CN112910699A - Intelligent fault detection method and device for power internet of things - Google Patents
Intelligent fault detection method and device for power internet of things Download PDFInfo
- Publication number
- CN112910699A CN112910699A CN202110116428.2A CN202110116428A CN112910699A CN 112910699 A CN112910699 A CN 112910699A CN 202110116428 A CN202110116428 A CN 202110116428A CN 112910699 A CN112910699 A CN 112910699A
- Authority
- CN
- China
- Prior art keywords
- data
- power
- congestion
- things
- internet
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 claims abstract description 66
- 230000005540 biological transmission Effects 0.000 claims description 140
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 5
- 230000008569 process Effects 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 7
- 230000003993 interaction Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
-
- 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
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- 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
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/35—Utilities, e.g. electricity, gas or water
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Computer Networks & Wireless Communication (AREA)
- Marketing (AREA)
- Geometry (AREA)
- Tourism & Hospitality (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Signal Processing (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Water Supply & Treatment (AREA)
- Mathematical Optimization (AREA)
- Accounting & Taxation (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Environmental & Geological Engineering (AREA)
- Pure & Applied Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention belongs to the technical field of Internet of things, and particularly relates to an intelligent fault detection method and device for an electric Internet of things. The method performs the steps of: step 1: constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of the fault detection map, and the power line in the power grid Internet of things is used as a connecting line of the fault detection map. The method has the advantages of high intelligent degree and high prediction accuracy by constructing a fault detection map, simulating a traffic map, analyzing flow and speed and predicting the fault at best; meanwhile, fault judgment is carried out on the basis of the physical parameters of the channels running in the power internet of things, and the judgment accuracy is higher.
Description
Technical Field
The invention belongs to the technical field of Internet of things, and particularly relates to an intelligent fault detection method and device for an electric Internet of things.
Background
The Internet of Things (The Internet of Things, IOT for short) is to collect any object or process needing monitoring, connection and interaction in real time and collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology and location through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors and laser scanners, and to realize ubiquitous connection of objects and people through various possible network accesses, so as to realize intelligent sensing, identification and management of objects and processes. The internet of things is an information bearer based on the internet, a traditional telecommunication network and the like, and all common physical objects which can be independently addressed form an interconnected network.
At present, the internet of things application based on the micro-service structure is prone to failure due to the reasons of limited equipment resources, small micro-service granularity, complex interaction, dynamic behavior and the like, and the failure is not easy to recur in a testing link, so that how to accurately judge the position and type of the failure through the running state of the application is the focus of attention of scholars at home and abroad.
Four common methods for fault location are: program slicing, program spectrum, graph mining, and model-specific. The program slicing is a program decomposition method, a specific program segment is intercepted according to requirements, the program segment is divided into a static state and a dynamic state according to a composition mode, the static state slicing intercepts the calling relation between the program control flow and the process, and the dynamic slicing considers the execution track of the program; the program spectrum describes information and characteristics contained in the execution of program statements, and fault location is carried out by calculating the suspicious degree of each element; based on the fault location of graph mining, dynamic calling tracks of a building program are constructed, and a specific algorithm is used for realizing the fault location; the fault location based on the model is to provide a model suitable for a specific program, and determine whether a fault exists in the program by defining a fault type and a fault model. The four methods focus on paying attention to the faults of the single application program, are not suitable for distributed internet of things application based on micro-services, and are even not suitable for analyzing the micro-service faults of specific internet of things application.
The patent numbers are: CN201610572722.3A discloses an internet of things fault device detection system. The system acquires real-time parameters of the fault equipment through the acquisition module, realizes information transmission among modules by using a GPRS communication module with a controller of SIM900B, analyzes and processes the fault equipment information by adopting a detection and diagnosis module with a core chip of an S7-200 type PLC, judges the reason and the position of the fault, and realizes real-time management and diagnosis of the fault equipment. In the software design process, the flow of the fault equipment detection system based on the Internet of things is analyzed in detail, and program codes for receiving and processing fault equipment data by the wireless sensing nodes are given.
Although fault analysis is carried out by collecting data and information, fault detection of the Internet of things is realized. However, when the data analysis is performed, manual work is often needed to participate, and when data is acquired, the data accuracy is reduced, the execution efficiency is low, and the accuracy of fault analysis is low due to interference of environmental factors. In addition, the failure rate can be improved due to the lack of the function of predicting the failure in the internet of things.
Disclosure of Invention
In view of the above, the main object of the present invention is to provide an intelligent fault detection method and apparatus for an electric power internet of things, which performs traffic map simulation, flow and speed analysis, and best fault prediction by constructing a fault detection map, and has the advantages of high intelligent degree and high prediction accuracy; meanwhile, fault judgment is carried out on the basis of the physical parameters of the channels running in the power internet of things, and the judgment accuracy is higher.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an intelligent fault detection method for an electric power internet of things, the method comprising the following steps:
step 1: constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map;
step 2: acquiring operating data and physical parameters of the power Internet of things; the operational data includes: the method comprises the following steps of (1) historical data transmission track of equipment, data transmission flow threshold of the equipment and data transmission real-time speed of the equipment; the physical parameters include: the method comprises the following steps of (1) counting the number of channels of a power line of the power internet of things, the error rate of the power internet of things, channel fixed power distribution, a data preamble sequence and a channel gain estimation value;
and step 3: calculating the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judging whether the internet of things breaks down or not according to the calculated signal-to-noise ratio;
and 4, step 4: and based on the operation data of the power Internet of things, performing fault prediction of the power Internet of things.
Further, the step 4 comprises: step 4.1: based on the historical transmission track of the equipment, after path matching is carried out, the transmission speed of the data passing through the first power line is calculated; step 4.2: calculating the average transmission speed and variance of the data on the first power line according to the transmission speed of the data on the first power line, and calculating a data transmission threshold according to the average transmission speed and variance; step 4.3: acquiring the instantaneous transmission speed of the current data on the first power line, judging whether the instantaneous transmission speed is less than a data transmission threshold value, and if so, judging that the data congestion occurs on the first power line; step 4.4: and traversing all the power lines, judging the power lines with the data congestion times larger than the preset times in each month as frequent data congestion power lines, and generating congestion subgraphs according to the frequent data congestion power lines.
Further, the step 4.3 comprises: acquiring the instantaneous transmission speed of the current data on the first power line, and judging whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value or not; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; and acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion.
Further, the step 4.4 comprises: traversing all the power lines, and judging the power lines with the data congestion times more than the preset times in each month as the power lines with frequent data congestion; adding frequent data congestion power lines into a congestion power line set, and extracting connected congestion roads to form congestion subgraphs of a congestion propagation model according to the topological relation among the roads in the congestion power line set; calculating the probability of data congestion of all connecting power lines in the congestion subgraph, and generating a data congestion probability graph model after calibrating the congestion subgraph; and predicting the data congestion condition of the power line according to the data congestion probability map model.
Further, the step 3 comprises: setting the number of useful channels in the power Internet of things as N and the error rate as BtargetAll channels are allocated according to fixed power; calculating the signal-to-noise ratio Yi of the channel according to the data leader sequence and the channel gain estimation value:
wherein | Hi|2And σi 2Respectively representing the channel gain and the noise power of the channel i bit; since the preamble sequence is known, the channel signal power P is knowniChannel gain | Hi|2Obtainable from channel estimation, noise power σi 2From the receiving end preamble sequence power and Pi×|Hi|2Subtracting to obtain the result; calculating a difference value according to the calculated signal-to-noise ratio of the channel and a set standard value; and if the calculated difference exceeds a set threshold value, indicating that a fault occurs.
Further, the step 4.1 comprises: acquiring a historical track of the terminal equipment of the power internet of things and a route of the terminal equipment of the power internet of things, performing path matching, and calculating a first transmission speed of the terminal equipment of the power internet of things in a first preset time through a first power line; acquiring a historical track of a terminal agent of the power internet of things and a path of the terminal agent of the power internet of things in a passenger carrying state, performing path matching, and calculating a second transmission speed of the terminal agent of the power internet of things passing through a first power line within a first preset time; acquiring a history track of a master station server of the power internet of things, performing path matching, and calculating a third transmission speed of the master station server of the power internet of things through a first power line within a first preset time; and acquiring a historical track of the regional data center, performing path matching, and calculating a fourth transmission speed of the regional data center through the first power line within a first preset time.
An intelligent fault detection device of an electric power internet of things, the device comprising: the fault detection map construction unit is used for constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map; the data acquisition unit is configured to acquire operating data and physical parameters of the power Internet of things; the operational data includes: the method comprises the following steps of (1) historical data transmission track of equipment, data transmission flow threshold of the equipment and data transmission real-time speed of the equipment; the physical parameters include: the method comprises the following steps of (1) counting the number of channels of a power line of the power internet of things, the error rate of the power internet of things, channel fixed power distribution, a data preamble sequence and a channel gain estimation value; the fault judgment unit is configured to calculate the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judge whether the internet of things has faults or not according to the calculated signal-to-noise ratio; and the fault prediction unit is configured to predict the fault of the power Internet of things based on the operation data of the power Internet of things.
Further, the failure prediction unit includes: the first calculation unit is configured to perform path matching based on a historical transmission track of the equipment and calculate the transmission speed of data passing through a first power line; the second calculation unit is configured to calculate the average transmission speed and the variance of the data on the first power line according to the transmission speed of the data on the first power line, and calculate a data transmission threshold according to the average transmission speed and the variance; the first judging unit is configured to acquire the instantaneous transmission speed of the current data on the first power line, judge whether the instantaneous transmission speed is smaller than a data transmission threshold value, and judge that the data congestion occurs on the first power line if the instantaneous transmission speed is smaller than the data transmission threshold value; and the second determination unit is configured to traverse all the power lines, determine the power lines with the data congestion times larger than the preset times in each month as the frequent data congestion power lines, and generate congestion subgraphs according to the frequent data congestion power lines.
Further, the method for acquiring the instantaneous transmission speed of the current data on the first power line by the first determination unit and determining whether the instantaneous transmission speed is less than the data transmission threshold value includes: acquiring the instantaneous transmission speed of the current data on the first power line, and judging whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value or not; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; and acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion.
Further, the second determination unit determines, as a frequent data congestion power line, a power line in which the number of times of data congestion occurring in each month is greater than a predetermined number of times by traversing all power lines, and the method for generating a congestion sub-graph according to the frequent data congestion power line includes: traversing all the power lines, and judging the power lines with the data congestion times more than the preset times in each month as the power lines with frequent data congestion; adding frequent data congestion power lines into a congestion power line set, and extracting connected congestion roads to form congestion subgraphs of a congestion propagation model according to the topological relation among the roads in the congestion power line set; calculating the probability of data congestion of all connecting power lines in the congestion subgraph, and generating a data congestion probability graph model after calibrating the congestion subgraph; and predicting the data congestion condition of the power line according to the data congestion probability map model.
The intelligent fault detection method and device for the power internet of things have the following beneficial effects:
the method has the advantages of high intelligent degree and high prediction accuracy by constructing a fault detection map, simulating a traffic map, analyzing flow and speed and predicting the fault at best; meanwhile, fault judgment is carried out on the basis of the physical parameters of the channels running in the power internet of things, and the judgment accuracy is higher. The method is mainly realized by the following steps:
1. constructing a fault detection map: according to the method, the fault prediction is carried out on the power Internet of things by constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map; the fault detection map is similar to the traffic map, and the fault prediction is realized by analyzing the fault detection map in a mode of analyzing the traffic map, so that the accuracy and efficiency of prediction are obviously improved;
2. and (3) fault detection: the method comprises the steps of calculating the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judging whether the internet of things breaks down or not according to the calculated signal-to-noise ratio; the fault detection is realized by the method, and compared with the conventional method for acquiring data to analyze the data, the method has higher efficiency; meanwhile, the physical parameters can better reflect the running state of the Internet of things, so that the fault detection accuracy is higher;
3. determination of data congestion: in the process of fault prediction, the instantaneous transmission speed of the current data on the first power line is obtained, and whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value is judged; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion; by means of the method, data congestion prediction is carried out, the prediction accuracy is higher, and further the accuracy of subsequent fault prediction is higher.
Drawings
Fig. 1 is a schematic method flow diagram of an intelligent fault detection method for an electric power internet of things according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an intelligent fault detection device of an electric power internet of things according to an embodiment of the present invention.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 1, an intelligent fault detection method for the power internet of things performs the following steps:
step 1: constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map;
step 2: acquiring operating data and physical parameters of the power Internet of things; the operational data includes: the method comprises the following steps of (1) historical data transmission track of equipment, data transmission flow threshold of the equipment and data transmission real-time speed of the equipment; the physical parameters include: the method comprises the following steps of (1) counting the number of channels of a power line of the power internet of things, the error rate of the power internet of things, channel fixed power distribution, a data preamble sequence and a channel gain estimation value;
and step 3: calculating the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judging whether the internet of things breaks down or not according to the calculated signal-to-noise ratio;
and 4, step 4: and based on the operation data of the power Internet of things, performing fault prediction of the power Internet of things.
Example 2
On the basis of the above embodiment, the step 4 includes: step 4.1: based on the historical transmission track of the equipment, after path matching is carried out, the transmission speed of the data passing through the first power line is calculated; step 4.2: calculating the average transmission speed and variance of the data on the first power line according to the transmission speed of the data on the first power line, and calculating a data transmission threshold according to the average transmission speed and variance; step 4.3: acquiring the instantaneous transmission speed of the current data on the first power line, judging whether the instantaneous transmission speed is less than a data transmission threshold value, and if so, judging that the data congestion occurs on the first power line; step 4.4: and traversing all the power lines, judging the power lines with the data congestion times larger than the preset times in each month as frequent data congestion power lines, and generating congestion subgraphs according to the frequent data congestion power lines.
Specifically, the Electric power Internet of Things (UEIOT) is an intelligent service system which fully applies modern information technologies and advanced communication technologies such as mobile interconnection and artificial intelligence around each link of an Electric power system, realizes the mutual object interconnection and man-machine interaction of each link of the Electric power system, and has the characteristics of comprehensive state perception, efficient information processing and convenient and flexible application.
Example 3
On the basis of the above embodiment, the step 4.3 includes: acquiring the instantaneous transmission speed of the current data on the first power line, and judging whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value or not; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; and acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion.
Specifically, The Internet of Things (IOT) is to collect any object or process needing monitoring, connection and interaction in real time and collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology and position thereof through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors and laser scanners, and to realize ubiquitous connection between objects and between objects and people through various possible network accesses, thereby realizing intelligent sensing, identification and management of objects and processes. The internet of things is an information bearer based on the internet, a traditional telecommunication network and the like, and all common physical objects which can be independently addressed form an interconnected network.
Example 4
On the basis of the above embodiment, the step 4.4 includes: traversing all the power lines, and judging the power lines with the data congestion times more than the preset times in each month as the power lines with frequent data congestion; adding frequent data congestion power lines into a congestion power line set, and extracting connected congestion roads to form congestion subgraphs of a congestion propagation model according to the topological relation among the roads in the congestion power line set; calculating the probability of data congestion of all connecting power lines in the congestion subgraph, and generating a data congestion probability graph model after calibrating the congestion subgraph; and predicting the data congestion condition of the power line according to the data congestion probability map model.
Particularly, the application field of the Internet of things relates to the aspects of the application in the infrastructure fields of industry, agriculture, environment, traffic, logistics, security and the like, and the intelligent development of the aspects is effectively promoted, so that the limited resources are more reasonably used and distributed, and the industry efficiency and the benefit are improved. The application in the fields which are closely related to life, such as home furnishing, medical health, education, finance, service industry, tourist industry and the like, greatly improves the aspects from service range, service mode to service quality and the like, and greatly improves the life quality of people; in the aspect of the field of national defense and military, although the field is still in a research and exploration stage, the influence brought by the application of the Internet of things can not be small and varied, the influence is large, equipment systems such as satellites, missiles, airplanes and submarines, the influence is small, the influence is single-soldier operational equipment, the technology of the Internet of things is embedded, the military intellectualization, informatization and precision are effectively improved, the military fighting capacity is greatly.
Example 5
On the basis of the above embodiment, the step 3 includes: setting the number of useful channels in the power Internet of things as N and the error rate as BtargetAll channels are allocated according to fixed power; calculating the signal-to-noise ratio Yi of the channel according to the data leader sequence and the channel gain estimation value:
wherein | Hi|2And σi 2Respectively representing the channel gain and the noise power of the channel i bit; since the preamble sequence is known, the channel signal power P is knowniChannel gain | Hi|2Obtainable from channel estimation, noise power σi 2From the receiving end preamble sequence power and Pi×|Hi|2Subtracting to obtain the result; calculating a difference value according to the calculated signal-to-noise ratio of the channel and a set standard value; and if the calculated difference exceeds a set threshold value, indicating that a fault occurs.
Specifically, the SIGNAL-to-NOISE RATIO, which is called SNR or S/N (Signal-NOISE RATIO), is also called SIGNAL-to-NOISE RATIO. Refers to the ratio of signal to noise in an electronic device or system. The signal refers to an electronic signal from the outside of the device to be processed by the device, the noise refers to an irregular extra signal (or information) which does not exist in the original signal generated after passing through the device, and the signal does not change along with the change of the original signal.
Similarly, the original signal does not exist or the original signal is distorted, distortion and noise actually have a certain relationship, and the difference between distortion and noise is that distortion is regular, and noise is irregular.
The unit of measurement of the signal-to-noise ratio is dB, and the calculation method is 10lg (Ps/Pn), wherein Ps and Pn respectively represent the effective power of the signal and the noise, and can also be converted into the ratio relation of the voltage amplitude: 20Lg (Vs/Vn), Vs and Vn represent "effective values" of the signal and noise voltages, respectively. In an audio amplifier, it is desirable that the amplifier should not add anything more than amplify the signal. Therefore, the higher the signal-to-noise ratio, the better.
In a narrow sense, the ratio of the power of the output signal of the amplifier to the power of the noise output at the same time, often expressed in decibels, a higher signal-to-noise ratio of the device indicates that it generates less noise. Generally, the larger the signal-to-noise ratio, the smaller the noise mixed in the signal, the higher the quality of sound playback, and vice versa. The signal-to-noise ratio should not be lower than 70dB generally, and the signal-to-noise ratio of the hi-fi loudspeaker box should reach more than 110 dB.
Example 6
On the basis of the above embodiment, the step 4.1 includes: acquiring a historical track of the terminal equipment of the power internet of things and a route of the terminal equipment of the power internet of things, performing path matching, and calculating a first transmission speed of the terminal equipment of the power internet of things in a first preset time through a first power line; acquiring a historical track of a terminal agent of the power internet of things and a path of the terminal agent of the power internet of things in a passenger carrying state, performing path matching, and calculating a second transmission speed of the terminal agent of the power internet of things passing through a first power line within a first preset time; acquiring a history track of a master station server of the power internet of things, performing path matching, and calculating a third transmission speed of the master station server of the power internet of things through a first power line within a first preset time; and acquiring a historical track of the regional data center, performing path matching, and calculating a fourth transmission speed of the regional data center through the first power line within a first preset time.
Specifically, the method constructs the fault detection map, simulates the traffic map, analyzes the flow and the speed, predicts the fault at best and has the advantages of high intelligent degree and high prediction accuracy; meanwhile, fault judgment is carried out on the basis of the physical parameters of the channels running in the power internet of things, and the judgment accuracy is higher. The method is mainly realized by the following steps:
1. constructing a fault detection map: according to the method, the fault prediction is carried out on the power Internet of things by constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map; the fault detection map is similar to the traffic map, and the fault prediction is realized by analyzing the fault detection map in a mode of analyzing the traffic map, so that the accuracy and efficiency of prediction are obviously improved;
2. and (3) fault detection: the method comprises the steps of calculating the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judging whether the internet of things breaks down or not according to the calculated signal-to-noise ratio; the fault detection is realized by the method, and compared with the conventional method for acquiring data to analyze the data, the method has higher efficiency; meanwhile, the physical parameters can better reflect the running state of the Internet of things, so that the fault detection accuracy is higher;
3. determination of data congestion: in the process of fault prediction, the instantaneous transmission speed of the current data on the first power line is obtained, and whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value is judged; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion; by means of the method, data congestion prediction is carried out, the prediction accuracy is higher, and further the accuracy of subsequent fault prediction is higher.
Example 7
An intelligent fault detection device of an electric power internet of things, the device comprising: the fault detection map construction unit is used for constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map; the data acquisition unit is configured to acquire operating data and physical parameters of the power Internet of things; the operational data includes: the method comprises the following steps of (1) historical data transmission track of equipment, data transmission flow threshold of the equipment and data transmission real-time speed of the equipment; the physical parameters include: the method comprises the following steps of (1) counting the number of channels of a power line of the power internet of things, the error rate of the power internet of things, channel fixed power distribution, a data preamble sequence and a channel gain estimation value; the fault judgment unit is configured to calculate the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judge whether the internet of things has faults or not according to the calculated signal-to-noise ratio; and the fault prediction unit is configured to predict the fault of the power Internet of things based on the operation data of the power Internet of things.
Example 8
On the basis of the above embodiment, the failure prediction unit includes: the first calculation unit is configured to perform path matching based on a historical transmission track of the equipment and calculate the transmission speed of data passing through a first power line; the second calculation unit is configured to calculate the average transmission speed and the variance of the data on the first power line according to the transmission speed of the data on the first power line, and calculate a data transmission threshold according to the average transmission speed and the variance; the first judging unit is configured to acquire the instantaneous transmission speed of the current data on the first power line, judge whether the instantaneous transmission speed is smaller than a data transmission threshold value, and judge that the data congestion occurs on the first power line if the instantaneous transmission speed is smaller than the data transmission threshold value; and the second determination unit is configured to traverse all the power lines, determine the power lines with the data congestion times larger than the preset times in each month as the frequent data congestion power lines, and generate congestion subgraphs according to the frequent data congestion power lines.
Example 9
On the basis of the above embodiment, the method for acquiring the instantaneous transmission speed of the current data on the first power line by the first determination unit and determining whether the instantaneous transmission speed is less than the data transmission threshold value includes: acquiring the instantaneous transmission speed of the current data on the first power line, and judging whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value or not; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; and acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion.
Example 10
On the basis of the above embodiment, the second determination unit determines, as a frequent data congestion power line, a power line with a data congestion occurrence frequency greater than a predetermined frequency in each month by traversing all power lines, and the method for generating a congestion subgraph according to the frequent data congestion power line includes: traversing all the power lines, and judging the power lines with the data congestion times more than the preset times in each month as the power lines with frequent data congestion; adding frequent data congestion power lines into a congestion power line set, and extracting connected congestion roads to form congestion subgraphs of a congestion propagation model according to the topological relation among the roads in the congestion power line set; calculating the probability of data congestion of all connecting power lines in the congestion subgraph, and generating a data congestion probability graph model after calibrating the congestion subgraph; and predicting the data congestion condition of the power line according to the data congestion probability map model.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or the functions of the units described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the elements, method steps may be located 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. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. 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 terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or unit/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or unit/apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent modifications or substitutions of the related art marks may be made by those skilled in the art without departing from the principle of the present invention, and the technical solutions after such modifications or substitutions will fall within the protective scope of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. The intelligent fault detection method of the power Internet of things is characterized by comprising the following steps:
step 1: constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map;
step 2: acquiring operating data and physical parameters of the power Internet of things; the operational data includes: the method comprises the following steps of (1) historical data transmission track of equipment, data transmission flow threshold of the equipment and data transmission real-time speed of the equipment; the physical parameters include: the method comprises the following steps of (1) counting the number of channels of a power line of the power internet of things, the error rate of the power internet of things, channel fixed power distribution, a data preamble sequence and a channel gain estimation value;
and step 3: calculating the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judging whether the internet of things breaks down or not according to the calculated signal-to-noise ratio;
and 4, step 4: and based on the operation data of the power Internet of things, performing fault prediction of the power Internet of things.
2. The method of claim 1, wherein step 4 comprises: step 4.1: based on the historical transmission track of the equipment, after path matching is carried out, the transmission speed of the data passing through the first power line is calculated; step 4.2: calculating the average transmission speed and variance of the data on the first power line according to the transmission speed of the data on the first power line, and calculating a data transmission threshold according to the average transmission speed and variance; step 4.3: acquiring the instantaneous transmission speed of the current data on the first power line, judging whether the instantaneous transmission speed is less than a data transmission threshold value, and if so, judging that the data congestion occurs on the first power line; step 4.4: and traversing all the power lines, judging the power lines with the data congestion times larger than the preset times in each month as frequent data congestion power lines, and generating congestion subgraphs according to the frequent data congestion power lines.
3. A method according to claim 3, wherein said step 4.3 comprises: acquiring the instantaneous transmission speed of the current data on the first power line, and judging whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value or not; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; and acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion.
4. The method of claim 3, wherein said step 4.4 comprises: traversing all the power lines, and judging the power lines with the data congestion times more than the preset times in each month as the power lines with frequent data congestion; adding frequent data congestion power lines into a congestion power line set, and extracting connected congestion roads to form congestion subgraphs of a congestion propagation model according to the topological relation among the roads in the congestion power line set; calculating the probability of data congestion of all connecting power lines in the congestion subgraph, and generating a data congestion probability graph model after calibrating the congestion subgraph; and predicting the data congestion condition of the power line according to the data congestion probability map model.
5. The method of claim 4, wherein step 3 comprises: setting the number of useful channels in the power Internet of things as N and the error rate as BtargetAll channels are allocated according to fixed power; calculating the signal-to-noise ratio Yi of the channel according to the data leader sequence and the channel gain estimation value:
wherein | Hi|2And σi 2Respectively representing the channel gain and the noise power of the channel i bit; since the preamble sequence is known, the channel signal power P is knowniChannel gain | Hi|2Obtainable from channel estimation, noise power σi 2From the receiving end preamble sequence power and Pi×|Hi|2Subtracting to obtain the result; calculating a difference value according to the calculated signal-to-noise ratio of the channel and a set standard value; and if the calculated difference exceeds a set threshold value, indicating that a fault occurs.
6. The method of claim 5, wherein the step 4.1 comprises: acquiring a historical track of the terminal equipment of the power internet of things and a route of the terminal equipment of the power internet of things, performing path matching, and calculating a first transmission speed of the terminal equipment of the power internet of things in a first preset time through a first power line; acquiring a historical track of a terminal agent of the power internet of things and a path of the terminal agent of the power internet of things in a passenger carrying state, performing path matching, and calculating a second transmission speed of the terminal agent of the power internet of things passing through a first power line within a first preset time; acquiring a history track of a master station server of the power internet of things, performing path matching, and calculating a third transmission speed of the master station server of the power internet of things through a first power line within a first preset time; and acquiring a historical track of the regional data center, performing path matching, and calculating a fourth transmission speed of the regional data center through the first power line within a first preset time.
7. An intelligent fault detection device of a power internet of things based on the method of any one of claims 1 to 6, wherein the device comprises: the fault detection map construction unit is used for constructing a fault detection map of the power Internet of things; the equipment in the power grid Internet of things is used as a node of a fault detection map, and a power line in the power grid Internet of things is used as a connecting line of the fault detection map; the data acquisition unit is configured to acquire operating data and physical parameters of the power Internet of things; the operational data includes: the method comprises the following steps of (1) historical data transmission track of equipment, data transmission flow threshold of the equipment and data transmission real-time speed of the equipment; the physical parameters include: the method comprises the following steps of (1) counting the number of channels of a power line of the power internet of things, the error rate of the power internet of things, channel fixed power distribution, a data preamble sequence and a channel gain estimation value; the fault judgment unit is configured to calculate the signal-to-noise ratio of a channel of the power internet of things based on the acquired physical parameters of the power internet of things, and judge whether the internet of things has faults or not according to the calculated signal-to-noise ratio; and the fault prediction unit is configured to predict the fault of the power Internet of things based on the operation data of the power Internet of things.
8. The apparatus of claim 7, wherein the failure prediction unit comprises: the first calculation unit is configured to perform path matching based on a historical transmission track of the equipment and calculate the transmission speed of data passing through a first power line; the second calculation unit is configured to calculate the average transmission speed and the variance of the data on the first power line according to the transmission speed of the data on the first power line, and calculate a data transmission threshold according to the average transmission speed and the variance; the first judging unit is configured to acquire the instantaneous transmission speed of the current data on the first power line, judge whether the instantaneous transmission speed is smaller than a data transmission threshold value, and judge that the data congestion occurs on the first power line if the instantaneous transmission speed is smaller than the data transmission threshold value; and the second determination unit is configured to traverse all the power lines, determine the power lines with the data congestion times larger than the preset times in each month as the frequent data congestion power lines, and generate congestion subgraphs according to the frequent data congestion power lines.
9. The apparatus of claim 8, wherein the first determining unit obtains an instantaneous transmission speed of the current data on the first power line, and the method of determining whether the instantaneous transmission speed is less than the data transmission threshold comprises: acquiring the instantaneous transmission speed of the current data on the first power line, and judging whether the instantaneous transmission speed of the current data on the first power line is smaller than a data transmission threshold value or not; if the instantaneous transmission speed is smaller than the data transmission threshold value, judging that the first power line has data congestion; and acquiring a second power line connected with the first power line, and if the second power line has data congestion in a second time period after the data congestion occurs on the first power line, transmitting the data congestion.
10. The apparatus of claim 9, wherein the second determining unit determines, by traversing all power lines, a power line with a number of times of data congestion occurring more than a predetermined number of times per month as a frequent data congestion power line, and the method for generating the congestion sub-graph from the frequent data congestion power line comprises: traversing all the power lines, and judging the power lines with the data congestion times more than the preset times in each month as the power lines with frequent data congestion; adding frequent data congestion power lines into a congestion power line set, and extracting connected congestion roads to form congestion subgraphs of a congestion propagation model according to the topological relation among the roads in the congestion power line set; calculating the probability of data congestion of all connecting power lines in the congestion subgraph, and generating a data congestion probability graph model after calibrating the congestion subgraph; and predicting the data congestion condition of the power line according to the data congestion probability map model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110116428.2A CN112910699A (en) | 2021-01-28 | 2021-01-28 | Intelligent fault detection method and device for power internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110116428.2A CN112910699A (en) | 2021-01-28 | 2021-01-28 | Intelligent fault detection method and device for power internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112910699A true CN112910699A (en) | 2021-06-04 |
Family
ID=76119410
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110116428.2A Pending CN112910699A (en) | 2021-01-28 | 2021-01-28 | Intelligent fault detection method and device for power internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112910699A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113504240A (en) * | 2021-07-07 | 2021-10-15 | 广东电力信息科技有限公司 | Power equipment fault detection method based on layered coding and visual perception |
| CN116930659A (en) * | 2023-07-25 | 2023-10-24 | 深圳粤能能源技术有限公司 | Electric power measurement device and method based on Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450259A (en) * | 2015-11-13 | 2016-03-30 | 湖南工业大学 | Smart meter reading system multicarrier communication module adaptive modulation method |
| CN106530694A (en) * | 2016-11-07 | 2017-03-22 | 深圳大学 | Traffic congestion prediction method and system based on traffic congestion propagation model |
| EP3291490A1 (en) * | 2016-09-06 | 2018-03-07 | Honeywell International Inc. | Systems and methods for generating a graphical representation of a fire system network and identifying network information for predicting network faults |
| CN111601270A (en) * | 2020-05-18 | 2020-08-28 | 黄春梅 | Internet of things power monitoring system and method of wireless sensor network topological structure |
-
2021
- 2021-01-28 CN CN202110116428.2A patent/CN112910699A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450259A (en) * | 2015-11-13 | 2016-03-30 | 湖南工业大学 | Smart meter reading system multicarrier communication module adaptive modulation method |
| EP3291490A1 (en) * | 2016-09-06 | 2018-03-07 | Honeywell International Inc. | Systems and methods for generating a graphical representation of a fire system network and identifying network information for predicting network faults |
| CN107800762A (en) * | 2016-09-06 | 2018-03-13 | 霍尼韦尔国际公司 | The system and method that figure for generating fire-fighting system network represents and is directed to the prediction network failure identification network information |
| CN106530694A (en) * | 2016-11-07 | 2017-03-22 | 深圳大学 | Traffic congestion prediction method and system based on traffic congestion propagation model |
| CN111601270A (en) * | 2020-05-18 | 2020-08-28 | 黄春梅 | Internet of things power monitoring system and method of wireless sensor network topological structure |
Non-Patent Citations (1)
| Title |
|---|
| 姚晓勇: ""面向泛在电力物联网的通信与业务适配研究"", 《通信电源技术》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113504240A (en) * | 2021-07-07 | 2021-10-15 | 广东电力信息科技有限公司 | Power equipment fault detection method based on layered coding and visual perception |
| CN113504240B (en) * | 2021-07-07 | 2022-06-21 | 广东电力信息科技有限公司 | Power equipment fault detection method based on layered coding and visual perception |
| CN116930659A (en) * | 2023-07-25 | 2023-10-24 | 深圳粤能能源技术有限公司 | Electric power measurement device and method based on Internet of things |
| CN116930659B (en) * | 2023-07-25 | 2024-02-23 | 深圳粤能能源技术有限公司 | Electric power measurement device and method based on Internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112364439B (en) | Simulation test method and device for automatic driving system and storage medium | |
| CN104853379B (en) | A kind of quality of wireless network appraisal procedure and device | |
| Sung et al. | Temporal spectrum sharing based on primary user activity prediction | |
| Eisenman et al. | Number and location of sensors for real-time network traffic estimation and prediction: Sensitivity analysis | |
| US7542436B2 (en) | Tactical cognitive-based simulation methods and systems for communication failure management in ad-hoc wireless networks | |
| CN111935820B (en) | Positioning implementation method based on wireless network and related equipment | |
| CN105203718A (en) | Air quality monitoring method and system | |
| CN108307427B (en) | LTE network coverage analysis and prediction method and system | |
| CN112910699A (en) | Intelligent fault detection method and device for power internet of things | |
| CN105376089A (en) | Network planning method and device | |
| CN108271157B (en) | Pseudo base station identification method and device | |
| CN112688310B (en) | Line loss analysis method and device applied to power distribution network | |
| CN115174416B (en) | Network planning system, method and device and electronic equipment | |
| Fernandes et al. | Scalable vanet simulations with ns-3 | |
| CN103686818A (en) | Simulation test method and equipment | |
| Bejarano-Luque et al. | A data-driven algorithm for indoor/outdoor detection based on connection traces in a LTE network | |
| CN113140109B (en) | Drive test data processing method and device, computer equipment and storage medium | |
| CN117406137B (en) | Method and system for monitoring lightning leakage current of power transmission line | |
| CN108574927B (en) | Mobile terminal positioning method and device | |
| CN112995893A (en) | Fingerprint positioning method, system, server and storage medium | |
| CN113839729A (en) | Device and method for predicting and evaluating coverage of frequency modulation broadcast signal | |
| CN104392101A (en) | Data sharing method and data sharing device | |
| CN114615693A (en) | Network capacity prediction method, device, electronic equipment and computer storage medium | |
| Tonchev et al. | Learning graph convolutional neural networks to predict radio environment maps | |
| CN115442814B (en) | Continuous weak coverage identification method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210604 |
|
| RJ01 | Rejection of invention patent application after publication |