CN112231493A - Method, device, equipment and medium for diagnosing machine room faults based on knowledge graph - Google Patents
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Abstract
The invention relates to a method, a device, equipment and a medium for diagnosing machine room faults based on a knowledge graph, wherein the method comprises the steps of obtaining historical operation data of machine room equipment for deep learning and knowledge extraction, constructing the machine room fault knowledge graph and storing the machine room fault knowledge graph to a graph database; acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault warning information; and acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database, and sending the fault diagnosis result to a user side associated with the fault diagnosis of the machine room. The invention can deeply diagnose the fault reason of the equipment in the machine room by utilizing the knowledge map technology, thereby improving the accuracy of fault diagnosis.
Description
Technical Field
The invention relates to the technical field of machine room fault diagnosis, in particular to a machine room fault diagnosis method, device, equipment and medium based on a knowledge graph.
Background
With the continuous deepening of electric power information construction and the development of an intelligent power grid, a machine room is used as an operation and maintenance management center, the equipment integration level and the complexity are higher and higher, and the maintenance is more and more difficult by relying on the experience diagnosis of maintenance personnel. At present, the intelligent verification link of a machine room acquires the operation data of various system devices in various sensor technologies, video technologies, messages and other modes, but the acquired data amount is small, and fault diagnosis is performed by adopting traditional data mining modes such as a decision tree and an SDG (software development group) model, so that the diagnosis effect is poor and the efficiency is low. Therefore, the inventor has certain defects in the accuracy of fault diagnosis of equipment in a machine room, and needs further improvement.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides a method, a device, equipment and a medium for diagnosing the fault of the machine room based on the knowledge graph, and the fault reason of the machine room equipment can be deeply diagnosed by utilizing the knowledge graph technology, so that the accuracy of fault diagnosis is improved.
The above object of the present invention is achieved by the following technical solutions:
a machine room fault diagnosis method based on knowledge graph includes:
acquiring historical operation data structures of equipment in a machine room, performing deep learning and knowledge extraction, constructing a fault knowledge map of the machine room and storing the fault knowledge map to a map database;
acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault warning information;
and acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database.
By adopting the technical scheme, the knowledge map is constructed by utilizing the historical operating data of the machine room equipment, and the fault diagnosis result is obtained by utilizing the fault warning information of the target diagnosis equipment by utilizing the knowledge map technology, so that the fault source can be effectively judged, and the accuracy of fault diagnosis is improved.
Optionally, after obtaining the fault diagnosis result of the target diagnosis device, the method further includes: and recommending a fault solution of the target diagnosis equipment by using a preset expert model library according to the fault diagnosis result.
By adopting the technical scheme, the fault solution of the target diagnosis equipment is recommended through the preset expert model library, the fault problem solution is provided by utilizing expert experience knowledge, the later-stage fault troubleshooting of operation and maintenance personnel is facilitated, and the fault solution efficiency is improved.
Optionally, obtaining historical operating data structures of the equipment in the machine room to perform deep learning and knowledge extraction, and constructing a machine room fault knowledge graph and storing the machine room fault knowledge graph to a graph database includes:
extracting the characteristics of the historical operating data by using a preset deep learning model to obtain fault category information of equipment in the machine room;
and extracting knowledge, extracting equipment, associating equipment and fault types according to the fault type information of the equipment in the machine room in an entity-relationship-attribute mode, and generating a machine room fault knowledge map.
By adopting the technical scheme, the characteristic extraction is carried out on the historical operating data through the deep learning model, the fault category information of the equipment in the machine room is obtained, the fault category information is extracted through a triple extraction mode, and the fault knowledge map in the machine room is generated, so that the data mining efficiency is improved, the relevance between the equipment and between the equipment and the fault reason is enhanced, and the generated fault knowledge map in the machine room is favorable for improving the accuracy of fault diagnosis.
Optionally, after obtaining a historical operating data structure of the equipment in the machine room to perform deep learning and knowledge extraction, and constructing a machine room fault knowledge graph and storing the machine room fault knowledge graph to a graph database, the method further includes:
and acquiring current operation data of the equipment in the machine room in real time, performing deep learning and knowledge extraction results according to the current operation data, and updating the current operation data to a graph database in real time.
By adopting the technical scheme, the database is updated in real time by acquiring the current operating data of the equipment in the machine room in real time, so that the data volume of the knowledge map can be continuously enriched, and the accuracy of fault diagnosis is improved.
Optionally, obtaining a fault diagnosis result of the target diagnostic device according to the fault warning information of the target diagnostic device and the graph database, including:
inquiring the fault warning information in the graph database to obtain the inquired data;
and displaying the inquired data according to the knowledge graph technology, acquiring fault reasons by using a preset service rule algorithm, respectively identifying the fault reasons in the knowledge graph, and acquiring the identified results as fault diagnosis results.
By adopting the technical scheme, the queried data is displayed by utilizing the knowledge graph technology, the fault reason is determined by utilizing the preset service rule algorithm, the accuracy of fault diagnosis is improved, the fault reason is respectively identified in the knowledge graph, and the marking result is used as the fault diagnosis result, so that visual output is facilitated.
Optionally, the fault diagnosis result includes at least one fault cause, the fault cause is matched and searched for a corresponding solution in a preset expert model library by using a RETE algorithm, and the solution is sent to a user side associated with the machine room fault diagnosis.
By adopting the technical scheme, the fault reason is matched and searched in the preset expert model library through the RETE algorithm, the solution is obtained, the matching efficiency and the fault troubleshooting efficiency are improved, and the time cost of manual troubleshooting is reduced.
The second aim of the invention is realized by the following technical scheme:
a machine room fault diagnosis device based on knowledge graph comprises:
the map construction module is used for acquiring historical operation data structures of the equipment in the machine room to perform deep learning and knowledge extraction, constructing a fault knowledge map of the machine room and storing the fault knowledge map to a map database;
the information acquisition module is used for acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault alarm information;
and the fault diagnosis module is used for acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the map database.
By adopting the technical scheme, the fault diagnosis method and the fault diagnosis system can realize that the fault diagnosis result is obtained by utilizing the fault alarm information of the target diagnosis equipment through constructing the knowledge map by utilizing the historical operation data of the equipment in the machine room, and the fault source is effectively judged, so that the accuracy of fault diagnosis is improved.
Optionally, the apparatus further comprises: and the scheme acquisition module is used for recommending a fault solution of the target diagnosis equipment by utilizing a preset expert model library according to the fault diagnosis result.
By adopting the technical scheme, the fault solution of the target diagnosis equipment can be recommended through the preset expert model library, the fault problem solution is provided by utilizing expert experience knowledge, the later-stage fault troubleshooting of operation and maintenance personnel is facilitated, and the fault solution efficiency is improved.
The third object of the invention is realized by the following technical scheme:
computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of the method for diagnosing a fault in a machine room based on a knowledge-graph.
The fourth object of the invention is realized by the following technical scheme:
a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for diagnosing a fault in a machine room based on a knowledge-graph.
In summary, the invention includes at least one of the following beneficial technical effects:
1. according to the method and the device, the data mining strength of historical operating data of the machine room equipment can be enhanced through the knowledge map technology, and the fault warning information is matched in the knowledge map to obtain the fault diagnosis result, so that the fault diagnosis accuracy of the machine room equipment is improved.
2. According to the method and the device, historical operating data are trained through the deep learning model, fault category information of the equipment in the machine room is obtained, the fault category information is extracted through a triple extraction mode, and a fault knowledge map of the machine room is generated, so that the effectiveness of data mining is improved, the relevance between the equipment and between the equipment and fault reasons is enhanced, and the generated fault knowledge map of the machine room is helpful for improving the accuracy of fault diagnosis.
3. According to the method and the device, the queried data are displayed by using the knowledge map technology, the fault reason is determined by using the preset business rule algorithm, the accuracy of fault diagnosis is improved, the fault reason is respectively identified in the knowledge map, and the marked result is used as the fault diagnosis result, so that visual output is facilitated.
Drawings
FIG. 1 is a flowchart of an implementation of a method for diagnosing a fault of a machine room based on a knowledge graph according to an embodiment of the present application;
FIG. 2 is a flowchart of another implementation of the method for diagnosing a fault of a machine room based on a knowledge graph according to the embodiment of the present application;
FIG. 3 is a schematic block diagram of a machine room fault diagnosis device based on a knowledge graph according to an embodiment of the present application;
FIG. 4 is a functional block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Example (b):
in this embodiment, as shown in fig. 1, the present application discloses a machine room fault diagnosis method based on a knowledge graph, including:
s10: and obtaining historical operation data structures of equipment in the machine room, performing deep learning and knowledge extraction, constructing a fault knowledge map of the machine room, and storing the fault knowledge map to a map database.
In this embodiment, the historical operating data includes text, image, message, and other data.
Specifically, historical operating data of the equipment can be acquired through a machine room equipment log or external industrial data. In this embodiment, real-time operation data of the device may be collected through monitoring sensors, metering devices, monitoring software, and the like.
Furthermore, deep learning can be performed on historical operating data by using a deep learning model to obtain fault category information, and then a machine room fault knowledge map is constructed by extracting knowledge of fault feature information.
The process of constructing the machine room fault knowledge graph according to the embodiment is specifically described as follows:
performing data preprocessing on historical operating data, including data cleaning and normalization processing, wherein the data cleaning comprises deleting abnormal values, compensating vacancy values and the like; and dividing the preprocessed historical operating data into two parts, wherein one part is used as data for deep learning model training, and the other part is used as data for constructing a knowledge graph. In this embodiment, the deep learning model may adopt a convolutional neural network model to perform BP algorithm cyclic iterative training on the historical operating data after data preprocessing, adjust network parameters, and obtain a trained convolutional neural network as the deep learning model. And then, performing feature extraction on the other part of preprocessed historical operating data by using a deep learning model to acquire fault category information.
And extracting knowledge of the fault category information according to an entity-relationship-attribute mode, extracting equipment, associated equipment and an associated data table of the fault category, such as an entity-attribute table and a relationship-attribute table, performing point-edge relationship operation according to characteristic data in the table to generate a machine room fault knowledge map, and storing the machine room fault knowledge map in a server map database.
Optionally, the current operation data of the equipment in the machine room can be acquired in real time, and the results of deep learning and knowledge extraction are performed through the current operation data and updated to the graph database in real time. The process of performing deep learning and knowledge extraction on the current operating data is the same as the process of processing the historical operating data, and is not described herein again.
In the embodiment, the database is updated in real time by acquiring the current operating data of the equipment in the machine room in real time, so that the data volume of the knowledge map can be enriched continuously, and the accuracy of fault diagnosis is improved.
S20: and acquiring fault diagnosis request information of the target diagnosis equipment, wherein the fault diagnosis request information comprises fault warning information.
In the present embodiment, the target diagnosis apparatus refers to an apparatus that is a fault diagnosis target. The fault diagnosis request information is request information that the user side requests the server to perform fault diagnosis on the target diagnosis device. The fault warning information refers to abnormal characteristic data of the target diagnosis equipment.
Specifically, the user-side operation and maintenance personnel send fault diagnosis request information to the server, and the server receives the fault diagnosis request information of the target diagnosis equipment and stores the fault diagnosis request information into the database.
S30: and acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database, and sending the fault diagnosis result to a user side associated with the fault diagnosis of the machine room.
Specifically, a graph database can be used for inquiring the possible fault reasons of the fault alarm information.
Further, according to the fault warning information, query is performed in the graph database to obtain queried data.
Furthermore, displaying the inquired data according to the knowledge graph technology, acquiring fault reasons by using a preset service rule algorithm, respectively identifying the fault reasons in a knowledge graph display page, and acquiring the identified results as fault diagnosis results. In this embodiment, the traffic rule algorithm may adopt a RETE algorithm. In this embodiment, the map database stores a plurality of machine room failure knowledge maps, queries are performed according to query statements, causes of possible failures are listed through a business rule algorithm, and the causes are identified on a display page of the knowledge maps, so that visualization is facilitated.
Optionally, as shown in fig. 2, the method for diagnosing a fault of a machine room based on a knowledge graph further includes:
s40: and recommending a fault solution of the target diagnosis equipment by using a preset expert model library according to the fault diagnosis result.
In this embodiment, the preset expert model base includes a knowledge base, which can be obtained through expert experience learning. In this embodiment, supervised neural network learning may be adopted to obtain solutions corresponding to different fault states of different devices. The learning data can be expert knowledge data in the field of operation and maintenance of the power equipment room.
Specifically, according to a fault diagnosis result obtained by fault diagnosis, the fault diagnosis result at least includes one fault cause, the fault cause may be matched and searched for a corresponding solution in the expert model library by using a RETE algorithm, and the solution is sent to the user side associated with the fault diagnosis of the machine room.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Example two:
in an embodiment, a machine room fault diagnosis device based on a knowledge graph is provided, and the machine room fault diagnosis device based on the knowledge graph corresponds to the machine room fault diagnosis method based on the knowledge graph in the embodiment one to one. As shown in fig. 3, the apparatus for diagnosing a fault in a machine room based on a knowledge graph includes a graph building module 10, an information acquiring module 20, and a fault diagnosing module 30. The functional modules are explained in detail as follows:
the map building module 10 is used for obtaining historical operation data structures of equipment in the machine room to perform deep learning and knowledge extraction, building a fault knowledge map of the machine room and storing the fault knowledge map to a map database;
the information acquisition module 20 is configured to acquire fault diagnosis request information of the target diagnostic device, where the fault diagnosis request information includes fault warning information;
and the fault diagnosis module 30 is configured to obtain a fault diagnosis result of the target diagnostic device according to the fault warning information of the target diagnostic device and the map database, and send the fault diagnosis result to the user side associated with the fault diagnosis of the machine room.
Optionally, the apparatus for diagnosing a fault in a machine room of this embodiment further includes:
and the scheme acquisition module is used for recommending a fault solution of the target diagnosis equipment by using a preset expert model library according to the fault diagnosis result.
The atlas-building module 10 includes:
the characteristic extraction unit is used for extracting the characteristics of the historical operating data by using a preset deep learning model and acquiring the fault category information of the equipment in the machine room;
and the map construction unit is used for extracting knowledge and extracting equipment, associated equipment and fault types according to the fault type information of the equipment in the machine room and an entity-relationship-attribute mode to generate a machine room fault knowledge map.
The fault diagnosis module 30 includes:
the data query unit is used for querying the fault alarm information in the graph database to obtain queried data;
and the fault diagnosis unit is used for displaying the inquired data according to the knowledge graph technology, acquiring fault reasons by using a preset service rule algorithm, respectively identifying the fault reasons in the knowledge graph, and acquiring the identified results as fault diagnosis results.
The scheme acquisition module comprises:
and the scheme matching unit is used for matching and searching a corresponding solution in a preset expert model library by using a RETE algorithm according to the fault diagnosis result at least comprising one fault reason, and sending the solution to a user side associated with the fault diagnosis of the machine room.
For specific limitations of the apparatus for diagnosing a fault in a machine room based on a knowledge graph, reference may be made to the above limitations of the method for diagnosing a fault in a machine room based on a knowledge graph, and details are not repeated here. All or part of the modules in the machine room fault diagnosis device based on the knowledge graph can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Example three:
in one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing historical operating data, fault diagnosis request information, fault warning information, fault diagnosis results and other data of the equipment in the computer room. The network interface of the computer device is used for communicating with an external terminal through a network connection. When being executed by a processor, the computer program realizes a machine room fault diagnosis method based on the knowledge graph, and specifically comprises the following steps:
taking a historical operation data structure of equipment in the machine room to perform deep learning and knowledge extraction, and constructing a fault knowledge map of the machine room and storing the fault knowledge map to a map database;
acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault warning information;
and acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database, and sending the fault diagnosis result to a user side associated with the fault diagnosis of the machine room.
Example four:
in one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring historical operation data structures of equipment in a machine room, performing deep learning and knowledge extraction, constructing a fault knowledge map of the machine room and storing the fault knowledge map to a map database;
acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault warning information;
and acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database, and sending the fault diagnosis result to a user side associated with the fault diagnosis of the machine room.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A machine room fault diagnosis method based on knowledge graph is characterized by comprising the following steps:
acquiring historical operation data of equipment in a machine room, performing deep learning and knowledge extraction, constructing a fault knowledge map of the machine room and storing the fault knowledge map to a map database;
acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault warning information;
and acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database, and sending the fault diagnosis result to a user side associated with the fault diagnosis of the machine room.
2. The machine room fault diagnosis method based on the knowledge-graph according to claim 1, wherein after obtaining the fault diagnosis result of the target diagnosis device, the method further comprises: and recommending a fault solution of the target diagnosis equipment by using a preset expert model library according to the fault diagnosis result.
3. The method for diagnosing the machine room fault based on the knowledge graph according to claim 1, wherein historical operation data of machine room equipment are acquired for deep learning and knowledge extraction, and the machine room fault knowledge graph is constructed and stored in a graph database, and the method comprises the following steps:
extracting the characteristics of the historical operating data by using a preset deep learning model to obtain fault category information of equipment in the machine room;
and extracting knowledge, extracting equipment, associating equipment and fault types according to the fault type information of the equipment in the machine room in an entity-relationship-attribute mode, and generating a machine room fault knowledge map.
4. The method for diagnosing faults of a machine room based on knowledge graph according to claim 1,
obtaining historical operation data of equipment in a machine room for deep learning and knowledge extraction, and after constructing a fault knowledge graph of the machine room and storing the fault knowledge graph in a graph database, the method further comprises the following steps:
and acquiring current operation data of the equipment in the machine room in real time, performing deep learning and knowledge extraction results according to the current operation data, and updating the current operation data to a graph database in real time.
5. The method for diagnosing faults of a machine room based on knowledge graph according to claim 1,
acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the graph database, wherein the fault diagnosis result comprises the following steps:
inquiring the fault warning information in the graph database to obtain the inquired data;
and displaying the inquired data according to the knowledge graph technology, acquiring fault reasons by using a preset service rule algorithm, respectively identifying the fault reasons in the knowledge graph, and acquiring the identified results as fault diagnosis results.
6. The machine room fault diagnosis method based on knowledge-graph according to claim 2,
according to the fault diagnosis result, a fault solution of the target diagnosis equipment is recommended by utilizing a preset expert model library, and the method comprises the following steps:
and matching and searching a corresponding solution in a preset expert model library by using a RETE algorithm, and sending the solution to a user side associated with the machine room fault diagnosis.
7. A machine room fault diagnosis device based on a knowledge graph is characterized by comprising:
the map building module is used for acquiring historical operating data of the equipment in the machine room to perform deep learning and knowledge extraction, building a fault knowledge map of the machine room and storing the fault knowledge map in a map database;
the information acquisition module is used for acquiring fault diagnosis request information of target diagnosis equipment, wherein the fault diagnosis request information comprises fault alarm information;
and the fault diagnosis module is used for acquiring a fault diagnosis result of the target diagnosis equipment according to the fault warning information of the target diagnosis equipment and the map database.
8. The apparatus for diagnosing machine room fault based on knowledge-graph according to claim 7, characterized in that the apparatus further comprises: and the scheme acquisition module is used for recommending a fault solution of the target diagnosis equipment by utilizing a preset expert model library according to the fault diagnosis result.
9. Computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program performs the steps of the method for knowledgegraph-based room fault diagnosis according to any of claims 1 to 6.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the method for diagnosing a fault in a knowledge-graph-based machine room according to any one of claims 1 to 6.
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Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112801315A (en) * | 2021-01-28 | 2021-05-14 | 国网河北省电力有限公司电力科学研究院 | State diagnosis method and device for power secondary equipment and terminal |
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