CN113792554A - Method and device for evaluating change influence based on knowledge graph - Google Patents
Method and device for evaluating change influence based on knowledge graph Download PDFInfo
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Abstract
The application discloses a change influence evaluation method and device based on a knowledge graph, which are used for analyzing a change instruction to obtain a change main body under the condition that the change instruction sent by a user is received. And selecting a knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change subject from the graph database as a target knowledge graph. From the target knowledge-graph, the entity with the same name as the name of the altering subject is searched. And searching other entities which have semantic association relation with the entities from the target knowledge graph. And counting the number of other entities to obtain a target value, and judging whether the target value is greater than a preset threshold value. And if the target value is larger than the preset threshold value, prompting the user that the influence of the change is not in a controllable range. The knowledge graph is used for assisting the user in evaluating the influence caused by the change, so that the error evaluation caused by insufficient experience of the user is avoided, and in addition, the knowledge graph is used as an evaluation basis, so that the method is scientific and objective, and the reliability of evaluation of the change influence is improved.
Description
Technical Field
The application relates to the field of big data, in particular to a change influence evaluation method and device based on a knowledge graph.
Background
The guarantee of safe and stable operation of the system is the first essential for operation and maintenance work of the data center, and with the rapid development of services, the association relationship of the application system and the architecture of the system become more and more complex, the update of the application system (particularly, related changes related to the production environment of the data center, including but not limited to the changes of the hardware, network, environment and other infrastructures, operating systems, middleware, databases and other basic software, applications, scripts and other related state, configuration, version and other changes of the equipment in the data center) becomes more and more frequent, the risks caused by the changes become more and more hidden, diversified and complicated, and in order to ensure the normal operation of the services, the changes need to be influenced and evaluated, thereby assisting in rapidly positioning faults caused by the changes.
At present, for the evaluation of the influence of change, usually, in a change application link, an influence analysis of change is compiled by combining manual operation and maintenance experience with operation and maintenance related information such as existing system architecture, infrastructure relationships such as software and hardware, and the like, and an emergency plan which may cause the influence is generated, when the change influence is caused by real change implementation, operation and maintenance personnel check performance, state and related alarms by means of monitoring systems in various fields, and perform change influence evaluation by combining the operation and maintenance experience. However, the manual operation and maintenance experience varies from person to person, and due to different operation and maintenance knowledge reserves and different operation and maintenance experiences of the operation and maintenance personnel, the quality of the change influence of manual experience combing cannot be strictly guaranteed, so that the reliability of the change influence evaluation is reduced.
Disclosure of Invention
The application provides a change influence evaluation method and device based on a knowledge graph, and aims to improve the reliability of change influence evaluation.
In order to achieve the above object, the present application provides the following technical solutions:
a change influence assessment method based on a knowledge graph comprises the following steps:
under the condition of receiving a change instruction sent by a user, analyzing the change instruction to obtain a change main body;
selecting a knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change main body from all knowledge graphs shown by a preset graph database as a target knowledge graph; the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data;
searching an entity with the same name as the name of the change subject from the target knowledge graph;
searching other entities which have semantic association relation with the entities from the target knowledge graph;
counting the number of the other entities to obtain a target value;
judging whether the target value is larger than a preset threshold value or not;
when the target value is larger than the preset threshold value, prompting the user that the influence of the change is not in a controllable range;
and prompting the user that the changing influence is within a controllable range when the target value is not larger than the preset threshold value.
Optionally, the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data, and includes:
acquiring expert experience information of various operation and maintenance fields in advance, and performing abstract processing on the expert experience information to obtain each class, the attribute of each class and the incidence relation among the classes; the attribute is used for indicating the operation and maintenance field related to the class;
classifying each class to obtain a plurality of class sets; dividing a plurality of classes related to the same operation and maintenance field into the same class set;
for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes;
and for each ontology model, capturing operation and maintenance data from a preset data source, butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
Optionally, after searching for another entity which has a semantic association relationship with the entity and the type of the semantic association relationship is a preset type from the target knowledge graph, the method further includes:
and displaying the attributes of the entity and the attributes of the other entities through a preset interface.
Optionally, the searching for another entity having a semantic association relationship with the entity from the target knowledge graph includes:
and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
A change influence evaluation apparatus based on a knowledge-graph, comprising:
the analysis unit is used for analyzing the change instruction to obtain a change main body under the condition of receiving the change instruction sent by a user;
the selection unit is used for selecting the knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change main body from all the knowledge graphs shown by the preset graph database as a target knowledge graph; the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data;
a first searching unit, configured to search, from the target knowledge graph, for an entity having a name that is the same as a name of the change subject;
the second searching unit is used for searching other entities which have semantic association relation with the entities from the target knowledge graph;
the counting unit is used for counting the number of the other entities to obtain a target value;
the judging unit is used for judging whether the target value is larger than a preset threshold value or not;
the first prompting unit is used for prompting that the influence of the change of the user is not in a controllable range under the condition that the target value is larger than the preset threshold value;
and the second prompting unit is used for prompting that the influence of the change of the user is within a controllable range when the target value is not larger than the preset threshold value.
Optionally, the selecting unit is specifically configured to:
acquiring expert experience information of various operation and maintenance fields in advance, and performing abstract processing on the expert experience information to obtain each class, the attribute of each class and the incidence relation among the classes; the attribute is used for indicating the operation and maintenance field related to the class;
classifying each class to obtain a plurality of class sets; dividing a plurality of classes related to the same operation and maintenance field into the same class set;
for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes;
and for each ontology model, capturing operation and maintenance data from a preset data source, butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
Optionally, the method further includes:
and the display unit is used for displaying the attributes of the entity and the attributes of the other entities through a preset interface.
Optionally, the second searching unit is specifically configured to:
and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
A computer-readable storage medium comprising a stored program, wherein the program performs the method for knowledge-graph based alteration impact assessment.
A change impact assessment apparatus based on a knowledge-graph, comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing a program, and the processor is used for executing the program, wherein the program executes the change influence evaluation method based on the knowledge-graph during the running process.
According to the technical scheme, when the change instruction sent by the user is received, the change instruction is analyzed to obtain the change main body. And selecting a knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change subject from all knowledge graphs shown by a preset graph database as a target knowledge graph, wherein the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data. From the target knowledge-graph, the entity with the same name as the name of the altering subject is searched. And searching other entities which have semantic association relation with the entities from the target knowledge graph. And counting the number of other entities to obtain a target value, and judging whether the target value is greater than a preset threshold value. And prompting the user that the influence of the change is not in a controllable range when the target value is larger than a preset threshold value. And prompting the user that the influence of the change is within a controllable range when the target value is not greater than the preset threshold value. Based on the scheme shown in the application, the knowledge graph is used for assisting the user in evaluating the influence caused by the change, so that the error evaluation caused by insufficient experience of the user is avoided, and in addition, the knowledge graph is used as an evaluation basis, so that the method is scientific and objective, and the reliability of evaluation of the change influence can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a change impact evaluation method based on a knowledge-graph according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an ontology model provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of another method for assessing change influence based on a knowledge-graph according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus for assessing change influence based on a knowledge graph according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, a schematic diagram of a change influence evaluation method based on a knowledge graph provided in an embodiment of the present application includes the following steps:
s101: the method comprises the steps of obtaining expert experience information of various operation and maintenance fields in advance, and carrying out abstract processing on the expert experience information to obtain various classes, attributes of the classes and incidence relations among the classes.
The types of the operation and maintenance field comprise a business field, a physical deployment architecture, a network field and a machine room field, and the expert experience information comprises but is not limited to knowledge points, concepts, terms and the like. The attributes of the classes are used to indicate the operation and maintenance domains to which the classes relate.
It is emphasized that the association between classes includes, but is not limited to: dependency/support, run/run, install/install, deploy/deploy, contain/belong to, connect/connected, backup relationships, and the like.
In particular, the method comprises the following steps of,
dependence/support: mainly refers to the dependency relationship between classes, that is, the relationship that the normal operation of the function of a class requires the normal operation of the function of other classes, for example, the service application depends on the middleware, otherwise, the middleware supports the service application.
Run/run at: it is mainly described that one class runs on top of another, e.g. an application instance runs on a host, whereas some application instance runs on a host.
Mounting/mounting to: the fingers are installed on another class, e.g., the database is installed on the host, whereas some databases are installed on the host.
Deployed/deployed at: the class shelf deployment installs another class of relationship, for example, logging the host shelf deployments on the cabinet, and vice versa which hosts are deployed on the cabinet.
Contains/belongs to: it is described that a plurality of classes constitute a single class and provide services to the outside, and each class for combination can independently provide services to the outside. For example. Multiple logical hosts belong to a cluster, whereas a cluster contains multiple logical hosts.
Connecting/connected: refers to a relationship where there is a physical connection between a class and another class, e.g., a host is connected to a switch, whereas a switch is connected by a host.
Backup relationship: the main description is the relation when a class is the backup of another class, for example, a server backs up another server by cold backup or device.
S102: and classifying each class to obtain a plurality of class sets.
A plurality of classes related to the same operation and maintenance field are divided into the same class set.
S103: and for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes.
The ontology model is a semantic data model used to define the type of an object in reality and describe the attributes of the object.
It should be noted that each ontology model shown in the embodiment of the present application includes a business domain ontology model, a physical deployment architecture ontology model, a network domain ontology model, and a machine room domain ontology model. Specifically, the machine room domain ontology model can be seen in fig. 2.
S104: and for each ontology model, capturing operation and maintenance data from a preset data source, and butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model.
The data source includes, but is not limited to, configuration data, log information, alarm information, etc. for the data center. In embodiments of the present application, the attributes of the entities shown in the knowledge-graph include configuration attributes, data attributes, and algorithm attributes.
So-called configuration attributes are used to indicate the identity of the entity, such as entity class, entity name, etc., and also include attributes of the entity's architectural management and asset management, such as device make, model, configuration, etc.
The data attribute is used for using a storage address and a pointer of an entity shown in the knowledge graph as a partial attribute of the entity, such as a time sequence database table name of a certain host, an index where a performance log is located, an index where an alarm log is located, and the like. In addition, as a lightweight logical store of operation and maintenance data, the knowledge-graph can be made to act as an adhesive to bond all of the operation and maintenance data together based on the data attributes of the entities shown in the knowledge-graph.
The so-called algorithm attribute is an attribute generated by mining the entity through a preset algorithm, such as a transaction amount operation baseline of a certain business system, an operation mode (e.g. clustering) of a transaction amount curve, a peak period and a valley period of the transaction amount, and the like.
S105: and storing each knowledge graph into a preset graph database, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
The knowledge maps shown in the embodiment of the application comprise a service field knowledge map, a physical deployment architecture knowledge map, a network field knowledge map and a machine room field knowledge map.
S106: and when a change instruction sent by a user is received, analyzing the change instruction to obtain a change main body.
S107: and selecting the knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change subject from all the knowledge graphs shown by the graph database as a target knowledge graph.
S108: from the target knowledge-graph, the entity with the same name as the name of the altering subject is searched.
S109: and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
S110: and counting the number of other entities to obtain a target value.
S111: and judging whether the target value is larger than a preset threshold value or not.
If the target value is greater than the preset threshold, executing S112, otherwise executing S113.
S112: and prompting the user that the influence of the change is not within the controllable range.
After execution of S112, execution continues with S114.
S113: the user is prompted that the impact of this change is within a controllable range.
After execution of S113, execution of S114 is continued.
S114: and displaying the attributes of the entity and the attributes of other entities through a preset interface.
The attributes of the entities and the attributes of other entities are displayed through the preset interface, and a user can conveniently check the information affected by the change. Specifically, the configuration attribute is used as an identifier of the entity, specifically, which device has changed, and this is described by the configuration attribute, for example, a cisco switch with an IP address of 1.1.1.1; when the influence of change is analyzed, a performance log, an alarm log and the like of an entity are obtained through data attributes, for example, after one server is changed, the performance log of the server is obtained through the data attributes, and the fact that the memory utilization rate is high, the transaction success rate of the host is low and the like is found; the transaction characteristics of the service system can be obtained through the algorithm attributes, for example, after a certain system is changed, the operation baseline of the transaction amount is compared, and the transaction amount is found to be lower than the baseline value, so that the change influence may exist.
Compared with the prior art, based on the process shown in S101-S114, by using the knowledge graph to combine the contents of the application system, such as architecture information, transaction path, configuration relationship, monitoring information, alarm, log, etc., the relationship integration is performed on the originally-fractured various operation and maintenance data with the application system as the core, and then the application change influence is deduced by combining with the assistance of the expert experience rule, so that a standard, efficient and reliable change influence evaluation method can be realized, and the operation and maintenance personnel can be helped to quickly identify the change risk. Meanwhile, the experience of operation and maintenance personnel can be continuously precipitated through the knowledge map, and knowledge is copied and popularized through the knowledge map, so that the purpose of integrally changing and improving the evaluation level is achieved.
In summary, based on the scheme shown in this embodiment, the knowledge graph is used to assist the user in evaluating the influence caused by the change, so as to avoid the occurrence of wrong evaluation due to insufficient experience of the user.
It should be noted that, in the above embodiment, the reference S101 is an alternative implementation manner of the change influence evaluation method based on the knowledge graph in the present application. In addition, S114 mentioned in the above embodiments is also an optional implementation of the change influence evaluation method based on the knowledge graph shown in this application. For this reason, the flow shown in the above embodiment can be summarized as the method shown in fig. 3.
As shown in fig. 3, a schematic diagram of another change influence evaluation method based on a knowledge graph provided in the embodiment of the present application includes the following steps:
s301: and when a change instruction sent by a user is received, analyzing the change instruction to obtain a change main body.
S302: and selecting the knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change main body from all the knowledge graphs shown by the preset graph database as a target knowledge graph.
The knowledge graph is constructed in advance based on expert experience information and operation and maintenance data.
S303: from the target knowledge-graph, the entity with the same name as the name of the altering subject is searched.
S304: and searching other entities which have semantic association relation with the entities from the target knowledge graph.
S305: and counting the number of other entities to obtain a target value.
S306: and judging whether the target value is larger than a preset threshold value or not.
If the target value is greater than the preset threshold, S307 is executed, otherwise S308 is executed.
S307: and prompting the user that the influence of the change is not within the controllable range.
S308: the user is prompted that the impact of this change is within a controllable range.
In summary, based on the scheme shown in this embodiment, the knowledge graph is used to assist the user in evaluating the influence caused by the change, so as to avoid the occurrence of wrong evaluation due to insufficient experience of the user.
Corresponding to the change influence evaluation method based on the knowledge graph provided by the embodiment of the application, the embodiment of the application also provides a change influence evaluation device based on the knowledge graph.
As shown in fig. 4, an architecture diagram of a change influence evaluation apparatus based on a knowledge graph provided in an embodiment of the present application includes:
the analysis unit 100 is configured to analyze the change instruction to obtain a change body when the change instruction transmitted by the user is received.
A selecting unit 200, configured to select, from the knowledge maps shown in the preset map database, a knowledge map having the same operation and maintenance field as the operation and maintenance field related to the change subject, as a target knowledge map; the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data.
Wherein, the selecting unit 200 is specifically configured to: acquiring expert experience information of various operation and maintenance fields in advance, and performing abstract processing on the expert experience information to obtain various classes, attributes of the classes and association relations among the classes; the attribute is used for indicating the operation and maintenance field related to the class; classifying each class to obtain a plurality of class sets; dividing a plurality of classes related to the same operation and maintenance field into the same class set; for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes; and for each ontology model, capturing operation and maintenance data from a preset data source, butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
A first searching unit 300, configured to search, from the target knowledge-graph, for an entity having a name that is the same as the name of the change subject.
And a second searching unit 400, configured to search, from the target knowledge graph, for other entities having semantic association with the entity.
The second searching unit 400 is specifically configured to: and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
The counting unit 500 is configured to count the number of other entities to obtain a target value.
A determining unit 600, configured to determine whether the target value is greater than a preset threshold.
The first prompting unit 700 is configured to prompt the user that the influence of the change is not within a controllable range when the target value is greater than a preset threshold.
And a second prompting unit 800, configured to prompt the user that the influence of the change is within a controllable range when the target value is not greater than the preset threshold.
The display unit 900 is configured to display the attribute of the entity and the attribute of the other entity through a preset interface.
In summary, based on the scheme shown in this embodiment, the knowledge graph is used to assist the user in evaluating the influence caused by the change, so as to avoid the occurrence of wrong evaluation due to insufficient experience of the user.
The present application also provides a computer-readable storage medium including a stored program, wherein the program performs the above-mentioned change influence evaluation method based on a knowledge-graph provided by the present application.
The present application further provides a change influence evaluation apparatus based on a knowledge graph, including: a processor, a memory, and a bus. The processor is connected with the memory through a bus, the memory is used for storing programs, and the processor is used for running the programs, wherein when the programs run, the change influence evaluation method based on the knowledge graph provided by the application is executed, and the change influence evaluation method based on the knowledge graph comprises the following steps:
under the condition of receiving a change instruction sent by a user, analyzing the change instruction to obtain a change main body;
selecting a knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change main body from all knowledge graphs shown by a preset graph database as a target knowledge graph; the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data;
searching an entity with the same name as the name of the change subject from the target knowledge graph;
searching other entities which have semantic association relation with the entities from the target knowledge graph;
counting the number of the other entities to obtain a target value;
judging whether the target value is larger than a preset threshold value or not;
when the target value is larger than the preset threshold value, prompting the user that the influence of the change is not in a controllable range;
and prompting the user that the changing influence is within a controllable range when the target value is not larger than the preset threshold value.
Optionally, the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data, and includes:
acquiring expert experience information of various operation and maintenance fields in advance, and performing abstract processing on the expert experience information to obtain each class, the attribute of each class and the incidence relation among the classes; the attribute is used for indicating the operation and maintenance field related to the class;
classifying each class to obtain a plurality of class sets; dividing a plurality of classes related to the same operation and maintenance field into the same class set;
for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes;
and for each ontology model, capturing operation and maintenance data from a preset data source, butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
Optionally, after searching for another entity which has a semantic association relationship with the entity and the type of the semantic association relationship is a preset type from the target knowledge graph, the method further includes:
and displaying the attributes of the entity and the attributes of the other entities through a preset interface.
Optionally, the searching for another entity having a semantic association relationship with the entity from the target knowledge graph includes:
and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A change influence evaluation method based on a knowledge graph is characterized by comprising the following steps:
under the condition of receiving a change instruction sent by a user, analyzing the change instruction to obtain a change main body;
selecting a knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change main body from all knowledge graphs shown by a preset graph database as a target knowledge graph; the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data;
searching an entity with the same name as the name of the change subject from the target knowledge graph;
searching other entities which have semantic association relation with the entities from the target knowledge graph;
counting the number of the other entities to obtain a target value;
judging whether the target value is larger than a preset threshold value or not;
when the target value is larger than the preset threshold value, prompting the user that the influence of the change is not in a controllable range;
and prompting the user that the changing influence is within a controllable range when the target value is not larger than the preset threshold value.
2. The method of claim 1, wherein the knowledge-graph is pre-constructed based on expert experience information and operation and maintenance data, and comprises:
acquiring expert experience information of various operation and maintenance fields in advance, and performing abstract processing on the expert experience information to obtain each class, the attribute of each class and the incidence relation among the classes; the attribute is used for indicating the operation and maintenance field related to the class;
classifying each class to obtain a plurality of class sets; dividing a plurality of classes related to the same operation and maintenance field into the same class set;
for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes;
and for each ontology model, capturing operation and maintenance data from a preset data source, butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
3. The method according to claim 1, wherein after searching for other entities from the target knowledge-graph, which have semantic association relationship with the entity and the type of the semantic association relationship is a preset type, the method further comprises:
and displaying the attributes of the entity and the attributes of the other entities through a preset interface.
4. The method of claim 1, wherein the searching for other entities having semantic associations with the entities from the target knowledge-graph comprises:
and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
5. A change influence evaluation device based on a knowledge graph, comprising:
the analysis unit is used for analyzing the change instruction to obtain a change main body under the condition of receiving the change instruction sent by a user;
the selection unit is used for selecting the knowledge graph with the same operation and maintenance field as the operation and maintenance field related to the change main body from all the knowledge graphs shown by the preset graph database as a target knowledge graph; the knowledge graph is constructed in advance based on expert experience information and operation and maintenance data;
a first searching unit, configured to search, from the target knowledge graph, for an entity having a name that is the same as a name of the change subject;
the second searching unit is used for searching other entities which have semantic association relation with the entities from the target knowledge graph;
the counting unit is used for counting the number of the other entities to obtain a target value;
the judging unit is used for judging whether the target value is larger than a preset threshold value or not;
the first prompting unit is used for prompting that the influence of the change of the user is not in a controllable range under the condition that the target value is larger than the preset threshold value;
and the second prompting unit is used for prompting that the influence of the change of the user is within a controllable range when the target value is not larger than the preset threshold value.
6. The apparatus according to claim 5, wherein the selecting unit is specifically configured to:
acquiring expert experience information of various operation and maintenance fields in advance, and performing abstract processing on the expert experience information to obtain each class, the attribute of each class and the incidence relation among the classes; the attribute is used for indicating the operation and maintenance field related to the class;
classifying each class to obtain a plurality of class sets; dividing a plurality of classes related to the same operation and maintenance field into the same class set;
for each class set, constructing an ontology model corresponding to each class set according to each class in the class set, the attribute of each class and the incidence relation among the classes;
and for each ontology model, capturing operation and maintenance data from a preset data source, butting the operation and maintenance data into the ontology models to obtain a knowledge graph corresponding to each ontology model, and identifying the operation and maintenance field related to each knowledge graph according to the attributes of the classes shown in each ontology model.
7. The apparatus of claim 5, further comprising:
and the display unit is used for displaying the attributes of the entity and the attributes of the other entities through a preset interface.
8. The apparatus according to claim 5, wherein the second lookup unit is specifically configured to:
and searching other entities which have semantic association relation with the entities and the type of the semantic association relation is a preset type from the target knowledge graph.
9. A computer-readable storage medium, comprising a stored program, wherein the program performs the method for knowledge-graph-based alteration impact assessment of any of claims 1-4.
10. A change influence evaluation apparatus based on a knowledge-graph, comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is configured to store a program and the processor is configured to execute the program, wherein the program executes the method for estimating influence of change based on a knowledge-graph of any one of claims 1 to 4.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113932817A (en) * | 2021-12-15 | 2022-01-14 | 思创数码科技股份有限公司 | Navigation mark offset evaluation method and system |
| CN114844786A (en) * | 2022-03-31 | 2022-08-02 | 广州大学 | Internet of things resource credibility assessment method based on heterogeneous information map |
| CN115081794A (en) * | 2022-04-21 | 2022-09-20 | 清华大学 | Method and system for evaluating influence range of cloud network change |
| WO2023207689A1 (en) * | 2022-04-27 | 2023-11-02 | 浙江大学 | Change risk assessment method and apparatus, and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113932817A (en) * | 2021-12-15 | 2022-01-14 | 思创数码科技股份有限公司 | Navigation mark offset evaluation method and system |
| CN114844786A (en) * | 2022-03-31 | 2022-08-02 | 广州大学 | Internet of things resource credibility assessment method based on heterogeneous information map |
| CN114844786B (en) * | 2022-03-31 | 2023-11-14 | 广州大学 | Internet of things resource credibility evaluation method based on heterogeneous information map |
| CN115081794A (en) * | 2022-04-21 | 2022-09-20 | 清华大学 | Method and system for evaluating influence range of cloud network change |
| CN115081794B (en) * | 2022-04-21 | 2023-08-29 | 清华大学 | Cloud network change influence range evaluation method and system |
| WO2023207689A1 (en) * | 2022-04-27 | 2023-11-02 | 浙江大学 | Change risk assessment method and apparatus, and storage medium |
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