CN113591586A - Power transmission line icing thickness calculation method and system based on 5G - Google Patents
Power transmission line icing thickness calculation method and system based on 5G Download PDFInfo
- Publication number
- CN113591586A CN113591586A CN202110748216.6A CN202110748216A CN113591586A CN 113591586 A CN113591586 A CN 113591586A CN 202110748216 A CN202110748216 A CN 202110748216A CN 113591586 A CN113591586 A CN 113591586A
- Authority
- CN
- China
- Prior art keywords
- layer
- transmission line
- neural network
- output
- icing
- 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
- 230000005540 biological transmission Effects 0.000 title claims abstract description 66
- 238000004364 calculation method Methods 0.000 title claims abstract description 18
- 238000013528 artificial neural network Methods 0.000 claims abstract description 54
- 238000012549 training Methods 0.000 claims abstract description 35
- 230000011218 segmentation Effects 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 46
- 238000011176 pooling Methods 0.000 claims description 22
- 230000008569 process Effects 0.000 claims description 21
- 230000004913 activation Effects 0.000 claims description 18
- 238000010276 construction Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 description 20
- 239000011248 coating agent Substances 0.000 description 3
- 238000000576 coating method Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a 5G-based power transmission line icing thickness calculation method, which comprises the following steps of: constructing a neural network, and training the neural network by using the icing image as a training set so as to identify and extract the icing image; erecting a camera at the side of the power transmission line, collecting images of the power transmission line by using the camera, identifying and extracting the icing images in the images of the power transmission line by using the neural network, and processing the images of the power transmission line based on a variational level set algorithm which utilizes a three-dimensional image to perform multi-item segmentation to obtain the thickness of the icing.
Description
Technical Field
The invention relates to the technical field of power transmission lines, in particular to a method and a system for calculating icing thickness of a power transmission line based on 5G.
Background
In the electric wire netting high-voltage transmission engineering, the geographical environment, the climatic conditions at remote high-voltage transmission line place are abominable, especially in winter, because temperature is lower, when humidity is higher, very easy icing on the surface of transmission cable, and along with temperature lasts the decline, the icing on cable conductor surface can be progressively thicker, let high-voltage transmission line's weight load bigger and bigger, can cause the phenomenon that transmission cable was broken by the pressure in serious time, very dangerous, the cost of later stage salvage maintenance is also very high moreover.
Therefore, how to detect the thickness of the ice coating on the power transmission line and timely perform the ice coating treatment is a technical problem in the prior art.
Disclosure of Invention
In view of this, the invention aims to provide a method for calculating the icing thickness of a power transmission line based on 5G, which can realize intelligent calculation of the icing thickness of the power transmission line and is convenient for technical personnel to monitor the icing thickness.
The purpose of the invention is realized by the following technical scheme:
a method for calculating icing thickness of a transmission line based on 5G comprises the following steps:
constructing a neural network, and training the neural network by using the icing image as a training set so as to identify and extract the icing image;
erecting a camera at the side of the power transmission line, collecting images of the power transmission line by using the camera, identifying and extracting the icing images in the images of the power transmission line by using the neural network, and processing the images of the power transmission line based on a variational level set algorithm which utilizes a three-dimensional image to perform multi-item segmentation to obtain the thickness of the icing.
Further, the neural network is constructed in a monitoring center, and the electric transmission line image collected by the camera is transmitted to the monitoring center through a 5G communication technology.
Further, the training process of the neural network comprises forward propagation and backward propagation; the forward propagation and the backward propagation are specifically:
forward propagation: training samples enter a network from an input layer, weighting and operation are carried out through nodes of the previous layer and corresponding connection weights, a bias term is added to the result, the result obtained through a nonlinear function is the output of the nodes of the layer, and the result of the output layer is obtained through layer-by-layer operation; if the actual output of the output layer is different from the expected output, turning to error back propagation; if the actual output of the output layer is the same as the expected output, ending;
and (3) back propagation: the difference between the expected output and the actual output is calculated according to the back transmission of the original path, the error is distributed to each unit of each layer in the back transmission process through the hidden layer back propagation until the error reaches the input layer, the error signal of each unit of each layer is obtained, the error signal is used as the basis for correcting the weight of each unit, and the process of correcting the weight of each unit is completed by using an Adagarad algorithm.
Further, the structure of the neural network is arranged according to an input layer, a convolution layer, a pooling layer, a convolution layer, a pooling layer and a full-connection layer, a ReLu function is selected as an activation function, a maximum pooling method is used for pooling, and the output of the current layer is represented as:
xe＝f(ue)
ue＝Wexe-1+be
wherein xeRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing the activation function, WeIs the weight of the current layer, beMay be biased.
Meanwhile, the invention provides an icing thickness calculation system applying the method, and the specific scheme is as follows: the device comprises a neural network construction module, a neural network training module, an icing image recognition and extraction module, a camera and an icing thickness calculation module;
the neural network construction module is used for constructing a neural network;
the neural network training module is used for training the neural network by using the icing image as a training set;
the icing image identification and extraction module is used for identifying and extracting the icing image by using the neural network;
the camera is used for collecting the image of the power transmission line;
the icing thickness calculation module processes the power transmission line image based on a variational level set algorithm of three-dimensional image multi-item segmentation to obtain the icing thickness.
Further, the neural network construction module, the neural network training module, the icing image identification and extraction module and the icing thickness calculation module are constructed in a monitoring center, and the electric transmission line image collected by the camera is transmitted to the monitoring center through a 5G communication technology.
Further, the training process of the neural network comprises forward propagation and backward propagation; the forward propagation and the backward propagation are specifically:
forward propagation: training samples enter a network from an input layer, weighting and operation are carried out through nodes of the previous layer and corresponding connection weights, a bias term is added to the result, the result obtained through a nonlinear function is the output of the nodes of the layer, and the result of the output layer is obtained through layer-by-layer operation; if the actual output of the output layer is different from the expected output, turning to error back propagation; if the actual output of the output layer is the same as the expected output, ending;
and (3) back propagation: the difference between the expected output and the actual output is calculated according to the back transmission of the original path, the error is distributed to each unit of each layer in the back transmission process through the hidden layer back propagation until the error reaches the input layer, the error signal of each unit of each layer is obtained, the error signal is used as the basis for correcting the weight of each unit, and the process of correcting the weight of each unit is completed by using an Adagarad algorithm.
Further, the structure of the neural network is arranged according to an input layer, a convolution layer, a pooling layer, a convolution layer, a pooling layer and a full connection layer, a ReLu function is selected as an activation function, the pooling uses a maximum pooling method, and the output of the current layer is represented as:
xe＝f(ue)
ue＝Wexe-1+be
wherein xeRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing the activation function, WeIs the weight of the current layer, beMay be biased.
The invention has the beneficial effects that: the method provided by the invention can realize intelligent calculation of the icing thickness of the power transmission line, and is convenient for technical personnel to monitor the icing thickness.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the present invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a 5G-based method for calculating the icing thickness of a power transmission line.
Fig. 2 is a schematic structural diagram of a 5G-based power transmission line icing thickness calculation system.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below 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, the method for calculating the icing thickness of the transmission line based on 5G of the present invention includes the following steps:
constructing a neural network, and training the neural network by using the icing image as a training set so as to identify and extract the icing image;
erecting a camera at the side of the power transmission line, collecting images of the power transmission line by using the camera, identifying and extracting the icing images in the images of the power transmission line by using the neural network, and processing the images of the power transmission line based on a variational level set algorithm which utilizes a three-dimensional image to perform multi-item segmentation to obtain the thickness of the icing.
In a specific implementation process, the neural network is constructed in a monitoring center, and the electric transmission line image collected by the camera is transmitted to the monitoring center through a 5G communication technology.
In a specific implementation process, the training process of the neural network comprises forward propagation and backward propagation; the forward propagation and the backward propagation are specifically:
forward propagation: training samples enter a network from an input layer, weighting and operation are carried out through nodes of the previous layer and corresponding connection weights, a bias term is added to the result, the result obtained through a nonlinear function is the output of the nodes of the layer, and the result of the output layer is obtained through layer-by-layer operation; if the actual output of the output layer is different from the expected output, turning to error back propagation; if the actual output of the output layer is the same as the expected output, ending;
and (3) back propagation: the difference between the expected output and the actual output is calculated according to the back transmission of the original path, the error is distributed to each unit of each layer in the back transmission process through the hidden layer back propagation until the error reaches the input layer, the error signal of each unit of each layer is obtained, the error signal is used as the basis for correcting the weight of each unit, and the process of correcting the weight of each unit is completed by using an Adagarad algorithm.
In a specific implementation process, the structure of the neural network is arranged according to an input layer-convolutional layer-pooling layer-convolutional layer-pooling layer-full connection layer, a ReLu function is selected as an activation function, a maximum pooling method is used for pooling, and the output of the current layer is represented as:
xe＝f(ue)
ue＝Wexe-1+be
wherein xeRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing the activation function, WeIs the weight of the current layer, beMay be biased.
Based on the design idea of the above method, as shown in fig. 2, the invention provides an ice coating thickness calculation system applying the above method, and the specific scheme is as follows: the device comprises a neural network construction module, a neural network training module, an icing image recognition and extraction module, a camera and an icing thickness calculation module;
the neural network construction module is used for constructing a neural network;
the neural network training module is used for training the neural network by using the icing image as a training set;
the icing image identification and extraction module is used for identifying and extracting the icing image by using the neural network;
the camera is used for collecting the image of the power transmission line;
the icing thickness calculation module processes the power transmission line image based on a variational level set algorithm of three-dimensional image multi-item segmentation to obtain the icing thickness.
In a specific implementation process, the neural network construction module, the neural network training module, the icing image identification and extraction module and the icing thickness calculation module are constructed in a monitoring center, and the electric transmission line image collected by the camera is transmitted to the monitoring center through a 5G communication technology.
In a specific implementation process, the training process of the neural network comprises forward propagation and backward propagation; the forward propagation and the backward propagation are specifically:
forward propagation: training samples enter a network from an input layer, weighting and operation are carried out through nodes of the previous layer and corresponding connection weights, a bias term is added to the result, the result obtained through a nonlinear function is the output of the nodes of the layer, and the result of the output layer is obtained through layer-by-layer operation; if the actual output of the output layer is different from the expected output, turning to error back propagation; if the actual output of the output layer is the same as the expected output, ending;
and (3) back propagation: the difference between the expected output and the actual output is calculated according to the back transmission of the original path, the error is distributed to each unit of each layer in the back transmission process through the hidden layer back propagation until the error reaches the input layer, the error signal of each unit of each layer is obtained, the error signal is used as the basis for correcting the weight of each unit, and the process of correcting the weight of each unit is completed by using an Adagarad algorithm.
In a specific implementation process, the structure of the neural network is arranged according to an input layer-convolutional layer-pooling layer-convolutional layer-pooling layer-full connection layer, a ReLu function is selected as an activation function, a maximum pooling method is used for pooling, and the output of the current layer is represented as:
xe＝f(ue)
ue＝Wexe-1+be
wherein xeRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing the activation function, WeIs the weight of the current layer, beMay be biased.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (8)
1. A method for calculating icing thickness of a power transmission line based on 5G is characterized by comprising the following steps: the method comprises the following steps:
constructing a neural network, and training the neural network by using the icing image as a training set so as to identify and extract the icing image;
erecting a camera at the side of the power transmission line, collecting images of the power transmission line by using the camera, identifying and extracting the icing images in the images of the power transmission line by using the neural network, and processing the images of the power transmission line based on a variational level set algorithm which utilizes a three-dimensional image to perform multi-item segmentation to obtain the thickness of the icing.
2. The method for calculating the icing thickness of the transmission line based on 5G according to claim 1, wherein the method comprises the following steps: the neural network is built in a monitoring center, and the electric transmission line images collected by the camera are transmitted to the monitoring center through a 5G communication technology.
3. The method for calculating the icing thickness of the transmission line based on 5G according to claim 1, wherein the method comprises the following steps: the process of training the neural network comprises forward propagation and backward propagation; the forward propagation and the backward propagation are specifically:
forward propagation: training samples enter a network from an input layer, weighting and operation are carried out through nodes of the previous layer and corresponding connection weights, a bias term is added to the result, the result obtained through a nonlinear function is the output of the nodes of the layer, and the result of the output layer is obtained through layer-by-layer operation; if the actual output of the output layer is different from the expected output, turning to error back propagation; if the actual output of the output layer is the same as the expected output, ending;
and (3) back propagation: the difference between the expected output and the actual output is calculated according to the back transmission of the original path, the error is distributed to each unit of each layer in the back transmission process through the hidden layer back propagation until the error reaches the input layer, the error signal of each unit of each layer is obtained, the error signal is used as the basis for correcting the weight of each unit, and the process of correcting the weight of each unit is completed by using an Adagarad algorithm.
4. The method for calculating the icing thickness of the transmission line based on 5G according to claim 1, wherein the method comprises the following steps: the structure of the neural network is arranged according to an input layer, a convolutional layer, a pooling layer and a full-connection layer, a ReLu function is selected as an activation function, a maximum pooling method is used for pooling, and the output of the current layer is represented as:
xe＝f(ue)
ue＝Wexe-1+be
wherein xeRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing the activation function, WeIs the weight of the current layer, beMay be biased.
5. The utility model provides a transmission line icing thickness calculation system based on 5G which characterized in that: the device comprises a neural network construction module, a neural network training module, an icing image recognition and extraction module, a camera and an icing thickness calculation module;
the neural network construction module is used for constructing a neural network;
the neural network training module is used for training the neural network by using the icing image as a training set;
the icing image identification and extraction module is used for identifying and extracting the icing image by using the neural network;
the camera is used for collecting the image of the power transmission line;
the icing thickness calculation module processes the power transmission line image based on a variational level set algorithm of three-dimensional image multi-item segmentation to obtain the icing thickness.
6. The system for calculating icing thickness of 5G-based power transmission line according to claim 5, wherein: the neural network construction module, the neural network training module, the icing image identification and extraction module and the icing thickness calculation module are constructed in a monitoring center, and the power transmission line image collected by the camera is transmitted to the monitoring center through a 5G communication technology.
7. The system for calculating icing thickness of 5G-based power transmission line according to claim 5, wherein: the process of training the neural network comprises forward propagation and backward propagation; the forward propagation and the backward propagation are specifically:
forward propagation: training samples enter a network from an input layer, weighting and operation are carried out through nodes of the previous layer and corresponding connection weights, a bias term is added to the result, the result obtained through a nonlinear function is the output of the nodes of the layer, and the result of the output layer is obtained through layer-by-layer operation; if the actual output of the output layer is different from the expected output, turning to error back propagation; if the actual output of the output layer is the same as the expected output, ending;
and (3) back propagation: the difference between the expected output and the actual output is calculated according to the back transmission of the original path, the error is distributed to each unit of each layer in the back transmission process through the hidden layer back propagation until the error reaches the input layer, the error signal of each unit of each layer is obtained, the error signal is used as the basis for correcting the weight of each unit, and the process of correcting the weight of each unit is completed by using an Adagarad algorithm.
8. The system for calculating icing thickness of 5G-based power transmission line according to claim 5, wherein: the structure of the neural network is arranged according to an input layer, a convolutional layer, a pooling layer and a full-connection layer, a ReLu function is selected as an activation function, a maximum pooling method is used for pooling, and the output of the current layer is represented as:
xe＝f(ue)
ue＝Wexe-1+be
wherein xeRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing the activation function, WeIs the weight of the current layer, beMay be biased.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110748216.6A CN113591586A (en) | 2021-07-01 | 2021-07-01 | Power transmission line icing thickness calculation method and system based on 5G |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110748216.6A CN113591586A (en) | 2021-07-01 | 2021-07-01 | Power transmission line icing thickness calculation method and system based on 5G |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113591586A true CN113591586A (en) | 2021-11-02 |
Family
ID=78246076
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110748216.6A Pending CN113591586A (en) | 2021-07-01 | 2021-07-01 | Power transmission line icing thickness calculation method and system based on 5G |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113591586A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115065169A (en) * | 2022-07-06 | 2022-09-16 | 贵州电网有限责任公司 | Electric power monitoring system based on 5G and Internet of things technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102679935A (en) * | 2012-03-02 | 2012-09-19 | 凯里供电局 | System and method for calculating icing thickness of power transmission line |
| CN110599538A (en) * | 2019-09-30 | 2019-12-20 | 山东信通电子股份有限公司 | Method and device for identifying icing thickness of transmission line conductor |
| CN112595276A (en) * | 2020-11-27 | 2021-04-02 | 哈尔滨工程大学 | Power transmission line icing thickness detection method based on deep learning |
-
2021
- 2021-07-01 CN CN202110748216.6A patent/CN113591586A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102679935A (en) * | 2012-03-02 | 2012-09-19 | 凯里供电局 | System and method for calculating icing thickness of power transmission line |
| CN110599538A (en) * | 2019-09-30 | 2019-12-20 | 山东信通电子股份有限公司 | Method and device for identifying icing thickness of transmission line conductor |
| CN112595276A (en) * | 2020-11-27 | 2021-04-02 | 哈尔滨工程大学 | Power transmission line icing thickness detection method based on deep learning |
Non-Patent Citations (2)
| Title |
|---|
| 藤田一弥 等: "实践深度学习", 31 October 2020, 机械工业出版社, pages: 28 * |
| 麻赛军: "输电线路覆冰远程监测与信息处理", 中国优秀硕士学位论文全文数据库 工程科技II辑（月刊）, pages 26 - 36 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115065169A (en) * | 2022-07-06 | 2022-09-16 | 贵州电网有限责任公司 | Electric power monitoring system based on 5G and Internet of things technology |
| CN115065169B (en) * | 2022-07-06 | 2023-05-02 | 贵州电网有限责任公司 | Electric power monitored control system based on 5G and internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109635928A (en) | A kind of voltage sag reason recognition methods based on deep learning Model Fusion | |
| CN111741133B (en) | Cloud-side-end-collaborative meteorological intelligent early warning system | |
| CN111833583B (en) | Training method, device, equipment and medium for power data anomaly detection model | |
| CN111241694B (en) | Big data processing-based aircraft fleet health assessment method, equipment and readable storage medium | |
| CN112883929B (en) | On-line video abnormal behavior detection model training and abnormal detection method and system | |
| CN113538235A (en) | Training method and device of image processing model, electronic equipment and storage medium | |
| CN113657269A (en) | Training method and device for face recognition model and computer program product | |
| CN115660262B (en) | Engineering intelligent quality inspection method, system and medium based on database application | |
| CN113591586A (en) | Power transmission line icing thickness calculation method and system based on 5G | |
| CN115346171A (en) | Power transmission line monitoring method, device, equipment and storage medium | |
| CN116021981A (en) | Method, device, equipment and storage medium for predicting ice coating faults of power distribution network line | |
| CN111488819A (en) | Disaster damage monitoring, sensing and collecting method and device for power equipment | |
| CN117292330B (en) | Intelligent monitoring system suitable for time sequence data operation and maintenance | |
| CN113283550A (en) | Abnormal identification model training method for vehicle network electric coupling data | |
| CN116933626A (en) | Data monitoring method and device based on digital twinning | |
| CN115600494A (en) | Low-voltage distribution area topology automatic identification method and device | |
| CN108761258A (en) | Transformer short period overload capability assessment system based on artificial intelligence and big data technology | |
| CN113971777A (en) | Equipment fault prediction method and device and server | |
| CN113486965A (en) | Training method for abnormity identification model of vehicle network electric coupling data | |
| CN113033489A (en) | Power transmission line insulator identification and positioning method based on lightweight deep learning algorithm | |
| CN117975275A (en) | Distribution line pole tower identification method and device, electronic equipment and storage medium | |
| CN116309276A (en) | Low false alarm ground lead defect detection method based on class balance sampling | |
| CN116626579A (en) | Intelligent fault diagnosis method and system for directional gradient KNN intelligent ammeter | |
| CN116452768A (en) | Three-dimensional reconstruction method, device, equipment and medium applied to transformer substation | |
| Yu et al. | Disparity estimation method of electric inspection robot based on lightweight neural network |
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 |