CN115878713B - Rapid query method and platform for complex large-scale SDN network entity - Google Patents
Rapid query method and platform for complex large-scale SDN network entity Download PDFInfo
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Abstract
The application discloses a complex large-scale SDN network entity rapid query method and a platform, which are used for rapidly querying all network entities meeting specific conditions and a specific ID list thereof. The application constructs a rule knowledge graph based on expert experience of operation and maintenance personnel, rapidly inquires network entities meeting requirements based on the rule knowledge graph, and inquires a specific network entity ID list on a multi-source distributed inquiry engine based on inquiry rules in the knowledge graph. The scheme can be deployed on SDN platforms such as cloud networks and the like, and the query of network entities can be completed rapidly at a second level. Compared with the existing network entity query method, the method uses the rule knowledge graph to avoid storing massive entity IDs of the network entities, and can quickly search all the network entities meeting the conditions and the specific ID list thereof.
Description
Technical Field
The application relates to the field of intelligent operation and maintenance of networks, in particular to a network entity rapid query method and a platform capable of being deployed in a complex large-scale SDN network.
Background
Modern large-scale SDN network structures are increasingly complex, various network entities such as network elements, devices, clusters and the like are various, and the relationship among the network entities is complex. Taking a complex large-scale SDN as an example of a representative cloud network, there are a large number of heterogeneous entities and virtual network elements, including various software and hardware switches, routers, load balancers, controllers, and the like. The network elements and the devices with a large number are complex in logical connection relation, the regions are widely distributed in space, and meanwhile, the network traffic forwarding relation among the network elements is complex. In addition, the underlying physical devices and network entities such as more abstract tenants, available areas, clusters and the like are also associated together through complex relationships, including network connection relationships, inter-device management relationships, network bandwidth sharing relationships and the like.
In the operation and maintenance of complex large-scale SDN, operators have a need to quickly query other network entities associated with a particular network entity. For example, in a network change, the device performing the network change may not normally perform an alarm due to the change or the provided alarm information is not reliable, and it is required to determine which entities are associated with the network entity of the change at this time, and pay attention to whether the device associated with the entity is affected by the change. In addition, when the equipment fails and the performance alarms, operation and maintenance personnel also need to quickly determine the affected associated equipment, and further position the failure source to accelerate the repair of network failure. The existing network entity query method still depends strongly on experience and manpower of operation and maintenance personnel. Because of complex connection and interdependence between network entities, operators need to query numerous configuration files including configuration entries, user information and other different data sources in the SDN controller to associate the network entities possibly involved, which is a manpower-consuming and inefficient solution.
In summary, it is very difficult to design an operation tool in a large-scale SDN that can cope with complex network characteristics and quickly query network entities.
Disclosure of Invention
The application aims at overcoming the defects of the prior art, and designs a set of network entity rapid query method and platform which can be deployed in a complex large-scale SDN network, so that network operation and maintenance personnel can rapidly query all network entities related to one network entity, and based on query results, the network operation and maintenance personnel can rapidly analyze and locate network elements with network faults and can also be used for analyzing the influence surfaces of the network faults and changes.
The application aims at realizing the following technical scheme:
according to a first aspect of the present specification, there is provided a complex large-scale SDN network entity fast query method, the method comprising the steps of:
s1: extracting metadata of SDN network entities according to expert experience by operation and maintenance personnel and storing the metadata in a graph database as nodes, and extracting relations among the network entities according to expert experience and storing the relations in the graph database; nodes and relations in the graph database form a rule knowledge graph;
s2: based on the rule knowledge graph, inputting query conditions containing network entity names, and rapidly querying all associated nodes meeting the query conditions by a graph database;
s3: based on the metadata of the node obtained by the query in the step S2, a multi-source distributed query engine is used for querying a data source actually storing the data of the node, and specific information (namely a network entity ID list) of the network entity represented by the node is obtained.
Further, the network entities include, but are not limited to, software and hardware network devices, such as virtual switches, edge routers, load balancers, etc.; SDN network management examples, such as tenants, available areas, clusters and the like in a cloud network; physical infrastructure such as physical servers, cabinets, etc.
Further, only metadata of the network entity is stored in the rule knowledge graph, but no specific network entity information such as device ID is stored; metadata of the network entity includes, but is not limited to, unique identifier names of the network entity in the SDN, entity types, service attributes, primary key information, and query rules;
the landmark names are unique names of network entities, such as edge routers, seven-layer load balancing and the like, and the landmark names are not specific device IDs;
the entity type and the service attribute are determined according to the actual classification of the network entity in the SDN, for example, the entity type comprises the types of cloud network logic objects, virtual network instances, specific applications, network equipment and the like; the business attributes comprise specific products, product lines and the like in the cloud network;
the primary key information is information necessary for inquiring a specific individual of the network entity, such as cloud network availability information;
the query rule is a formatted query statement, contains data source information and the like, and can be an SQL query statement or an API call command containing complete parameter definition.
Further, in S1, the relationship between network entities is a directed edge between nodes in the graph data, and one relationship needs to include the following contents: relationship name (uniquely identified by the connected node); relationship types such as "composition/composed", "mount/carried", "controlled/controlled", and the like; a quantity attribute such as one-to-many, one-to-one, many-to-many, etc.; directional attributes such as out and in.
Further, in S1, any database supporting a graph data structure may be used to store the rule knowledge graph; any network entity that can abstract the necessary metadata and can establish a certain relationship with other network entities can be stored in the rule knowledge graph.
Further, in S2, the query condition information required to be input at least includes the following information of a node in the rule knowledge graph in S1: the network entity landmark names and the associated hops, such as (load balancer, 3), represent all network entities that the query and load balancer can reach on the rule knowledge graph in S1 at most with three hops; there may also be a network entity ID, a specific query primary key, etc.
Further, in S2, the plurality of node information supporting the rule knowledge graph in S1 is input, and the network entity represented by all the nodes on the shortest reachable path between the plurality of nodes is queried.
When the shortest path between the nodes is inquired, the shortest path between every two nodes is obtained by utilizing a shortest path searching algorithm, and the number of nodes on the path between source and destination nodes is reduced, so that the inquiry operation of a subsequent multi-source distributed inquiry engine on a database is reduced, and the response speed of the system is improved; specifically, the complex structure of the cloud network is highly abstract by utilizing the knowledge graph obtained by metadata abstraction, so that the scale of the knowledge graph can be controlled at a smaller scale although the scale of the cloud network is huge and the equipment instance reaches tens of millions; the conventional Dijkstra algorithm, the a algorithm and the like can be used for searching the shortest path on the selected path planning algorithm, the a algorithm is selected in the design, the weight of the edge between any two nodes is set to be 1 in the design, the weight between certain nodes can be increased according to the preference of searching, for example, the weight of the edge between the physical equipment and other nodes is set to be smaller under the condition of setting to be preferential to inquiring the physical equipment.
Further, in S2, the network entity list is visualized in the form of a knowledge graph, the graph nodes are network entities, and the graph edges are relationships between entities.
Further, in S3, query condition information input by the user is filled into a query rule in the metadata of the network entity, so as to generate a query sentence which can be effectively and actually executed. Specifically, the query rules of metadata can be divided into two types, one is an SQL class query and the other is a call to an API; for the former, an SQL parsing algorithm, such as a guide written in Java language, a KingStandard written in Go language and the like, is used for parsing the query rule to obtain an abstract syntax tree, and an actually executable SQL query statement is generated based on the abstract syntax tree and the filled primary key information; for the latter, in order to ensure the correct call to the API, a semantic recognition rule is set to carry out format verification on the filled primary key information, the specific semantic recognition rule is provided by an API designer, and the verification is directly filled into the query API in the metadata, so that an effective call command can be generated. For example, a "virtual machine" is input, a specific region is given, and in the specific query rule obtained by the query in S2, such as a predefined SQL statement, a query statement executable on the SQL database can be obtained.
Further, in S3, based on the obtained executable query statement, a multi-source distributed query engine (e.g., prest) is used to query in a database specifically storing the corresponding network entity data, so that the ID list of all the queried specific network entities can be further obtained; meanwhile, the fuzzy query function and the function of generating query sentences of adjacent nodes are designed. Specifically, according to the queried network entity ID list and the input query conditions, the query rule in the metadata of the adjacent nodes can be filled and the query is continuously executed, for example, after a certain region virtual machine list is queried, the query rule of the virtual switch node is filled according to the region and the available region information of the virtual machine, and all virtual switches connected with the virtual machine can be further queried and obtained.
Further, in S3, the queried network entity ID list may be displayed on the basis of the knowledge graph visualization in S2, and the network entity node may be selected, so that all ID lists and other relevant information corresponding to the entity may be displayed.
Further, queries are performed using a multi-source distributed query engine that supports execution of multiple types of query statements, which may access multiple data sources.
Further, according to the number of input nodes and connected edges of operation and maintenance personnel, the knowledge graph scale can be synchronously enlarged, the calculation power of a needed multi-source distributed query engine can be synchronously increased, and the problem of adding calculation resources by adopting simple elastic expansion can be solved.
Further, the results of the multi-source distributed query engine query are cached, more caching time is obtained for hot spot data in the query results, and data with lower query frequency are replaced, so that the query speed of the hot spot data is ensured by using a caching technology.
According to a second aspect of the present disclosure, there is provided a complex large-scale SDN network entity fast query platform that may be deployed on SDNs of various scales and structures, including but not limited to cloud networks, carrier networks, data center networks, enterprise local area networks, etc., the platform comprising the following modules:
rule knowledge graph module: the operation and maintenance personnel construct a rule knowledge graph by taking SDN network entity metadata as nodes and the relationship between network entities as the connection edges according to expert experience; the metadata stored by the nodes of the rule knowledge graph comprises unique marker names, entity types, service attributes, primary key information and query rules of network entities in the SDN; the continuous edges of the rule knowledge graph are the relation among the entities; the rule knowledge graph is stored in any database supporting graph data structure, the user inquires information, and the graph database returns all nodes of the composite inquiry condition and the relation among the nodes;
a multi-source query module: based on the query conditions input by the user and the query rules in the node metadata, generating a specific executable query statement or API call command and executing the specific executable query statement or API call command on a multi-source distributed query engine, querying all entities and related information meeting the requirement of a query main key in a database actually storing network entity information, and returning a query result;
and a visual query module: the visual query module is used for inputting query conditions by a user and visualizing query results in a knowledge graph mode; under the condition that a user inputs network entity IDs or inquires a primary key, firstly, network entity nodes and relations returned by a rule knowledge graph module are displayed in the form of a graph comprising nodes and connected edges, then, a network entity ID list inquired by a multi-source inquiry module is displayed below the graph, and the nodes in the selected graph can display ID lists of different network entities. If the user does not input the network entity ID or inquire the primary key, the network entity ID list is not displayed.
Compared with the prior art, the application has the following advantages:
firstly, through a rule knowledge graph, the method can finish the storage of complex network entities and complex relations between complex network entities in a large-scale SDN by using lower storage cost, thereby saving mass storage and calculation cost caused by direct storage and query of network entities;
secondly, through flexibly definable relationships among entities and simplified network entity metadata definition, a wide range of network entities can be abstracted and stored, so that a wide use scene is obtained;
thirdly, query computing power adapting to data volume and extending is obtained through metadata filling query conditions and based on design executed by the multi-source distributed query engine.
Fourth, the number of actual executed queries is reduced by the caching mechanism for the hot spot query data, and the process of extracting the required query results is accelerated.
Drawings
Fig. 1 is a flowchart of a complex large-scale SDN network entity fast query method provided by an embodiment of the present application;
fig. 2 is a block diagram of a complex large-scale SDN network entity fast query platform provided by an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the method for quickly querying an SDN network entity on a complex large scale according to the embodiment of the present application may be specifically implemented in three steps.
(1) The operation and maintenance personnel extract the metadata of the network entities according to the expert experience and store the metadata in the graph database as nodes, and extract the relations among the network entities according to the expert experience and store the relations into the graph database as continuous edges. Nodes and edges in the graph database form a rule knowledge graph.
Wherein the network entity includes, but is not limited to, software and hardware network devices such as virtual switches, edge routers, load balancers, etc.; SDN network management examples, such as tenants, available areas, clusters and the like in a cloud network; physical infrastructure such as physical servers, cabinets, etc. Relationships between network instances, including but not limited to composition, mounting, control relationships, may abstract various new network entities and relationships according to the actual deployment scenario.
The metadata of the network entity comprises, but is not limited to, unique identifier names of the network entity in the SDN, entity types, service attributes, primary key information and query rules; the landmark names are unique names of network entities, such as seven-layer load balancing; the entity type and the service attribute are determined according to the actual classification of the network entity in the SDN, such as a virtual network instance and a virtual network operation and maintenance product; the primary key information is information necessary for inquiring a specific individual of the network entity, such as cloud network availability information; the query rule is a formatted query statement, contains data source information and the like, and can be SQL or an API call command.
Wherein the relationship needs to include the following: relationship name (uniquely identified by the connected node); relationship types such as "composition/composed", "mount/carried", "controlled/controlled", and the like; a quantity attribute such as one-to-many, one-to-one, many-to-many, etc.; directional attributes such as out and in.
(2) Queries are performed in a graph database storing regular knowledge-graphs to obtain network entities and their relationships to each other.
The query condition information required to be input at least comprises the following information of one node in the rule knowledge graph in the step (1): the network entity landmark names and the associated hop numbers N, and the graph database returns the relationships between and among all network entities which are reachable by the appointed network entity N hops, for example (load balancer, 3) represents that all network entities which are reachable by the query and load balancer on the rule knowledge graph in the step (1) at most are reachable by three hops; there may also be a network entity ID, a specific query primary key, etc.
The query return result contains metadata represented by the rule knowledge graph nodes and relationship information represented by the continuous edges. The query results may be presented in any suitable manner, including but not limited to in the form of a graph, list.
Further, the method supports inputting the information of a plurality of nodes of the rule knowledge graph in the step (1), and queries network entities represented by all nodes on the shortest reachable path among the plurality of nodes. When the shortest path between the nodes is inquired, the shortest path between every two nodes is obtained by utilizing a shortest path searching algorithm, and the number of nodes on the path between source and destination nodes is reduced, so that the inquiry operation of a subsequent multi-source distributed inquiry engine on a database is reduced, and the response speed of the system is improved; specifically, the complex structure of the cloud network is highly abstract by utilizing the knowledge graph obtained by metadata abstraction, so that the scale of the knowledge graph can be controlled at a smaller scale although the scale of the cloud network is huge and the equipment instance reaches tens of millions; the conventional Dijkstra algorithm, the a algorithm and the like can be used for searching the shortest path on the selected path planning algorithm, the a algorithm is selected in the design, the weight of the edge between any two nodes is set to be 1 in the design, the weight between certain nodes can be increased according to the preference of searching, for example, the weight of the edge between the physical equipment and other nodes is set to be smaller under the condition of setting to be preferential to inquiring the physical equipment.
(3) The multi-source distributed query engine fills effective query sentences according to the query rules in the network entity metadata obtained in the step (2) and the input query conditions in the step (2). The generated effective query statement is executed, and a specific network entity ID and other stored information can be obtained from the corresponding data source query.
The network entity ID list returned in the step (3) may be displayed together with the result in the step (2) in a suitable manner, for example, the nodes and the edges obtained by the query are displayed in a graph form, and the selected nodes may obtain the network entity ID list.
Further, the query rules of metadata can be divided into two types, one is an SQL class query and the other is a call to an API; for the former, an SQL parsing algorithm, such as a guide written in Java language, a KingStandard written in Go language and the like, is used for parsing the query rule to obtain an abstract syntax tree, and an actually executable SQL query statement is generated based on the abstract syntax tree and the filled primary key information; for the latter, in order to ensure the correct call to the API, a semantic recognition rule is set to perform format verification on the filled primary key information, and if verification is correct, the primary key information is directly filled into the query API in the metadata, so that an effective call command can be generated.
Further, based on the obtained executable query statement, using a multi-source distributed query engine (such as prest) to query in a database specifically storing corresponding network entity data, an ID list of all specific queried network entities can be further obtained; meanwhile, the fuzzy query function and the function of generating query sentences of adjacent nodes are designed. And for the cloud network, using the available area as a condition to inquire an ID list of the network entity virtual switch in the available area, further filling inquiry rules of adjacent network entity nodes according to the virtual switch ID list, and generating inquiry sentences for the adjacent node network entity ID list.
Further, the results of the multi-source distributed query engine query are cached, more caching time is obtained for hot spot data in the query results, and data with lower query frequency are replaced, so that the query speed of the hot spot data is ensured by using a caching technology.
As shown in fig. 2, the complex large-scale SDN network entity fast query platform provided by the embodiment of the present application includes the following modules:
rule knowledge graph module: the operation and maintenance personnel construct a rule knowledge graph by taking SDN network entity metadata as nodes and the relationship between network entities as the connection edges according to expert experience; the metadata stored by the nodes of the rule knowledge graph comprises unique marker names, entity types, service attributes, primary key information and query rules of network entities in the SDN; the continuous edges of the rule knowledge graph are the relation among the entities; the rule knowledge graph is stored in any database supporting graph data structure, the user queries information, and the graph database returns all nodes and relations among nodes of the composite query condition.
A multi-source query module: based on the query conditions input by the user and the query rules in the node metadata, a specific executable query statement or API call command is generated and executed on the multi-source distributed query engine, all entities and related information meeting the requirement of the query main key are queried in the database actually storing the network entity information, and the query result is returned.
And a visual query module: the visual query module is used for inputting query conditions by a user and visualizing query results in a knowledge graph mode. Under the condition that a user inputs network entity IDs or inquires a primary key, firstly, network entity nodes and relations returned by a rule knowledge graph module are displayed in the form of a graph comprising nodes and connected edges, then, a network entity ID list inquired by a multi-source inquiry module is displayed below the graph, and the nodes in the selected graph can display ID lists of different network entities.
In addition, when the scale of the deployed SDN is enlarged or reduced, the scale of the graph data and the multi-source distributed query engine can be flexibly scaled so as to maintain the query speed and save resources.
The embodiment of the application realizes a prototype system on the cloud network platform and tests the effect of the method. But according to the different types and scales of SDN networks, the rule knowledge graph can be flexibly defined and a proper multi-source distributed query engine can be selected.
The foregoing is merely a preferred embodiment of the present application, and the present application has been disclosed in the above description of the preferred embodiment, but is not limited thereto. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present application or modifications to equivalent embodiments using the methods and technical contents disclosed above, without departing from the scope of the technical solution of the present application. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present application still fall within the scope of the technical solution of the present application.
Claims (6)
1. A complex large-scale SDN network entity rapid query method is characterized by comprising the following steps:
s1: extracting metadata of SDN network entities according to expert experience by operation and maintenance personnel and storing the metadata in a graph database as nodes, and extracting relations among the network entities according to expert experience and storing the relations in the graph database; nodes and relations in the graph database form a rule knowledge graph;
the metadata of the network entity comprises a unique mark name, entity type, service attribute, primary key information and query rule of the network entity in the SDN; the query rule is a formatted query statement, and is an SQL or API call command;
s2: based on the rule knowledge graph, inputting query conditions containing network entity names, and rapidly querying all associated nodes meeting the query conditions by a graph database;
the query condition information required to be input comprises the following information of one node in the rule knowledge graph in S1: network entity landmark names, associated hop counts, network entity IDs, and specific query primary keys;
supporting to input a plurality of node information of the rule knowledge graph in the S1, and inquiring network entities represented by all nodes on the shortest reachable path among the plurality of nodes; when the shortest path between the nodes is inquired, the shortest path between every two nodes is obtained by utilizing a shortest path searching algorithm, and the number of nodes on the path between the source node and the destination node is reduced, so that the inquiry operation of a subsequent multi-source distributed inquiry engine on the database is reduced; the shortest path searching algorithm adopts an A-type algorithm, and the weight of the edge between any two nodes is set to be 1, or the weight between certain nodes is increased according to the searching preference;
the query return result contains metadata represented by the rule knowledge graph nodes and relationship information represented by the continuous edges;
s3: based on the metadata of the node obtained by the query in the step S2, a multi-source distributed query engine is used for querying a data source actually storing the data of the node, and specific information of a network entity represented by the node is obtained, and the method comprises the following steps:
filling the query condition information input by the user into a query rule in the network entity metadata to generate a query statement which can be effectively and practically executed; the query rules of the metadata are divided into two types, namely SQL type query and API call;
for the first type, an SQL parsing algorithm is used for parsing the query rule to obtain an abstract syntax tree, and an actually executable SQL query statement is generated based on the abstract syntax tree and the filled primary key information;
for the second type, in order to ensure the correct call to the API, a semantic recognition rule is set to perform format verification on the filled primary key information, and if verification is correct, the primary key information is directly filled into the query API in the metadata, so that an effective call command can be generated.
2. The method of claim 1, wherein the network entity comprises a software and hardware network device, an SDN management instance, a physical infrastructure.
3. The method of claim 1, wherein the relationship comprises: relationship name, relationship type, quantity attribute, and direction attribute.
4. The method of claim 1, wherein in S3, inputting necessary information can query a specific network entity list, and simultaneously, a fuzzy query function and a function of generating a neighboring node query statement are designed; and for the cloud network, using the available area as a condition to inquire an ID list of the network entity virtual switch in the available area, filling inquiry rules of adjacent network entity nodes according to the virtual switch ID list, and generating inquiry sentences for the adjacent node network entity ID list.
5. The method of claim 1, wherein in S3, the query is executed using a multi-source distributed query engine supporting execution of multiple types of query statements, accessible to multiple data sources; the multi-source distributed query engine queries results are cached, hot spot data in the query results can obtain more caching time, and subsequent data with lower query frequency can be replaced, so that the query speed of the hot spot data is ensured by using a caching technology.
6. A complex large-scale SDN network entity fast query platform implemented by a method as claimed in any one of claims 1-5, comprising:
rule knowledge graph module: the operation and maintenance personnel construct a rule knowledge graph by taking SDN network entity metadata as nodes and the relationship between network entities as the connection edges according to expert experience; the metadata stored by the nodes of the rule knowledge graph comprises unique marker names, entity types, service attributes, primary key information and query rules of network entities in the SDN; the rule knowledge graph is stored in any database supporting graph data structure, the user inquires information, and the graph database returns all nodes meeting the inquiry condition and the relation among the nodes;
a multi-source query module: based on the query conditions input by the user and the query rules in the node metadata, generating a specific executable query statement or API call command and executing the specific executable query statement or API call command on a multi-source distributed query engine, querying all entities and related information meeting the requirement of a query main key in a database actually storing network entity information, and returning a query result;
and a visual query module: the visual query module is used for inputting query conditions by a user and visualizing query results in a knowledge graph mode; firstly, network entity nodes and relations returned by the rule knowledge graph module are displayed in the form of a graph containing nodes and edges, and then a network entity ID list obtained by inquiring the multi-source inquiry module is displayed.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109922075A (en) * | 2019-03-22 | 2019-06-21 | 中国南方电网有限责任公司 | Network security knowledge map construction method and apparatus, computer equipment |
| WO2020056984A1 (en) * | 2018-09-19 | 2020-03-26 | 平安科技（深圳）有限公司 | Shortest path query method, system, computer device and storage medium |
| CN113139002A (en) * | 2020-01-19 | 2021-07-20 | 上海静客网络科技有限公司 | Hot spot data caching method based on Redis |
| CN113507486A (en) * | 2021-09-06 | 2021-10-15 | 中国人民解放军国防科技大学 | Method and device for constructing knowledge graph of important infrastructure of internet |
| CN114153980A (en) * | 2020-09-07 | 2022-03-08 | 中兴通讯股份有限公司 | Knowledge graph construction method and device, inspection method and storage medium |
| CN114745286A (en) * | 2022-04-13 | 2022-07-12 | 电信科学技术第五研究所有限公司 | Intelligent network situation perception system facing dynamic network based on knowledge graph technology |
| CN114867052A (en) * | 2022-06-10 | 2022-08-05 | 中国电信股份有限公司 | Wireless network fault diagnosis method and device, electronic equipment and medium |
| CN115033657A (en) * | 2022-08-10 | 2022-09-09 | 广东美的暖通设备有限公司 | Inquiry method, device and equipment based on knowledge graph and storage medium |
| CN115203337A (en) * | 2022-05-10 | 2022-10-18 | 电子科技大学 | Database metadata relation knowledge graph generation method |
-
2022
- 2022-10-27 CN CN202211327659.9A patent/CN115878713B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020056984A1 (en) * | 2018-09-19 | 2020-03-26 | 平安科技（深圳）有限公司 | Shortest path query method, system, computer device and storage medium |
| CN109922075A (en) * | 2019-03-22 | 2019-06-21 | 中国南方电网有限责任公司 | Network security knowledge map construction method and apparatus, computer equipment |
| CN113139002A (en) * | 2020-01-19 | 2021-07-20 | 上海静客网络科技有限公司 | Hot spot data caching method based on Redis |
| CN114153980A (en) * | 2020-09-07 | 2022-03-08 | 中兴通讯股份有限公司 | Knowledge graph construction method and device, inspection method and storage medium |
| CN113507486A (en) * | 2021-09-06 | 2021-10-15 | 中国人民解放军国防科技大学 | Method and device for constructing knowledge graph of important infrastructure of internet |
| CN114745286A (en) * | 2022-04-13 | 2022-07-12 | 电信科学技术第五研究所有限公司 | Intelligent network situation perception system facing dynamic network based on knowledge graph technology |
| CN115203337A (en) * | 2022-05-10 | 2022-10-18 | 电子科技大学 | Database metadata relation knowledge graph generation method |
| CN114867052A (en) * | 2022-06-10 | 2022-08-05 | 中国电信股份有限公司 | Wireless network fault diagnosis method and device, electronic equipment and medium |
| CN115033657A (en) * | 2022-08-10 | 2022-09-09 | 广东美的暖通设备有限公司 | Inquiry method, device and equipment based on knowledge graph and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于知识图谱的IT运维辅助系统设计与实现;方苏东;《中国优秀硕士学位论文全文数据库信息科技辑》(第6期);全文 * |
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