ represents the partition threshold.

[0122] Specifically, the network node set of the hub site and the number and length of the shortest paths between the multiple network nodes are determined. The shortest path can be solved by an existing algorithm, which will not be described in detail 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 It represents the maximum number of shortest paths that may pass through node i among all the shortest paths of network node pairs. It represents the number of shortest paths that actually pass through node i in the shortest paths between each pair of nodes in 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 value range of is (0, 1].

[0129] The threshold for defining community structure division is as follows:

[0130]

[0131] in, Indicates the number of all node pairs.

[0132] The clustering of community nodes based on the shortest path feature is to use the number of paths through a certain node in the shortest path number of any node pair in the network to describe the importance and influence of the node in the network topology. Based on this, the node influence coefficient of each node is obtained and sorted from large to small. By comparing the difference in the shortest path length from any two nodes in the network to any other node, the similarity of the node pair is judged, and the average similarity of all node pairs is used as the division threshold. The clustering of community nodes is performed according to the clustering idea. The specific process is as follows:

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

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

[0135] 3) Find the division threshold of 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 division rule, the similarity value between the node and the community center node is compared with the division threshold, the nodes greater than or equal to the threshold are clustered, and the divided nodes are deleted from the node set;

[0138] 6) Determine whether the network node set is non-empty. If it is not empty, repeat 4) to 6). Otherwise, the clustering is completed and multiple node communities are obtained.

[0139] like Figure 7 FIG. 1 is another step flow chart of step S103 provided in an embodiment of the present invention, referring to FIG. Figure 7 As an optional implementation method, the network node resources of the hub site are dynamically configured according to the node community, which specifically includes:

[0140] S1036, determining the average value of the node influence coefficient of each node community;

[0141] S1037. Divide the node community into a key node community and a common node community according to the average value of the node influence coefficient;

[0142] S1038. Dynamically configure network node resources for hub sites based on key node communities and common node communities.

[0143] Specifically, the average value of the node influence coefficient of each network node in each node community is determined. The average value can represent the importance of the corresponding node community, so that all node communities of the hub site can be divided into key node communities and ordinary node communities according to the average value of the node influence coefficient. Different levels of resource allocation can be performed based on the division of key node communities and ordinary node communities, thereby realizing dynamic configuration of network node resources at edge sites.

[0144] The above is an explanation of the method steps of the embodiment of the present invention. It can be recognized that the embodiment of the present invention builds a space-based transmission network health management architecture based on OSA-CBM, determines the node association strength between network nodes of edge sites according to the maximum mutual information coefficient between node business indicators, dynamically configures network node resources for edge sites according to the node association strength, divides the node community of the hub site by a community clustering algorithm based on the shortest path feature, and dynamically configures network node resources for the hub site according to the node community, thereby improving the efficiency and accuracy of network node resource configuration, thereby improving the data transmission efficiency and network stability of the space-based transmission network.

[0145] like Figure 8 FIG. 1 is a schematic diagram of the structure of a space-based transmission network resource configuration device provided by an embodiment of the present invention, referring to FIG. Figure 8The embodiment of the present invention provides a space-based transmission network resource configuration device, including:

[0146] A health management architecture building module, which is used to build a health management architecture for space-based transmission networks based on OSA-CBM and determine edge sites and hub sites in the health management architecture for space-based transmission networks;

[0147] An 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 according to the maximum mutual information coefficient, and then dynamically configure network node resources for the edge site according to the node association strength;

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

[0149] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present 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] The embodiment of the present invention further provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned space-based transmission network resource configuration method. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0151] like Fig. 9 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Fig. 9 , an embodiment of the present invention provides an electronic device, including:

[0152] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an 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 in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the space-based transmission network resource configuration method of the embodiment of the present invention;

[0154] Input / output interface 903, used to implement information input and output;

[0155] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

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

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

[0158] like Fig.10 FIG. 1 is a schematic diagram of a storage medium according to an embodiment of the present invention. Fig.10 An embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs 1001, and the one or more programs 1001 can be executed by one or more processors to implement the above-mentioned space-based transmission network resource configuration method.

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

[0160] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are 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, so that the computer device executes Figure 1 The method shown.

[0161] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0162] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can 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 present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0163] If the above functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

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

[0165] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0166] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0167] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

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

[0169] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A space-based transmission network resource configuration method, characterized in that: The following steps are involved: Constructing a space-based transmission network health management architecture based on OSA-CBM, and determining edge sites and hub sites in the space-based transmission network health management architecture; Determine a maximum mutual information coefficient between multiple node service indicators of the edge site, determine a node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configure network node resources of the edge site according to the node association strength; The multiple network nodes of the hub site 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 site are dynamically configured according to the node communities.

2. A space-based transmission network resource configuration method according to claim 1, characterized in that: The construction of the OSA-CBM-based space-based transmission network health management framework and the determination of edge sites and hub sites in the space-based transmission network health management framework specifically include: Divide the space-based transmission network into multiple levels based on OSA-CBM, and determine the health management function and service type provided by each level to obtain the health management architecture of the space-based transmission network; Dividing the network devices at each level into the edge sites and the hub sites according to the health management function; Among them, the health management functions of the edge site include data collection, data processing, status detection and automatic health assessment, and the health management functions of the hub site include comprehensive health assessment, fault prediction and decision support processing.

3. A space-based transmission network resource configuration method according to claim 1, characterized in that: The determining of the maximum mutual information coefficient between the multiple node service indicators of the edge site specifically includes: Determine a two-dimensional coordinate plane of the two node business indicators, and generate a scatter plot on the two-dimensional coordinate plane according to the data pairs of the two node business indicators; Dividing the two-dimensional coordinate plane into grids, and calculating the joint distribution probability of the two node service indicators in each grid and the marginal distribution probability of the two node service indicators respectively; Determine the mutual information value between the two node service indicators under the corresponding grid division according to 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 eigenvalue under multiple grid divisions with the same number of rows and columns according to the maximum mutual information value and the minimum value of the number of rows and the number of columns; An eigenvalue matrix of two node service indicators is generated according to the mutual information eigenvalues ​​corresponding to different numbers of rows and columns, and the maximum mutual information coefficient of the two node service indicators is determined according to the largest mutual information eigenvalue in the eigenvalue matrix.

4. A space-based transmission network resource configuration method according to claim 3, characterized in that: The determining the node association strength between the plurality of network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configuring the network node resources of the edge site according to the node association strength, specifically includes: Determine the network node corresponding to each of the node service indicators; Determine the node association strength between the corresponding two network nodes according to the maximum mutual information coefficient between the two node service indicators; Determine an average value of association strength between each network node of the edge site and other network nodes according to the node association strength; The key network nodes and common network nodes of the edge site are determined according to the average value of the association strength, and then the network node resources of the edge site are dynamically configured according to the key network nodes and the common network nodes.

5. The method for configuring space-based transmission network resources according to claim 1, characterized in that: The method of dividing the multiple network nodes of the hub site into multiple node communities by using a community clustering algorithm based on the shortest path feature specifically includes: Determine a set of network nodes at the hub site and the number and length of shortest paths between a plurality of network nodes; Determining a node influence coefficient of each network node according to the number of shortest paths; Determine the node similarity between two network nodes according to the shortest path length, and determine the partition threshold according to the average of the node similarities between all network nodes; Taking out the network node with the largest node influence coefficient from the network node set as the current community center node, and determining the node similarity between other network nodes in the network node set and the current community center node; Other network nodes whose node similarity is greater than or equal to the division threshold are taken out from the network node set, and form a node community with the current community center node, and then the network node with the largest node influence coefficient is returned from the network node set as the current community center node, until the network node set is empty.

6. A space-based transmission network resource configuration method according to claim 5, characterized in that: The node influence coefficient is calculated by the following formula: Among them, B i represents the node influence coefficient of node i, p jk represents the number of shortest paths between node j and node k, represents the number of shortest paths between node j and node k passing through node i, and n represents the total number of network nodes; The node similarity is calculated by the following formula: Among them, S (j,k) represents the node similarity between node j and node k, d ji represents the distance between node j and node i, d ki represents the distance between node k and node i; The division threshold is calculated by the following formula: Among them, λ represents the partition threshold.

7. A space-based transmission network resource configuration method according to claim 5, characterized in that: The dynamically configuring network node resources of the hub site according to the node community specifically includes: Determine the average value of the node influence coefficient of each of the node communities; Dividing the node community into a key node community and a common node community according to the average value of the node influence coefficient; The network node resources of the hub site are dynamically configured according to the key node community and the common node community.

8. A space-based transmission network resource configuration device, characterized in that: include: A health management architecture building module, used to build a health management architecture of a space-based transmission network based on OSA-CBM, and determine edge sites and hub sites in the health management architecture of the space-based transmission network; An edge site resource configuration module, used to determine the maximum mutual information coefficient between multiple node service indicators of the edge site, determine the node association strength between multiple network nodes of the edge site according to the maximum mutual information coefficient, and then dynamically configure network node resources for the edge site according to the node association strength; The hub site resource configuration module is used to divide the multiple network nodes of the hub site into multiple node communities through a community clustering algorithm based on the shortest path feature, and dynamically configure the network node resources of the hub site according to the node communities.

9. 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 realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the space-based transmission network resource configuration method as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs 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 7.

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