CN114745286B - Intelligent network situation awareness system oriented to dynamic network based on knowledge graph technology - Google Patents
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Abstract
The invention discloses a knowledge graph technology-based intelligent network situation awareness system oriented to a dynamic network, which comprises situation awareness nodes, a network situation awareness fusion center and network situation awareness application. The invention provides a knowledge graph technology-oriented intelligent network situation awareness system for a 5G dynamic network, which solves the problem of the intelligent flexible dynamic network in the 5G communication era, controls the overall network situation awareness of the carrier network distribution of a transmission network, the interweaving relation of various sub-networks, the trend of network flow, network load, network paths, network element entity information and the like, and meets the urgent need of construction optimization, operation maintenance, network safety and network supervision.
Description
Technical Field
The invention belongs to the technical field of network situation awareness, and particularly relates to a dynamic network intelligent network situation awareness system based on a knowledge graph technology.
Background
At present, network situation awareness technology hotspots collect monitoring awareness management along with the state of network equipment, and belong to the category of network management system equipment. An intelligent network management method based on network situation awareness is disclosed in a chinese patent application document with application number of 201811409929.4, which is applicable to state awareness of network equipment of a targeted machine room and management and maintenance thereof.
In the current 5G communication era, any to any pan connection, SNP network slice access network, transmission pipeline clouding and intelligent full mesh are adopted, and the method is popularized and applied in the software defined network SDN technology. Communication networks are entering the era of intelligent dynamic networks, and the access of the IP Internet, private networks, signaling networks and private networks of various enterprise groups is convenient at any time and any place. The blueprint planning bearing network is transferred into the bearing network which changes along with the need, so that the difficulty of network state cognition is brought to the construction optimization, operation maintenance and network security monitoring management of the network.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a knowledge-graph-based intelligent network situation awareness system for a dynamic network.
The aim of the invention is achieved by the following technical scheme: a knowledge graph technology-based intelligent network situation awareness system for a dynamic network comprises situation awareness nodes, a network situation awareness fusion center and a network situation awareness application;
the situation awareness node is used for extracting and completing knowledge conversion processing based on network sensor node data, and completing network situation awareness of network situation sensor node layout to construct a local network situation knowledge map base;
the network situation awareness fusion center performs knowledge graph library fusion disambiguation processing and reasoning analysis complement on the basis of the local network situation knowledge graph model generated by each network sensing node of the networking to construct a network situation knowledge graph model; the research and analysis completes the establishment of a standardized network situation knowledge graph body model; meanwhile, the network situation knowledge graph data sharing and network situation change early warning analysis are realized, the network reasoning analysis service is realized, and the situation awareness node networking management is carried out;
the network situation awareness application is used for providing visual configuration ontology knowledge creation, network map relation and network situation information display, and the network entity network element node information analysis and early warning application service system.
Further: the network situation knowledge graph model is constructed based on an ontology model, and the network situation knowledge graph model object entity network element knowledge comprises link, network and session protocol parameters belonging to static properties, flow, failure frequency, networking element association and associated region dynamic properties; the network situation knowledge graph model object entity network element interweaving relation knowledge comprises lines, ports, interaction cooperative flow and bearing service attributes.
Further: the data extraction and knowledge transformation are specifically as follows: through knowledge extraction, network element entities, relations and attribute knowledge elements can be extracted from the original data, and then a structured and networked knowledge system is finally obtained through knowledge processing, wherein the knowledge processing comprises the following steps: knowledge representation, knowledge reasoning, knowledge updating and quality assessment.
Further: the knowledge representation may be constructed manually by means of manual editing, or the ontology may be constructed in a data-driven automated manner comprising 3 phases: and calculating the similarity of the entity parallel relationship, extracting the upper and lower relationship of the entity and generating the ontology.
Further: the knowledge reasoning is to start from the existing entity network element relation data in the knowledge base, conduct computer reasoning, and establish new association between entity network elements, so as to expand and enrich the knowledge network elements.
Further: the knowledge reasoning method utilizes 3 major classes: ontology reasoning, rule-based reasoning and graph-based reasoning.
Further: the knowledge updating comprises updating of a concept layer and updating of a data layer, wherein the updating of the concept layer means that a new concept is obtained after Schema data is newly added, and the new concept needs to be automatically added into the concept layer of a knowledge base; the update of the data layer mainly comprises the steps of adding or updating entity, relationship and attribute value; updating the data layer needs to consider the reliability of the data sources and the consistency of the data, and select the fact and the attribute with high occurrence frequency in each data source to add into the knowledge base.
Further: the quality assessment can quantify the credibility of the knowledge, and the quality of the knowledge base is guaranteed by discarding the knowledge with lower credibility.
Further: the knowledge map base fusion disambiguation process is aimed at data of different layers of network fields in an ISO seven-layer network structure, and a body model is combined to research a data fusion algorithm of a plurality of situation awareness nodes, so that multi-node network situation map data fusion and complementation are realized, and the fusion is divided into pattern layer fusion and data layer fusion;
the fusion of the mode layer is oriented to schema and comprises concepts, upper and lower levels of the concepts and unification of attributes; the fusion of the data layers is to fuse the same entities of different data sources in different expression forms, and comprises merging of the entities, merging of entity attributes and relations and a TransE model related to entity similarity.
Further: the peer-to-peer system architecture adopted by the networking is composed of one or more network sensing nodes.
The foregoing inventive concepts and various further alternatives thereof may be freely combined to form multiple concepts, all of which are contemplated and claimed herein. Various combinations will be apparent to those skilled in the art from a review of the present disclosure, and are not intended to be exhaustive or all of the present disclosure.
The invention has the beneficial effects that: the invention provides a knowledge graph technology-oriented intelligent network situation awareness system for a 5G dynamic network, which solves the problem of the intelligent flexible dynamic network in the 5G communication era, controls the overall network situation awareness of the carrier network distribution of a transmission network, the interweaving relation of various sub-networks, the trend of network flow, network load, network paths, network element entity information and the like, and meets the urgent need of construction optimization, operation maintenance, network safety and network supervision.
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FIG. 1 is a schematic diagram of the components of the system of the present invention;
FIG. 2 is a schematic diagram of the operation of the system of the present invention;
FIG. 3 is a schematic diagram of the composition of an ontology model according to an embodiment;
FIG. 4 is a schematic diagram of a network situation awareness ontology model in an embodiment;
FIG. 5 is a schematic diagram of data knowledge extraction in an embodiment;
FIG. 6 is a basic flow diagram of a knowledge fusion technique in an embodiment;
fig. 7 is a schematic diagram of a networking model in an embodiment.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that, for the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments.
In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like indicate orientations or positional relationships in which the inventive product is conventionally placed in use, and are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements being referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal," "vertical," "overhang," and the like do not denote a requirement that the component be absolutely horizontal or overhang, but rather may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, in the present invention, if a specific structure, connection relationship, position relationship, power source relationship, etc. are not specifically written, the structure, connection relationship, position relationship, power source relationship, etc. related to the present invention can be known by those skilled in the art without any creative effort.
Example 1:
referring to fig. 1, the invention discloses a knowledge-graph-based technology-oriented intelligent network situation awareness system for a dynamic network. The system mainly comprises 3 layers, namely situation awareness nodes, a network situation awareness fusion center and network situation awareness application. And constructing a communication cable and a communication node knowledge graph by integrating data through a data networking, and simultaneously carrying out situation analysis and reasoning on rich graph data to finally obtain a network graph relation network and a situation information visualization result. And (3) carrying out data networking analysis, intelligently constructing knowledge maps of network information data resources transmitted, loaded, stored and exchanged on the IP internet, private networks, signaling networks and various deep network communication networks, realizing dynamic network situation awareness analysis, changing and identifying monitoring capability, and reasoning rich map data to finally obtain a network map relation network and situation information visualization result.
Component functions:
1. and the situation sensing node is used for extracting and finishing knowledge conversion processing based on the network sensing node data and finishing the local network situation sensing of the network situation sensing node to construct a local network situation knowledge map base.
2. The network situation awareness fusion center is used for carrying out knowledge map base fusion disambiguation treatment and reasoning analysis complement on the basis of the local network situation knowledge map base generated by each network sensing node of the networking to construct a whole network situation knowledge map base; the research and analysis completes the establishment of a standardized network situation knowledge graph body model; meanwhile, the system has the services of network situation knowledge graph data sharing, network situation change early warning analysis, network reasoning analysis and the like.
3. And the network situation awareness application is an application service system for providing visual configuration ontology knowledge creation, network map relation and network situation information display, network entity network element node information analysis, early warning and the like.
The working principle is shown in figure 2. The network situation awareness system needs a corresponding data platform as a support to ensure the systemization and the flow of data acquisition, processing and application. Unified data format and data standard, and further mining and complementing the database-entering data by using machine learning algorithms such as clustering, association, regression and the like, and providing a data basis for the map analysis service.
And the fusion service constructs a mapping file of the ontology and the data according to the pre-constructed ontology model and combines the source data, and stores the data into a graph database. Meanwhile, knowledge reasoning analysis service is provided, and implicit information can be obtained based on logic reasoning, rule reasoning and other technologies.
And the information visualization service is applied to realize the functions of human-computer interface interaction and related data display, each query data is displayed in a form of a newspaper, and analysis results (such as topology, association, path diagram and the like) are displayed in a visual view mode. The layer comprises interfaces such as situation change early warning, node information, ontology management, network map relation, network situation information and the like.
The network situation knowledge graph model is shown in fig. 3 and fig. 4. The network situation knowledge graph model management is constructed based on an ontology model, and the ontology construction can be considered by adopting creation software. Prot g e is a JRE-based cross-platform knowledge graph and ontology editing software.
The network situation knowledge graph model object entity network element knowledge comprises attributes such as link, network, session and other protocol parameters, flow, failure frequency, networking element association, associated region dynamics and the like.
The network situation knowledge graph model object entity network element interweaving relation knowledge comprises attributes such as lines, ports, interactive cooperative flow, bearing service and the like.
The data extraction and knowledge transformation are shown in fig. 5. Knowledge elements such as network element entities, relationships, attributes and the like can be extracted from the original data through knowledge extraction, and a knowledge processing process is required to finally obtain a structured and networked knowledge system. Knowledge processing mainly comprises 4 aspects: knowledge representation (ontology construction), knowledge reasoning, knowledge updating and quality assessment.
Knowledge representation, which can be constructed manually (by means of ontology editing software, such as protein) by means of manual editing, or in a data-driven automated manner, comprises 3 phases: and calculating the similarity of the entity parallel relationship, extracting the upper and lower relationship of the entity and generating the ontology.
The knowledge reasoning is to start from the existing entity network element relation data in the knowledge base, conduct computer reasoning, and establish new association between entity network elements, so as to expand and enrich the knowledge network elements. The reasoning method of knowledge utilizes 3 major classes: ontology reasoning, rule-based reasoning and graph-based reasoning.
The update of the knowledge base comprises the update of a concept layer and the update of a data layer from the logic view, wherein the update of the concept layer means that new concepts are obtained after Schema data are newly added, and the new concepts need to be automatically added into the concept layer of the knowledge base. The update of the data layer mainly comprises adding or updating entity, relationship and attribute values. Updating the data layer needs to consider reliable data sources such as reliability of the data sources, consistency of the data (whether contradiction or redundancy exists or not) and the like, and select facts and attributes with high occurrence frequency in each data source to add into a knowledge base. The update modes include full update and incremental update.
The quality assessment can quantify the credibility of the knowledge, and the quality of the knowledge base is guaranteed by discarding the knowledge with lower confidence.
The knowledge-graph fusion process is shown in fig. 6. The network element target and event disambiguation and fusion function supports the function of automatic disambiguation and fusion according to rules or extension programs; the knowledge online streaming application function performs streaming identification of targets and events according to knowledge by utilizing rules or extension operators and performs reporting and early warning;
aiming at the network field data of different layers in the ISO seven-layer network structure, the data fusion algorithm of a plurality of situation awareness nodes is researched by combining an ontology model, and the multi-node network situation map data fusion and complementation are realized. It is mainly divided into the fusion of the mode layer and the fusion of the data layer. The fusion of the mode layer is oriented to schema and mainly comprises concepts, upper and lower levels of the concepts and unification of attributes; the fusion of the data layers mainly comprises the steps of fusing the same entities of different data sources in different expression forms, including the combination of the entities, the combination of entity attributes and relations, a TransE model related to entity similarity and the like. The main technical difficulty is that the data quality is challenged, such as data input errors, loss, format inconsistencies, abbreviations, naming ambiguity and the like, which cause the problems that entities and bodies are difficult to align or match; with the expansion of data size, the problems of parallel computation, data variety diversity and the like are required to be increased.
Networking management technology
The service networking is that each network node network sensing node performs quasi-real-time dynamic network element data extraction and converts the network element data into network situation knowledge data, then an intelligent network situation center completes the basis of the network situation knowledge data fusion of each node, how to add a plurality of network sensing nodes with dispersed regions into the network situation awareness networking, and ensures the stable reliability of service operation and message receiving and transmitting under the condition of smooth network; while feedback or protection mechanisms in case of network failure need to be considered.
The wide area network has instability relative to a local area network due to the complexity of the wide area network, the problems of unstable network among centers and changeable online state of node service of each array are faced, and meanwhile, the dynamic and extensible requirements of the system are met, so that the dynamic change of the network map of the system becomes a normal state.
Aiming at the network sensor node networking requirements and the real environment, a peer-to-peer networking model is designed, and the model can timely discover system fault service nodes and dynamically expanded service nodes, intelligently reconstruct a network map structure, and has good adaptability to network faults, offline service, node expansion and other situations. The automatic joining and exiting capability of network sensing node service, the automatic electing capability of network sensing main nodes, the network state maintenance among all nodes, the anti-damage capability of fault self-recombination and the high-efficiency cross-domain retrieval are important points of the whole system design.
The network sensing node networking system adopts a peer-to-peer system architecture, and the network sensing node networking consists of one network sensing node or a plurality of network sensing nodes. Peer-to-peer means that the network sensing nodes are completely equal in position, and there is neither a control center nor a resource center, and they are integrally interconnected by a network.
The system networking model is shown in fig. 7, and in addition, the networking further includes registration management, fault recovery, state management, and cross-node data service, which are not further described.
The invention is based on knowledge graph technology, and the realization method of intelligent network situation awareness system of 5G dynamic network is oriented to easy cognition, sharable and knowledge layer service, which comprises network element knowledge, network element interleaving relation, network element interleaving parameter, network element flow fault change state, network structure change recognition and the like, and mainly has the following technical points:
1. the networking network sensing node supports dynamic expansion;
2. the network perception knowledge form can be customized, and supports network perception of IP Internet, private network, signaling network and various deep network communication networks;
3. the map knowledge meeting the standard of the map ontology model of the knowledge map of the same network situation can be shared.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (4)
1. The intelligent network situation awareness system based on the knowledge graph technology is oriented to a dynamic network and is characterized by comprising situation awareness nodes, a network situation awareness fusion center and a network situation awareness application;
the situation awareness node is used for extracting and completing knowledge conversion processing based on network sensor node data, and completing network situation awareness of network situation sensor node layout to construct a local network situation knowledge map base;
the network situation awareness fusion center performs knowledge graph library fusion disambiguation processing and reasoning analysis complement on the basis of the local network situation knowledge graph model generated by each network sensing node of the networking to construct a network situation knowledge graph model; the research and analysis completes the establishment of a standardized network situation knowledge graph body model; meanwhile, the network situation knowledge graph data sharing and network situation change early warning analysis are realized, the network reasoning analysis service is realized, and the situation awareness node networking management is carried out;
the network situation awareness application is used for providing visual configuration ontology knowledge creation, network map relation and network situation information display, and the network entity network element node information analysis and early warning application service system;
the network situation knowledge graph model is constructed based on an ontology model, and the network situation knowledge graph model object entity network element knowledge comprises link, network and session protocol parameters belonging to static properties, flow, failure frequency, networking element association and associated region dynamic properties; the network situation knowledge graph model object entity network element interweaving relation knowledge comprises lines, ports, interaction cooperative flow and bearing service attributes;
the data extraction and knowledge transformation are specifically as follows: through knowledge extraction, network element entities, relations and attribute knowledge elements can be extracted from the original data, and then a structured and networked knowledge system is finally obtained through knowledge processing, wherein the knowledge processing comprises the following steps: knowledge representation, knowledge reasoning, knowledge updating and quality assessment;
the knowledge representation may be constructed manually by means of manual editing, or the ontology may be constructed in a data-driven automated manner comprising 3 phases: calculating similarity of entity parallel relationship, extracting upper and lower relationship of entity and generating an ontology;
the knowledge reasoning is to start from the existing entity network element relation data in the knowledge base, conduct computer reasoning, establish new association between entity network elements, and expand and enrich knowledge network elements;
the knowledge updating comprises updating of a concept layer and updating of a data layer, wherein the updating of the concept layer means that a new concept is obtained after Schema data is newly added, and the new concept needs to be automatically added into the concept layer of a knowledge base; the update of the data layer mainly comprises the steps of adding or updating entity, relationship and attribute value; updating the data layer needs to consider the reliability of the data sources and the consistency of the data, and select the fact and the attribute with high occurrence frequency in each data source to be added into a knowledge base;
the quality assessment can quantify the credibility of the knowledge, and the quality of the knowledge base is guaranteed by discarding the knowledge with lower credibility.
2. The knowledge-graph-based technology-oriented dynamic network intelligent network situation awareness system according to claim 1, wherein the knowledge reasoning method utilizes 3 major classes: ontology reasoning, rule-based reasoning and graph-based reasoning.
3. The intelligent network situation awareness system based on the knowledge graph technology and oriented to the dynamic network according to claim 1 is characterized in that the knowledge graph base fusion disambiguation process is aimed at data of different layers of network fields in an ISO seven-layer network structure, and a body model is combined to research a data fusion algorithm of a plurality of situation awareness nodes so as to realize multi-node network situation graph data fusion and complementation, and the fusion is divided into pattern layer fusion and data layer fusion;
the fusion of the mode layer is oriented to schema and comprises concepts, upper and lower levels of the concepts and unification of attributes; the fusion of the data layers is to fuse the same entities of different data sources in different expression forms, and comprises merging of the entities, merging of entity attributes and relations and a TransE model related to entity similarity.
4. The intelligent network situation awareness system based on knowledge-graph technology, according to claim 1, wherein the peer-to-peer system architecture adopted by the networking is composed of one or more network sensing nodes.
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