CN114048821A - Monitoring method, monitoring system, electronic device and storage medium for multi-dimensional data fusion - Google Patents
Monitoring method, monitoring system, electronic device and storage medium for multi-dimensional data fusion Download PDFInfo
- Publication number
- CN114048821A CN114048821A CN202111388266.4A CN202111388266A CN114048821A CN 114048821 A CN114048821 A CN 114048821A CN 202111388266 A CN202111388266 A CN 202111388266A CN 114048821 A CN114048821 A CN 114048821A
- Authority
- CN
- China
- Prior art keywords
- data
- historical
- real
- time
- multidimensional
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/251—Fusion techniques of input or preprocessed data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
Abstract
The application discloses a monitoring method for multi-dimensional data fusion, which comprises the following steps: acquiring historical multivariate data, and preprocessing a historical multivariate data source to obtain historical multidimensional data in a uniform data format; extracting characteristics of historical multidimensional data in a uniform data format to obtain a historical multidimensional characteristic vector; establishing an electrical equipment state monitoring data model by using the historical multi-dimensional characteristic vector; acquiring real-time multivariate data, and preprocessing a real-time multivariate data source to obtain real-time multidimensional data with a uniform data format; extracting features of real-time multidimensional data in a uniform data format to obtain a real-time multidimensional feature vector; projecting the real-time multidimensional characteristic vector to an electrical equipment state monitoring data model to obtain a projection result; and comparing the projection result with a preset threshold value to obtain a data monitoring result. The application also discloses a monitoring system, an electronic device and a storage medium. The method and the system solve the problems of different data key points and structures, information isolated islands and repeated intersection of all business systems of the enterprise.
Description
Technical Field
The invention belongs to the field of data processing, and particularly relates to a monitoring method, a monitoring system, electronic equipment and a storage medium for multi-dimensional data fusion.
Background
The petrochemical industry currently has the following problems: the electric equipment has various types and large quantity, and the data source is up to 100 communication protocols; all the electrical monitoring systems are not communicated with each other, and a plurality of information isolated islands are formed; the data sources of all systems are independent, a unified data bin is lacked, sharing and sharing of data cannot be realized, fault early warning cannot be realized by applying a data mining technology, and technical and management benefits cannot be obtained from big data. Moreover, due to the lack of a unified alarm information platform, when the operation and maintenance personnel deal with the sudden defect, the problems of information interleaving, information explosion and different time scales can be encountered. In addition, each platform data is not open to the outside, and the high interface fee needs to be paid to the equipment manufacturer during access, so that the data belonging to the user is owned. The equipment intelligent modification has larger one-time investment; the labor intensity of the inspection work is high, the quality and the efficiency are to be improved, and the like.
In the prior art, at present, research on online state monitoring of specific electrical equipment is more at home and abroad, and research on an overall electrical state monitoring platform is very little. The existing literature has proposed the online state monitoring of the potential transformer realizes method and apparatus structure; some documents detail the transformer failure mechanism, theory and method of on-line monitoring; similarly, other documents also respectively study the failure mechanism and online monitoring and analysis method of specific electrical equipment, such as GIS switch cabinets, power transmission lines, and the like. The specific research contents and results belong to the basic work of an electrical equipment state monitoring platform, form a part of a sensing layer or a data layer of the whole platform system, and provide basic technical support for a multi-dimensional data fusion monitoring method.
Aiming at the problem that a plurality of data sources can not be fused on a unified platform for unified management and data state monitoring in the prior art, no effective technical scheme is provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the application provides a monitoring method, a monitoring system, electronic equipment and a storage medium for multi-dimensional data fusion, which apply a multi-dimensional characteristic data fusion technology and get through a plurality of information islands, so that data from different sources are unified in the multi-dimensional data fusion monitoring system, and an accurate data base is provided for next data state detection.
In a first aspect, the present application provides a monitoring method for multidimensional data fusion, including the following steps:
acquiring historical multi-element data, and preprocessing the historical multi-element data source to obtain historical multi-dimensional data in a unified data format;
extracting features aiming at the historical multidimensional data with the uniform data format to obtain a historical multidimensional feature vector;
establishing an electrical equipment state monitoring data model by using the historical multi-dimensional characteristic vector;
storing the historical multidimensional data, the historical multidimensional characteristic vector and the electrical equipment state monitoring data model into an electrical equipment state monitoring data bin according to a pre-established index;
acquiring real-time multivariate data, and preprocessing the real-time multivariate data source to obtain real-time multidimensional data with a uniform data format;
extracting features aiming at the real-time multidimensional data with the uniform data format to obtain a real-time multidimensional feature vector;
projecting the real-time multidimensional characteristic vector to the established electrical equipment state monitoring data model to obtain a projection result;
and comparing the projection result with a preset threshold value to obtain a data monitoring result.
The historical multivariate data and the real-time multivariate data comprise: the system comprises equipment information data, power grid topological data, measurement data, state monitoring data, geographic information data, weather data, customer information data, metering data and power failure information data.
The condition monitoring data includes: the method comprises the following steps of high-voltage electrical equipment running state monitoring, secondary equipment state monitoring, sensor state monitoring, communication state monitoring, environment state monitoring and auxiliary state monitoring.
The method for extracting the features comprises the following steps: an expert system method, an association analysis method, a neural network method, and a cluster analysis method.
The multi-dimensional feature vector includes: static feature vectors, dynamic feature vectors, timing feature vectors, spatial feature vectors, multi-time scale vectors, multi-space dimension vectors.
The acquiring historical multivariate data or real-time multivariate data comprises the following steps:
establishing corresponding interfaces aiming at different historical multivariate data or real-time multivariate data;
and acquiring different historical multivariate data or real-time multivariate data through the corresponding interfaces.
The preprocessing the historical multi-element data or the real-time multi-element data to obtain the historical multi-dimensional data or the real-time multi-element data in a unified data format comprises the following steps:
filtering the acquired different historical data or real-time multivariate data, and removing invalid data to obtain filtered data;
removing the duplicate of the filtered data, and removing repeated data to obtain the data after the duplicate is removed;
and performing data transformation on the data after the duplication removal to obtain historical multidimensional data or real-time multivariate data in a unified data format.
The pre-established index comprises: time index, feature index, vector index, data model index.
And storing the real-time multidimensional data with the uniform data format and the real-time multidimensional characteristic vector into an electrical equipment state monitoring data bin according to a pre-established index.
In a second aspect, the present application provides a monitoring system for multi-dimensional data fusion, including: the system comprises a historical data acquisition module, a feature extraction module, a model establishment module, a real-time data acquisition module, a projection module and a comparison module;
the historical data acquisition module and the real-time data acquisition module are respectively connected with the feature extraction module, the feature extraction module is respectively connected with the model establishment module and the projection module, and the projection module is connected with the comparison module;
the historical data acquisition module is used for acquiring historical multivariate data and preprocessing the historical multivariate data source to obtain historical multidimensional data in a uniform data format;
the characteristic extraction module is used for extracting characteristics of historical multi-dimensional data or real-time multi-dimensional data in the unified data format to obtain historical multi-dimensional characteristic vectors or real-time multi-dimensional data;
the model establishing module is used for establishing an electrical equipment state monitoring data model by using the historical multidimensional characteristic vector;
the projection module is used for projecting the real-time multidimensional characteristic vector to the established electrical equipment state monitoring data model to obtain a projection result;
and the comparison module is used for comparing the projection result with a preset threshold value to obtain a data monitoring result.
In a third aspect, the present application provides an electronic device, comprising:
one or more processors;
a memory;
one or more applications stored in the memory and configured to be loaded and executed by the one or more processors to perform the multi-dimensional data fusion monitoring method.
In a fourth aspect, the present application proposes a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, enables monitoring of a multidimensional data fusion as described in the first aspect or any one of the possible implementations of the first aspect.
The beneficial effect that this application reached:
according to the monitoring method, the monitoring system, the electronic device and the storage medium for the multi-dimensional data fusion, a plurality of information islands are opened, so that data from different sources are unified in the monitoring system for the multi-dimensional data fusion, and an accurate data base is provided for next data state detection.
The method solves the problems of huge information and uncertainty of information for monitoring the state of the electrical equipment, establishes a unified electrical equipment state monitoring data model, effectively overcomes the problems of different data key points and structures, information isolated islands and repeated intersection of all business systems of an enterprise, is favorable for fully mining the big data value of the electrical equipment, and provides powerful calculation and analysis conditions for operation analysis, risk identification, state early warning, fault processing and the like of the electrical equipment.
Drawings
Fig. 1 is a flowchart of a monitoring method for multidimensional data fusion according to an embodiment of the present application;
FIG. 2 is a flow chart of data acquisition according to an embodiment of the present application;
FIG. 3 is a flow chart of a pre-process according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a monitoring system for multi-dimensional data fusion according to an embodiment of the present disclosure;
fig. 5 is an exemplary diagram of an electronic device according to an embodiment of the application.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Example (b):
in the embodiment, the monitoring method for multi-dimensional data fusion provided by the application is applied to the Jinling petrochemical industry, and the Jinling petrochemical industry faces the following problems 1. the electrical equipment is huge in quantity, wide in distribution and difficult to manage: at present, the tomb petrochemical equipment has longer operation life, more quantity and wide distribution. Whether the equipment running state is normal or not needs to be combined with running information in inspection and the running state of a process device, so that the equipment is difficult to manage. Taking a common-scale distribution substation as an example, about 20 running devices needing inspection and measurement are needed, and one hour is needed for finishing the power transformation according to the specified items. A large amount of workload is required to be invested in completing the daily routing inspection of all the operating electrical equipment of the whole plant. 2. The operation and maintenance personnel are few, the manual inspection efficiency is low: corresponding to the huge amount of inspection equipment, the operation and maintenance personnel who actually invest in are fewer, and the efficiency of manual work and process unit operation communication is lower. With the increasing of new devices, the number of substations and switchyards in charge of equipment in the electric operation department is increased, and the number of distribution equipment is increased, but operation and maintenance personnel is decreased, so that the operation and maintenance difficulty is increased substantially. 3. The operation and maintenance of secondary equipment are not in place, and the hidden production trouble cannot be eliminated as soon as possible: the condition of unplanned shutdown of low-pressure pump equipment can not be avoided in daily operation of a production site, particularly the pump equipment which is a non-important link can not directly cause the shutdown of equipment with serious faults; the shutdown of these devices often reduces the operational reliability of critical links or is an inducing factor in the occurrence of major accidents. Due to the reasons, the problems cannot be found in time because the daily operation and maintenance work is difficult to be in place, so that the hidden production trouble cannot be eliminated as soon as possible, and the reliability of production and operation is greatly reduced.
In a first aspect, the present application provides a monitoring method for multidimensional data fusion, as shown in fig. 1, including the following steps:
step S1: acquiring historical multi-element data, and preprocessing the historical multi-element data source to obtain historical multi-dimensional data in a unified data format;
step S2: extracting features aiming at the historical multidimensional data with the uniform data format to obtain a historical multidimensional feature vector;
step S3: establishing an electrical equipment state monitoring data model by using the historical multi-dimensional characteristic vector;
step S4: storing the historical multidimensional data, the historical multidimensional characteristic vector and the electrical equipment state monitoring data model into an electrical equipment state monitoring data bin according to a pre-established index;
step S5: acquiring real-time multivariate data, and preprocessing the real-time multivariate data source to obtain real-time multidimensional data with a uniform data format;
step S6: extracting features aiming at the real-time multidimensional data with the uniform data format to obtain a real-time multidimensional feature vector;
step S7: projecting the real-time multidimensional characteristic vector to the established electrical equipment state monitoring data model to obtain a projection result;
step S8: and comparing the projection result with a preset threshold value to obtain a data monitoring result.
The historical multivariate data and the real-time multivariate data comprise: the system comprises equipment information data, power grid topological data, measurement data, state monitoring data, geographic information data, weather data, customer information data, metering data and power failure information data.
The condition monitoring data includes: the method comprises the following steps of high-voltage electrical equipment running state monitoring, secondary equipment state monitoring, sensor state monitoring, communication state monitoring, environment state monitoring and auxiliary state monitoring.
(1) Monitoring of operating states of high-voltage electrical devices
High-voltage electrical equipment mainly comprises transformers, circuit breakers, capacitors, lightning arresters and the like. The operating state of the high-voltage electrical device is not entirely contained in the SCADA. Taking a transformer as an example, the oil chromatogram state, the partial discharge state, the iron core state, the winding temperature state, etc. need to be monitored. Such information is extremely important and requires access to the system. Data already contained in the SCADA, such as equipment voltage, current, real power, power factor and the like, are accessed into the system through an interface.
(2) Secondary equipment condition monitoring
The secondary equipment state comprises state information such as faults and abnormity of equipment such as a digital electrical measuring instrument, digital relay protection, a conventional relay protection device, an automatic control device and the like.
(3) Sensor condition monitoring
The sensor state comprises effective/invalid state information of devices such as a current transformer, a voltage transformer, a temperature sensor, a vibration sensor, an arc sensor and the like.
(4) Communication status monitoring
The communication state includes information such as communication on/off, bit error rate, and the like of the secondary device and the communication device.
(5) Environmental condition monitoring
The environmental conditions include temperature, humidity, water immersion, lightning strike, etc. information in the station, the room, the cabinet, and the loop.
(6) Assisted condition monitoring
The auxiliary condition monitoring is the condition monitoring of other auxiliary systems, such as: independent arc light protection system, wireless temperature measurement system, temperature and humidity control system, fire alarm system, online insulation monitoring system, video monitoring system all can insert this platform, fuse data to the equipment that corresponds in.
The method for extracting the features comprises the following steps: an expert system method, an association analysis method, a neural network method, and a cluster analysis method.
The extraction of features by the expert system method, the association analysis method, the neural network method and the cluster analysis method is a technical method which can be realized by all the technicians in the field, and the detailed description is omitted in the present application.
The multi-dimensional feature vector includes: static feature vectors, dynamic feature vectors, timing feature vectors, spatial feature vectors, multi-time scale vectors, multi-space dimension vectors.
The acquiring historical multivariate data or real-time multivariate data, as shown in fig. 2, includes:
step S100: establishing corresponding interfaces aiming at different historical multivariate data or real-time multivariate data;
step S101: and acquiring different historical multivariate data or real-time multivariate data through the corresponding interfaces.
The preprocessing the historical multi-metadata or the real-time multi-metadata to obtain the historical multi-dimensional data or the real-time multi-metadata in a unified data format, as shown in fig. 3, includes:
step S200: filtering the acquired different historical data or real-time multivariate data, and removing invalid data to obtain filtered data;
step S201: removing the duplicate of the filtered data, and removing repeated data to obtain the data after the duplicate is removed;
step S202: and performing data transformation on the data after the duplication removal to obtain historical multidimensional data or real-time multivariate data in a unified data format.
The pre-established index comprises: time index, feature index, vector index, data model index.
And storing the real-time multidimensional data with the uniform data format and the real-time multidimensional characteristic vector into an electrical equipment state monitoring data bin according to a pre-established index.
In a second aspect, the present application provides a monitoring system for multi-dimensional data fusion, as shown in fig. 4, including: the system comprises a historical data acquisition module, a feature extraction module, a model establishment module, a real-time data acquisition module, a projection module and a comparison module;
the historical data acquisition module and the real-time data acquisition module are respectively connected with the feature extraction module, the feature extraction module is respectively connected with the model establishment module and the projection module, and the projection module is connected with the comparison module;
the historical data acquisition module is used for acquiring historical multivariate data and preprocessing the historical multivariate data source to obtain historical multidimensional data in a uniform data format;
the characteristic extraction module is used for extracting characteristics of historical multi-dimensional data or real-time multi-dimensional data in the unified data format to obtain historical multi-dimensional characteristic vectors or real-time multi-dimensional data;
the model establishing module is used for establishing an electrical equipment state monitoring data model by using the historical multidimensional characteristic vector;
the projection module is used for projecting the real-time multidimensional characteristic vector to the established electrical equipment state monitoring data model to obtain a projection result;
and the comparison module is used for comparing the projection result with a preset threshold value to obtain a data monitoring result.
In a third aspect, the present application provides an electronic device, comprising:
one or more processors;
a memory;
one or more applications stored in the memory and configured to be loaded and executed by the one or more processors to perform the multi-dimensional data fusion monitoring method.
As shown in fig. 5, the electronic apparatus 100 includes: a processor 101 and a memory 103. Wherein the processor 101 is coupled to the memory 103, such as via a bus 102.
The structure of the electronic device 100 is not limited to the embodiment of the present application.
The processor 101 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 101 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors.
The memory 103 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an EEPROM, a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the monitoring method for multidimensional data fusion according to the first aspect or any possible implementation manner of the first aspect.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. A monitoring method for multi-dimensional data fusion is characterized by comprising the following steps:
acquiring historical multi-element data, and preprocessing the historical multi-element data source to obtain historical multi-dimensional data in a unified data format;
extracting features aiming at the historical multidimensional data with the uniform data format to obtain a historical multidimensional feature vector;
establishing an electrical equipment state monitoring data model by using the historical multi-dimensional characteristic vector;
storing the historical multidimensional data, the historical multidimensional characteristic vector and the electrical equipment state monitoring data model into an electrical equipment state monitoring data bin according to a pre-established index;
acquiring real-time multivariate data, and preprocessing the real-time multivariate data source to obtain real-time multidimensional data with a uniform data format;
extracting features aiming at the real-time multidimensional data with the uniform data format to obtain a real-time multidimensional feature vector;
projecting the real-time multidimensional characteristic vector to the established electrical equipment state monitoring data model to obtain a projection result;
and comparing the projection result with a preset threshold value to obtain a data monitoring result.
2. The method for monitoring multidimensional data fusion of claim 1, wherein: the historical multivariate data and the real-time multivariate data comprise: the system comprises equipment information data, power grid topological data, measurement data, state monitoring data, geographic information data, weather data, customer information data, metering data and power failure information data.
3. The method for monitoring multidimensional data fusion according to claim 2, wherein: the condition monitoring data includes: the method comprises the following steps of high-voltage electrical equipment running state monitoring, secondary equipment state monitoring, sensor state monitoring, communication state monitoring, environment state monitoring and auxiliary state monitoring.
4. The method for monitoring multidimensional data fusion of claim 1, wherein: the method for extracting the features comprises the following steps: an expert system method, an association analysis method, a neural network method, and a cluster analysis method.
5. The method for monitoring multidimensional data fusion of claim 1, wherein: the acquiring historical multivariate data or real-time multivariate data comprises the following steps:
establishing corresponding interfaces aiming at different historical multivariate data or real-time multivariate data;
and acquiring different historical multivariate data or real-time multivariate data through the corresponding interfaces.
6. The method for monitoring multidimensional data fusion of claim 1, wherein: the preprocessing the historical multi-element data or the real-time multi-element data to obtain the historical multi-dimensional data or the real-time multi-element data in a unified data format comprises the following steps:
filtering the acquired different historical data or real-time multivariate data, and removing invalid data to obtain filtered data;
removing the duplicate of the filtered data, and removing repeated data to obtain the data after the duplicate is removed;
and performing data transformation on the data after the duplication removal to obtain historical multidimensional data or real-time multivariate data in a unified data format.
7. The method for monitoring multidimensional data fusion of claim 1, wherein: also comprises the following steps: and storing the real-time multidimensional data with the uniform data format and the real-time multidimensional characteristic vector into an electrical equipment state monitoring data bin according to a pre-established index.
8. A monitoring system for multi-dimensional data fusion is characterized in that: the method comprises the following steps: the system comprises a historical data acquisition module, a feature extraction module, a model establishment module, a real-time data acquisition module, a projection module and a comparison module;
the historical data acquisition module and the real-time data acquisition module are respectively connected with the feature extraction module, the feature extraction module is respectively connected with the model establishment module and the projection module, and the projection module is connected with the comparison module;
the historical data acquisition module is used for acquiring historical multivariate data and preprocessing the historical multivariate data source to obtain historical multidimensional data in a uniform data format;
the characteristic extraction module is used for extracting characteristics of historical multi-dimensional data or real-time multi-dimensional data in the unified data format to obtain historical multi-dimensional characteristic vectors or real-time multi-dimensional data;
the model establishing module is used for establishing an electrical equipment state monitoring data model by using the historical multidimensional characteristic vector;
the projection module is used for projecting the real-time multidimensional characteristic vector to the established electrical equipment state monitoring data model to obtain a projection result;
and the comparison module is used for comparing the projection result with a preset threshold value to obtain a data monitoring result.
9. An electronic device, comprising:
one or more processors;
a memory;
one or more applications stored in the memory and configured to be loaded and executed by the one or more processors to perform the method of monitoring multi-dimensional data fusion of any of claims 1-7.
10. A computer-readable storage medium, characterized in that,
stored thereon a computer program which can be loaded and run by a processor to perform the method for monitoring a multi-dimensional data fusion according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111388266.4A CN114048821A (en) | 2021-11-22 | 2021-11-22 | Monitoring method, monitoring system, electronic device and storage medium for multi-dimensional data fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111388266.4A CN114048821A (en) | 2021-11-22 | 2021-11-22 | Monitoring method, monitoring system, electronic device and storage medium for multi-dimensional data fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114048821A true CN114048821A (en) | 2022-02-15 |
Family
ID=80210319
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111388266.4A Pending CN114048821A (en) | 2021-11-22 | 2021-11-22 | Monitoring method, monitoring system, electronic device and storage medium for multi-dimensional data fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114048821A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114884987A (en) * | 2022-04-24 | 2022-08-09 | 青岛海信医疗设备股份有限公司 | Method, device and storage medium for acquiring equipment state information |
-
2021
- 2021-11-22 CN CN202111388266.4A patent/CN114048821A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114884987A (en) * | 2022-04-24 | 2022-08-09 | 青岛海信医疗设备股份有限公司 | Method, device and storage medium for acquiring equipment state information |
| CN114884987B (en) * | 2022-04-24 | 2024-03-29 | 青岛海信医疗设备股份有限公司 | Method, device and storage medium for acquiring equipment state information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| RU2525859C2 (en) | Controlling power outage and faulty state of electrical power system | |
| JP5616330B2 (en) | Method and system for managing a power grid | |
| CN101601057A (en) | Electrical substation monitoring and diagnosis | |
| CN110879327B (en) | 10KV line monitoring method by multi-data fusion | |
| Peharda et al. | Using data from SCADA for centralized transformer monitoring applications | |
| CN108876197A (en) | A kind of power equipment cluster and cohort analysis system and method | |
| CN114048821A (en) | Monitoring method, monitoring system, electronic device and storage medium for multi-dimensional data fusion | |
| CN113344364B (en) | Power failure plan risk analysis method and device, electronic equipment and storage medium | |
| Guo et al. | Power transmission risk assessment considering component condition | |
| CN114140266A (en) | Intelligent low-voltage power grid fault studying and judging algorithm | |
| CN112766783B (en) | Method and system for evaluating operation quality of power equipment, terminal equipment and medium | |
| CN107896002A (en) | 10kV feeder loads monitor active alarm system | |
| CN110489729B (en) | Automatic conversion method and system for D5000-matpower grid multi-disaster coupling cascading failure model | |
| CN115267616A (en) | Transformer running state monitoring system and method based on enterprise data middling station | |
| CN112510826B (en) | Method for determining configuration change boundary of modification and extension intelligent recorder | |
| Xiaoyu et al. | The Effect of secondary-DC-loss Protection on the Reliability of substation protection system | |
| Huang et al. | Extracting substation cyber-physical architecture through intelligent electronic devices' data | |
| CN113708489A (en) | Electric power secondary equipment warning method and system | |
| CN106709158B (en) | Performance improvement method of power grid feeder automation system | |
| CN110852606A (en) | Production early report data object analysis method based on regulation cloud | |
| Fazaeli et al. | A novel approach for modeling and maintenance of power system substation automation | |
| CN114040002A (en) | Internet of things-based state monitoring system, state monitoring method, electronic device and storage medium | |
| RU2800630C1 (en) | System for monitoring the quality of electric energy by measuring electric power quantities and parameters | |
| Shokooh et al. | Real-time simulation and supervisory control; the next logical step after system monitoring | |
| CN114374205B (en) | Load transfer analysis method and device |
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 |