CN116467459A - Internet of things equipment fault reporting method and device, computer equipment and storage medium - Google Patents
Internet of things equipment fault reporting method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a method, a device, a computer device and a storage medium for reporting faults of equipment of the Internet of things, wherein the method comprises the following steps: acquiring sample data for constructing a fault prediction model, wherein a sample device comprises device information of a plurality of sample devices; constructing a knowledge graph based on the sample equipment, wherein the knowledge graph is used for identifying the association relation between equipment information of the sample equipment; constructing a scoring model based on the sample equipment and the knowledge graph; and inputting the equipment information of the target equipment into a scoring model to obtain the fault score of the target equipment. The scoring value obtained by the method is more in line with the actual situation of the user.
Description
Technical Field
The invention relates to the technical field of data measurement processing, in particular to a method and a device for reporting faults of equipment of the Internet of things, computer equipment and a storage medium.
Background
The existing intelligent internet of things system realizes the mutual and mutual communication among different intelligent terminal devices, different system platforms and different application scenes, everything is mutually fused, each connected sub-device in the internet of things system is used as an independent intelligent device, and a plurality of independent intelligent devices bring intelligent experience to a user after being connected into an intelligent network.
The existing intelligent Internet of things system brings intelligent experience to users, and meanwhile, the users can hardly intuitively search related fault equipment through time, space, trigger events and other conditions by using the mobile terminals of the users, so that the fault equipment report link analysis and equipment fault early warning can hardly be realized in a floor scene.
Disclosure of Invention
Based on the method, the device, the computer equipment and the storage medium for reporting the faults of the equipment of the Internet of things are provided.
An internet of things equipment fault reporting method, comprising:
acquiring sample data for constructing a fault prediction model, wherein the sample data comprises equipment information of a plurality of sample equipment;
constructing a knowledge graph based on the sample data, wherein the knowledge graph is used for identifying the association relation between the equipment information of the sample equipment;
constructing a scoring model based on the sample data and the knowledge-graph;
and inputting the equipment information of the target equipment into the scoring model to obtain the fault score of the target equipment.
In one embodiment, after obtaining the failure score of the target device, the method further includes:
judging whether the fault score of the target equipment is larger than a scoring threshold value, if so, determining report category information which is supposed to be of the target equipment according to the fault score;
determining whether the target device is in a reporting state based on the determined reporting category information;
and in response to determining that the target device is in a reporting state, sending reporting information to at least one second terminal, the reporting information including reporting category information, location information of the target device.
In one embodiment, the sending report information to at least one second terminal includes:
detecting whether at least one second terminal connected with the first terminal is in a state of receiving report information;
and transmitting report information to the at least one second terminal in response to the at least one second terminal being in a state of receiving report information.
In one embodiment, the constructing a knowledge-graph based on the sample data includes:
screening the target equipment information data, selecting the equipment information data representing the relation between sample equipment and sample equipment for deep analysis, and mining the association relation between the target equipment information data to construct the target equipment information knowledge graph; and selecting the rest equipment information data except the equipment information data representing the relation between the sample equipment and the sample equipment for constructing a relation database.
In one embodiment, the constructing a knowledge-graph based on the sample data further includes:
and establishing a mapping between the target equipment information knowledge graph and the relational database, and calling related data in the relational database through the target equipment information knowledge graph.
In one embodiment, the inputting the device information of the target device into the scoring model, to obtain the failure score of the target device, includes:
in the knowledge graph, according to a random walk algorithm, obtaining a preset number of node devices corresponding to the target device, wherein the node devices and the target device have an association relationship, the association relationship comprises a direct association relationship of direct connection between the target device and the node device, and an indirect association relationship of indirect connection between the target device and the node device;
determining the fault score of each node device and the associated weight of each node device, and carrying out weighted sum calculation according to the fault score of each node and the associated weight of each node device to obtain the fault score of the target device.
An internet of things equipment fault reporting method, comprising:
a data acquisition unit configured to acquire sample data for constructing a failure prediction model, wherein the sample data includes device information of a plurality of sample devices;
a knowledge graph unit, configured to construct a knowledge graph based on the sample data, where the knowledge graph is used to identify an association relationship between device information of the sample device;
a model unit for constructing a scoring model based on the sample data and the knowledge-graph;
and the scoring unit is used for inputting the equipment information of the target equipment into the scoring model to obtain the fault score of the target equipment.
In one embodiment, the apparatus further comprises: the report calculation unit is used for judging whether the fault score of the target equipment is larger than a scoring threshold value, and if so, determining report category information which is supposed to be of the target equipment according to the fault score;
a category determining unit configured to determine whether the target device is in a reporting state based on the determined reporting category information;
and a reporting unit, configured to send reporting information to at least one second terminal in response to determining that the target device is in a reporting state, where the reporting information includes reporting category information and location information of the target device.
A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the internet of things device fault reporting method described above.
A storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the internet of things device fault reporting method described above.
According to the method, the device, the computer equipment and the storage medium for reporting the equipment faults of the Internet of things, the knowledge graph constructed by the equipment information in the sample equipment is introduced, and the scoring model is constructed according to the equipment information in the data and the constructed knowledge graph, so that the scoring model is more objective, the scoring model is used for scoring the users to be evaluated, and the output scoring value can determine the probability that the equipment is in a fault state; the intelligent terminal device and the intelligent terminal system have the advantages that the mutual and mutual communication among different intelligent terminal devices, different system platforms and different application scenes is realized, and everything is mutually fused, so that intelligent experience is brought to users. And (3) rapidly realizing the link analysis of the fault equipment report and the equipment fault early warning in the floor scene.
Drawings
FIG. 1 is a block diagram of the internal architecture of a computer device in one embodiment;
FIG. 2 is a flow chart of a method of reporting an Internet of things device failure in one embodiment;
fig. 3 is a block diagram of a device for reporting a fault of an internet of things device in one embodiment.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element.
FIG. 1 is a schematic diagram of the internal architecture of a computer device in one embodiment. As shown in fig. 2, the computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected by a system bus. The nonvolatile storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store a control information sequence, and when the computer readable instructions are executed by a processor, the processor can realize an Internet of things device fault reporting method. The processor of the computer device is used to provide computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer readable instructions that, when executed by the processor, cause the processor to perform a method for reporting faults of the internet of things device. The network interface of the computer device is for communicating with a terminal connection. It will be appreciated by those skilled in the art that the structure shown in fig. 2 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
As shown in fig. 2, in one embodiment, an internet of things device fault reporting method is provided, and the internet of things device fault reporting method may be applied to the above-mentioned computer device 110, and may specifically include the following steps:
step 101, obtaining sample data for constructing a fault prediction model, wherein the sample data comprises equipment information of a plurality of sample equipment;
the data format of the acquired sample data may include formatting information data, semi-formatting information data, and non-formatting information data. Wherein the formatted information data may comprise, in particular, multi-source data capable of being represented in data or in a unified structure; the semi-formatted information data can comprise data in forms such as XML, JASON and the like, wherein the data is self-description, data structure and content of which are not obviously distinguished; unstructured information data may include office documents, text, pictures, HTML, various types of reports, image and audio/video information, etc. in all formats.
Step 102, constructing a knowledge graph based on the sample data, wherein the knowledge graph is used for identifying the association relation between the equipment information of the sample equipment.
The knowledge graph is essentially a knowledge base called semantic network, i.e. a knowledge base with a directed graph structure. Wherein, the nodes of the knowledge graph represent entities or concepts, and the edges of the knowledge graph connecting the nodes represent various association relations between the entities or concepts, such as similarity relations between two entities, etc.
In this embodiment, the data obtained in step 101 is used as a basis for knowledge graph construction, and the data is extracted from different data sources. Knowledge extraction is carried out on the structured data and the semi-structured data, and entities and attributes are extracted to serve as nodes for constructing an equipment information knowledge graph; the relationships are extracted as edges for building the knowledge-graph of the device information, e.g., device a and device B are homogeneous. And carrying out knowledge fusion by taking the processed data as the ontology for constructing the knowledge graph, namely ontology alignment and entity matching. The unstructured data is processed by natural language processing technology to extract structured information.
Further, in processing unstructured data using natural language processing techniques, specific steps may include:
entity extraction, which is used to identify entity information from semi-structured and unstructured data, and to determine the category of the entity while determining the front and back boundaries of yes.
Extracting a relation to obtain a semantic relation between two entities, wherein the semantic relation can be a unitary relation such as the type of the entity; binary relationships, such as attributes of an entity; even higher order relationships are possible.
Attribute extraction, which collects attribute information of specific entities from different information sources.
Event extraction, namely extracting event information related to the construction equipment information knowledge graph from information data describing the event, and presenting the event information in a structured form.
In one embodiment of the invention, the sample equipment data is screened, and the data which has little effect on analysis relation and low access frequency is put into a relation database through the angles of efficiency and redundancy principle, and the mapping between the data and the equipment information knowledge graph is established, so that related data can be conveniently called when needed.
Further, constructing an attribute graph according to the equipment information; the attribute map can be said to be the most widely adopted-generic map data model currently adopted by the map database industry.
The attribute map is composed of node sets and edge sets, and satisfies the following properties:
each node has a unique id;
each node is provided with a plurality of outgoing edges;
each node is provided with a plurality of incoming edges;
each node has a set of attributes, each attribute being a key-value pair;
each edge has a unique id;
each edge has a head node;
each edge has a tail node;
each edge has a label representing a contact;
each edge has a set of attributes, each attribute being a key-value pair.
On the knowledge graph data model, query operation is required by means of knowledge graph query language. Currently, the query language on RDF graphs is SPARQL; query languages on attribute graphs are commonly used by cytor and Gremlin.
SPARQL is a whole set of knowledge service standards.
Cypher was originally the attribute map data query language implemented in the map database Neo4 j. Like SPARQL, cytor is also a declarative language, i.e., the user only needs to declare "what to look up" and does not have to care about "what to look up. The language has the advantages of facilitating learning and mastering of users and simultaneously giving the database a space for query optimization, and has the disadvantage of not meeting the requirements of advanced user navigation type query, and the query execution plan planned by the database is possibly not an optimal scheme.
In this embodiment, in order to construct a small and light data storage carrier, efficient real-time data query is facilitated, data searching involving depth of relationship is performed more quickly, knowledge maps may be stored in a graph database such as janus graph, and relationship databases may be stored in a conventional relational database such as Hbase.
In some embodiments, constructing a knowledge-graph based on the sample device includes:
screening the equipment information data, selecting the equipment information data representing the relation between the sample equipment and the sample equipment for deep analysis, and mining the association relation between the sample equipment information data to construct a target equipment information knowledge graph; the remaining device information data, other than the device information data characterizing the user and user relationship, is selected for use in constructing a relationship database.
In the process of acquiring the entity and the attribute related to the entity by adopting a machine learning mode, firstly, training is carried out according to data recorded under a plurality of nodes in a knowledge graph, so that the entity is automatically identified, the information such as the types of the entity and the attribute of the entity is obtained, then the obtained information such as the types of the entity and the attribute is disassembled and analyzed, an analysis result is obtained, and the analysis result is recorded under the corresponding nodes in the knowledge graph, so that the entity and the attribute with the same analysis result are located in the nodes.
In one embodiment, constructing the knowledge-graph based on the sample device further comprises: and establishing mapping between the equipment information knowledge graph and the relational database, and calling related data in the relational database through the equipment information knowledge graph.
It can be appreciated that the mapping relationship between the device information knowledge graph and the relational database is established, so that the relational database can be directly called by the device information knowledge graph when needed.
It can be understood that the invention relates to an Internet of things equipment relationship graph system established based on a knowledge graph technology, which comprises a main control module and a mobile terminal in communication connection with the main control module: the control module is internally provided with a drawing unit and a processing unit, the main control module is connected with a plurality of intelligent devices, and each intelligent device comprises a sensing unit, a fog calculating unit and an executing unit;
the drawing unit is used for constructing an equipment relation graph of the intelligent Internet of things, a data layer is firstly established in the equipment relation graph construction process, a corresponding mode layer is established on the data layer, finally, the equipment relation graph is constructed according to the mode layer, the data layer stores real running data of each intelligent equipment and intelligent scene data commonly constructed by a plurality of intelligent equipment, the mode layer is used for analyzing the data layer to obtain a first analysis result which is used as knowledge for refining stored data, the equipment relation graph is constructed according to the knowledge, each intelligent equipment in the equipment relation graph is used as an independent entity, and each entity is used as an independent node in the equipment relation graph.
Step 103, constructing a scoring model based on the sample data and the knowledge graph;
the knowledge graph is used for identifying the association relation among all sample devices. And taking the sample equipment as a node in the knowledge graph, and if the two different sample equipment have the relation data, establishing the connection relation between the two different sample equipment.
And 104, inputting the equipment information of the target equipment into a scoring model to obtain the fault score of the target equipment.
The step 104 may specifically include:
in the knowledge graph, acquiring a preset number of node devices corresponding to target devices according to a random walk algorithm, wherein the node devices and the target devices have an association relationship, and the association relationship comprises a direct association relationship of direct connection between the target devices and the node devices, and an indirect association relationship of indirect connection between the target devices and the node devices;
determining the fault score of each node device and the associated weight of each node device, and carrying out weighted sum calculation according to the fault score of each node and the associated weight of each node device to obtain the fault score of the target device.
In some embodiments, after obtaining the failure score of the target device in step 104, further comprises:
judging whether the fault score of the target equipment is larger than a scoring threshold value, if so, determining report category information which is supposed to be of the target equipment according to the fault score;
determining whether the target device is in a reporting state based on the determined reporting category information;
and in response to determining that the target device is in a reporting state, sending reporting information to at least one second terminal, the reporting information including reporting category information, location information of the target device.
Further specifically, the sending report information to at least one second terminal includes:
detecting whether at least one second terminal connected with the first terminal is in a state of receiving report information;
and transmitting report information to the at least one second terminal in response to the at least one second terminal being in a state of receiving report information.
Compared with the prior art, the method and the device have the advantages that the knowledge graph constructed by the equipment information in the sample equipment is introduced, the scoring model is constructed according to the equipment information in the data and the constructed knowledge graph, the scoring model is obtained, the obtained scoring model is more objective, the user to be evaluated is scored through the scoring model, and the output scoring value can be used for determining whether the equipment is in a fault state or not.
Furthermore, the processing unit is used for constructing a neural network model, the neural network model comprises a plurality of neurons, and a plurality of nodes in the equipment relation map and a plurality of neurons in the neural network model are mapped correspondingly;
the processing unit monitors the neural network model and the equipment relation map in time and space, and generates a second analysis result by real-time link early warning analysis of the monitoring result;
the processing unit inputs a triggering event which is particularly an induction factor and is generated in the system operation process into the neural network model, the neural network model generates corresponding stimulation, the stimulation generated each time is fed back to the processing unit, the processing unit analyzes the neural network model and the equipment relationship graph to generate a third analysis result, the processing unit integrates the second analysis result and the third analysis result into a feedback result and uploads the feedback result to the main control module, the main control module sends the feedback result to the mobile terminal, and the mobile terminal rapidly retrieves fault equipment associated with the mobile terminal according to the feedback result through the equipment relationship graph and the neural network model and timely sends early warning.
As shown in fig. 3, in an embodiment, an apparatus for reporting a fault of an internet of things device is provided, where the apparatus for reporting a fault of an internet of things device may be integrated in the computer device, and may specifically include:
a data acquisition unit configured to acquire sample data for constructing a failure prediction model, wherein the sample data includes device information of a plurality of sample devices;
the knowledge graph unit is used for constructing a knowledge graph based on the sample data, and the knowledge graph is used for identifying the association relationship between the equipment information of the sample equipment;
the model unit is used for constructing a scoring model based on the sample data and the knowledge graph;
and the scoring unit is used for inputting the equipment information of the target equipment into the scoring model to obtain the fault score of the target equipment.
The report calculation unit is used for judging whether the fault score of the target equipment is larger than a scoring threshold value, and if so, determining report category information which is supposed to be of the target equipment according to the fault score;
a category determination unit 316 for determining whether the target device is in a reporting state based on the determined reporting category information;
and a reporting unit, configured to send reporting information to at least one second terminal in response to determining that the target device is in a reporting state, where the reporting information includes reporting category information and location information of the target device.
In one embodiment, a computer device is presented, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring sample data for constructing a fault prediction model, wherein the sample data comprises equipment information of a plurality of sample equipment; constructing a knowledge graph based on the sample data, wherein the knowledge graph is used for identifying the association relation between the equipment information of the sample equipment; constructing a scoring model based on the sample data and the knowledge graph; and inputting the equipment information of the target equipment into a scoring model to obtain the fault score of the target equipment.
In one embodiment, a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: acquiring sample data for constructing a fault prediction model, wherein the sample data comprises equipment information of a plurality of sample equipment; constructing a knowledge graph based on the sample data, wherein the knowledge graph is used for identifying the association relation between the equipment information of the sample equipment; constructing a scoring model based on the sample data and the knowledge graph; and inputting the equipment information of the target equipment into a scoring model to obtain the fault score of the target equipment.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored in a computer-readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Randoma Access Memory, RAM).
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The method for reporting the faults of the equipment of the Internet of things is characterized by comprising the following steps of:
acquiring sample data for constructing a fault prediction model, wherein the sample data comprises equipment information of a plurality of sample equipment;
constructing a knowledge graph based on the sample data, wherein the knowledge graph is used for identifying the association relation between the equipment information of the sample equipment;
constructing a scoring model based on the sample data and the knowledge-graph;
and inputting the equipment information of the target equipment into the scoring model to obtain the fault score of the target equipment.
2. The method for reporting the fault of the internet of things device according to claim 1, wherein after obtaining the fault score of the target device, the method further comprises:
judging whether the fault score of the target equipment is larger than a scoring threshold value, if so, determining report category information which is supposed to be of the target equipment according to the fault score;
determining whether the target device is in a reporting state based on the determined reporting category information;
and in response to determining that the target device is in a reporting state, sending reporting information to at least one second terminal, the reporting information including reporting category information, location information of the target device.
3. The method for reporting the fault of the internet of things device according to claim 2, wherein the sending report information to the at least one second terminal comprises:
detecting whether at least one second terminal connected with the first terminal is in a state of receiving report information;
and transmitting report information to the at least one second terminal in response to the at least one second terminal being in a state of receiving report information.
4. The method for reporting the fault of the internet of things equipment according to claim 1, wherein the constructing a knowledge graph based on the sample data comprises:
screening the target equipment information data, selecting the equipment information data representing the relation between sample equipment and sample equipment for deep analysis, and mining the association relation between the target equipment information data to construct the target equipment information knowledge graph; and selecting the rest equipment information data except the equipment information data representing the relation between the sample equipment and the sample equipment for constructing a relation database.
5. The method for reporting the fault of the internet of things device according to claim 1, wherein the constructing a knowledge graph based on the sample data further comprises:
and establishing a mapping between the target equipment information knowledge graph and the relational database, and calling related data in the relational database through the target equipment information knowledge graph.
6. The method for reporting the fault of the internet of things device according to claim 1, wherein the step of inputting the device information of the target device into the scoring model to obtain the fault score of the target device comprises the steps of:
in the knowledge graph, according to a random walk algorithm, obtaining a preset number of node devices corresponding to the target device, wherein the node devices and the target device have an association relationship, the association relationship comprises a direct association relationship of direct connection between the target device and the node device, and an indirect association relationship of indirect connection between the target device and the node device;
determining the fault score of each node device and the associated weight of each node device, and carrying out weighted sum calculation according to the fault score of each node and the associated weight of each node device to obtain the fault score of the target device.
7. The method for reporting the faults of the equipment of the Internet of things is characterized by comprising the following steps of:
a data acquisition unit configured to acquire sample data for constructing a failure prediction model, wherein the sample data includes device information of a plurality of sample devices;
a knowledge graph unit, configured to construct a knowledge graph based on the sample data, where the knowledge graph is used to identify an association relationship between device information of the sample device;
a model unit for constructing a scoring model based on the sample data and the knowledge-graph;
and the scoring unit is used for inputting the equipment information of the target equipment into the scoring model to obtain the fault score of the target equipment.
8. The internet of things device fault reporting apparatus of claim 7, wherein the apparatus further comprises: the report calculation unit is used for judging whether the fault score of the target equipment is larger than a scoring threshold value, and if so, determining report category information which is supposed to be of the target equipment according to the fault score;
a category determining unit configured to determine whether the target device is in a reporting state based on the determined reporting category information;
and a reporting unit, configured to send reporting information to at least one second terminal in response to determining that the target device is in a reporting state, where the reporting information includes reporting category information and location information of the target device.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the internet of things device failure reporting method of any one of claims 1 to 6.
10. A storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the internet of things device failure reporting method of any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117647697A (en) * | 2023-11-21 | 2024-03-05 | 广东电网有限责任公司江门供电局 | Knowledge graph-based fault positioning method and system for electric power metering assembly line |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117647697A (en) * | 2023-11-21 | 2024-03-05 | 广东电网有限责任公司江门供电局 | Knowledge graph-based fault positioning method and system for electric power metering assembly line |
| CN117647697B (en) * | 2023-11-21 | 2024-05-14 | 广东电网有限责任公司江门供电局 | Knowledge graph-based fault positioning method and system for electric power metering assembly line |
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