CN116976859A - Intelligent campus management dormitory warranty maintenance method and system based on big data application - Google Patents
Intelligent campus management dormitory warranty maintenance method and system based on big data application Download PDFInfo
- Publication number
- CN116976859A CN116976859A CN202311006704.5A CN202311006704A CN116976859A CN 116976859 A CN116976859 A CN 116976859A CN 202311006704 A CN202311006704 A CN 202311006704A CN 116976859 A CN116976859 A CN 116976859A
- Authority
- CN
- China
- Prior art keywords
- real
- maintenance
- time monitoring
- monitoring data
- dormitory
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 98
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000012544 monitoring process Methods 0.000 claims abstract description 161
- 230000005540 biological transmission Effects 0.000 claims description 13
- 238000013075 data extraction Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 12
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000012545 processing Methods 0.000 description 17
- 230000001537 neural effect Effects 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/38—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
The invention discloses a maintenance method and a system for intelligent campus management dormitory based on big data application, and belongs to the technical field of intelligent campus technology and operation and maintenance. According to the invention, by acquiring the real-time monitoring data of the distributed monitoring nodes, whether the dormitory equipment fails or not is monitored in real time, so that the real-time discovery of the failure is ensured, and the maintenance efficiency is improved; obtaining metadata by expanding the attribute information; according to the metadata, a knowledge graph corresponding to the single dormitory equipment is constructed, so that the fault cause can be more comprehensively known in the fault discovery process, and the efficiency is improved and the cost is reduced in the real-time maintenance process; through the knowledge graph, the single dormitory equipment faults are predicted and analyzed, so that the faults are not only timely found, but also can be predicted before the faults occur, the influence of the equipment faults on the use is avoided, the efficiency is improved, and the user experience is further improved.
Description
Technical Field
The invention relates to the technical field of intelligent campus technologies and operation and maintenance technologies, in particular to a maintenance method and a maintenance system for intelligent campus management dormitory based on big data application.
Background
The intelligent campus is an intelligent campus, and is also established according to intelligent standards, so that the organic connection of a physical space and an information space is realized, and resources and services can be conveniently acquired by anyone, any time and any place; the smart campus is typically composed of a campus infrastructure with sensor network and intelligent hardware as cores, and an intelligent software system deployed on a cloud server in a data center.
In the field of maintenance of management dormitory warranty, the intelligent campus technology can realize the dispatch of maintenance tasks through the supporting platform and the application platform on the basis of reporting by maintenance personnel or students, so that the maintenance personnel can be parked on site to realize maintenance.
However, the prior art cannot find faults in time, so that maintenance is required to wait for maintenance when dormitory equipment fails, and the use of teachers and students is affected.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the invention provides a maintenance method and a maintenance system for intelligent campus management dormitory based on big data application. The technical scheme is as follows:
in one aspect, there is provided a smart campus management dormitory warranty maintenance method based on big data application, the method being applied to a system including a plurality of distributed monitoring nodes, the method comprising:
acquiring real-time monitoring data of a plurality of distributed monitoring nodes, wherein the distributed monitoring nodes correspond to single dormitory equipment;
extracting attribute information of the plurality of real-time monitoring data and expanding the attribute information;
constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
according to the metadata, constructing a knowledge graph corresponding to the single dormitory equipment;
predicting and analyzing the single dormitory equipment failure based on the knowledge graph;
and executing a corresponding operation and maintenance strategy according to the fault information of the single dormitory equipment.
Optionally, before the acquiring the real-time monitoring data of the plurality of distributed monitoring nodes, the method further includes:
dynamically configuring a real-time monitoring data uploading mode corresponding to the distributed monitoring nodes.
Optionally, the acquiring real-time monitoring data of the plurality of distributed monitoring nodes includes:
setting a central node of the plurality of distributed monitoring nodes and a data transmission strategy corresponding to the central node;
and acquiring the real-time monitoring data according to the data transmission strategy.
Optionally, extracting attribute information of the plurality of real-time monitoring data and expanding the attribute information include:
presetting an identification model based on a neural network;
identifying attribute information in the real-time monitoring data from a plurality of dimensions according to the identification model;
and expanding the attribute information to obtain the expanded attribute information.
Optionally, the constructing metadata corresponding to the real-time monitoring data according to the extended attribute information includes:
setting a description model corresponding to the real-time monitoring data according to the extended attribute information, the source of the real-time monitoring data and the application scene;
and constructing metadata corresponding to the real-time monitoring data according to the description model.
Optionally, the constructing, according to the metadata, a knowledge graph corresponding to the single dormitory device includes:
calculating the relationship data of the single dormitory equipment and the real-time monitoring data time according to the metadata and the real-time monitoring data;
and constructing a knowledge graph corresponding to the single dormitory according to the metadata and the relationship data.
Optionally, the predicting and analyzing the single dormitory equipment fault based on the knowledge graph includes:
setting a data extraction model;
identifying target relation data according to the data extraction model;
identifying a plurality of real-time monitoring data according to the target relation data and the knowledge graph;
presetting a fault identification model;
and inputting the identified multiple real-time monitoring data into the fault identification model to obtain fault information, wherein the fault information is used for indicating whether the dormitory equipment is faulty or not and the fault reason.
Optionally, the executing the corresponding operation and maintenance policy according to the fault information of the single dormitory device includes:
the operation and maintenance strategy comprises remote operation and local operation and maintenance;
if remote operation and maintenance are needed, executing a corresponding operation and maintenance strategy according to the fault reason in the fault information;
and if the local operation and maintenance is needed, acquiring a local available operation and maintenance resource, and distributing operation and maintenance tasks according to the local available operation and maintenance resource.
In another aspect, a smart campus management dormitory warranty maintenance system based on big data applications is provided, wherein the system includes a plurality of distributed monitoring nodes, the method comprising:
the acquisition device is used for acquiring real-time monitoring data of a plurality of distributed monitoring nodes, and the distributed monitoring nodes correspond to single dormitory equipment;
the extracting device is used for extracting attribute information of the plurality of real-time monitoring data and expanding the attribute information;
the construction device is used for constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
the construction device is also used for constructing a knowledge graph corresponding to the single dormitory equipment according to the metadata;
analysis means for predicting and analyzing the single dormitory equipment failure based on the knowledge graph;
and the executing device is used for executing the corresponding operation and maintenance strategy according to the fault information of the single dormitory equipment.
Optionally, the executing device is specifically configured to:
the operation and maintenance strategy comprises remote operation and local operation and maintenance;
if remote operation and maintenance are needed, executing a corresponding operation and maintenance strategy according to the fault reason in the fault information;
and if the local operation and maintenance is needed, acquiring a local available operation and maintenance resource, and distributing operation and maintenance tasks according to the local available operation and maintenance resource.
The technical scheme provided by the embodiment of the invention has the beneficial effects that:
acquiring real-time monitoring data of a plurality of distributed monitoring nodes, wherein the distributed monitoring nodes correspond to single dormitory equipment;
extracting attribute information of the plurality of real-time monitoring data and expanding the attribute information;
constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
according to the metadata, constructing a knowledge graph corresponding to the single dormitory equipment;
predicting and analyzing the single dormitory equipment failure based on the knowledge graph;
and executing a corresponding operation and maintenance strategy according to the fault information of the single dormitory equipment.
1. The real-time monitoring data of the distributed monitoring nodes are obtained to monitor whether the dormitory equipment fails or not in real time, so that the real-time discovery of the failure is ensured, and the maintenance efficiency is improved;
2. obtaining metadata by expanding the attribute information; according to the metadata, a knowledge graph corresponding to the single dormitory equipment is constructed, so that the fault cause can be more comprehensively known in the fault discovery process, and the efficiency is improved and the cost is reduced in the real-time maintenance process;
3. through the knowledge graph, the single dormitory equipment faults are predicted and analyzed, so that the faults are not only timely found, but also can be predicted before the faults occur, the influence of the equipment faults on the use is avoided, the efficiency is improved, and the user experience is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a smart campus management dormitory warranty maintenance method based on big data application provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a smart campus management dormitory warranty maintenance system based on big data applications provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a smart campus management dormitory warranty maintenance system based on big data application provided in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, there is provided a smart campus management dormitory warranty maintenance method based on big data application, the method being applied to a system including a plurality of distributed monitoring nodes, the method comprising:
101. acquiring real-time monitoring data of a plurality of distributed monitoring nodes, wherein the distributed monitoring nodes correspond to single dormitory equipment;
specifically, in practical application, the distributed deployment of the distributed monitoring node may be:
for each dormitory equipment such as power supply equipment, living equipment (such as air conditioner and access control) and network equipment, a corresponding monitoring node is arranged; the monitoring node is configured with a network module;
for a plurality of monitoring nodes in a single dormitory, realizing distributed deployment through an IP address by using a network module self-subnet;
the monitoring nodes broadcast own network parameters and performance parameters to other nodes in real time; namely, for each monitoring node, dynamically maintaining a monitoring node performance table through the broadcasting, wherein the monitoring node performance table comprises network parameters and performance parameters;
selecting a monitoring node with network parameters meeting preset network conditions as a data transmission node in a preset time period, wherein the network parameters of the monitoring node are optimal if a monitoring node performance table indicates that the monitoring node is in a preset network condition;
selecting a monitoring node with performance parameters meeting preset performance conditions as an edge processing node in a preset time period, wherein the monitoring node performance table indicates that the performance parameters of the monitoring node are optimal;
the edge processing node acquires an edge processing algorithm or an edge processing model from a server side through a data transmission node; the edge processing algorithm or the edge processing model is used for processing data and faults transmitted by other monitoring nodes, so that the response speed is improved through edge calculation on the basis of ensuring accuracy through updating the edge processing algorithm or the edge processing model in real time.
102. Extracting attribute information of a plurality of real-time monitoring data and expanding the attribute information;
103. constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
104. according to the metadata, constructing a knowledge graph corresponding to the single dormitory equipment;
105. predicting and analyzing single dormitory equipment faults based on the knowledge graph;
106. and executing corresponding operation and maintenance strategies according to the fault information of the single dormitory equipment.
Optionally, before step 101, before acquiring the real-time monitoring data of the plurality of distributed monitoring nodes, the method further includes:
dynamically configuring a real-time monitoring data uploading mode corresponding to the distributed monitoring nodes, wherein an uploading object is an edge processing node.
The process may be specifically:
the distributed monitoring nodes are configured with real-time monitoring data reporting conditions;
the monitoring data reporting condition may be that the data transmission node is obtained from the server side and then distributed to other distributed monitoring nodes; the updating time can be periodic, or can be judged by combining feedback information after fault maintenance, if maintenance is unsuccessful, or if fault is occurred, but real-time monitoring data is not reported, the updating is performed.
Because the cloud has the advantage of big data processing, the accuracy can be further improved on the premise of big data processing by updating the monitoring data reporting condition in real time.
Optionally, step 101 obtaining real-time monitoring data of the plurality of distributed monitoring nodes includes:
301. setting a central node of a plurality of distributed monitoring nodes and a data transmission strategy corresponding to the central node;
the central node is the data transmission node and the edge processing node described in step 101.
The data transmission strategy comprises the following steps:
after the real-time monitoring data meet the reporting condition, the distributed monitoring node establishes independent communication connection with the central node;
the central node stops broadcasting and receiving information of other distributed monitoring nodes;
and receiving real-time monitoring data transmitted by the distributed monitoring nodes.
302. And acquiring real-time monitoring data according to the data transmission strategy.
Optionally, step 102 extracts attribute information of the plurality of real-time monitoring data, and extends the attribute information to include:
401. presetting an identification model based on a neural network;
the neural network may be a self-learning text recognition model; the embodiment of the invention does not limit the specific text recognition model and the training process thereof.
402. Identifying attribute information in the real-time monitoring data from a plurality of dimensions according to the identification model;
the attribute information comprises names of components, running states, maintenance records, types of real-time monitoring data (fault or non-fault), fault information and management information;
the fault information includes a fault description of the device;
the management information includes update time of the real-time monitoring data and update content.
And filling the default content of the part by a preset value.
403. And expanding the attribute information to obtain the expanded attribute information.
Specifically, attribute information including a null rate, a unique value rate, a column number, a line number, and the like of the real-time monitoring data is expanded.
The real-time monitoring data uploaded by the partial distributed monitoring nodes further comprises fault information corresponding to an edge processing algorithm or an edge processing model, and the steps can be as follows:
and expanding attribute information including accuracy, recall, auc, f-1score, model parameters, model frames and the like of an edge processing algorithm or an edge processing model.
By expanding the attribute information, the expanded attribute information is obtained, and the comprehensive acquisition of the real-time monitoring data is realized, so that the accuracy is improved.
Optionally, step 103 includes constructing metadata corresponding to the real-time monitoring data according to the extended attribute information:
501. setting a description model corresponding to the real-time monitoring data according to the extended attribute information, the source of the real-time monitoring data and the application scene;
the application scene comprises a fault scene, namely fault content indicated by fault information.
The description model can be realized through a knowledge graph corresponding to the monitoring data, and the process can be as follows:
acquiring the relativity of all contents contained in the attribute information; the correlation may be preset by the system, such as a correlation between names of set components, operation states, maintenance records, and types of real-time monitoring data.
The dimension of the content contained in the attribute information is identified, and in practical application, the name, the running state, the maintenance record, the type (fault or non-fault) of the real-time monitoring data, the fault information and the management information of the components can be set as one dimension respectively;
in practical application, if the contents represented by the dimensions are similar, if the contents represented by the running state and the maintenance record are abnormal operation of the component, the two dimensions of the running state and the maintenance record are combined to improve accuracy.
502. And constructing metadata corresponding to the real-time monitoring data according to the description model.
Defining a plurality of dimensions in the description model through schema, and establishing an initial knowledge graph;
filling the content contained in the correlation and the attribute information corresponding to the dimension into the initial knowledge graph so as to perfect the description of dormitory equipment corresponding to the distributed monitoring node;
taking the filled knowledge graph as metadata corresponding to the real-time monitoring data,
The metadata describe the information of the monitoring data, and the description dimension of the monitoring data is increased, so that the usability of the monitoring data is enhanced.
Optionally, step 104 includes constructing, according to the metadata, a knowledge graph corresponding to the single dormitory device:
601. calculating the relationship data of the single dormitory equipment and the real-time monitoring data time according to the metadata and the real-time monitoring data;
specifically, constructing a map relationship among all dormitory equipment in a single dormitory based on nlp technology and a knowledge map, and obtaining relationship data;
the relationship data is used to indicate all affected dormitory equipment when a fault occurs.
602. And constructing a knowledge graph corresponding to the single dormitory according to the metadata and the relationship data.
In practical application, based on nlp technology, algorithms including named entity recognition, entity extraction, relation link, entity disambiguation and the like are adopted, specific monitoring data corresponding to metadata and relation data are put into a map one by one according to a designed schema, and synchronous increase and update are carried out along with the increase of access and production monitoring data.
Optionally, referring to fig. 2, step 105 of predicting and analyzing a single dormitory equipment failure based on the knowledge-graph includes:
701. setting a data extraction model; in practical applications, the data extraction model may be a neural algorithm-based text recognition model, which may be a self-learning neural algorithm model.
The neural algorithm model in the above process is not limited in the specific training process and training mode.
702. Identifying target relation data according to the data extraction model; the destination relation data are monitoring data with similar attribute information or similar source information;
the attribute information or the source information includes monitoring data from the same equipment, the same area or the same system (such as a power supply system, an access control system and the like);
the extraction of the target relation data is realized through a self-learning neural algorithm, and the identification accuracy is further improved on the basis of saving the labor cost.
703. Identifying a plurality of real-time monitoring data according to the target relation data and the knowledge graph; i.e. a plurality of associated real-time monitoring data is identified.
704. Presetting a fault identification model;
in practical application, the fault recognition model may be a markov decision model, and the embodiment of the present invention does not limit the specific markov decision model.
705. And inputting the identified multiple real-time monitoring data into a fault identification model, and acquiring fault information which is used for indicating whether the dormitory equipment is faulty or not and the fault reason.
Optionally, step 106 includes, according to the fault information of the single dormitory device, executing the corresponding operation and maintenance policy:
the operation and maintenance strategy comprises remote operation and local operation and maintenance;
if remote operation and maintenance are needed, executing a corresponding operation and maintenance strategy according to the fault reason in the fault information;
if the local operation and maintenance is needed, the local available operation and maintenance resources are obtained, and operation and maintenance tasks are distributed according to the local available operation and maintenance resources.
According to the technical scheme provided by the embodiment of the invention, whether the dormitory equipment fails or not is monitored in real time by acquiring the real-time monitoring data of the distributed monitoring nodes, so that the real-time discovery of the failure is ensured, and the maintenance efficiency is improved; obtaining metadata by expanding the attribute information; according to the metadata, a knowledge graph corresponding to the single dormitory equipment is constructed, so that the fault cause can be more comprehensively known in the fault discovery process, and the efficiency is improved and the cost is reduced in the real-time maintenance process; through the knowledge graph, the single dormitory equipment faults are predicted and analyzed, so that the faults are not only timely found, but also can be predicted before the faults occur, the influence of the equipment faults on the use is avoided, the efficiency is improved, and the user experience is further improved.
Referring to fig. 3, there is provided a smart campus management dormitory warranty maintenance system based on big data application, wherein the system includes a plurality of distributed monitoring nodes, and the method includes:
the acquisition device is used for acquiring real-time monitoring data of a plurality of distributed monitoring nodes, and the distributed monitoring nodes correspond to single dormitory equipment;
the extraction device is used for extracting attribute information of a plurality of real-time monitoring data and expanding the attribute information;
the construction device is used for constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
the construction device is also used for constructing a knowledge graph corresponding to the single dormitory equipment according to the metadata;
analysis means for predicting and analyzing a single dormitory equipment failure based on the knowledge graph;
and the executing device is used for executing the corresponding operation and maintenance strategy according to the fault information of the single dormitory equipment.
Optionally, before acquiring the real-time monitoring data of the plurality of distributed monitoring nodes, the method further includes:
in a single dormitory device and respectively
Dynamically configuring a real-time monitoring data uploading mode corresponding to the distributed monitoring nodes.
Optionally, the acquiring device is configured to:
setting a central node of a plurality of distributed monitoring nodes and a data transmission strategy corresponding to the central node;
and acquiring real-time monitoring data according to the data transmission strategy.
Optionally, the extracting device is configured to:
presetting an identification model based on a neural network;
identifying attribute information in the real-time monitoring data from a plurality of dimensions according to the identification model;
and expanding the attribute information to obtain the expanded attribute information.
Optionally, the construction device is configured to:
setting a description model corresponding to the real-time monitoring data according to the extended attribute information, the source of the real-time monitoring data and the application scene;
and constructing metadata corresponding to the real-time monitoring data according to the description model.
Optionally, the construction device is configured to:
calculating the relationship data of the single dormitory equipment and the real-time monitoring data time according to the metadata and the real-time monitoring data;
and constructing a knowledge graph corresponding to the single dormitory according to the metadata and the relationship data.
Optionally, the analysis device is specifically configured to:
setting a data extraction model;
identifying target relation data according to the data extraction model;
according to the target relation data and the knowledge graph, a plurality of real-time monitoring data are identified;
presetting a fault identification model;
and inputting the identified multiple real-time monitoring data into a fault identification model, and acquiring fault information which is used for indicating whether the dormitory equipment is faulty or not and the fault reason.
Optionally, the executing device is specifically configured to:
the operation and maintenance strategy comprises remote operation and local operation and maintenance;
if remote operation and maintenance are needed, executing a corresponding operation and maintenance strategy according to the fault reason in the fault information;
if the local operation and maintenance is needed, the local available operation and maintenance resources are obtained, and operation and maintenance tasks are distributed according to the local available operation and maintenance resources.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present invention, which is not described herein.
It should be noted that: the smart campus management dormitory warranty maintenance system based on the big data application provided in the above embodiment is only exemplified by the division of the above functional modules when executing the smart campus management dormitory warranty maintenance method based on the big data application, and in practical application, the above functional allocation may be completed by different functional modules according to needs, i.e. the internal structure of the system is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the embodiments of the system and the method for maintaining the intelligent campus management dormitory based on the big data application provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the system and the method embodiments are detailed in the foregoing embodiments, and are not repeated herein.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A smart campus management dormitory warranty maintenance method based on big data applications, the method being applied to a system comprising a plurality of distributed monitoring nodes, the method comprising:
acquiring real-time monitoring data of a plurality of distributed monitoring nodes, wherein the distributed monitoring nodes correspond to single dormitory equipment;
extracting attribute information of the plurality of real-time monitoring data and expanding the attribute information;
constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
according to the metadata, constructing a knowledge graph corresponding to the single dormitory equipment;
predicting and analyzing the single dormitory equipment failure based on the knowledge graph;
and executing a corresponding operation and maintenance strategy according to the fault information of the single dormitory equipment.
2. The method of claim 1, wherein prior to the acquiring real-time monitoring data for the plurality of distributed monitoring nodes, the method further comprises:
dynamically configuring a real-time monitoring data uploading mode corresponding to the distributed monitoring nodes.
3. The method of claim 2, wherein the acquiring real-time monitoring data of the plurality of distributed monitoring nodes comprises:
setting a central node of the plurality of distributed monitoring nodes and a data transmission strategy corresponding to the central node;
and acquiring the real-time monitoring data according to the data transmission strategy.
4. The method of claim 3, wherein extracting and expanding attribute information of the plurality of real-time monitoring data comprises:
presetting an identification model based on a neural network;
identifying attribute information in the real-time monitoring data from a plurality of dimensions according to the identification model;
and expanding the attribute information to obtain the expanded attribute information.
5. The method of claim 4, wherein constructing metadata corresponding to the real-time monitoring data according to the extended attribute information comprises:
setting a description model corresponding to the real-time monitoring data according to the extended attribute information, the source of the real-time monitoring data and the application scene;
and constructing metadata corresponding to the real-time monitoring data according to the description model.
6. The method of claim 5, wherein constructing a knowledge-graph corresponding to the single dormitory device from the metadata comprises:
calculating the relationship data of the single dormitory equipment and the real-time monitoring data time according to the metadata and the real-time monitoring data;
and constructing a knowledge graph corresponding to the single dormitory according to the metadata and the relationship data.
7. The method of claim 6, wherein predicting and analyzing the single dormitory equipment failure based on the knowledge-graph comprises:
setting a data extraction model;
identifying target relation data according to the data extraction model;
identifying a plurality of real-time monitoring data according to the target relation data and the knowledge graph;
presetting a fault identification model;
and inputting the identified multiple real-time monitoring data into the fault identification model to obtain fault information, wherein the fault information is used for indicating whether the dormitory equipment is faulty or not and the fault reason.
8. The method of claim 7, wherein the executing the corresponding operation and maintenance policy according to the failure information of the single dormitory device comprises:
the operation and maintenance strategy comprises remote operation and local operation and maintenance;
if remote operation and maintenance are needed, executing a corresponding operation and maintenance strategy according to the fault reason in the fault information;
and if the local operation and maintenance is needed, acquiring a local available operation and maintenance resource, and distributing operation and maintenance tasks according to the local available operation and maintenance resource.
9. An intelligent campus management dormitory warranty maintenance system based on big data applications, the system comprising a plurality of distributed monitoring nodes, the method comprising:
the acquisition device is used for acquiring real-time monitoring data of a plurality of distributed monitoring nodes, and the distributed monitoring nodes correspond to single dormitory equipment;
the extracting device is used for extracting attribute information of the plurality of real-time monitoring data and expanding the attribute information;
the construction device is used for constructing metadata corresponding to the real-time monitoring data according to the extended attribute information;
the construction device is also used for constructing a knowledge graph corresponding to the single dormitory equipment according to the metadata;
analysis means for predicting and analyzing the single dormitory equipment failure based on the knowledge graph;
and the executing device is used for executing the corresponding operation and maintenance strategy according to the fault information of the single dormitory equipment.
10. The system according to claim 9, wherein the execution means is specifically configured to:
the operation and maintenance strategy comprises remote operation and local operation and maintenance;
if remote operation and maintenance are needed, executing a corresponding operation and maintenance strategy according to the fault reason in the fault information;
and if the local operation and maintenance is needed, acquiring a local available operation and maintenance resource, and distributing operation and maintenance tasks according to the local available operation and maintenance resource.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311006704.5A CN116976859A (en) | 2023-08-11 | 2023-08-11 | Intelligent campus management dormitory warranty maintenance method and system based on big data application |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311006704.5A CN116976859A (en) | 2023-08-11 | 2023-08-11 | Intelligent campus management dormitory warranty maintenance method and system based on big data application |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116976859A true CN116976859A (en) | 2023-10-31 |
Family
ID=88484891
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311006704.5A Pending CN116976859A (en) | 2023-08-11 | 2023-08-11 | Intelligent campus management dormitory warranty maintenance method and system based on big data application |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116976859A (en) |
-
2023
- 2023-08-11 CN CN202311006704.5A patent/CN116976859A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2020211561A1 (en) | Data processing method and device, storage medium and electronic device | |
| WO2013189110A1 (en) | Power communication fault early warning analysis method and system | |
| CN113176948B (en) | Edge gateway, edge computing system and configuration method thereof | |
| CN113542039A (en) | Method for positioning 5G network virtualization cross-layer problem through AI algorithm | |
| CN108924007B (en) | Big data acquisition and storage system and method of communication operation information | |
| CN110995859A (en) | Intelligent transformer substation supporting platform system based on ubiquitous Internet of things | |
| CN111966289A (en) | Partition optimization method and system based on Kafka cluster | |
| CN110659180A (en) | Data center infrastructure management system based on cluster technology | |
| CN114615718A (en) | Network topology construction method and device based on digital twin technology | |
| CN113448947B (en) | Method and device for distributed deployment operation and maintenance of mongo database | |
| Li et al. | Research and application of AI in 5G network operation and maintenance | |
| CN111077851A (en) | Chemical process fault diagnosis system based on gas chromatography fog calculation framework | |
| CN112486666A (en) | Model-driven reference architecture method and platform | |
| CN112463892A (en) | Early warning method and system based on risk situation | |
| CN112256490A (en) | Data processing method and device | |
| CN116976859A (en) | Intelligent campus management dormitory warranty maintenance method and system based on big data application | |
| CN113824801B (en) | Intelligent integration terminal unified access management component system | |
| CN110855757A (en) | Soft start equipment management system and method based on cloud service | |
| CN109861846A (en) | Using call relation acquisition methods, system and storage medium | |
| CN112769620B (en) | Network deployment method, equipment and computer readable storage medium | |
| CN114648223A (en) | Smart city energy consumption data mining system and method based on Internet of things | |
| CN114091610A (en) | Intelligent decision method and device | |
| CN112486667A (en) | Method and device for accurately processing data based on edge calculation | |
| CN113037867B (en) | Unified data access and processing system for multiple unmanned aerial vehicles | |
| WO2024037345A1 (en) | Fault processing method and apparatus, and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |