A method and device for evaluating change impact based on knowledge graph

Abstract

The present application discloses a change impact assessment method and device based on a knowledge graph. When a change instruction sent by a user is received, the change instruction is parsed to obtain the change subject. From the graph database, a knowledge graph with the same operation and maintenance field as the operation and maintenance field involved in the change subject is selected as the target knowledge graph. From the target knowledge graph, search for entities with the same name as the name of the change subject. From the target knowledge graph, search for other entities that have a semantic association relationship with the entity. Count the number of other entities, obtain a target value, and determine whether the target value is greater than a preset threshold. If the target value is greater than the preset threshold, the user is prompted that the impact of this change is not within the controllable range. Using knowledge graphs to assist users in evaluating the impact of changes avoids incorrect evaluations due to lack of user experience. In addition, using knowledge graphs as the basis for evaluation is more scientific and objective, which improves the reliability of change impact assessments.

Description

Technical Field

[0001] The present application relates to the field of big data, and in particular to a method and device for evaluating the impact of changes based on knowledge graphs. Background Art

[0002] Ensuring the safe and stable operation of the system is the top priority of data center operation and maintenance. With the rapid development of the business, the relationship between application systems and the system architecture are becoming more and more complex. Changes and upgrades to application systems (specifically referring to changes related to the production environment of the data center, including but not limited to: hardware, network, environment and other infrastructure of equipment in the data center, operating systems, middleware, databases and other basic software and applications, scripts and other related status, configuration, version, etc.) are becoming more and more frequent. The risks brought about by changes are becoming more and more hidden, diverse and complex. In order to ensure the normal operation of the business, it is necessary to conduct an impact assessment of the changes to assist in quickly locating faults caused by the changes.

[0003] Currently, impact assessments for changes typically involve manually applying operational experience during the change application process, combining existing system architecture, software and hardware infrastructure relationships, and other operational information to create an impact analysis and corresponding contingency plans for potential impacts. Once the actual change is implemented and impacts occur, operational personnel use monitoring systems in various fields to review performance, status, and related alarms, and then conduct a change impact assessment based on their operational experience. However, operational experience varies from person to person, and due to differences in operational knowledge and experience, the quality of the impact of changes compiled through manual experience cannot be strictly guaranteed, thereby reducing the reliability of change impact assessments. Summary of the Invention

[0004] This application provides a knowledge graph-based change impact assessment method and device, aiming to improve the reliability of change impact assessment.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] A change impact assessment method based on knowledge graph, including:

[0007] When receiving a change instruction sent by a user, parsing the change instruction to obtain a change subject;

[0008] From various knowledge graphs shown in a preset graph database, a knowledge graph having the same operation and maintenance field as the operation and maintenance field involved in the change subject is selected as a target knowledge graph; wherein the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data;

[0009] Searching the target knowledge graph for an entity whose name is the same as the name of the change subject;

[0010] Searching for other entities that have a semantic association relationship with the entity in the target knowledge graph;

[0011] Counting the number of the other entities to obtain a target value;

[0012] Determining whether the target value is greater than a preset threshold;

[0013] If the target value is greater than the preset threshold, the user is prompted that the impact of the change is not within the controllable range;

[0014] When the target value is not greater than the preset threshold, the user is prompted that the impact of the change is within a controllable range.

[0015] Optionally, the knowledge graph is pre-built based on expert experience information and operation and maintenance data, including:

[0016] Acquire expert experience information in various operation and maintenance fields in advance, and abstract the expert experience information to obtain various classes, attributes of each class, and associations between the classes; the attributes are used to indicate the operation and maintenance field involved in the class;

[0017] Classifying each of the classes to obtain multiple class sets; wherein multiple classes related to the same operation and maintenance field are classified into the same class set;

[0018] For each of the class sets, constructing an ontology model corresponding to each of the class sets based on the classes in the class set, the attributes of the classes, and the associations between the classes;

[0019] For each of the ontology models, operation and maintenance data is captured from a preset data source, and the operation and maintenance data is connected to the ontology model to obtain a knowledge graph corresponding to each of the ontology models, and based on the attributes of the classes shown in each of the ontology models, the operation and maintenance fields involved in each of the knowledge graphs are identified.

[0020] Optionally, after searching the target knowledge graph for other entities that have a semantic association relationship with the entity and the type of the semantic association relationship is a preset type, the method further includes:

[0021] The attributes of the entity and the attributes of the other entities are displayed through a preset interface.

[0022] Optionally, searching the target knowledge graph for other entities that have a semantic association relationship with the entity includes:

[0023] From the target knowledge graph, search for other entities that have a semantic association relationship with the entity, and the type of the semantic association relationship is a preset type.

[0024] A change impact assessment device based on a knowledge graph, comprising:

[0025] a parsing unit, configured to parse a change instruction sent by a user to obtain a change subject upon receiving the change instruction;

[0026] a selection unit configured to select, from among various knowledge graphs shown in a preset graph database, a knowledge graph having the same operation and maintenance field as the operation and maintenance field involved in the change subject, as a target knowledge graph; wherein the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data;

[0027] A first search unit is configured to search the target knowledge graph for an entity whose name is the same as that of the change subject;

[0028] A second search unit is used to search other entities that have a semantic association relationship with the entity from the target knowledge graph;

[0029] A counting unit, configured to count the number of the other entities to obtain a target value;

[0030] a judging unit, configured to judge whether the target value is greater than a preset threshold;

[0031] A first prompting unit is configured to prompt the user that the impact of the change is not within the controllable range when the target value is greater than the preset threshold;

[0032] The second prompting unit is configured to prompt the user that the impact of the change is within a controllable range when the target value is not greater than the preset threshold.

[0033] Optionally, the selection unit is specifically configured to:

[0034] Acquire expert experience information in various operation and maintenance fields in advance, and abstract the expert experience information to obtain various classes, attributes of each class, and associations between the classes; the attributes are used to indicate the operation and maintenance field involved in the class;

[0035] Classifying each of the classes to obtain multiple class sets; wherein multiple classes related to the same operation and maintenance field are classified into the same class set;

[0036] For each of the class sets, constructing an ontology model corresponding to each of the class sets based on the classes in the class set, the attributes of the classes, and the associations between the classes;

[0037] For each of the ontology models, operation and maintenance data is captured from a preset data source, and the operation and maintenance data is connected to the ontology model to obtain a knowledge graph corresponding to each of the ontology models, and based on the attributes of the classes shown in each of the ontology models, the operation and maintenance fields involved in each of the knowledge graphs are identified.

[0038] Optionally, also include:

[0039] The display unit is used to display the attributes of the entity and the attributes of other entities through a preset interface.

[0040] Optionally, the second searching unit is specifically configured to:

[0041] From the target knowledge graph, search for other entities that have a semantic association relationship with the entity, and the type of the semantic association relationship is a preset type.

[0042] A computer-readable storage medium comprising a stored program, wherein the program executes the knowledge graph-based change impact assessment method.

[0043] A knowledge graph-based change impact assessment device includes: a processor, a memory, and a bus; the processor and the memory are connected via the bus;

[0044] The memory is used to store a program, and the processor is used to run the program, wherein the knowledge graph-based change impact assessment method is executed when the program is run.

[0045] The technical solution provided by the present application parses the change instruction and obtains the change subject when receiving the change instruction sent by the user. From the various knowledge graphs shown in the preset graph database, a knowledge graph with the same operation and maintenance field as the operation and maintenance field involved in the change subject is selected as the target knowledge graph, wherein the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data. From the target knowledge graph, search for entities with the same name as the name of the change subject. From the target knowledge graph, search for other entities that have a semantic association relationship with the entity. Count the number of other entities to obtain a target value, and determine whether the target value is greater than a preset threshold. When the target value is greater than the preset threshold, the user is prompted that the impact of this change is not within the controllable range. When the target value is not greater than the preset threshold, the user is prompted that the impact of this change is within the controllable range. Based on the solution shown in the present application, the knowledge graph is used to assist users in evaluating the impact of the change, avoiding incorrect evaluation due to insufficient user experience. In addition, using the knowledge graph as the evaluation basis is more scientific and objective, and can improve the reliability of the change impact evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic diagram of a knowledge graph-based change impact assessment method provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of an ontology model provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of another knowledge graph-based change impact assessment method provided in an embodiment of the present application;

[0050] Figure 4 A schematic diagram of the architecture of a knowledge graph-based change impact assessment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] like Figure 1 FIG. 1 is a schematic diagram of a method for evaluating the impact of a change based on a knowledge graph provided in an embodiment of the present application, comprising the following steps:

[0053] S101: Acquire expert experience information in various operation and maintenance fields in advance, and abstract the expert experience information to obtain various classes, attributes of various classes, and associations between various classes.

[0054] The types of operation and maintenance domains include business domain, physical deployment architecture, network domain, and computer room domain. Expert experience information includes, but is not limited to, knowledge points, concepts, and terminology. The attributes of a class are used to indicate the operation and maintenance domain to which the class relates.

[0055] It should be emphasized that the relationship between each class includes but is not limited to: depend on / support, run / run on, install / install on, deploy / deploy on, contain / belong to, connect / be connected to, backup relationship and other relationships.

[0056] Specifically,

[0057] Dependency / Support: Mainly refers to the dependency relationship between classes, that is, the relationship when the normal operation of a class's functions requires the normal operation of other classes' functions. For example, a business application depends on middleware, and vice versa, the middleware supports the business application.

[0058] Run / Run on: Mainly describes that a class runs on another class. For example, an application instance runs on a certain host, and vice versa, some application instances run on the host.

[0059] Installed / Installed on: refers to a class being installed on another class, for example, a database is installed on a host, and conversely some databases are installed on the host.

[0060] Deployment / Deployed on: Refers to the relationship between one type of deployment and installation on a rack, for example, entering the host deployment on a rack and vice versa, which hosts are deployed on the rack.

[0061] Contains / Belongs to: Describes that multiple classes form a single class and provide external services, and each class in the combination can independently provide external services. For example, multiple logical hosts belong to a cluster, and conversely, a cluster contains multiple logical hosts.

[0062] Connection / connected: refers to the physical connection between one class and another class, for example, a host is connected to a switch, and vice versa, a switch is connected to a host.

[0063] Backup relationship: Mainly describes the relationship when a class is a backup of another class, such as a server backing up another server through cold standby or device.

[0064] S102: Classify each class to obtain multiple class sets.

[0065] Among them, multiple classes involving the same operation and maintenance field are divided into the same class set.

[0066] S103: For each class set, construct an ontology model corresponding to each class set based on each class in the class set, the attributes of each class, and the association relationship between each class.

[0067] Among them, the ontology model is a semantic data model used to define the types of things in reality and describe the attributes of things.

[0068] It should be noted that the various ontology models shown in the embodiments of this application include business domain ontology model, physical deployment architecture ontology model, network domain ontology model, and computer room domain ontology model. Figure 2 shown.

[0069] S104: For each ontology model, operation and maintenance data is captured from a preset data source, and the operation and maintenance data is connected to the ontology model to obtain a knowledge graph corresponding to each ontology model.

[0070] The data source includes but is not limited to configuration data of the data center, log information, alarm information, etc. In the embodiment of the present application, the attributes of the entity shown in the knowledge graph include configuration attributes, data attributes and algorithm attributes.

[0071] The so-called configuration attributes are used to indicate the identity of the entity itself, such as entity classification, entity name, etc., and also include the attributes of the entity's architecture management and asset management, such as device brand, model, configuration, etc.

[0072] Data attributes are used to represent the storage addresses and pointers of entities shown in the knowledge graph as partial attributes of the entities. For example, the name of a host's time series database table, the index where performance logs are located, the index where alarm logs are located, and so on. Furthermore, as a lightweight logical storage for operational data, based on the data attributes of entities shown in the knowledge graph, the knowledge graph can act as a glue to bind all operational data together.

[0073] The so-called algorithmic attributes are the attributes generated by mining entities through preset algorithms, such as the transaction volume operating baseline of a business system, the operating mode of the transaction volume curve (such as clustering), the peak and trough periods of transaction volume, etc.

[0074] S105: Store each knowledge graph in a preset graph database, and identify the operation and maintenance field involved in each knowledge graph based on the attributes of the classes shown in each ontology model.

[0075] Among them, the various knowledge graphs shown in the embodiments of the present application include a business domain knowledge graph, a physical deployment architecture knowledge graph, a network domain knowledge graph, and a computer room domain knowledge graph.

[0076] S106: When receiving the change instruction sent by the user, the change instruction is parsed to obtain the change subject.

[0077] S107: From the various knowledge graphs shown in the graph database, select a knowledge graph whose operation and maintenance field is the same as the operation and maintenance field involved in the change subject as the target knowledge graph.

[0078] S108: Search the target knowledge graph for an entity whose name is the same as the name of the change subject.

[0079] S109: Searching for other entities in the target knowledge graph that have a semantic association relationship with the entity and whose semantic association relationship type is a preset type.

[0080] S110: Count the number of other entities to obtain a target value.

[0081] S111: Determine whether the target value is greater than a preset threshold.

[0082] If the target value is greater than the preset threshold, execute S112; otherwise, execute S113.

[0083] S112: Prompt the user that the impact of this change is beyond the controllable range.

[0084] After executing S112 , continue executing S114 .

[0085] S113: Prompt the user that the impact of this change is within a controllable range.

[0086] After executing S113 , continue to execute S114 .

[0087] S114: Displaying the attributes of the entity and the attributes of other entities through a preset interface.

[0088] Among them, the attributes of the entity and other entities are displayed through the preset interface, which can facilitate users to view the information affected by the change. Specifically, the configuration attributes are used as the entity's identification. The specific device that made the change is described by the configuration attributes, such as a Cisco switch with an IP address of 1.1.1.1. When analyzing the impact of the change, the performance logs and alarm logs of the entity are obtained through the data attributes. For example, after a server is changed, the performance log of the server is obtained through the data attributes, and it is found that the memory usage rate is high and the transaction success rate of the host is low. The transaction characteristics of the business system can be obtained through the algorithm attributes. For example, after a system is changed, the operating baseline of the transaction volume is compared and it is found that the transaction volume is lower than the baseline value, which may be affected by the change.

[0089] It should be noted that compared to existing technologies, based on the process shown in S101-S114 above, by utilizing the knowledge graph to combine the application system's architecture information, transaction paths, configuration relationships, monitoring information, alarms, logs, and other content, the originally fragmented types of operation and maintenance data are integrated with the application system as the core. Combined with expert experience rules to assist in deducing the impact of application changes, a standard, efficient, and reliable change impact assessment method can be implemented to help operation and maintenance personnel quickly identify change risks. At the same time, the experience of operation and maintenance personnel can be continuously accumulated through the knowledge graph, and knowledge can be replicated and promoted through the graph, thereby achieving the goal of improving the overall change assessment level.

[0090] To sum up, based on the solution shown in this embodiment, the knowledge graph is used to assist users in evaluating the impact of changes, avoiding incorrect evaluations due to insufficient user experience. In addition, using the knowledge graph as the basis for evaluation is more scientific and objective, and can improve the reliability of change impact assessments.

[0091] It should be noted that S101 mentioned in the above embodiment is an optional implementation of the change impact assessment method based on the knowledge graph shown in this application. In addition, S114 mentioned in the above embodiment is also an optional implementation of the change impact assessment method based on the knowledge graph shown in this application. To this end, the process shown in the above embodiment can be summarized as follows: Figure 3 The method shown.

[0092] like Figure 3 FIG. 1 is a schematic diagram of another method for assessing the impact of a change based on a knowledge graph provided in an embodiment of the present application, comprising the following steps:

[0093] S301: When a change instruction sent by a user is received, the change instruction is parsed to obtain a change subject.

[0094] S302: From the various knowledge graphs shown in the preset graph database, select a knowledge graph whose operation and maintenance field is the same as the operation and maintenance field involved in the change subject as the target knowledge graph.

[0095] Among them, the knowledge graph is pre-built based on expert experience information and operation and maintenance data.

[0096] S303: Search the target knowledge graph for an entity whose name is the same as the name of the change subject.

[0097] S304: Search the target knowledge graph for other entities that have semantic associations with the entity.

[0098] S305: Count the number of other entities to obtain the target value.

[0099] S306: Determine whether the target value is greater than a preset threshold.

[0100] If the target value is greater than the preset threshold, execute S307; otherwise, execute S308.

[0101] S307: Prompt the user that the impact of this change is beyond the controllable range.

[0102] S308: Prompt the user that the impact of this change is within a controllable range.

[0103] To sum up, based on the solution shown in this embodiment, the knowledge graph is used to assist users in evaluating the impact of changes, avoiding incorrect evaluations due to insufficient user experience. In addition, using the knowledge graph as the basis for evaluation is more scientific and objective, and can improve the reliability of change impact assessments.

[0104] Corresponding to the knowledge graph-based change impact assessment method provided in the above-mentioned embodiment of the present application, the embodiment of the present application also provides a knowledge graph-based change impact assessment device.

[0105] like Figure 4 FIG. 1 is a schematic diagram of the architecture of a knowledge graph-based change impact assessment device provided in an embodiment of the present application, including:

[0106] The parsing unit 100 is configured to parse the change instruction and obtain the change subject upon receiving the change instruction sent by the user.

[0107] The selection unit 200 is used to select a knowledge graph with the same operation and maintenance field as the operation and maintenance field involved in the change subject from various knowledge graphs shown in a preset graph database as a target knowledge graph; wherein the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data.

[0108] Among them, the selection unit 200 is specifically used to: pre-acquire expert experience information in various operation and maintenance fields, and abstractly process the expert experience information to obtain each class, the attributes of each class, and the relationship between each class; the attributes are used to indicate the operation and maintenance field involved in the class; classify each class to obtain multiple class sets; among them, multiple classes involving the same operation and maintenance field are divided into the same class set; for each class set, based on the classes in the class set, the attributes of each class, and the relationship between each class, construct an ontology model corresponding to each class set; for each ontology model, capture operation and maintenance data from a preset data source, and connect the operation and maintenance data to the ontology model to obtain a knowledge graph corresponding to each ontology model, and identify the operation and maintenance field involved in each knowledge graph based on the attributes of the class shown in each ontology model.

[0109] The first search unit 300 is used to search for an entity with the same name as the change subject from the target knowledge graph.

[0110] The second search unit 400 is used to search for other entities that have a semantic association relationship with the entity from the target knowledge graph.

[0111] Among them, the second search unit 400 is specifically used to: search from the target knowledge graph for other entities that have a semantic association relationship with the entity and the type of the semantic association relationship is a preset type.

[0112] The counting unit 500 is used to count the number of other entities to obtain a target value.

[0113] The judgment unit 600 is used to judge whether the target value is greater than a preset threshold.

[0114] The first prompting unit 700 is used to prompt the user that the impact of the change is not within the controllable range when the target value is greater than a preset threshold.

[0115] The second prompting unit 800 is used to prompt the user that the impact of the change is within a controllable range when the target value is not greater than a preset threshold.

[0116] The display unit 900 is used to display the attributes of the entity and the attributes of other entities through a preset interface.

[0117] To sum up, based on the solution shown in this embodiment, the knowledge graph is used to assist users in evaluating the impact of changes, avoiding incorrect evaluations due to insufficient user experience. In addition, using the knowledge graph as the basis for evaluation is more scientific and objective, and can improve the reliability of change impact assessments.

[0118] The present application also provides a computer-readable storage medium, which includes a stored program, wherein the program executes the change impact assessment method based on the knowledge graph provided by the present application.

[0119] This application also provides a knowledge graph-based change impact assessment device, comprising: a processor, a memory, and a bus. The processor and the memory are connected via the bus, the memory is used to store programs, and the processor is used to run the programs. When the programs are run, the change impact assessment method based on the knowledge graph provided in this application is executed, including the following steps:

[0120] When receiving a change instruction sent by a user, parsing the change instruction to obtain a change subject;

[0121] From various knowledge graphs shown in a preset graph database, a knowledge graph having the same operation and maintenance field as the operation and maintenance field involved in the change subject is selected as a target knowledge graph; wherein the knowledge graph is pre-constructed based on expert experience information and operation and maintenance data;

[0122] Searching the target knowledge graph for an entity whose name is the same as the name of the change subject;

[0123] Searching for other entities that have a semantic association relationship with the entity in the target knowledge graph;

[0124] Counting the number of the other entities to obtain a target value;

[0125] Determining whether the target value is greater than a preset threshold;

[0126] If the target value is greater than the preset threshold, the user is prompted that the impact of the change is not within the controllable range;

[0127] When the target value is not greater than the preset threshold, the user is prompted that the impact of the change is within a controllable range.

[0128] Optionally, the knowledge graph is pre-built based on expert experience information and operation and maintenance data, including:

[0129] Acquire expert experience information in various operation and maintenance fields in advance, and abstract the expert experience information to obtain various classes, attributes of each class, and associations between the classes; the attributes are used to indicate the operation and maintenance field involved in the class;

[0130] Classifying each of the classes to obtain multiple class sets; wherein multiple classes related to the same operation and maintenance field are classified into the same class set;

[0131] For each of the class sets, constructing an ontology model corresponding to each of the class sets based on the classes in the class set, the attributes of the classes, and the associations between the classes;

[0132] For each of the ontology models, operation and maintenance data is captured from a preset data source, and the operation and maintenance data is connected to the ontology model to obtain a knowledge graph corresponding to each of the ontology models, and based on the attributes of the classes shown in each of the ontology models, the operation and maintenance fields involved in each of the knowledge graphs are identified.

[0133] Optionally, after searching the target knowledge graph for other entities that have a semantic association relationship with the entity and the type of the semantic association relationship is a preset type, the method further includes:

[0134] The attributes of the entity and the attributes of the other entities are displayed through a preset interface.

[0135] Optionally, searching the target knowledge graph for other entities that have a semantic association relationship with the entity includes:

[0136] From the target knowledge graph, search for other entities that have a semantic association relationship with the entity, and the type of the semantic association relationship is a preset type.

[0137] If the functions described in the method of the embodiment of the present application are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a storage medium readable by a computing device. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0138] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0139] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A change impact assessment method based on knowledge graph, characterized in that: include: When receiving a change instruction sent by a user, parsing the change instruction to obtain a change subject; From the various knowledge graphs shown in the preset graph database, a knowledge graph with the same operation and maintenance field as the operation and maintenance field involved in the change subject is selected as the target knowledge graph; wherein the construction process of the knowledge graph is: obtaining expert experience information in various operation and maintenance fields in advance, and abstracting the expert experience information to obtain various classes, attributes of each class, and the association relationship between each class; the attributes are used to indicate the operation and maintenance field involved in the class; each class is classified to obtain multiple class sets; wherein multiple classes involving the same operation and maintenance field are divided into the same class set; for each class set, an ontology model corresponding to each class set is constructed based on the various classes, attributes of each class, and the association relationship between each class in the class set; for each ontology model, operation and maintenance data is captured from a preset data source, and the operation and maintenance data is docked into the ontology model to obtain a knowledge graph corresponding to each ontology model, and the operation and maintenance field involved in each knowledge graph is identified based on the attributes of the class shown in each ontology model; Searching the target knowledge graph for an entity whose name is the same as the name of the change subject; Searching for other entities that have a semantic association relationship with the entity in the target knowledge graph; Counting the number of the other entities to obtain a target value; Determining whether the target value is greater than a preset threshold; If the target value is greater than the preset threshold, the user is prompted that the impact of the change is not within the controllable range; When the target value is not greater than the preset threshold, the user is prompted that the impact of the change is within a controllable range.

2. The method according to claim 1, characterized in that After searching the target knowledge graph for other entities that have a semantic association relationship with the entity and the type of the semantic association relationship is a preset type, the method further includes: The attributes of the entity and the attributes of the other entities are displayed through a preset interface.

3. The method according to claim 1, characterized in that The step of searching the target knowledge graph for other entities that have a semantic association relationship with the entity includes: From the target knowledge graph, search for other entities that have a semantic association relationship with the entity, and the type of the semantic association relationship is a preset type.

4. A change impact assessment device based on knowledge graph, characterized in that: include: a parsing unit, configured to parse a change instruction sent by a user to obtain a change subject upon receiving the change instruction; A selection unit is used to select, from various knowledge graphs shown in a preset graph database, a knowledge graph with the same operation and maintenance field as the operation and maintenance field involved in the change subject as the target knowledge graph; wherein, the construction process of the knowledge graph is: obtaining expert experience information in various operation and maintenance fields in advance, and abstracting the expert experience information to obtain various classes, attributes of each class, and associations between each class; the attributes are used to indicate the operation and maintenance field involved in the class; classifying each class to obtain multiple class sets; wherein, multiple classes involving the same operation and maintenance field are divided into the same class set; for each class set, an ontology model corresponding to each class set is constructed based on the various classes, attributes of each class, and associations between each class in the class set; for each ontology model, operation and maintenance data is captured from a preset data source, and the operation and maintenance data is docked into the ontology model to obtain a knowledge graph corresponding to each ontology model, and the operation and maintenance field involved in each knowledge graph is identified based on the attributes of the class shown in each ontology model; A first search unit is configured to search the target knowledge graph for an entity whose name is the same as that of the change subject; A second search unit is used to search other entities that have a semantic association relationship with the entity from the target knowledge graph; A counting unit, configured to count the number of the other entities to obtain a target value; a judging unit, configured to judge whether the target value is greater than a preset threshold; A first prompting unit is configured to prompt the user that the impact of the change is not within the controllable range when the target value is greater than the preset threshold; The second prompting unit is configured to prompt the user that the impact of the change is within a controllable range when the target value is not greater than the preset threshold.

5. The device according to claim 4, characterized in that Also includes: The display unit is used to display the attributes of the entity and the attributes of other entities through a preset interface.

6. The device according to claim 4, characterized in that The second searching unit is specifically configured to: From the target knowledge graph, search for other entities that have a semantic association relationship with the entity, and the type of the semantic association relationship is a preset type.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein the program executes the change impact assessment method based on knowledge graph according to any one of claims 1-3.

8. A change impact assessment device based on knowledge graph, characterized in that: include: processor, memory, and bus; The processor is connected to the memory via the bus; The memory is used to store a program, and the processor is used to run the program, wherein the program executes the change impact assessment method based on the knowledge graph according to any one of claims 1 to 3 when running.

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