CN112749861A - Ubiquitous power Internet of things edge calculation method and system - Google Patents
Ubiquitous power Internet of things edge calculation method and system Download PDFInfo
- Publication number
- CN112749861A CN112749861A CN201911051288.4A CN201911051288A CN112749861A CN 112749861 A CN112749861 A CN 112749861A CN 201911051288 A CN201911051288 A CN 201911051288A CN 112749861 A CN112749861 A CN 112749861A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- electrical equipment
- life cycle
- sensing
- 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
- 238000004364 calculation method Methods 0.000 title claims abstract description 33
- 238000007726 management method Methods 0.000 claims abstract description 74
- 238000004458 analytical method Methods 0.000 claims abstract description 68
- 238000003745 diagnosis Methods 0.000 claims abstract description 45
- 238000000034 method Methods 0.000 claims abstract description 24
- 230000008447 perception Effects 0.000 claims abstract description 24
- 238000011065 in-situ storage Methods 0.000 claims abstract description 19
- 230000006870 function Effects 0.000 claims description 19
- 238000011161 development Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 abstract description 17
- 238000005265 energy consumption Methods 0.000 abstract description 7
- 238000002955 isolation Methods 0.000 abstract description 6
- 238000012423 maintenance Methods 0.000 description 14
- 238000004519 manufacturing process Methods 0.000 description 10
- 238000013461 design Methods 0.000 description 9
- 238000003860 storage Methods 0.000 description 9
- 238000006073 displacement reaction Methods 0.000 description 8
- 238000007689 inspection Methods 0.000 description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 230000005540 biological transmission Effects 0.000 description 6
- 239000013307 optical fiber Substances 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000009529 body temperature measurement Methods 0.000 description 4
- 238000013499 data model Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000007774 longterm Effects 0.000 description 4
- 230000003449 preventive effect Effects 0.000 description 4
- 238000012502 risk assessment Methods 0.000 description 4
- 230000001960 triggered effect Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000010249 in-situ analysis Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Images
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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- 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"
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Computing Systems (AREA)
- Signal Processing (AREA)
- Medical Informatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a ubiquitous power Internet of things edge calculation method and a ubiquitous power Internet of things edge calculation system, wherein the method comprises the following steps: the method comprises the steps of acquiring original data acquired by an integrated sensor on the electrical equipment through an interface module, judging whether the original data meet sensing conditions through a sensing module, and if so, performing: the data meeting the defined perception conditions are analyzed in situ through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module; predicting the health state of the electrical equipment on the basis of a fault diagnosis expert knowledge base in the full life cycle management module through a prediction module according to the data meeting the defined sensing conditions; and storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through a full life cycle management module. The invention realizes the isolation of invalid data and valid data, realizes the in-situ edge calculation of the data, and further meets the requirements of real-time performance, safety, low energy consumption and the like of big data processing.
Description
Technical Field
The invention relates to the technical field of power Internet of things, in particular to a ubiquitous power Internet of things edge calculation method and system.
Background
In recent years, in order to promote the construction of smart power grids, various electrical equipment manufacturers develop devices such as merging units, intelligent terminals, and on-line monitoring IEDs, so as to realize integrated transmission of sensor data and reception and transmission of control signals. With the development of the internet of things technology, the linearly-increased centralized cloud computing capability cannot be matched with the explosively-increased massive edge data, the number of electrical devices in a power grid is large, the number of sensors is huge, and the requirements of instantaneity, safety, low energy consumption and the like of large data processing cannot be met by a single computing resource based on a cloud computing model.
Therefore, how to meet the requirements of real-time performance, safety, low energy consumption and the like of big data processing by using the electrical equipment as a key link in the operation of a power grid is a problem to be solved urgently.
Disclosure of Invention
In view of this, the invention provides a ubiquitous power internet of things edge calculation method, which can meet the requirements of real-time performance, safety, low energy consumption and the like of big data processing.
The invention provides a ubiquitous power Internet of things edge calculation method, which comprises the following steps:
acquiring original data acquired by a sensor integrated on electrical equipment through an interface module;
judging whether the original data meet the perception condition through a perception module, if so, performing:
the data meeting the defined perception conditions are analyzed in situ through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module;
predicting the health state of the electrical equipment on the basis of a fault diagnosis expert knowledge base in the full life cycle management module through a prediction module, wherein the data meet defined perception conditions;
and storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through the full life cycle management module.
Preferably, the method further comprises:
and accessing ubiquitous electric power Internet of things electrical equipment through a data service gateway.
Preferably, the method further comprises:
and accessing the mobile terminal through the data service gateway.
Preferably, the method further comprises:
customized development functions of the in-place edge computing application are provided through the application extension module.
Preferably, the sensing condition includes at least one of a dead zone condition, a threshold condition and a logical operation condition.
A ubiquitous power internet of things edge computing system, comprising:
the interface module is used for acquiring original data acquired by a sensor integrated on the electrical equipment;
the sensing module is used for judging whether the original data meets the sensing condition;
the analysis module is used for carrying out on-site analysis on the data meeting the defined perception conditions based on a fault diagnosis expert knowledge base in the full life cycle management module when the original data meet the perception conditions;
the prediction module is used for predicting the health state of the electrical equipment for the data meeting the defined perception conditions based on the fault diagnosis expert knowledge base in the full life cycle management module when the original data meet the perception conditions;
and the full life cycle management module is used for storing the results of the local analysis and the prediction results of the health state of the electrical equipment in the form of operation data respectively.
Preferably, the system further comprises:
and the data service gateway is used for accessing the ubiquitous power Internet of things electrical equipment.
Preferably, the data service gateway is further configured to access a mobile terminal.
Preferably, the system further comprises:
and the application extension module is used for providing the customized development function of the in-place edge computing application program.
Preferably, the sensing condition includes at least one of a dead zone condition, a threshold condition and a logical operation condition.
In summary, the invention discloses a ubiquitous power internet of things edge calculation method, when edge calculation needs to be performed on a ubiquitous power internet of things, firstly, original data acquired by a sensor integrated on electrical equipment is acquired through an interface module, then, whether the original data meet sensing conditions is judged through a sensing module, and if yes, then: the data meeting the defined sensing conditions are analyzed in situ through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module, and the health state of the electrical equipment is predicted through a prediction module based on the fault diagnosis expert knowledge base in the full life cycle management module; and storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through a full life cycle management module. According to the invention, the isolation of invalid data and valid data is realized through the sensing conditions of the sensing module, the in-situ edge calculation of the data is realized through the in-situ preset fault diagnosis expert knowledge base, and the requirements of real-time performance, safety, low energy consumption and the like of big data processing are further met.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method according to embodiment 1 of a ubiquitous power internet of things edge calculation method disclosed in the present invention;
fig. 2 is a flowchart of a method according to embodiment 2 of the ubiquitous power internet of things disclosed in the present invention;
fig. 3 is a schematic structural diagram of an embodiment 1 of a ubiquitous power internet of things according to the present invention;
fig. 4 is a schematic structural diagram of an embodiment 2 of a ubiquitous power internet of things disclosed in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, which is a flowchart of a method in embodiment 1 of a ubiquitous power internet of things edge computing method disclosed in the present invention, the method may include the following steps:
s101, acquiring original data acquired by a sensor integrated on electrical equipment through an interface module;
when the edge calculation of the ubiquitous power internet of things is needed, the raw data collected by the sensors is firstly acquired through the interface modules connected with various sensors integrated on the electrical equipment. The interface module provides various interfaces and protocols for connecting with the integrated sensor on the electrical equipment, wherein the interfaces comprise cables, RJ45, optical fibers, RS485, RS232 and the like, and the protocols comprise CANopen, Modbus, RFID, Bluetooth, wireless local area network and the like. The integrated sensor on the electrical equipment can comprise an SF6 sensor, a partial discharge sensor, a lightning arrester sensor, a position sensor, a Hall current sensor, a displacement sensor, a current transformer, a temperature and humidity sensor, a wireless temperature measurement sensor and the like.
S102, judging whether the original data meet the sensing condition through a sensing module, if so, entering S103 and S104:
after the raw data acquired by the sensor integrated on the electrical equipment is acquired through the interface module, the raw data transmitted by the interface module connected with the sensing module is received through the sensing module, and the raw data is judged according to the defined sensing conditions.
Wherein the sensing condition comprises a dead zone condition, a threshold condition, a logical operation condition, or a combination of a plurality of conditions. Such as: the sensing module judges the pressure, micro water and other information of SF6 sent by an SF6 sensor through a dead zone condition and a threshold condition, the threshold condition judgment is carried out on the data when the data value exceeds the dead zone condition, the normal sensing judgment is given out when the data value is within the threshold range, and the abnormal sensing judgment is given out when the data value is outside the threshold range; the sensing module respectively carries out threshold condition judgment on current data of the opening and closing coils sent by the Hall current sensor and contact travel data sent by the displacement sensor, and then carries out logic OR operation, namely when the current data of the opening and closing coils or the contact travel data exceed a preset threshold value, the two data are simultaneously sensed and judged, namely the two data are bound into a group. For long-term operation data of the electrical equipment, such as SF6 pressure, micro water and other information, the data are immediately judged after sensing, and the judgment result and the original data are transmitted to the analysis module and the prediction module for further analysis. For data which can be triggered only when the electrical equipment mechanically acts, such as coil opening and closing current data and contact travel data, after sensing, a cache memory is applied in an edge computing system immediately, the data are stored in the cache memory in sequence, and when the time scale of the stored data reaches a preset value, the sensing module transmits the cache data to an analysis module and a prediction module for further analysis.
S103, carrying out local analysis on the data meeting the defined perception conditions through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module;
the analysis module is connected with the sensing module and the life cycle management module, and when the original data meet the sensing condition, the analysis module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the life cycle management module to realize the on-site analysis of the data.
S104, predicting the health state of the electrical equipment on the basis of a fault diagnosis expert knowledge base in the full life cycle management module through a prediction module, wherein the data meet the defined perception conditions;
and the prediction module is connected with the sensing module and the full life cycle management module, and when the original data meet the sensing condition, the prediction module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the full life cycle management module to realize risk assessment of the health state of the electrical equipment. All data from design, production, factory inspection, operation, maintenance to final service expiration of the electrical equipment are stored in the full life cycle management module, and a data model of the electrical equipment is established through a digital twin technology to realize data expression of the physical electrical equipment.
And S105, storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data through the full life cycle management module.
When the analysis module performs on-site analysis on the data meeting the defined sensing conditions based on a fault diagnosis expert knowledge base in the full life cycle management module, the analysis result is stored in the full life cycle management module in the form of operation data; and when the prediction module predicts the health state of the electrical equipment according to the data meeting the defined sensing conditions on the basis of a fault diagnosis expert knowledge base in the life cycle management module, the prediction result is stored in the life cycle management module in the form of operation data.
In summary, when edge calculation needs to be performed on the ubiquitous power internet of things, the raw data acquired by the integrated sensor on the electrical device is acquired through the interface module, and then whether the raw data meets the sensing condition is judged through the sensing module, if yes, then: the data meeting the defined sensing conditions are analyzed in situ through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module, and the health state of the electrical equipment is predicted through a prediction module based on the fault diagnosis expert knowledge base in the full life cycle management module; and storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through a full life cycle management module. According to the invention, the isolation of invalid data and valid data is realized through the sensing conditions of the sensing module, the in-situ edge calculation of the data is realized through the in-situ preset fault diagnosis expert knowledge base, and the requirements of real-time performance, safety, low energy consumption and the like of big data processing are further met.
As shown in fig. 2, which is a flowchart of a method in embodiment 2 of the ubiquitous power internet of things edge computing method disclosed in the present invention, the method may include the following steps:
s201, accessing ubiquitous power Internet of things electrical equipment and a mobile terminal through a data service gateway;
when the edge calculation of the ubiquitous power Internet of things is needed, the ubiquitous power Internet of things electric equipment and the mobile terminal are accessed through the data service gateway. Specifically, the data service gateway can enable the electrical equipment to be plugged into a ubiquitous power internet of things main network or platform in a plug-and-play mode through means such as optical fibers, WIFI, 4G/5G and two-dimensional codes, and can be connected into the mobile terminal.
S202, providing a customized development function of the in-place edge computing application program through an application extension module;
and the customized development functions of the in-situ edge computing application program, such as functions of fault early warning and alarming, preventive maintenance and maintenance decision support, matching of tools and accessories, operation guidance and guidance, and the like, are provided through the application extension module.
S203, acquiring original data acquired by a sensor integrated on the electrical equipment through an interface module;
after the ubiquitous power internet of things electrical equipment is accessed, the raw data collected by the sensors is acquired through the interface modules connected with various sensors integrated on the electrical equipment. The interface module provides various interfaces and protocols for connecting with the integrated sensor on the electrical equipment, wherein the interfaces comprise cables, RJ45, optical fibers, RS485, RS232 and the like, and the protocols comprise CANopen, Modbus, RFID, Bluetooth, wireless local area network and the like. The integrated sensor on the electrical equipment can comprise an SF6 sensor, a partial discharge sensor, a lightning arrester sensor, a position sensor, a Hall current sensor, a displacement sensor, a current transformer, a temperature and humidity sensor, a wireless temperature measurement sensor and the like.
S204, judging whether the original data meet the sensing condition through a sensing module, if so, entering S205 and S206:
after the raw data acquired by the sensor integrated on the electrical equipment is acquired through the interface module, the raw data transmitted by the interface module connected with the sensing module is received through the sensing module, and the raw data is judged according to the defined sensing conditions.
Wherein the sensing condition comprises a dead zone condition, a threshold condition, a logical operation condition, or a combination of a plurality of conditions. Such as: the sensing module judges the pressure, micro water and other information of SF6 sent by an SF6 sensor through a dead zone condition and a threshold condition, the threshold condition judgment is carried out on the data when the data value exceeds the dead zone condition, the normal sensing judgment is given out when the data value is within the threshold range, and the abnormal sensing judgment is given out when the data value is outside the threshold range; the sensing module respectively carries out threshold condition judgment on current data of the opening and closing coils sent by the Hall current sensor and contact travel data sent by the displacement sensor, and then carries out logic OR operation, namely when the current data of the opening and closing coils or the contact travel data exceed a preset threshold value, the two data are simultaneously sensed and judged, namely the two data are bound into a group. For long-term operation data of the electrical equipment, such as SF6 pressure, micro water and other information, the data are immediately judged after sensing, and the judgment result and the original data are transmitted to the analysis module and the prediction module for further analysis. For data which can be triggered only when the electrical equipment mechanically acts, such as coil opening and closing current data and contact travel data, after sensing, a cache memory is applied in an edge computing system immediately, the data are stored in the cache memory in sequence, and when the time scale of the stored data reaches a preset value, the sensing module transmits the cache data to an analysis module and a prediction module for further analysis.
S205, performing local analysis on the data meeting the defined perception conditions through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module;
the analysis module is connected with the sensing module and the life cycle management module, and when the original data meet the sensing condition, the analysis module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the life cycle management module to realize the on-site analysis of the data.
S206, predicting the health state of the electrical equipment on the basis of a fault diagnosis expert knowledge base in the full life cycle management module through the prediction module according to the data meeting the defined sensing conditions;
and the prediction module is connected with the sensing module and the full life cycle management module, and when the original data meet the sensing condition, the prediction module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the full life cycle management module to realize risk assessment of the health state of the electrical equipment. All data from design, production, factory inspection, operation, maintenance to final service expiration of the electrical equipment are stored in the full life cycle management module, and a data model of the electrical equipment is established through a digital twin technology to realize data expression of the physical electrical equipment.
And S207, storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through a full life cycle management module.
When the analysis module performs on-site analysis on the data meeting the defined sensing conditions based on a fault diagnosis expert knowledge base in the full life cycle management module, the analysis result is stored in the full life cycle management module in the form of operation data; and when the prediction module predicts the health state of the electrical equipment according to the data meeting the defined sensing conditions on the basis of a fault diagnosis expert knowledge base in the life cycle management module, the prediction result is stored in the life cycle management module in the form of operation data.
Therefore, data access is realized, and the electrical equipment can be accessed to a ubiquitous power Internet of things main network or platform in a plug-and-play manner; secondly, the plug-and-play access of the sensor on the electrical equipment is realized through a unified communication protocol; and thirdly, the access of personnel, materials, tools and the like to the electrical equipment can be realized. From the physical perspective, the data interconnection between the ubiquitous Internet of things and the underlying sensor network and man-machine-object network is realized;
meanwhile, the invention realizes storage and calculation, and the existing online monitoring device for the smart power grid mainly realizes the transmission, access and control of signals as core functions; the invention not only can realize the existing on-line monitoring, but also has the functions of local storage and calculation, and embodies the storage, processing and decision of data. The data of the manufacturing process of the electrical equipment (design data, process data, production data and inspection data) can be stored, and various operation data generated in the operation process of the equipment can be stored. In addition, data can be analyzed and processed from the source of the equipment, and only the result of local data processing or data needing further processing needs to be sent, so that the requirement of centralized data processing of the ubiquitous Internet of things is greatly reduced, and the network efficiency is improved;
meanwhile, the invention realizes the application extension function, and can develop various application programs in a customized manner and build various application models and scenes based on the storage and calculation functions, such as fault early warning and alarming, preventive maintenance and maintenance decision support, matching of tools and accessories, operation guidance and the like.
In summary, the invention realizes the isolation of invalid data and valid data through the sensing conditions of the sensing module, and the sensing module performs pre-judgment on the data, thereby reducing the data transmission amount and reducing the workload of data processing in the subsequent link; the in-situ edge calculation of the data is realized through a fault diagnosis expert knowledge base preset in situ; by collecting data of various sensors on the electrical equipment and adopting digital twin calculation, the datamation expression of the physical electrical equipment is realized; the whole life cycle management of the electrical equipment is realized by providing the access function of design data, process data, production data, inspection data and maintenance data; meanwhile, data interconnection between the ubiquitous Internet of things and a sensor network and a man-machine-object network at the bottom layer of the electrical equipment is realized.
As shown in fig. 3, which is a schematic structural diagram of an embodiment 1 of a ubiquitous power internet of things disclosed in the present invention, the system may include:
the interface module 301 is configured to obtain raw data acquired by a sensor integrated on an electrical device;
when the edge calculation of the ubiquitous power internet of things is needed, the raw data collected by the sensors is firstly acquired through the interface modules connected with various sensors integrated on the electrical equipment. The interface module provides various interfaces and protocols for connecting with the integrated sensor on the electrical equipment, wherein the interfaces comprise cables, RJ45, optical fibers, RS485, RS232 and the like, and the protocols comprise CANopen, Modbus, RFID, Bluetooth, wireless local area network and the like. The integrated sensor on the electrical equipment can comprise an SF6 sensor, a partial discharge sensor, a lightning arrester sensor, a position sensor, a Hall current sensor, a displacement sensor, a current transformer, a temperature and humidity sensor, a wireless temperature measurement sensor and the like.
A perception module 302, configured to determine whether the raw data meets a perception condition;
after the raw data acquired by the sensor integrated on the electrical equipment is acquired through the interface module, the raw data transmitted by the interface module connected with the sensing module is received through the sensing module, and the raw data is judged according to the defined sensing conditions.
Wherein the sensing condition comprises a dead zone condition, a threshold condition, a logical operation condition, or a combination of a plurality of conditions. Such as: the sensing module judges the pressure, micro water and other information of SF6 sent by an SF6 sensor through a dead zone condition and a threshold condition, the threshold condition judgment is carried out on the data when the data value exceeds the dead zone condition, the normal sensing judgment is given out when the data value is within the threshold range, and the abnormal sensing judgment is given out when the data value is outside the threshold range; the sensing module respectively carries out threshold condition judgment on current data of the opening and closing coils sent by the Hall current sensor and contact travel data sent by the displacement sensor, and then carries out logic OR operation, namely when the current data of the opening and closing coils or the contact travel data exceed a preset threshold value, the two data are simultaneously sensed and judged, namely the two data are bound into a group. For long-term operation data of the electrical equipment, such as SF6 pressure, micro water and other information, the data are immediately judged after sensing, and the judgment result and the original data are transmitted to the analysis module and the prediction module for further analysis. For data which can be triggered only when the electrical equipment mechanically acts, such as coil opening and closing current data and contact travel data, after sensing, a cache memory is applied in an edge computing system immediately, the data are stored in the cache memory in sequence, and when the time scale of the stored data reaches a preset value, the sensing module transmits the cache data to an analysis module and a prediction module for further analysis.
The analysis module 303 is configured to perform in-situ analysis on data meeting the defined sensing conditions based on the fault diagnosis expert knowledge base in the full life cycle management module when the raw data meets the sensing conditions;
the analysis module is connected with the sensing module and the life cycle management module, and when the original data meet the sensing condition, the analysis module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the life cycle management module to realize the on-site analysis of the data.
The prediction module 304 is used for predicting the health state of the electrical equipment for the data meeting the defined perception conditions based on the fault diagnosis expert knowledge base in the full life cycle management module when the original data meet the perception conditions;
and the prediction module is connected with the sensing module and the full life cycle management module, and when the original data meet the sensing condition, the prediction module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the full life cycle management module to realize risk assessment of the health state of the electrical equipment. All data from design, production, factory inspection, operation, maintenance to final service expiration of the electrical equipment are stored in the full life cycle management module, and a data model of the electrical equipment is established through a digital twin technology to realize data expression of the physical electrical equipment.
The life cycle management module 305 is configured to store the result of the local analysis and the result of the prediction of the health state of the electrical device in the form of operation data.
When the analysis module performs on-site analysis on the data meeting the defined sensing conditions based on a fault diagnosis expert knowledge base in the full life cycle management module, the analysis result is stored in the full life cycle management module in the form of operation data; and when the prediction module predicts the health state of the electrical equipment according to the data meeting the defined sensing conditions on the basis of a fault diagnosis expert knowledge base in the life cycle management module, the prediction result is stored in the life cycle management module in the form of operation data.
In summary, when edge calculation needs to be performed on the ubiquitous power internet of things, the raw data acquired by the integrated sensor on the electrical device is acquired through the interface module, and then whether the raw data meets the sensing condition is judged through the sensing module, if yes, then: the data meeting the defined sensing conditions are analyzed in situ through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module, and the health state of the electrical equipment is predicted through a prediction module based on the fault diagnosis expert knowledge base in the full life cycle management module; and storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through a full life cycle management module. According to the invention, the isolation of invalid data and valid data is realized through the sensing conditions of the sensing module, the in-situ edge calculation of the data is realized through the in-situ preset fault diagnosis expert knowledge base, and the requirements of real-time performance, safety, low energy consumption and the like of big data processing are further met.
As shown in fig. 4, which is a schematic structural diagram of an embodiment 2 of a ubiquitous power internet of things disclosed in the present invention, the system may include:
the data service gateway 401 is used for accessing ubiquitous power internet of things electrical equipment and a mobile terminal;
when the edge calculation of the ubiquitous power Internet of things is needed, the ubiquitous power Internet of things electric equipment and the mobile terminal are accessed through the data service gateway. Specifically, the data service gateway can enable the electrical equipment to be plugged into a ubiquitous power internet of things main network or platform in a plug-and-play mode through means such as optical fibers, WIFI, 4G/5G and two-dimensional codes, and can be connected into the mobile terminal.
An application extension module 402 for providing customized development functionality for in-place edge computing applications;
and the customized development functions of the in-situ edge computing application program, such as functions of fault early warning and alarming, preventive maintenance and maintenance decision support, matching of tools and accessories, operation guidance and guidance, and the like, are provided through the application extension module.
An interface module 403, configured to obtain raw data acquired by a sensor integrated on an electrical device;
after the ubiquitous power internet of things electrical equipment is accessed, the raw data collected by the sensors is acquired through the interface modules connected with various sensors integrated on the electrical equipment. The interface module provides various interfaces and protocols for connecting with the integrated sensor on the electrical equipment, wherein the interfaces comprise cables, RJ45, optical fibers, RS485, RS232 and the like, and the protocols comprise CANopen, Modbus, RFID, Bluetooth, wireless local area network and the like. The integrated sensor on the electrical equipment can comprise an SF6 sensor, a partial discharge sensor, a lightning arrester sensor, a position sensor, a Hall current sensor, a displacement sensor, a current transformer, a temperature and humidity sensor, a wireless temperature measurement sensor and the like.
A sensing module 404, configured to determine whether the raw data meets a sensing condition;
after the raw data acquired by the sensor integrated on the electrical equipment is acquired through the interface module, the raw data transmitted by the interface module connected with the sensing module is received through the sensing module, and the raw data is judged according to the defined sensing conditions.
Wherein the sensing condition comprises a dead zone condition, a threshold condition, a logical operation condition, or a combination of a plurality of conditions. Such as: the sensing module judges the pressure, micro water and other information of SF6 sent by an SF6 sensor through a dead zone condition and a threshold condition, the threshold condition judgment is carried out on the data when the data value exceeds the dead zone condition, the normal sensing judgment is given out when the data value is within the threshold range, and the abnormal sensing judgment is given out when the data value is outside the threshold range; the sensing module respectively carries out threshold condition judgment on current data of the opening and closing coils sent by the Hall current sensor and contact travel data sent by the displacement sensor, and then carries out logic OR operation, namely when the current data of the opening and closing coils or the contact travel data exceed a preset threshold value, the two data are simultaneously sensed and judged, namely the two data are bound into a group. For long-term operation data of the electrical equipment, such as SF6 pressure, micro water and other information, the data are immediately judged after sensing, and the judgment result and the original data are transmitted to the analysis module and the prediction module for further analysis. For data which can be triggered only when the electrical equipment mechanically acts, such as coil opening and closing current data and contact travel data, after sensing, a cache memory is applied in an edge computing system immediately, the data are stored in the cache memory in sequence, and when the time scale of the stored data reaches a preset value, the sensing module transmits the cache data to an analysis module and a prediction module for further analysis.
An analysis module 405, configured to perform in-situ analysis on data meeting the defined sensing conditions based on a fault diagnosis expert knowledge base in the full life cycle management module when the raw data meets the sensing conditions;
the analysis module is connected with the sensing module and the life cycle management module, and when the original data meet the sensing condition, the analysis module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the life cycle management module to realize the on-site analysis of the data.
The prediction module 406 is used for predicting the health state of the electrical equipment for the data meeting the defined sensing conditions based on the fault diagnosis expert knowledge base in the full life cycle management module when the original data meet the sensing conditions;
and the prediction module is connected with the sensing module and the full life cycle management module, and when the original data meet the sensing condition, the prediction module compares and analyzes the data transmitted by the sensing module with a fault diagnosis expert knowledge base in the full life cycle management module to realize risk assessment of the health state of the electrical equipment. All data from design, production, factory inspection, operation, maintenance to final service expiration of the electrical equipment are stored in the full life cycle management module, and a data model of the electrical equipment is established through a digital twin technology to realize data expression of the physical electrical equipment.
The life cycle management module 407 is configured to store the result of the local analysis and the result of the prediction of the health state of the electrical device in the form of operation data.
When the analysis module performs on-site analysis on the data meeting the defined sensing conditions based on a fault diagnosis expert knowledge base in the full life cycle management module, the analysis result is stored in the full life cycle management module in the form of operation data; and when the prediction module predicts the health state of the electrical equipment according to the data meeting the defined sensing conditions on the basis of a fault diagnosis expert knowledge base in the life cycle management module, the prediction result is stored in the life cycle management module in the form of operation data.
Therefore, data access is realized, and the electrical equipment can be accessed to a ubiquitous power Internet of things main network or platform in a plug-and-play manner; secondly, the plug-and-play access of the sensor on the electrical equipment is realized through a unified communication protocol; and thirdly, the access of personnel, materials, tools and the like to the electrical equipment can be realized. From the physical perspective, the data interconnection between the ubiquitous Internet of things and the underlying sensor network and man-machine-object network is realized;
meanwhile, the invention realizes storage and calculation, and the existing online monitoring device for the smart power grid mainly realizes the transmission, access and control of signals as core functions; the invention not only can realize the existing on-line monitoring, but also has the functions of local storage and calculation, and embodies the storage, processing and decision of data. The data of the manufacturing process of the electrical equipment (design data, process data, production data and inspection data) can be stored, and various operation data generated in the operation process of the equipment can be stored. In addition, data can be analyzed and processed from the source of the equipment, and only the result of local data processing or data needing further processing needs to be sent, so that the requirement of centralized data processing of the ubiquitous Internet of things is greatly reduced, and the network efficiency is improved;
meanwhile, the invention realizes the application extension function, and can develop various application programs in a customized manner and build various application models and scenes based on the storage and calculation functions, such as fault early warning and alarming, preventive maintenance and maintenance decision support, matching of tools and accessories, operation guidance and the like.
In summary, the invention realizes the isolation of invalid data and valid data through the sensing conditions of the sensing module, and the sensing module performs pre-judgment on the data, thereby reducing the data transmission amount and reducing the workload of data processing in the subsequent link; the in-situ edge calculation of the data is realized through a fault diagnosis expert knowledge base preset in situ; by collecting data of various sensors on the electrical equipment and adopting digital twin calculation, the datamation expression of the physical electrical equipment is realized; the whole life cycle management of the electrical equipment is realized by providing the access function of design data, process data, production data, inspection data and maintenance data; meanwhile, data interconnection between the ubiquitous Internet of things and a sensor network and a man-machine-object network at the bottom layer of the electrical equipment is realized.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A ubiquitous power Internet of things edge computing method is characterized by comprising the following steps:
acquiring original data acquired by a sensor integrated on electrical equipment through an interface module;
judging whether the original data meet the perception condition through a perception module, if so, performing:
the data meeting the defined perception conditions are analyzed in situ through an analysis module based on a fault diagnosis expert knowledge base in a full life cycle management module;
predicting the health state of the electrical equipment on the basis of a fault diagnosis expert knowledge base in the full life cycle management module through a prediction module, wherein the data meet defined perception conditions;
and storing the result of the local analysis and the prediction result of the health state of the electrical equipment in the form of operation data respectively through the full life cycle management module.
2. The method of claim 1, further comprising:
and accessing ubiquitous electric power Internet of things electrical equipment through a data service gateway.
3. The method of claim 2, further comprising:
and accessing the mobile terminal through the data service gateway.
4. The method of claim 3, further comprising:
customized development functions of the in-place edge computing application are provided through the application extension module.
5. The method of claim 1, wherein the sensing condition comprises at least one of a dead zone condition, a threshold condition, and a logical operation condition.
6. A ubiquitous power internet of things edge computing system, comprising:
the interface module is used for acquiring original data acquired by a sensor integrated on the electrical equipment;
the sensing module is used for judging whether the original data meets the sensing condition;
the analysis module is used for carrying out on-site analysis on the data meeting the defined perception conditions based on a fault diagnosis expert knowledge base in the full life cycle management module when the original data meet the perception conditions;
the prediction module is used for predicting the health state of the electrical equipment for the data meeting the defined perception conditions based on the fault diagnosis expert knowledge base in the full life cycle management module when the original data meet the perception conditions;
and the full life cycle management module is used for storing the results of the local analysis and the prediction results of the health state of the electrical equipment in the form of operation data respectively.
7. The system of claim 6, further comprising:
and the data service gateway is used for accessing the ubiquitous power Internet of things electrical equipment.
8. The system of claim 7, wherein the data service gateway is further configured to access a mobile terminal.
9. The system of claim 8, further comprising:
and the application extension module is used for providing the customized development function of the in-place edge computing application program.
10. The system of claim 6, wherein the sensing condition comprises at least one of a dead zone condition, a threshold condition, and a logical operation condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911051288.4A CN112749861A (en) | 2019-10-31 | 2019-10-31 | Ubiquitous power Internet of things edge calculation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911051288.4A CN112749861A (en) | 2019-10-31 | 2019-10-31 | Ubiquitous power Internet of things edge calculation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112749861A true CN112749861A (en) | 2021-05-04 |
Family
ID=75641259
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911051288.4A Pending CN112749861A (en) | 2019-10-31 | 2019-10-31 | Ubiquitous power Internet of things edge calculation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112749861A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313853A (en) * | 2021-05-31 | 2021-08-27 | 国网冀北电力有限公司检修分公司 | Direct current converter station valve room system of patrolling and examining based on unmanned aerial vehicle |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105302087A (en) * | 2014-07-10 | 2016-02-03 | 上海哈德电力工程技术有限公司 | Intelligent monitoring and managing system for distribution network medium-voltage switch devices |
| US20170160328A1 (en) * | 2015-12-02 | 2017-06-08 | Prophecy Sensors, Llc | Distributed iot based sensor analytics for power line diagnosis |
| KR20170088736A (en) * | 2016-01-25 | 2017-08-02 | 한국전자통신연구원 | Electricity-safety managing server, apparatus and method for managing of electricity-safety |
| CN109118732A (en) * | 2018-08-27 | 2019-01-01 | 中国科学院上海微系统与信息技术研究所 | Sensor perception event judgement and alarm system and method based on marginal information |
| CN110138870A (en) * | 2019-05-21 | 2019-08-16 | 国网湖北省电力有限公司电力科学研究院 | A kind of the sensing layer detection system and its detection method of ubiquitous electric power Internet of Things power equipment |
| CN110362068A (en) * | 2019-08-02 | 2019-10-22 | 苏州容思恒辉智能科技有限公司 | A kind of mechanical equipment fault method for early warning, system and readable storage medium storing program for executing based on industrial Internet of Things |
-
2019
- 2019-10-31 CN CN201911051288.4A patent/CN112749861A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105302087A (en) * | 2014-07-10 | 2016-02-03 | 上海哈德电力工程技术有限公司 | Intelligent monitoring and managing system for distribution network medium-voltage switch devices |
| US20170160328A1 (en) * | 2015-12-02 | 2017-06-08 | Prophecy Sensors, Llc | Distributed iot based sensor analytics for power line diagnosis |
| KR20170088736A (en) * | 2016-01-25 | 2017-08-02 | 한국전자통신연구원 | Electricity-safety managing server, apparatus and method for managing of electricity-safety |
| CN109118732A (en) * | 2018-08-27 | 2019-01-01 | 中国科学院上海微系统与信息技术研究所 | Sensor perception event judgement and alarm system and method based on marginal information |
| CN110138870A (en) * | 2019-05-21 | 2019-08-16 | 国网湖北省电力有限公司电力科学研究院 | A kind of the sensing layer detection system and its detection method of ubiquitous electric power Internet of Things power equipment |
| CN110362068A (en) * | 2019-08-02 | 2019-10-22 | 苏州容思恒辉智能科技有限公司 | A kind of mechanical equipment fault method for early warning, system and readable storage medium storing program for executing based on industrial Internet of Things |
Non-Patent Citations (1)
| Title |
|---|
| 江秀臣等: "输配电设备泛在电力物联网建设思路与发展趋势", 高电压技术, vol. 45, no. 5, pages 1345 - 1351 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313853A (en) * | 2021-05-31 | 2021-08-27 | 国网冀北电力有限公司检修分公司 | Direct current converter station valve room system of patrolling and examining based on unmanned aerial vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110401262B (en) | GIS equipment state intelligent monitoring system and method based on edge computing technology | |
| CN109102189B (en) | Electrical equipment health management system and method | |
| CN111710122A (en) | Safe power utilization management system based on ubiquitous power Internet of things | |
| EP3644581B1 (en) | Edge-cloud collaboration system for analyzing internet of things data and operating method thereof | |
| CN111062651A (en) | Safe power utilization management system and method based on edge calculation and big data analysis | |
| Alavi et al. | An IoT-based data collection platform for situational awareness-centric microgrids | |
| CN110658807A (en) | Vehicle fault diagnosis method, device and system | |
| CN111426345A (en) | Distributed cable channel environment monitoring system, method and computer equipment | |
| CN113763667B (en) | Fire disaster early warning and state monitoring device and method based on 5G edge calculation | |
| CN112947127A (en) | Intelligent electricity consumption control management system | |
| CN113676534B (en) | Bridge data uploading method based on edge calculation | |
| CN110988559A (en) | Online monitoring method for full life cycle of transformer substation direct current system based on Internet of things | |
| CN111890933A (en) | Battery management method and system for vehicle, vehicle and server | |
| CN112685472A (en) | Power grid multi-station fusion monitoring method and device, electronic equipment and storage medium | |
| CN113055424B (en) | Internet of things data acquisition and transmission performance optimization method and device and computer equipment | |
| CN114414955A (en) | Method and system for monitoring and evaluating state of switch cabinet | |
| CN112749861A (en) | Ubiquitous power Internet of things edge calculation method and system | |
| CN112702219B (en) | Internet of things network monitoring method, device, equipment and storage medium | |
| CN212009318U (en) | Bridge monitoring facilities based on edge calculation | |
| CN107765618A (en) | Sewage monitoring system and its monitoring method based on Internet of Things | |
| KR102103251B1 (en) | IoT based embedded monitoring system | |
| CN116499529A (en) | Equipment running state monitoring method, device, management terminal and storage medium | |
| CN112308176B (en) | Mold management method, device, equipment and storage medium based on NB-IOT technology | |
| CN116056117A (en) | Network anomaly prediction method, device and storage medium | |
| CN114500613A (en) | Online monitoring Internet of things system for power grid |
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 |