CN114978946A - Node fault diagnosis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides a node fault diagnosis method and device, electronic equipment and a storage medium, and relates to the technical field of knowledge graphs. The method comprises the following steps: acquiring a target node object to be diagnosed and relationship information; screening out a node object which accords with the relation information with the target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relation information; detecting whether the screened node object meets a preset fault condition; and marking and displaying the node objects meeting the preset fault conditions. According to the method and the device, the fault node can be rapidly checked out from the large-scale node data and marked and displayed, the operation and maintenance efficiency is improved, and the user experience is improved.
Description
Technical Field
The present disclosure relates to the field of knowledge graph technologies, and in particular, to a node fault diagnosis method and apparatus, an electronic device, and a storage medium.
Background
Data of operator resource nodes, service capability nodes, product nodes and the like are important components of a new generation of cloud network operation system. At present, the integration level of telecommunication Product Service Resources (PSR) is not high, the data display dimensionality is not sufficient, and data dispersion is caused. When faced with large-scale data, troubleshooting potentially faulty nodes is inefficient. This will hinder intensive research efforts on the service resources of telecommunication products. In order to promote the research of a new generation of cloud network operation system, the research work of integrating the service resources of various existing telecommunication products and accelerating the automatic troubleshooting of fault node data is urgently needed.
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
The present disclosure provides a node fault diagnosis method, apparatus, electronic device and storage medium, which at least to some extent overcome the technical problem in the related art that it is difficult to quickly troubleshoot and diagnose a faulty node in the face of large-scale node data.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a node fault diagnosis method, including: acquiring a target node object to be diagnosed and relationship information; screening out a node object which accords with the relation information with a target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relation information; detecting whether the screened node object meets a preset fault condition; and marking and displaying the node objects meeting the preset fault conditions.
In some embodiments, the method further comprises: obtaining a node type of the target node object, wherein the node type comprises: a source node and/or a destination node.
In some embodiments, the relationship information comprises: object relationships and object region relationships; according to a target node object to be diagnosed and relationship information, screening out a node object which accords with the relationship information with the target node object from a pre-constructed product service resource knowledge graph, wherein the method comprises the following steps: screening out a product service resource node object which accords with the object relation with a target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the object relation; and according to the target node object to be diagnosed and the object region relation, screening out the region node object which is in line with the target node object and the object region relation from a pre-constructed product service resource knowledge graph.
In some embodiments, the target node object is a node object that provides a product service or resource for an operator, and the object relationship includes at least: including, accommodating, carrying, relying on, dividing, continuing, perforating; the object region relationships include at least: in design, to be audited, in audit, on-line, off-line and off-line.
In some embodiments, before screening out a node object that matches the target node object with the relationship information from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relationship information, the method further includes: acquiring a product service resource object file, wherein the product service resource object file comprises attribute information of one or more product service resource objects; obtaining a product service resource object relation file, wherein the product service resource object relation file comprises the relation between product service resource objects; acquiring a region file, wherein the region file comprises attribute information of one or more regions; acquiring an object area relation file, wherein the object area relation file comprises the relation between each object and each area; and constructing a product service resource knowledge graph according to the product service resource object file, the product service resource object relation file, the region file and the object region relation file.
In some embodiments, constructing a product service resource knowledge graph from the product service resource object file, the product service resource object relationship file, the region file, and the object region relationship file comprises: and importing the product service resource object file, the product service resource object relation file, the area file and the object area relation file into a graph database to generate a product service resource knowledge graph.
In some embodiments, the graph database is a Neo4J database.
In some embodiments, the method further comprises: vue and D3.js are used for displaying the network topology information of the product service resource knowledge graph at the front end; the Spring Boot framework is used to enable the back-end to respond to requests from the front-end.
According to an aspect of the present disclosure, there is also provided a node fault diagnosis apparatus including: the data acquisition module is used for acquiring a target node object to be diagnosed and relationship information; the data screening module is used for screening out node objects which accord with the target node objects with the relationship information from a pre-constructed product service resource knowledge graph according to the target node objects to be diagnosed and the relationship information; the fault diagnosis module is used for detecting whether the screened node objects meet preset fault conditions or not; and the fault display module is used for marking and displaying the node objects meeting the preset fault conditions.
According to an aspect of the present disclosure, there is also provided an electronic device including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to perform any one of the above described node fault diagnosis methods via execution of executable instructions.
According to an aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the node fault diagnosis method of any one of the above.
According to the node fault diagnosis method, the node fault diagnosis device, the electronic equipment and the storage medium, the product service resource knowledge graph is constructed, the node objects which accord with the relation information with the target node objects can be screened out from the product service resource knowledge graph constructed in advance according to the target node objects to be diagnosed and the relation information, whether the screened node objects meet the preset fault conditions or not is detected, and finally the node objects which meet the preset fault conditions are marked and displayed. Through the embodiment of the disclosure, the fault node can be rapidly checked out from the large-scale node data and marked and displayed, so that the operation and maintenance efficiency is improved, and the user experience is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 is a flow chart illustrating a node fault diagnosis method in an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating node object screening in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating an object relationship selection page in an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating an object region relationship selection page in an embodiment of the present disclosure;
FIG. 5 illustrates a knowledge graph building flow diagram in an embodiment of the disclosure;
FIG. 6 is a flow chart illustrating a specific implementation of a node fault diagnosis method according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram illustrating a failed node display page in an embodiment of the present disclosure;
FIG. 8 is a schematic diagram illustrating a node fault diagnosis apparatus according to an embodiment of the disclosure;
FIG. 9 is a block diagram of an electronic device in an embodiment of the present disclosure;
FIG. 10 is a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
For convenience of understanding, before describing the embodiments of the present disclosure, several terms referred to in the embodiments of the present disclosure are first explained as follows:
knowledge graph: a data structure is composed of objects, relationships, and attributes.
PSR: product Service resource, Product Service resource.
Specific embodiments of the disclosed embodiments are described in detail below with reference to the accompanying drawings.
Firstly, the embodiment of the present disclosure provides a node fault diagnosis method to achieve the purpose of quickly finding out a fault node from large-scale node data and marking and displaying the fault node, where the method may be executed by any electronic device with computing processing capability.
In some embodiments, the node fault diagnosis method provided in the embodiments of the present disclosure may be performed by a terminal device; in other embodiments, the node fault diagnosis method provided in the embodiments of the present disclosure may be performed by a server; in other embodiments, the node fault diagnosis method provided in the embodiments of the present disclosure may be implemented by a terminal device and a server in an interactive manner.
The terminal device may be a variety of electronic devices including, but not limited to, a smartphone, a tablet, a laptop portable computer, a desktop computer, a wearable device, an augmented reality device, a virtual reality device, and the like.
Optionally, the clients of the applications installed in different terminal devices are the same, or clients of the same type of application based on different operating systems. The specific form of the application client may also be different based on different terminal platforms, for example, the application client may be a mobile phone client, a PC client, or the like.
The server may be a server that provides various services, such as a background management server that provides support for devices operated by the user with the terminal device. The background management server can analyze and process the received data such as the request and feed back the processing result to the terminal equipment.
Optionally, the server may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server that provides basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, middleware service, a domain name service, a security service, a CDN (Content Delivery Network), and a big data and artificial intelligence platform.
The medium providing the communication link between the terminal device and the server may be a wired network or a wireless network. Alternatively, the wireless network or wired network uses standard communication techniques and/or protocols. The Network is typically the Internet, but may be any Network including, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wireline or wireless Network, a private Network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including Hypertext Mark-up Language (HTML), Extensible markup Language (XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet protocol Security (IPsec). In other embodiments, custom and/or dedicated data communication techniques may also be used in place of, or in addition to, the data communication techniques described above.
Fig. 1 shows a flowchart of a node fault diagnosis method in an embodiment of the present disclosure, and as shown in fig. 1, the node fault diagnosis method provided in the embodiment of the present disclosure includes the following steps:
s102, target node objects to be diagnosed and relationship information are obtained.
It should be noted that the target node object to be diagnosed in S102 may be any node object, in some embodiments, may be a node object serving as a source node, in other embodiments, may be a node object serving as a destination node, and for more comprehensively analyzing the node object, the target node object may be simultaneously selected as the source node and the destination node to analyze all node objects associated when the node object serves as the source node and the destination node. The relationship information in S102 refers to information on the relationship between the target node object and other node objects.
It should be noted that the node object in the embodiment of the present disclosure may be, but is not limited to, a product service resource node and an area node, where the product service resource node corresponds to a provided product service resource; the area node corresponds to a preset area (which may be not limited to a geographical administrative division area).
Thus, in some embodiments, the method further comprises: obtaining a node type of the target node object, wherein the node type comprises: a source node and/or a destination node.
And S104, screening out the node object which accords with the relation information with the target node object from the pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relation information.
In the embodiment of the disclosure, in order to realize analysis of large-scale node data, a product service resource knowledge graph including a plurality of product service resource nodes and area nodes is constructed according to the large-scale node data, so that other node objects having a certain relation with a target node are quickly analyzed based on the product service resource knowledge graph.
And S106, detecting whether the screened node object meets a preset fault condition.
It should be noted that the preset fault condition in the embodiment of the present disclosure may be a condition that represents that node data is abnormal, and after node objects that conform to relationship information with a target node object are screened from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relationship information, whether data of the node objects satisfy the preset fault condition is detected one by one.
And S108, marking and displaying the node objects meeting the preset fault conditions.
In order to facilitate operation and maintenance personnel to quickly know a fault node, after node objects meeting preset fault conditions are detected, the node objects can be marked and displayed. In some embodiments, node objects that meet a preset fault condition may be highlighted.
As can be seen from the above, according to the node fault diagnosis method, the node fault diagnosis device, the electronic device, and the storage medium provided in the embodiments of the present disclosure, by constructing the product service resource knowledge graph, the node object that matches the target node object with the relationship information can be screened from the pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relationship information, and then whether the screened node object satisfies the preset fault condition is detected, and finally, the node object that satisfies the preset fault condition is displayed in a marked manner. Through the embodiment of the disclosure, the fault node can be rapidly checked out from the large-scale node data and marked and displayed, so that the operation and maintenance efficiency is improved, and the user experience is improved.
It should be noted that the relationship information may include, but is not limited to: object relationships and object region relationships; the object relation is used for representing the relation between the node objects, and the object area relation is used for representing the relation between the node objects and the area node objects.
In some embodiments, in a case that the relationship information includes an object relationship and an object region relationship, as shown in fig. 2, the node fault diagnosis method provided in the embodiments of the present disclosure may screen out a node object that conforms to the relationship information with a target node object by:
s202, according to a target node object to be diagnosed and an object relation, screening out a product service resource node object which accords with the object relation with the target node object from a pre-constructed product service resource knowledge graph;
and S204, according to the target node object to be diagnosed and the object region relation, screening out the region node object which is in accordance with the object region relation with the target node object from the pre-constructed product service resource knowledge graph.
In some embodiments, the target node object is a node object for providing product services or resources for an operator, and the object relationship in the embodiments of the present disclosure at least includes: including, accommodating, carrying, relying on, dividing, continuing, perforating; the object region relationship in the embodiments of the present disclosure at least includes: during design, to be checked, during checking, online, offline and disabled. FIG. 3 illustrates an object relationship selection page; fig. 4 shows an object region relationship selection page. As shown in FIG. 3, the object relationships support all-select, single-select, or multiple-select; the target node object may be selected as a source node, the target node may be selected as a destination node, or the target node may be selected as the source node and the target node. As shown in fig. 4, in the object region relationship selection page, the object region relationship supports full selection, single selection, or multiple selection.
In some embodiments, as shown in fig. 5, the node fault diagnosis method provided in the embodiments of the present disclosure may construct the product service resource knowledge graph by:
s502, acquiring a product service resource object file, wherein the product service resource object file comprises attribute information of one or more product service resource objects;
s504, obtaining a product service resource object relation file, wherein the product service resource object relation file comprises the relation between each product service resource object;
s506, acquiring a region file, wherein the region file comprises attribute information of one or more regions;
s508, obtaining an object area relation file, wherein the object area relation file comprises the relation between each object and each area;
s510, constructing a product service resource knowledge graph according to the product service resource object file, the product service resource object relation file, the area file and the object area relation file.
It should be noted that, the information stored in the product service resource object file in the embodiment of the present disclosure includes, but is not limited to: name, code, attribute information (JSON array structure) and the like of the product service resource object; the specific field information is shown in table 1.
TABLE 1
The information stored in the product service resource object relationship file in the embodiments of the present disclosure includes, but is not limited to: the method comprises the following steps that (1) a primary key ID of a source node, a name of the source node, a code of the source node, a primary key ID of a destination node, a name of the destination node, a code and an object relation name of the destination node and the like are obtained; the specific field information is shown in table 2.
TABLE 2
The information stored in the zone file in the embodiments of the present disclosure includes, but is not limited to: the name, code, etc. of the region; the specific field information is shown in table 3.
TABLE 3
| Field(s) | Explanation of the invention | Sample examples |
| id | Primary key id | 111 |
| regionName | Area name | Guangdong province |
| regionCode | Region coding | 1880000 |
The information stored in the zone file in the embodiments of the present disclosure includes, but is not limited to: the primary key ID of the product service resource object, the name of the product service resource object, the code of the product service resource object, the primary key ID of the area, the name of the area, the code of the area, the object area relationship and the like; the specific field information is shown in table 4.
TABLE 4
| Field(s) | Explanation of the invention | Sample examples |
| id | Primary key id | 1 |
| PSRId | PSR Primary Key id | 2 |
| PSRCode | PSR coding | cfsFixAccessNetLine |
| PSRName | PSR name | Fixed special line of networkCFS |
| regionName | Area name | Guangdong province |
| regionCode | Region coding | 1880000 |
| relationName | The state of the PSR object in the current region | Threading |
In some embodiments, constructing a product service resource knowledge graph from the product service resource object file, the product service resource object relationship file, the region file, and the object region relationship file comprises: and importing the product service resource object file, the product service resource object relation file, the area file and the object area relation file into a graph database to generate a product service resource knowledge graph.
In some embodiments, the graph database is a Neo4J database.
In some embodiments, the node fault diagnosis method provided in the embodiments of the present disclosure further includes the following steps: vue and D3.js are used for displaying the network topology information of the product service resource knowledge graph at the front end; the Spring Boot framework is used to enable the back-end to respond to requests from the front-end.
After data in the product service resource object file, the product service resource object relation file, the region file and the object region relation file are imported into a Neo4J database, the front end displays network topology data by combining technologies such as Vue, D3.js and the like, and the rear end responds to a front end request by using a Spring Boot framework.
When a user selects a certain node object, the page displays all PSR object nodes which take the node as a source node and/or a destination node and conform to the selected relation type and area nodes of which each node conforms to the selected object-area relation, then, according to a corresponding object node data logic specification table defined in advance, a program automatically checks whether the data conforms to the specification, the specific field information is shown in a table 5, if not, the corresponding PSR object node is set to be highlighted in red for further checking and verification by the user.
TABLE 5
| Field(s) | Explanation of the invention | Sample examples |
| id | Primary key id | 1111 |
| PSRId | PSR Primary Key id | 2 |
| PSRCode | PSR coding | cfsFixAccessNetLine |
| PSRName | PSR name | Fixed special line CFS that passes through network |
| isTgt | Whether target node identification | TRUE |
| conformanceSpecification | Object-region relationship conformance specification | "on-line" and "audit"] |
Fig. 6 shows a specific implementation flow of the node fault diagnosis method in the embodiment of the present disclosure, and as shown in fig. 6, the method includes:
s602, constructing a product service resource data set;
s604, constructing a product service resource object file according to the product service resource information;
s606, constructing a product service resource object relation file;
s608, constructing a region file according to the region information;
s610, constructing an object region relation file;
s612, constructing a node data logic specification for detecting whether the node object meets a preset fault condition;
s614, importing the new 4J database;
s616, selecting a target node object to be diagnosed;
s618, detecting whether other node data associated with the target node object meets the preset data logic specification (if not, meets the preset fault condition) (i.e., the node data meets the node data logic specification table); if yes, go to S620; if not, S622 is executed.
And S620, normally displaying the node object.
And S622, highlighting the fault node object.
As can be seen from the above, the node fault diagnosis method provided in the embodiment of the present disclosure fuses product service resources of an operator and local network topology data, and constructs a product service resource knowledge graph with the help of the NEO4J database, so that the work of automatically troubleshooting a faulty node in large-scale data can be realized, which is beneficial to analysis and mining of important data information by a user, improves user experience, and can provide help for construction and further optimization of a new generation cloud network operation system.
Suppose that when a user selects an A node object, and selects the node type as a source node, the object relationship selects dependency, and the object region relationship selects on-line; at this time, the back-end program performs object region relationship check on all the associated node objects according to the object region relationship conformity criterion in table 5, and highlights the associated node objects that do not conform to the "on-line". As shown in fig. 7, the a node object depends on B, C, D, E node objects, and the a node object belongs to "up" status in four areas, so that the B, C, D, E node object also belongs to "up" status for four areas to meet logic, and therefore the B node in the diagram is in "down" status for area "tianjin" and does not meet data logic, and is highlighted for the user to further manually judge and review data.
Based on the same inventive concept, the embodiment of the present disclosure further provides a node fault diagnosis apparatus, as described in the following embodiments. Because the principle of the embodiment of the apparatus for solving the problem is similar to that of the embodiment of the method, the embodiment of the apparatus can be implemented by referring to the implementation of the embodiment of the method, and repeated details are not described again.
Fig. 8 is a schematic diagram illustrating a node fault diagnosis apparatus according to an embodiment of the present disclosure, and as shown in fig. 8, the apparatus includes: a data acquisition module 81, a data screening module 82, a fault diagnosis module 83, and a fault display module 84.
The data acquisition module 81 is configured to acquire a target node object to be diagnosed and relationship information; the data screening module 82 is used for screening out node objects which accord with the relation information with the target node objects from a pre-constructed product service resource knowledge graph according to the target node objects to be diagnosed and the relation information; a fault diagnosis module 83, configured to detect whether the screened node object meets a preset fault condition; and a fault display module 84, configured to mark and display the node objects that meet the preset fault condition.
It should be noted that the data obtaining module 81, the data filtering module 82, the fault diagnosing module 83 and the fault displaying module 84 correspond to S102 to S108 in the method embodiment, and the modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure of the method embodiment. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.
As can be seen from the above, the node fault diagnosis device provided in the embodiment of the present disclosure, by constructing the product service resource knowledge graph, after the target node object to be diagnosed and the relationship information are acquired by the data acquisition module 81, the node object that meets the relationship information with the target node object is screened out from the product service resource knowledge graph that is constructed in advance by the data screening module 82 according to the target node object to be diagnosed and the relationship information, and then whether the screened node object meets the preset fault condition is detected by the fault diagnosis module 83, and finally, the node object that meets the preset fault condition is marked and displayed by the fault display module 84. Through the embodiment of the disclosure, the fault node can be rapidly checked out from the large-scale node data and marked and displayed, so that the operation and maintenance efficiency is improved, and the user experience is improved.
In some embodiments, the data obtaining module 81 is further configured to: acquiring the node type of a target node object, wherein the node type comprises: a source node and/or a destination node.
In some embodiments, the relationship information includes: object relationships and object region relationships; the data filtering module 82 is further configured to: screening out a product service resource node object which accords with the object relation with the target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the object relation; and according to the relation between the target node object to be diagnosed and the object region, screening out the region node object which is in accordance with the object region relation with the target node object from the pre-constructed product service resource knowledge graph.
In some embodiments, the target node object is a node object that provides a product service or resource for an operator, and the object relationship includes at least: including, accommodating, carrying, relying on, dividing, continuing, perforating; the object region relationships include at least: during design, to be checked, during checking, online, offline and disabled.
In some embodiments, the node fault diagnosis apparatus provided in the embodiments of the present disclosure further includes: a knowledge graph building module 85 configured to: acquiring a product service resource object file, wherein the product service resource object file comprises attribute information of one or more product service resource objects; acquiring a product service resource object relation file, wherein the product service resource object relation file comprises the relation between product service resource objects; acquiring a region file, wherein the region file comprises attribute information of one or more regions; acquiring an object area relation file, wherein the object area relation file comprises the relation between each object and each area; and constructing a product service resource knowledge graph according to the product service resource object file, the product service resource object relation file, the area file and the object area relation file.
In some embodiments, the knowledge-graph building module is further configured to: and importing the product service resource object file, the product service resource object relation file, the area file and the object area relation file into a graph database to generate a product service resource knowledge graph.
In some embodiments, the graph database is a Neo4J database.
In some embodiments, the node fault diagnosis apparatus provided in the embodiments of the present disclosure further includes: a front-end display module 86 for displaying network topology information of the product service resource knowledge graph at the front end using Vue and D3. js; and a back-end response module 87, configured to enable the back-end to respond to the request from the front-end by using the Spring Boot framework.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 900 according to this embodiment of the disclosure is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one memory unit 920, and a bus 930 that couples various system components including the memory unit 920 and the processing unit 910.
Wherein the storage unit stores program code that is executable by the processing unit 910 to cause the processing unit 910 to perform steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary method" of the present specification. For example, the processing unit 910 may perform the following steps of the above method embodiments: acquiring a target node object to be diagnosed and relationship information; screening out a node object which accords with the relation information with the target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relation information; detecting whether the screened node object meets a preset fault condition; and marking and displaying the node objects meeting the preset fault conditions.
In some embodiments, the processing unit 910 may further perform the following steps of the above method embodiments: acquiring the node type of a target node object, wherein the node type comprises: a source node and/or a destination node.
In some embodiments, the relationship information includes: object relationships and object region relationships; in some embodiments, the processing unit 910 may further perform the following steps of the above method embodiments: screening out a product service resource node object which accords with the object relation with the target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the object relation; and according to the relation between the target node object to be diagnosed and the object region, screening out the region node object which is in accordance with the object region relation with the target node object from the pre-constructed product service resource knowledge graph.
In some embodiments, the target node object is a node object that provides a product service or resource for an operator, and the object relationship includes at least: including, accommodating, carrying, relying on, dividing, continuing, perforating; the object region relationships include at least: in design, to be audited, in audit, on-line, off-line and off-line.
In some embodiments, the processing unit 910 may further perform the following steps of the above method embodiments: acquiring a product service resource object file, wherein the product service resource object file comprises attribute information of one or more product service resource objects; acquiring a product service resource object relation file, wherein the product service resource object relation file comprises the relation between product service resource objects; acquiring a region file, wherein the region file comprises attribute information of one or more regions; acquiring an object area relation file, wherein the object area relation file comprises the relation between each object and each area; and constructing a product service resource knowledge graph according to the product service resource object file, the product service resource object relation file, the region file and the object region relation file.
In some embodiments, the processing unit 910 may further perform the following steps of the above method embodiments: and importing the product service resource object file, the product service resource object relation file, the area file and the object area relation file into a graph database to generate a product service resource knowledge graph.
In some embodiments, the graph database is a Neo4J database.
In some embodiments, the processing unit 910 may further perform the following steps of the above method embodiments: vue and D3.js are used for displaying the network topology information of the product service resource knowledge graph at the front end; the Spring Boot framework is used to enable the back-end to respond to requests from the front-end.
The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM)9201 and/or a cache memory unit 9202, and may further include a read only memory unit (ROM) 9203.
The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 900, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 950. Also, the electronic device 900 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 960. As shown, the network adapter 960 communicates with the other modules of the electronic device 900 via the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, which may be a readable signal medium or a readable storage medium. Fig. 10 is a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure, and as shown in fig. 10, the computer-readable storage medium 100 has a program product stored thereon, which is capable of implementing the above-mentioned method of the disclosure. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
More specific examples of the computer-readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, 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 include a propagated data signal with readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Alternatively, program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In particular implementations, program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and 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).
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. A node fault diagnosis method, comprising:
acquiring a target node object to be diagnosed and relationship information;
screening out a node object which accords with the relation information with a target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relation information;
detecting whether the screened node object meets a preset fault condition;
and marking and displaying the node objects meeting the preset fault conditions.
2. The node fault diagnosis method according to claim 1, wherein before screening out node objects that conform to the relationship information with a target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relationship information, the method further comprises:
obtaining a node type of the target node object, wherein the node type comprises: a source node and/or a destination node.
3. The node fault diagnosis method according to claim 1, characterized in that the relationship information includes: object relationships and object region relationships; according to a target node object to be diagnosed and relationship information, screening out a node object which accords with the relationship information with the target node object from a pre-constructed product service resource knowledge graph, wherein the method comprises the following steps:
screening out a product service resource node object which accords with the object relation with a target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the object relation;
and according to the target node object to be diagnosed and the object region relation, screening out the region node object which is in line with the target node object and the object region relation from a pre-constructed product service resource knowledge graph.
4. The node fault diagnosis method according to claim 3, wherein the target node object is a node object providing product services or resources for an operator, and the object relationship at least includes: including, accommodating, carrying, relying on, dividing, continuing, perforating; the object region relationships include at least: during design, to be checked, during checking, online, offline and disabled.
5. The node fault diagnosis method according to claim 1, wherein before screening out node objects that conform to the relationship information with a target node object from a pre-constructed product service resource knowledge graph according to the target node object to be diagnosed and the relationship information, the method further comprises:
acquiring a product service resource object file, wherein the product service resource object file comprises attribute information of one or more product service resource objects;
obtaining a product service resource object relation file, wherein the product service resource object relation file comprises the relation between product service resource objects;
acquiring a region file, wherein the region file comprises attribute information of one or more regions;
acquiring an object area relation file, wherein the object area relation file comprises the relation between each object and each area;
and constructing a product service resource knowledge graph according to the product service resource object file, the product service resource object relation file, the area file and the object area relation file.
6. The node fault diagnosis method according to claim 5, wherein constructing a product service resource knowledge graph according to the product service resource object file, the product service resource object relationship file, the region file, and the object region relationship file comprises:
and importing the product service resource object file, the product service resource object relation file, the area file and the object area relation file into a graph database to generate a product service resource knowledge graph.
7. The node fault diagnosis method according to claim 6, characterized in that the database is a Neo4J database.
8. The node fault diagnostic method of claim 7, further comprising:
vue and D3.js are used for displaying the network topology information of the product service resource knowledge graph at the front end;
the Spring Boot framework is used to enable the back-end to respond to requests from the front-end.
9. A node fault diagnosis apparatus characterized by comprising:
the data acquisition module is used for acquiring a target node object to be diagnosed and relationship information;
the data screening module is used for screening out node objects which accord with the target node objects with the relationship information from a pre-constructed product service resource knowledge graph according to the target node objects to be diagnosed and the relationship information;
the fault diagnosis module is used for detecting whether the screened node objects meet preset fault conditions or not;
and the fault display module is used for marking and displaying the node objects meeting the preset fault conditions.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the node fault diagnosis method of any one of claims 1 to 8 via execution of the executable instructions.
11. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the node fault diagnosis method according to any one of claims 1 to 8.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110727804A (en) * | 2019-10-11 | 2020-01-24 | 北京明略软件系统有限公司 | Method and device for processing maintenance case by using knowledge graph and electronic equipment |
| CN111209409A (en) * | 2019-12-27 | 2020-05-29 | 南京医康科技有限公司 | Data matching method and device, storage medium and electronic terminal |
| CN112148887A (en) * | 2020-09-16 | 2020-12-29 | 珠海格力电器股份有限公司 | Equipment fault diagnosis method and device, storage medium and electronic equipment |
| CN112231493A (en) * | 2020-11-10 | 2021-01-15 | 泽恩科技有限公司 | Method, device, equipment and medium for diagnosing machine room faults based on knowledge graph |
| CN112579789A (en) * | 2020-12-04 | 2021-03-30 | 珠海格力电器股份有限公司 | Equipment fault diagnosis method and device and equipment |
| CN112583640A (en) * | 2020-12-02 | 2021-03-30 | 厦门渊亭信息科技有限公司 | Service fault detection method and device based on knowledge graph |
| CN113254249A (en) * | 2021-06-07 | 2021-08-13 | 博彦物联科技(北京)有限公司 | Cold station fault analysis method and device and storage medium |
| CN113886120A (en) * | 2021-09-28 | 2022-01-04 | 济南浪潮数据技术有限公司 | Server fault diagnosis method, device, equipment and readable storage medium |
| CN114254950A (en) * | 2021-12-27 | 2022-03-29 | 中国电信股份有限公司 | Telecommunication resource data processing method and device, electronic equipment and storage medium |
-
2022
- 2022-05-17 CN CN202210541752.3A patent/CN114978946B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110727804A (en) * | 2019-10-11 | 2020-01-24 | 北京明略软件系统有限公司 | Method and device for processing maintenance case by using knowledge graph and electronic equipment |
| CN111209409A (en) * | 2019-12-27 | 2020-05-29 | 南京医康科技有限公司 | Data matching method and device, storage medium and electronic terminal |
| CN112148887A (en) * | 2020-09-16 | 2020-12-29 | 珠海格力电器股份有限公司 | Equipment fault diagnosis method and device, storage medium and electronic equipment |
| CN112231493A (en) * | 2020-11-10 | 2021-01-15 | 泽恩科技有限公司 | Method, device, equipment and medium for diagnosing machine room faults based on knowledge graph |
| CN112583640A (en) * | 2020-12-02 | 2021-03-30 | 厦门渊亭信息科技有限公司 | Service fault detection method and device based on knowledge graph |
| CN112579789A (en) * | 2020-12-04 | 2021-03-30 | 珠海格力电器股份有限公司 | Equipment fault diagnosis method and device and equipment |
| CN113254249A (en) * | 2021-06-07 | 2021-08-13 | 博彦物联科技(北京)有限公司 | Cold station fault analysis method and device and storage medium |
| CN113886120A (en) * | 2021-09-28 | 2022-01-04 | 济南浪潮数据技术有限公司 | Server fault diagnosis method, device, equipment and readable storage medium |
| CN114254950A (en) * | 2021-12-27 | 2022-03-29 | 中国电信股份有限公司 | Telecommunication resource data processing method and device, electronic equipment and storage medium |
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Application publication date: 20220830 Assignee: Tianyiyun Technology Co.,Ltd. Assignor: CHINA TELECOM Corp.,Ltd. Contract record no.: X2024110000020 Denomination of invention: Node fault diagnosis methods, devices, electronic devices, and storage media Granted publication date: 20231003 License type: Common License Record date: 20240315 |
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