CN115658424A

CN115658424A - Monitoring method, apparatus, device, medium and program product based on knowledge graph

Info

Publication number: CN115658424A
Application number: CN202211187224.9A
Authority: CN
Inventors: 李晓龙
Original assignee: CCB Finetech Co Ltd
Current assignee: CCB Finetech Co Ltd
Priority date: 2022-09-27
Filing date: 2022-09-27
Publication date: 2023-01-31

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 following steps: determining knowledge change operation of the target knowledge graph in response to data change information of the data source; responding to knowledge change operation of a target knowledge graph, and acquiring position information, changed knowledge types and changed types of changed knowledge in the target knowledge graph; generating an automatic handling scheme aiming at the data change information according to the position information, the changed knowledge type and the change type, wherein the automatic handling scheme is used for the monitoring end to carry out follow-up updating on the data change of a data source; sending the automated handling protocol to an automation platform. The present disclosure also provides a monitoring apparatus, a device, a storage medium, and a program product based on the knowledge-graph.

Description

Monitoring method, apparatus, device, medium and program product based on knowledge graph

Technical Field

The present disclosure relates to the field of artificial intelligence technologies, and in particular, to the field of automatic operation and maintenance technologies, and in particular, to a monitoring method, apparatus, device, medium, and program product based on a knowledge graph.

Background

With the development of computer technology, information technology, cloud technology and other technologies, electronic systems are increasingly complex, accordingly, various monitoring tools and monitoring methods are also evolved, and monitoring types and monitoring indexes are continuously abundant. In order to ensure the reliability and safety of the service, monitoring tools need to be deployed and configured as soon as possible for various newly added or changed resources, applications and the like in the system, so as to ensure the continuous and stable operation of the service.

In the related technology, the deployment and the configuration of the monitoring tool are manually performed, a small number of steps realize automatic deployment and configuration, but the triggering and the series connection of the monitoring tool still need to be manually completed, and the change of resources, application and the like in the system can not be quickly reflected at the monitoring end, so that the deployment of the monitoring tool at the monitoring end is delayed, the deployment efficiency is low, and even the normal operation of the service is influenced.

It is to be noted that the information disclosed in the above background section is only for enhancement of 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 method, apparatus, device, medium, and program product for knowledgegraph-based monitoring that improves monitoring deployment efficiency.

According to a first aspect of the present disclosure, there is provided a monitoring method based on a knowledge-graph, comprising: determining a knowledge change operation of a target knowledge graph in response to data change information of a data source, wherein the target knowledge graph is constructed in advance according to data of the data source, and the target knowledge graph is connected with the data source;

responding to knowledge change operation of a target knowledge graph, and acquiring position information, changed knowledge types and changed types of changed knowledge in the target knowledge graph;

generating an automatic handling scheme aiming at the data change information according to the position information, the changed knowledge type and the change type, wherein the automatic handling scheme is used for the monitoring end to carry out follow-up updating on the data change of a data source; and

sending the automated handling protocol to an automation 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 of the object to be monitored 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 and 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 performing 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 present disclosure, the knowledge-fusing the first, second, and third knowledge-graphs to generate a target knowledge-graph comprises:

according to the corresponding relation between the packaging version and the configuration version of the monitoring tool, merging the deployment knowledge and the configuration knowledge 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 the 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 plan for the data change information according to the location information, the changed knowledge type, and the change type includes:

determining local knowledge structure information of the variation knowledge according to the position information;

arranging an action scheme aiming at the data change information according to the changed knowledge type and the change type; and

populating the local knowledge structure information into the action plan to generate an automated treatment plan for the data variation 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 a knowledge change operation of the target knowledge-graph in response to the data change information of the data source includes:

and determining corresponding knowledge change operation in the target knowledge graph according to data change types, wherein the data change types comprise addition, update and deletion.

According to an embodiment of the present disclosure, further comprising:

carrying out knowledge reasoning on a target knowledge graph to determine missing knowledge information of the target knowledge graph;

and updating the target knowledge graph according to the missing knowledge information.

According to an embodiment of the present disclosure, the performing knowledge inference on the target knowledge-graph includes:

reasoning 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 a inferred knowledge graph schema according to the first topological relation and the second topological relation; and

determining missing knowledge information of the target knowledge graph according to the graph of the target knowledge graph and the inferred knowledge graph.

A second aspect of the present disclosure provides a monitoring device based on a knowledge-graph, comprising: the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for responding to data change information of a data source and determining knowledge change operation of a target knowledge graph, the target knowledge graph is constructed in advance according to data of the data source, and the target knowledge graph is connected with the data source;

the acquisition module is used for responding to the knowledge change operation of the target knowledge graph and acquiring the position information, the changed knowledge type and the changed type of the changed knowledge in the target knowledge graph;

a generating module, configured to generate an automatic handling scheme for the data change information according to the location information, the changed knowledge type, and the change type, where the automatic handling scheme is used for a monitoring end to perform follow-up update on data change of a data source;

and the sending module is used for sending the automatic handling scheme to an automatic platform.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; 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 method of knowledgegraph-based monitoring.

The 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 method for knowledgegraph-based monitoring.

The 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-mentioned method of knowledge-graph based monitoring.

By the monitoring method based on the knowledge graph, the knowledge change operation of the target knowledge graph is determined by monitoring the data change information of the data source in real time, wherein the target knowledge graph is constructed in advance according to the data of the data source and is connected with the data source; acquiring the position information, the changed knowledge type and the changed type of the changed knowledge in the target knowledge graph; generating an automated treatment plan for the data change information according to the location information, the changed knowledge type and the change type; sending the automated handling protocol to an automation 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, arranges the follow-up response required to be made by the monitoring end, sends the follow-up response to the automation platform in real time, can quickly respond to the change of the object to be monitored in the system, makes the corresponding configuration change at the monitoring end, improves the follow-up efficiency of the monitoring tool to a greater extent, and provides the monitoring deployment efficiency.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds 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 diagram of a knowledge-graph based monitoring method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart of a method of construction of a target knowledge-graph according to an embodiment of the present disclosure;

FIG. 4a schematically illustrates a structural schematic of a first knowledge-graph according to an embodiment of the present disclosure;

FIG. 4b schematically illustrates a structural schematic of a second knowledge-graph according to an embodiment of the present disclosure;

FIG. 4c schematically shows a structural schematic of a third knowledge-graph according to an embodiment of the present disclosure;

FIG. 4d schematically shows a structural schematic of a target knowledge-graph according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a flow diagram of an automated treatment protocol generation method provided in accordance with an embodiment of the present disclosure;

FIG. 6 schematically illustrates a flow diagram for providing knowledge reasoning about a target knowledge-graph, in accordance with an embodiment of the present disclosure;

FIG. 7 schematically illustrates a block diagram of a knowledge-graph based monitoring device according to an embodiment of the present disclosure; and

FIG. 8 schematically illustrates a block diagram of an electronic device suitable for implementing a knowledge-graph based monitoring method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that these descriptions are illustrative only and are 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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 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 to which this invention belongs, unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).

Based on the above technical problem, an embodiment of the present disclosure provides a monitoring method based on a knowledge graph, where the method includes: determining knowledge change operation of a target knowledge graph in response to data change information of a data source, wherein the target knowledge graph is constructed in advance according to data of the data source, and the target knowledge graph is connected with the data source; responding to knowledge change operation of a target knowledge graph, and acquiring position information, changed knowledge types and changed types of changed knowledge in the target knowledge graph; generating an automatic handling scheme aiming at the data change information according to the position information, the changed knowledge type and the change type, wherein the automatic handling scheme is used for the monitoring end to carry out follow-up updating on the data change of the data source; and sending the automated handling protocol to an automation 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 present 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 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server can collect and identify the state change information of the object to be monitored and the monitoring tool in real time based on the knowledge graph, arrange the follow-up response required to be made by the monitoring end and send the follow-up response to the automation platform, and therefore follow-up updating of the monitoring end is achieved.

It should be noted that the monitoring method based on knowledge graph provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the monitoring device based on knowledge graph provided by the embodiment of the present disclosure can be generally disposed in the server 105. The monitoring method based on knowledge graph provided by the embodiment of the present disclosure may also be performed by a server or a server cluster which is different from the server 105 and can communicate with the

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the monitoring apparatus based on knowledge graph provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and 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 monitoring method based on the knowledge-graph of the disclosed embodiment will be described in detail through fig. 2 to 6 based on the scenario described in fig. 1.

FIG. 2 schematically illustrates a flow diagram of a knowledge-graph based monitoring method according to an embodiment of the disclosure.

As shown in FIG. 2, the monitoring method based on knowledge-graph of the embodiment includes operations S210-S240, and the monitoring method based on knowledge-graph can be executed by a server or other computing device.

In operation S210, a knowledge change operation of the target knowledge-graph is determined in response to data change information of the data source.

According to an embodiment of the present disclosure, the target knowledge-graph is pre-constructed from data of the data source, and the target knowledge-graph remains connected to 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 corresponding knowledge change operation in the target knowledge graph according to data change types, wherein the data change types comprise addition, update and deletion.

In one example, the object to be monitored can be divided into two categories, namely, resource and application, and the monitoring types can be specifically divided 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 each type includes various monitoring indexes. Monitoring tools need to be deployed and configured as soon as possible for various newly added or changed resources, applications and the like in the system. However, different objects to be monitored correspond to different monitoring tools and components, and are deployed in different servers in a distributed manner, and configuration data is also stored in different types of databases.

In order to more intuitively know the addition or the 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, when the data of the data source changes, for example, a certain hardware is newly installed, a certain application or service is newly deployed, a certain application configuration is updated, a certain service is offline, and the like, the data of the object to be monitored is newly added, deleted and updated, and accordingly, the target knowledge graph changes. The knowledge change operation of the target knowledge graph is adaptive to the data change of the data source connected with the target knowledge graph, namely when certain data of the data source is updated, the knowledge change operation of the target knowledge graph is also updated. The knowledge graph is stably connected with each data source, data structures on two sides are butted, and a polling mechanism or an active triggering mechanism of data source change is adopted to return the data change condition in real time; when the data change information of the data source is identified, the new addition, the update and the deletion of the data are changed by the knowledge graph, and corresponding data change operation is carried out.

In operation S220, in response to a knowledge change operation of a target knowledge graph, location information of changed knowledge in the target knowledge graph, a changed knowledge type, and a changed type are acquired.

In one example, after determining that the knowledge of the target knowledge graph changes, in order to complete the follow-up update of the monitoring end as soon as possible, the type, the change type, and the specific position in the knowledge graph of the changed knowledge need to be obtained, so as to obtain information related to the changed object to be monitored or the monitoring tool, including version information, configuration version, configuration content, and the like, to prepare for a subsequent automated handling scheme.

In operation S230, an automated treatment plan for the data change information is generated according to the location information, the changed knowledge type, and the change type.

In operation S240, the automated handling protocol is sent to an automation platform.

According to the embodiment of the disclosure, the automatic treatment scheme is used for the monitoring end to perform follow-up updating on data change of the data source.

In one example, different treatment schemes may be pre-programmed for different combination manners of a changed type (such as addition, update, and deletion) and a changed knowledge type (such as correlation with an object to be monitored, correlation with a monitoring tool version, and correlation with configuration), where the treatment scheme pointer determines a target treatment scheme according to the changed knowledge type and the changed type for an automated processing flow in a certain scenario, where the different combination manners, i.e., the different scenarios, correspond to the different treatment schemes; preferably, an automated treatment plan for the data change information may be generated in real time based on the location information, the changed knowledge type, and the change type. The generation scheme of the automated handling scheme may specifically refer to operations S231 to S233 shown in fig. 5, which are not described herein again.

The automated handling scheme determined in operation S230 is sent to the automation platform, and is mapped to a series of actions similar to the task flow in the automation platform, including but not limited to issuing, installing, initializing, starting, stopping, updating the configuration, and the like, and the follow-up update of the monitoring end is completed by executing the series of actions.

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 to be made 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, the 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 improved.

The method for constructing the target knowledge-graph in the embodiment of the present disclosure will be described below with reference to fig. 3 to 4 d. FIG. 3 schematically shows a flow chart of a method of constructing a target knowledge-graph according to an embodiment of the present disclosure. FIG. 4a schematically illustrates a structural schematic of a first knowledge-graph according to an embodiment of the present disclosure; FIG. 4b schematically illustrates a structural schematic of a second knowledge-graph according to an embodiment of the present disclosure; FIG. 4c schematically shows a structural schematic of a third knowledge-graph according to an embodiment of the present disclosure; FIG. 4d schematically shows a structural schematic of a target knowledge-graph according to an embodiment of the present disclosure; as shown 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 its related components, 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 topological relations and deployment position information of various objects to be monitored, such as resources, applications and the like, and other related components in the system is constructed, wherein the related components can be middleware, service components and the like which are related to the objects to be monitored in a broad sense. Specifically, data related to a system architecture in the CMDB of the enterprise is obtained, and resources and application type data in the CMDB are focused; designing a schema of a knowledge graph according to the extracted data of types such as an entity table, an attribute table, a relation table and the like, or using a knowledge graph dynamic schema, namely a global structure of the entity, the attribute thereof and the interrelation between the entities, so that the input data has a corresponding knowledge structure for storage; and (3) a collection module of the data platform is used for carrying out corresponding data collection on the CMDB in a butt joint mode, finishing work such as data deletion, mapping and the like is carried out, and finally data entry is carried out by using a data entry interface of the knowledge graph or a direct writing base to generate a first knowledge graph, wherein a formed knowledge structure is shown in figure 4 a.

In operation S320, a second knowledge graph of monitoring tools, packaged versions, and installation deployment location information is constructed from the data of the automation platform database.

In one example, an automation platform-related database may be interfaced with, for example, an enterprise CMDB, an automation platform project configuration library or table (e.g., redis, nacos, ES), etc.; extracting related data, mainly mapping each packaged installation version of the monitoring tool and corresponding deployment position information, relative position information of the installed configuration files, configuration version information and the like into triple relation and attribute data, storing the triple relation and attribute data into a second knowledge graph, and forming a knowledge structure shown in fig. 4 b.

In operation S330, a third knowledge-graph of the monitoring tool configuration file version and corresponding configuration information is constructed according to 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) for each monitoring tool item, and the like; extracting related data, mainly mapping configuration versions corresponding to each packaging installation version of the monitoring tool and specific configuration content information thereof to triple relationship and attribute data, storing the triple relationship and attribute data in a knowledge graph, and forming a knowledge structure as shown in fig. 4 c.

In operation S340, the first, second, and third knowledge-graphs are subjected to knowledge fusion to generate a target knowledge-graph.

Operation S340 further includes operation S341 and operation S342, according to an embodiment of the present disclosure.

In operation S341, the deployment knowledge and the configuration knowledge of the monitoring tool are fused according to the corresponding relationship between the monitoring tool packaging version and the configuration version.

In operation S342, a monitoring relationship between a monitoring tool and an 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 between 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, monitoring tool knowledge is fused, deployment and configuration knowledge of a monitoring tool are fused according to the corresponding relation between a monitoring tool packaging version and a configuration version, and it is required to know that the knowledge may come from an automation platform or a monitoring tool configuration library and a knowledge source is selected according to actual conditions; and fusing the monitoring tool and the knowledge of the object to be monitored, and fusing the monitoring relation of the monitoring tool to the object to be monitored according to the correspondence of the deployment position information (or the deployment position information in the configuration information) of the monitoring tool and the object to be monitored. The structure of knowledge of the target knowledge-graph is as shown in figure 4 d.

Fig. 5 schematically shows a flowchart of an automated treatment plan generation method provided according to an embodiment of the present disclosure. As shown in fig. 5, operation S230 includes operations S231 through S233.

In operation S231, local knowledge structure information of the variation knowledge is determined according to the location information.

In operation S232, an action scheme for the data change information is arranged according to the changed knowledge type and the change type.

In operation S233, the local knowledge structure information is populated into the action plan to generate an automated treatment plan for the data variation information.

In one example, for a knowledge change place, relevant information is combined, a set of treatment actions is arranged and sent to an automation platform, and follow-up updating of a monitoring end is completed. Specifically, different disposal schemes are arranged according to different combination modes of the changed types (such as addition, update and deletion) and the changed knowledge types (such as correlation of objects to be monitored, correlation of versions of monitoring tools and correlation of configuration), for example, if a new monitored object is added, monitoring tools of the same type of monitored object need to be configured, for example, if the configuration of the monitoring tools changes, configuration files of corresponding positions need 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 having a topological relation with the object, wherein the knowledge structure information comprises a monitoring tool, a related component, deployment position information, monitoring tool version information, monitoring configuration information and the like. The knowledge information is filled into the action plan determined in operation S232, and a specific executable automated treatment plan is generated. The automatic handling scheme is similar to the task flow workflow and is automatically executed by an automatic platform, and compared with manual configuration, the method has the advantages that the monitoring and deployment efficiency is higher, and the method is more sensitive to the change of resources or applications in the system.

After the knowledge fusion, the target knowledge graph can be further updated and refined by knowledge inference, and fig. 6 schematically shows a flow chart of knowledge inference on the target knowledge graph provided according to an embodiment of the present disclosure.

As shown in fig. 6, operation S410 and operation S420 are included.

Performing intellectual inference on a target knowledge graph to determine missing knowledge information of the target knowledge graph in operation S410;

in operation S420, the target knowledge-graph is updated according to the missing knowledge information.

According to the embodiment of the disclosure, a first topological relation between types of objects to be monitored is inferred according to the topological relation between 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 a inferred knowledge graph schema according to the first topological relation and the second topological relation; determining missing knowledge information of the target knowledge graph according to the graph of the target knowledge graph and the inferred knowledge graph.

In one example, in order to further improve the knowledge graph, knowledge reasoning needs to be performed on the target knowledge graph, and more complex topology relations are deduced according to the existing topology relations, including using the topology relations among specific objects to be monitored, deducing a first topology relation among types of the objects to be monitored, using the topology relations of specific monitoring tools for monitoring the specific objects to be monitored, deducing a second topology relation of the types of the monitoring tools for the types of the objects to be monitored, which is extensible, and also can be used for deducing the monitoring relations of a packed version of the objects to be monitored. And comparing the graph schema of the inference knowledge graph determined according to the topological relations with the existing graph schema of the target knowledge graph, determining the knowledge missing information, marking the knowledge missing part, and sending prompt information to the user. Optionally, for the missing knowledge part, the data source corresponding to the missing part is identified, and the supplementation work of the missing information is prompted, so as to update and perfect the target knowledge graph.

Based on the monitoring method based on the knowledge graph, the disclosure also provides a monitoring device based on the knowledge graph. The apparatus will be described in detail below with reference to fig. 7.

FIG. 7 schematically shows a block diagram of a knowledge-graph based monitoring device according to an embodiment of the present disclosure.

As shown in fig. 7, the monitoring apparatus 700 based on knowledge-graph of this embodiment includes a first determining module 710, an obtaining module 720, a generating module 730, and a sending module 740.

The first determining module 710 is configured to determine a change-of-knowledge operation of a target knowledge-graph in response to data change 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 connected to 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 again.

The obtaining module 720 is configured to obtain, in response to the knowledge change operation of the target knowledge-graph, location information of the changed knowledge in the target knowledge-graph, a changed knowledge type, and a changed type. In an embodiment, the obtaining module 720 may be configured to perform the operation S220 described above, which is not described herein again.

The generating module 730 is configured to generate an automatic handling scheme for the data change information according to the location information, the changed knowledge type, and the change type, where the automatic handling scheme is used for the monitoring end to perform follow-up update for 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 again.

The sending module 740 is configured to send the automated handling plan to an automation platform. In an embodiment, the sending module 730 may be configured to perform the operation S240 described above, which is not described herein again.

According to an embodiment of the present disclosure, any plurality of the first determining module 710, the obtaining module 720, the generating module 730, and the sending module 740 may be combined into one module to be implemented, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first determining module 710, the obtaining module 720, the generating module 730, and the sending module 740 may be implemented at least partially as a hardware circuit, 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 manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the first determining module 710, the obtaining module 720, the generating module 730 and the sending module 740 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.

As shown in fig. 8, an electronic apparatus 900 according to an embodiment of the present disclosure includes a processor 901 which can perform various appropriate actions and processes in accordance with 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. Processor 901 can include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or related chipset(s) and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 901 may also include on-board memory for caching purposes. The processor 901 may comprise a single processing unit or a plurality of 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 apparatus 900 are stored. The processor 901, ROM 902, and RAM 903 are connected to each other by a bus 904. The processor 901 performs various operations of the method flows 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 also 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 flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 900 may also include input/output (I/O) interface 905, input/output (I/O) interface 905 also connected to bus 904, according to an embodiment of the present disclosure. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. 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 necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary so that a computer program read out therefrom is mounted into the storage section 908 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement a method according to an embodiment of the 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 present 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, a computer-readable storage medium may include the ROM 902 and/or the RAM 903 described above and/or one or more memories other than the ROM 902 and the RAM 903.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the monitoring method based on the knowledge graph provided by the embodiment of the disclosure.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 901. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted 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 in the form of a signal over a network medium, distributed, and downloaded and installed via the communication section 909 and/or installed from the removable medium 911. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The computer program, when executed by the processor 901, performs the above-described functions defined in the system of the embodiment of the present disclosure. The above described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).

The flowchart 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 various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments of the present disclosure and/or the claims may be made without departing from the spirit and teachings of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A monitoring method based on knowledge graph is characterized in that the monitoring method comprises the following steps:

determining a knowledge change operation of a target knowledge graph in response to data change information of a data source, wherein the target knowledge graph is constructed in advance according to data of the data source, and the target knowledge graph is connected with the data source;

sending the automated handling protocol to an automation platform.

2. The monitoring method of claim 1, wherein pre-constructing a target knowledge-graph from the data of the data source comprises:

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;

3. The monitoring method of claim 2, wherein the knowledge-fusing the first, second, and third knowledge-graphs to generate a target knowledge-graph comprises:

according to the corresponding relation between the monitoring tool packaging version and the configuration version, merging the deployment knowledge and the configuration knowledge of the monitoring tool;

4. The method of claim 1, wherein the generating an automated treatment plan for the data change information based on the location information, the changed knowledge type, and the change type comprises:

5. The monitoring method of claim 1, wherein the data change information includes data source tags and data change type tags, and wherein the operation of determining a change of knowledge of the target knowledge-graph in response to the data change information of the data source comprises:

6. The monitoring method according to claim 3, further comprising:

carrying out knowledge reasoning on a target knowledge graph to determine the missing knowledge information of the target knowledge graph;

and updating the target knowledge graph according to the knowledge missing information.

7. The monitoring method of claim 6, wherein the knowledgeable reasoning about the target knowledge-graph includes:

8. A monitoring device based on a knowledge-graph, comprising:

the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for responding to data change information of a data source and determining knowledge change operation of a target knowledge graph, the target knowledge graph is constructed in advance according to data of the data source, and the target knowledge graph is connected with the data source;

9. An electronic device, comprising:

one or more processors;

a storage device 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 monitoring method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out a monitoring method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program which, when executed by a processor, carries out the monitoring method according to any one of claims 1 to 7.