CN115658424B - Monitoring methods, devices, equipment, media and program products based on knowledge graphs - Google Patents
Monitoring methods, devices, equipment, media and program products based on knowledge graphsInfo
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- CN115658424B CN115658424B CN202211187224.9A CN202211187224A CN115658424B CN 115658424 B CN115658424 B CN 115658424B CN 202211187224 A CN202211187224 A CN 202211187224A CN 115658424 B CN115658424 B CN 115658424B
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Abstract
The disclosure provides a monitoring method based on a knowledge graph, which can be applied to the technical field of automatic operation and maintenance. The method comprises the steps of responding to data change information of a data source, determining knowledge change operation of a target knowledge graph, responding to the knowledge change operation of the target knowledge graph, obtaining position information of changed knowledge in the target knowledge graph, changed knowledge type and changed type, generating an automatic treatment scheme aiming at the data change information according to the position information, the changed knowledge type and the changed type, wherein the automatic treatment scheme is used for carrying out follow-up update on the data change of the data source by a monitoring end, and sending the automatic treatment scheme to an automatic platform. The disclosure also provides a knowledge-graph-based monitoring device, equipment, a storage medium and a program product.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of automatic operation and maintenance, and especially relates to a monitoring method, device, equipment, medium and program product based on a knowledge graph.
Background
Along with the development of computer technology, information technology, cloud technology and other technologies, electronic systems are increasingly complex, correspondingly, various monitoring tools and monitoring methods also evolve, and monitoring types and monitoring indexes are continuously enriched. In order to ensure the reliability and the safety of the service, monitoring tools are required to be deployed and configured as soon as possible for various newly-added or changed resources, applications and the like in the system, and the continuous and stable operation of the service is ensured.
In the related art, deployment and configuration of the monitoring tool are manually performed, and automatic deployment configuration is realized by a small number of steps, but the triggering and the serial connection of the monitoring tool still need to be manually completed, and the changes of resources, applications and the like in the system cannot be rapidly reflected at a monitoring end, so that the monitoring tool at the monitoring end is delayed in deployment, the deployment efficiency is low, and even the normal operation of the service is influenced.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a knowledge-graph-based monitoring method, apparatus, device, medium, and program product that improve the efficiency of monitoring deployment.
According to a first aspect of the present disclosure, there is provided a knowledge graph-based monitoring method, including determining a knowledge variation operation of a target knowledge graph in response to data variation information of a data source, wherein the target knowledge graph is pre-constructed according to data of the data source, and the target knowledge graph is kept connected with the data source;
Responding to knowledge variation operation of a target knowledge graph, and acquiring position information of variation knowledge in the target knowledge graph, a varied knowledge type and a varied type;
Generating an automated treatment scheme for the data change information according to the position information, the changed knowledge type and the changed type, wherein the automated treatment scheme is used for carrying out follow-up update on the data change of a data source by a monitoring end, and
The automated treatment protocol is sent to an automated platform.
According to an embodiment of the present disclosure, pre-constructing a target knowledge-graph from data of the data source includes:
constructing an object to be monitored, a topological relation between the object to be monitored and related components thereof and a first knowledge graph of deployment position information of the object to be monitored according to data of a database of a system architecture;
Constructing a second knowledge graph of the monitoring tool, the packaging version and the installation deployment position information according to the data of the automation platform database;
constructing a third knowledge graph of the monitoring tool configuration file version and the corresponding configuration information according to the data of the application configuration database;
And carrying out knowledge fusion on the first knowledge graph, the second knowledge graph and the third knowledge graph to generate a target knowledge graph.
According to an embodiment of the disclosure, performing knowledge fusion on the first knowledge-graph, the second knowledge-graph, and the third knowledge-graph to generate a target knowledge-graph includes:
fusing deployment knowledge and configuration knowledge of a monitoring tool according to the corresponding relation between the packaging version and the configuration version of the monitoring tool;
and determining the monitoring relation between the monitoring tool and the object to be monitored according to the deployment position information of the monitoring tool and the object to be monitored so as to complete knowledge fusion of the monitoring tool and the object to be monitored.
According to an embodiment of the present disclosure, the generating an automated treatment regimen for the data change information from the location information, the changed knowledge type, and the change type comprises:
determining local knowledge structure information of the change knowledge according to the position information;
Scheduling an action scheme for the data change information based on the changed knowledge type and the changed type, and
The local knowledge structure information is populated into the action plan to generate an automated treatment plan for the data volatility information.
According to an embodiment of the present disclosure, the data change information includes a data source tag and a data change type tag, and the determining the knowledge change operation of the target knowledge graph in response to the data change information of the data source includes:
And determining a corresponding knowledge change operation in the target knowledge graph according to the data change type, wherein the data change type comprises addition, update and deletion.
According to an embodiment of the present disclosure, further comprising:
Carrying out knowledge reasoning on the target knowledge graph to determine knowledge loss information of the target knowledge graph;
and updating the target knowledge graph according to the knowledge deletion information.
According to an embodiment of the disclosure, the performing knowledge-based reasoning on the target knowledge-graph includes:
deducing a first topological relation among the types of the objects to be monitored according to the topological relation among the objects to be monitored;
reasoning a second topological relation between the type of the monitoring tool and the type of the object to be monitored according to the topological relation between the monitoring tool and the object to be monitored;
determining an inferred knowledge-graph schema from the first topological relationship and the second topological relationship, and
Determining knowledge loss information of the target knowledge graph according to the drawing of the target knowledge graph and the inferred knowledge graph drawing.
The second aspect of the disclosure provides a knowledge graph-based monitoring device, which comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining knowledge change operation of a target knowledge graph in response to data change information of a data source, the target knowledge graph is pre-constructed according to data of the data source, and the target knowledge graph is kept connected with the data source;
The acquisition module is used for responding to the knowledge variation operation of the target knowledge graph and acquiring the position information of variation knowledge in the target knowledge graph, the varied knowledge type and the varied type;
The generation module is used for generating an automatic treatment scheme aiming at the data change information according to the position information, the changed knowledge type and the changed type, wherein the automatic treatment scheme is used for carrying out follow-up update on the data change of a data source by a monitoring end;
and the sending module is used for sending the automatic treatment scheme to an automatic platform.
A third aspect of the present disclosure provides an electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described knowledge-graph-based monitoring method.
A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described knowledge-graph-based monitoring method.
A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described knowledge-graph-based monitoring method.
According to the knowledge graph-based monitoring method, knowledge change operation of a target knowledge graph is determined by monitoring data change information of a data source in real time, wherein the target knowledge graph is pre-constructed according to data of the data source, the target knowledge graph is connected with the data source, position information of change knowledge in the target knowledge graph, changed knowledge type and changed type are obtained, an automatic treatment scheme aiming at the data change information is generated according to the position information, the changed knowledge type and the changed type, and the automatic treatment scheme is sent to an automatic platform. Compared with the prior art, the monitoring method provided by the embodiment of the disclosure collects and identifies the state change information of the object to be monitored and the monitoring tool in real time based on the knowledge graph, and composes the follow-up response required by the monitoring end, and sends the follow-up response to the automation platform in real time, so that the response to the change of the object to be monitored in the system can be fast, the corresponding configuration change is made at the monitoring end, the follow-up efficiency of the monitoring tool is improved to a great extent, and the monitoring deployment efficiency is provided.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of a knowledge-graph-based monitoring method, apparatus, device, medium, and program product, in accordance with an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a knowledge-graph based monitoring method, in accordance with an embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a method of constructing a target knowledge-graph, in accordance with an embodiment of the disclosure;
Fig. 4a schematically shows a schematic structural diagram of a first knowledge-graph, according to an embodiment of the present disclosure;
fig. 4b schematically shows a schematic structural diagram of a second knowledge-graph, according to an embodiment of the present disclosure;
Fig. 4c schematically illustrates a structural schematic of a third knowledge-graph, according to an embodiment of the present disclosure;
FIG. 4d schematically illustrates a structural schematic of a target knowledge-graph, according to an embodiment of the present disclosure;
Fig. 5 schematically illustrates a flow chart of an automated treatment plan generation method provided in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flow chart for knowledge-based reasoning about a target knowledge-graph, provided in accordance with an embodiment of the present disclosure;
fig. 7 schematically shows a block diagram of a knowledge-graph-based monitoring device in accordance with an embodiment of the disclosure, and
Fig. 8 schematically illustrates a block diagram of an electronic device adapted to implement a knowledge-graph based monitoring method, in accordance with an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Based on the technical problems, the embodiment of the disclosure provides a knowledge graph-based monitoring method, which comprises the steps of responding to data change information of a data source, determining knowledge change operation of a target knowledge graph, wherein the target knowledge graph is pre-constructed according to data of the data source, the target knowledge graph is kept connected with the data source, responding to the knowledge change operation of the target knowledge graph, acquiring position information of change knowledge in the target knowledge graph, changed knowledge type and changed type, generating an automatic treatment scheme aiming at the data change information according to the position information, the changed knowledge type and the changed type, wherein the automatic treatment scheme is used for carrying out follow-up update on data change of the data source by a monitoring end, and sending the automatic treatment scheme to an automatic platform.
Fig. 1 schematically illustrates an application scenario diagram of a knowledge-graph-based monitoring method, apparatus, device, medium and program product according to an embodiment of the disclosure.
As shown in fig. 1, the application scenario 100 according to this embodiment may include an automatic operation and maintenance scenario. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server can collect and identify state change information of the object to be monitored and the monitoring tool in real time based on the knowledge graph, and arrange follow-up response required to be made by the monitoring end, and send the follow-up response to the automation platform to realize follow-up update of the monitoring end.
It should be noted that the knowledge-graph-based monitoring method provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the knowledge-graph-based monitoring device provided by the embodiments of the present disclosure may be generally disposed in the server 105. The knowledge-graph-based monitoring method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the knowledge-graph-based monitoring apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The knowledge-graph-based monitoring method of the disclosed embodiment will be described in detail below based on the scenario described in fig. 1 through fig. 2 to 6.
Fig. 2 schematically illustrates a flow chart of a knowledge-graph based monitoring method, in accordance with an embodiment of the disclosure.
As shown in fig. 2, the knowledge-graph-based monitoring method of the embodiment includes operations S210 to S240, and the knowledge-graph-based monitoring method may be executed by a server or other computing devices.
In operation S210, a knowledge-change operation of the target knowledge-graph is determined in response to the data-change information of the data source.
According to an embodiment of the disclosure, the target knowledge-graph is pre-constructed according to data of the data source, and the target knowledge-graph is kept connected with the data source.
According to an embodiment of the present disclosure, the data change information includes a data source tag and a data change type tag. And determining a corresponding knowledge change operation in the target knowledge graph according to the data change type, wherein the data change type comprises addition, update and deletion.
In one example, the objects to be monitored can be classified into two major categories, namely, resource and application, and the monitoring types can be specifically classified into hardware monitoring, system monitoring, database monitoring, application monitoring, network monitoring, log monitoring, security monitoring, service monitoring, performance monitoring, service monitoring and the like, and various monitoring indexes are included under each type. For various newly added or changed resources, applications and the like in the system, monitoring tools need to be deployed and configured as soon as possible. However, as different objects to be monitored correspond to different monitoring tools and components, the objects to be monitored are distributed and deployed in different servers, and configuration data is also stored in different types of databases.
In order to more intuitively understand the new or change of various resources and applications in the system, the embodiment of the disclosure constructs a target knowledge graph according to the data of each data source, and represents the relationship among the object to be monitored, the monitoring tool, the version information and the configuration content information through the knowledge graph. The knowledge change operation of the target knowledge graph is adapted to the data change of the data source connected with the knowledge change operation, namely, when certain data of the data source is updated, the knowledge change operation of the target knowledge graph is updated. The knowledge graph is stably connected with each data source, the data structures on two sides are butted, a polling mechanism or an active triggering mechanism for data source change is adopted, the data change condition is returned in real time, and when the data change information of the data source is identified, the knowledge graph performs corresponding data change operation on the data change.
In operation S220, in response to the knowledge modification operation of the target knowledge graph, position information of modified knowledge in the target knowledge graph, a modified knowledge type, and a modified type are acquired.
In one example, after determining knowledge variation of the target knowledge graph, in order to complete follow-up updating of the monitoring end as soon as possible, the type of knowledge, the variation type and the specific position in the knowledge graph of the variation need to be acquired, so as to acquire information related to the object to be monitored or the monitoring tool, including version information, configuration version and configuration content, and the like, which are varied, and prepare for a subsequent automated treatment scheme.
In operation S230, an automated treatment plan for the data fluctuation information is generated from the location information, the fluctuating knowledge type, and the fluctuating type.
In operation S240, the automated treatment protocol is sent to an automated platform.
According to an embodiment of the disclosure, the automated treatment scheme is used for the monitoring end to update with follow-up for data changes of the data source.
In one example, different treatment schemes can be preset according to different combination modes of the type of the change (such as adding, updating and deleting) and the type of the change knowledge (such as related to an object to be monitored, related to a monitoring tool version and related to configuration), the treatment scheme refers to an automatic treatment flow under a certain scene, different combination modes, namely, different scenes, correspond to different treatment schemes, a target treatment scheme is determined according to the changed knowledge type and the changed type, and preferably, the automatic treatment scheme aiming at the data change information can also be generated in real time according to the position information, the changed knowledge type and the changed type. The generation scheme of the automated treatment scheme can be specifically referred to operations S231 to S233 shown in fig. 5, and will not be described herein.
The automated treatment plan determined in operation S230 is sent to the automation platform and mapped to a series of actions in the automation platform like task flows including, but not limited to, issuing, installing, initializing, starting, stopping, configuration updating, etc., and the series of actions is performed to complete the follow-up update at the monitoring end.
According to the monitoring method based on the knowledge graph, the state change information of the object to be monitored and the monitoring tool is collected and identified in real time based on the knowledge graph, the follow-up response required by the monitoring end is arranged and sent to the automation platform in real time, the change of the object to be monitored in the system can be responded rapidly, corresponding configuration change is made at the monitoring end, the follow-up efficiency of the monitoring tool is improved to a large extent, and the monitoring deployment efficiency is provided.
The method for constructing the target knowledge graph in the embodiment of the disclosure will be described with reference to fig. 3 to fig. 4 d. Fig. 3 schematically illustrates a flowchart of a method of constructing a target knowledge-graph, in accordance with an embodiment of the disclosure. Fig. 4a schematically illustrates a structure diagram of a first knowledge-graph according to an embodiment of the present disclosure, fig. 4b schematically illustrates a structure diagram of a second knowledge-graph according to an embodiment of the present disclosure, fig. 4c schematically illustrates a structure diagram of a third knowledge-graph according to an embodiment of the present disclosure, fig. 4d schematically illustrates a structure diagram of a target knowledge-graph according to an embodiment of the present disclosure, and as illustrated in fig. 3, operations S310 to S340 are included.
In operation S310, a first knowledge graph of an object to be monitored, a topological relation between the object to be monitored and related components thereof, and deployment location information of the object to be monitored is constructed according to data of a database of a system architecture.
In one example, a knowledge graph of the topological relation and the deployment position information of various objects to be monitored and other related components such as resources, applications and the like in the system is constructed, wherein the related components can be middleware, service components and the like all components related to the objects to be monitored in a broad sense. The method comprises the steps of obtaining data related to a system architecture in an enterprise CMDB, focusing on resources and application type data in the data, designing a schema of a knowledge graph according to the extracted data of the types of an entity table, an attribute table, a relation table and the like, or using the knowledge graph dynamic schema, namely a global structure of the entity and the attribute and the relationship among the entities, to enable the input data to have a corresponding knowledge structure for storage, using an acquisition module of a data platform to acquire the corresponding data by abutting against the CMDB, performing data deletion, mapping and other finishing work, finally performing data input by using a data input interface or a direct writing library of the knowledge graph to generate a first knowledge graph, and forming a knowledge structure shown in fig. 4 a.
In operation S320, a second knowledge-graph of the monitoring tool, the packaged version, and the installation deployment location information is constructed from the data of the automation platform database.
In one example, the docking automation platform related database may include an enterprise CMDB, an automation platform project configuration library or table (e.g. Redis, nacos, ES), etc., and the related data is extracted, mainly including each packaged installation version of the monitoring tool and corresponding deployment location information thereof, relative location information of the installed configuration file, configuration version information, etc., mapped into triplet relationship and attribute data, and stored in a second knowledge graph, and the formed knowledge structure is shown in fig. 4 b.
In operation S330, a third knowledge graph of the monitoring tool configuration file version and the corresponding configuration information is constructed from the data of the application configuration database.
In one example, the docking application configuration related database may include an enterprise CMDB, a configuration library or table (e.g. Redis, nacos, ES) of each monitoring tool item, etc., and the related data is extracted, mainly including configuration versions corresponding to each packaged installation version of the monitoring tool and specific configuration content information thereof, etc., mapped into triplet relationship and attribute data, and stored in a knowledge graph, and the formed knowledge structure is shown in fig. 4 c.
In operation S340, knowledge fusion is performed on the first knowledge-graph, the second knowledge-graph, and the third knowledge-graph to generate a target knowledge-graph.
Operation S340 further includes operations S341 and S342 according to an embodiment of the present disclosure.
In operation S341, deployment knowledge and configuration knowledge of the monitoring tool are fused according to the correspondence between the packaged version and the configuration version of the monitoring tool.
In operation S342, a monitoring relationship between the monitoring tool and the object to be monitored is determined according to deployment location information of the monitoring tool and the object to be monitored, so as to complete knowledge fusion of the monitoring tool and the object to be monitored.
In one example, the first knowledge graph, the second knowledge graph and the third knowledge graph are subjected to knowledge fusion to generate a target knowledge graph, specifically, knowledge of a monitoring tool is fused, deployment and configuration knowledge of the monitoring tool are fused according to a corresponding relation between a packaging version and a configuration version of the monitoring tool, knowledge sources are required to be selected according to actual conditions, knowledge sources of the monitoring tool and knowledge of an object to be monitored are fused, and the monitoring relation of the monitoring tool to the object to be monitored is fused according to deployment position information (or deployment position information in configuration information) of the two knowledge of the object to be monitored. The knowledge structure of the target knowledge graph is different from that of fig. 4 d.
Fig. 5 schematically illustrates a flow chart of an automated treatment plan generation method provided in accordance with an embodiment of the present disclosure. As shown in fig. 5, operation S230 includes operations S231 to S233.
In operation S231, local knowledge structure information of the varying knowledge is determined according to the location information.
In operation S232, an operation scheme for the data fluctuation information is organized according to the knowledge type and the fluctuation type of the fluctuation.
In operation S233, the local knowledge structure information is filled into the action plan to generate an automated treatment plan for the data change information.
In one example, for knowledge variation, relevant information is combined, complete treatment actions are arranged and sent to an automation platform, and follow-up updating of a monitoring end is completed. Specifically, different treatment schemes are arranged according to different combinations of the type of the change (such as adding, updating and deleting) and the type of the knowledge of the change (such as related to the object to be monitored, related to the version of the monitoring tool and related to the configuration), for example, if the monitoring object is added, the monitoring tool of the same type of monitoring object needs to be configured, for example, if the monitoring tool is configured to be changed, the configuration file of the corresponding position needs to be changed. And acquiring local knowledge structure information of the position in a target knowledge graph obtained after knowledge fusion according to the knowledge change position information, for example, if the object to be monitored changes, acquiring knowledge structure information which has topological relation with the object, wherein the knowledge structure information comprises a monitoring tool, related components, deployment position information, monitoring tool version information, monitoring configuration information and the like. These knowledge information are filled into the action plan determined in operation S232, and a specific executable automated treatment plan is generated. The automated handling scheme is similar to the task flow workflow being automatically executed by an automated platform, and the method of the disclosed embodiments monitors deployment more efficiently and is more sensitive to variations in resources or applications in the system than manual configuration.
After knowledge fusion, further updating and perfecting the target knowledge graph can be performed by knowledge reasoning, and fig. 6 schematically shows a flowchart of knowledge reasoning on the target knowledge graph according to an embodiment of the disclosure.
As shown in fig. 6, operations S410 and S420 are included.
In operation S410, performing knowledge reasoning on the target knowledge graph to determine knowledge missing information of the target knowledge graph;
In operation S420, the target knowledge graph is updated according to the knowledge missing information.
According to the embodiment of the disclosure, a first topological relation among the types of the objects to be monitored is inferred according to the topological relation among the objects to be monitored, a second topological relation between the types of the monitoring tool and the types of the objects to be monitored is inferred according to the topological relation between the monitoring tool and the objects to be monitored, an inferred knowledge graph diagram is determined according to the first topological relation and the second topological relation, and knowledge missing information of a target knowledge graph is determined according to the diagram of the target knowledge graph and the inferred knowledge graph diagram.
In one example, in order to further refine the knowledge graph, knowledge reasoning needs to be performed on the target knowledge graph, and more complex topological relations are deduced according to the existing topological relations, including using the topological relation among specific objects to be monitored, reasoning the first topological relation among the types of the objects to be monitored, using the specific monitoring tool to monitor the topological relation of the specific objects to be monitored, reasoning the second topological relation of the types of the monitoring tool to the types of the objects to be monitored, which is expandable, and can also infer the monitoring relation of the packaged version to the objects to be monitored. Comparing the graphic schema of the reasoning knowledge graph determined according to the topological relations with the graphic schema of the existing target knowledge graph, determining knowledge missing information, marking the knowledge missing position, and sending prompt information to a user. Optionally, for the knowledge missing part, identifying the data source corresponding to the missing part, and the like, and prompting the supplementing work of the missing information so as to update and perfect the target knowledge graph.
Based on the knowledge graph-based monitoring method, the disclosure also provides a knowledge graph-based monitoring device. The device will be described in detail below in connection with fig. 7.
Fig. 7 schematically illustrates a block diagram of a knowledge-graph-based monitoring device, in accordance with an embodiment of the disclosure.
As shown in fig. 7, the knowledge-graph-based monitoring apparatus 700 of this embodiment includes a first determining module 710, an acquiring module 720, a generating module 730, and a transmitting module 740.
The first determining module 710 is configured to determine, in response to data change information of a data source, a knowledge change operation of a target knowledge graph, where the target knowledge graph is pre-constructed according to data of the data source, and the target knowledge graph is kept connected with the data source. In an embodiment, the first determining module 710 may be configured to perform the operation S210 described above, which is not described herein.
The obtaining module 720 is configured to obtain, in response to a knowledge modification operation of a target knowledge graph, location information of modified knowledge in the target knowledge graph, a modified knowledge type, and a modified type. In an embodiment, the obtaining module 720 may be configured to perform the operation S220 described above, which is not described herein.
The generating module 730 is configured to generate an automated treatment scheme for the data change information according to the location information, the knowledge type of the change, and the change type, where the automated treatment scheme is used for a monitoring end to update the data change of the data source. In an embodiment, the generating module 730 may be configured to perform the operation S230 described above, which is not described herein.
The sending module 740 is configured to send the automated treatment plan to an automated platform. In an embodiment, the sending module 730 may be configured to perform the operation S240 described above, which is not described herein.
According to an embodiment of the present disclosure, any of the first determining module 710, the acquiring module 720, the generating module 730, and the transmitting module 740 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. According to embodiments of the present disclosure, at least one of the first determination module 710, the acquisition module 720, the generation module 730, and the transmission module 740 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuitry, or in any one of or a suitable combination of any of the three. Or at least one of the first determining module 710, the obtaining module 720, the generating module 730 and the transmitting module 740 may be at least partially implemented as a computer program module, which may perform the corresponding functions when being run.
Fig. 8 schematically illustrates a block diagram of an electronic device adapted to implement a knowledge-graph based monitoring method, in accordance with an embodiment of the disclosure.
As shown in fig. 8, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. The processor 901 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 901 may also include on-board memory for caching purposes. Processor 901 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. The processor 901 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the program may be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.
According to an embodiment of the disclosure, the electronic device 900 may also include an input/output (I/O) interface 905, the input/output (I/O) interface 905 also being connected to the bus 904. The electronic device 900 may also include one or more of an input portion 906 including a keyboard, a mouse, etc., an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc., a storage portion 908 including a hard disk, etc., and a communication portion 909 including a network interface card such as a LAN card, a modem, etc., connected to the I/O interface 905. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.
The present disclosure also provides a computer-readable storage medium that may be included in the apparatus/device/system described in the above embodiments, or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 902 and/or RAM 903 and/or one or more memories other than ROM 902 and RAM 903 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, is configured to cause the computer system to implement the knowledge-graph based monitoring method provided by the embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, via communication portion 909, and/or installed from removable medium 911. The computer program may comprise program code that is transmitted using any appropriate network medium, including but not limited to wireless, wireline, etc., or any suitable combination of the preceding.
In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
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| CN113971236A (en) * | 2020-07-23 | 2022-01-25 | 北京金山数字娱乐科技有限公司 | Data monitoring method and device of knowledge graph |
| CN114706994A (en) * | 2022-03-21 | 2022-07-05 | 华迪计算机集团有限公司 | Operation and maintenance management system and method based on knowledge base |
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| CN111460167A (en) * | 2020-03-19 | 2020-07-28 | 平安国际智慧城市科技股份有限公司 | Method and related equipment for locating sewage objects based on knowledge graph |
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| CN113971236A (en) * | 2020-07-23 | 2022-01-25 | 北京金山数字娱乐科技有限公司 | Data monitoring method and device of knowledge graph |
| CN114706994A (en) * | 2022-03-21 | 2022-07-05 | 华迪计算机集团有限公司 | Operation and maintenance management system and method based on knowledge base |
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