CN113132689A - Low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research - Google Patents
Low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research Download PDFInfo
- Publication number
- CN113132689A CN113132689A CN202110423538.3A CN202110423538A CN113132689A CN 113132689 A CN113132689 A CN 113132689A CN 202110423538 A CN202110423538 A CN 202110423538A CN 113132689 A CN113132689 A CN 113132689A
- Authority
- CN
- China
- Prior art keywords
- module
- maintenance
- learning algorithm
- simulation device
- device based
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
- H02J13/00024—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission by means of mobile telephony
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Human Computer Interaction (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a low-voltage centralized reading, transporting and maintaining simulation device based on AI deep learning algorithm research, which comprises an unmanned aerial vehicle, an inspection robot, a fixed camera position, a learning module, an electric power operation and inspection image library, a maintenance APP and a remote maintenance system, wherein the unmanned aerial vehicle is used for high-altitude patrol type shooting of images of electric power equipment, the inspection robot is used for carrying a thermal imaging sensor to patrol type shooting of images of the electric power equipment on land, the fixed camera position is used for fixed type shooting of images of the electric power equipment, and the learning module is used for learning an equipment initialization model; the invention has an AI deep learning function, reduces the manual input work, improves the troubleshooting speed, can directly navigate after a maintenance worker receives a work order, avoids missing the best maintenance opportunity, can carry out voice interaction through the voice interaction module in the whole process, liberates two hands and improves the working efficiency, and in addition, greatly reduces the communication and exchange cost and improves the fault diagnosis and repair capability.
Description
Technical Field
The invention relates to the field of operation and maintenance simulation devices, in particular to a low-voltage centralized reading operation and maintenance simulation device based on AI deep learning algorithm research.
Background
The power equipment needs to be monitored and maintained continuously during working, so that a low-voltage centralized meter-reading, operation and maintenance simulation device is needed, the purpose is to ensure the normal operation of a power system, and when a fault occurs, the power equipment can be maintained in time, so that the influence of the fault on the use of the powered equipment is avoided.
The existing low-voltage centralized meter reading, operation and maintenance simulation device has certain disadvantages to be improved, firstly, the complexity of a power grid structure is obviously increased, the power grid operation mode changes frequently and faces greater uncertainty, the existing low-voltage centralized meter reading, operation and maintenance simulation device has no learning function, models of all power equipment need to be manually input, the workload is huge, and the use is seriously influenced; secondly, the existing low-voltage centralized meter reading, operation and maintenance simulation device is usually combined by a plurality of systems, and information coverage is slow, so that the optimal time of operation and maintenance is easily influenced.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the complexity of a power grid structure is obviously increased, the operation mode of the power grid changes frequently, and the power grid faces larger uncertainty, while the existing low-voltage centralized meter-reading operation and maintenance simulation device has no learning function, models of all power equipment need to be manually input, the workload is huge, and the use is seriously influenced; secondly, the existing low-voltage centralized meter reading, operation and maintenance simulation device is usually combined by a plurality of systems, and information coverage is slow, so that the optimal time of operation and maintenance is easily influenced.
The invention solves the technical problems through the following technical scheme, and provides a low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research, which comprises an unmanned aerial vehicle, an inspection robot, a fixed camera position, a learning module, an electric operation and inspection image library, an operation APP and a remote operation and maintenance system;
the unmanned aerial vehicle is used for shooting pictures of the power equipment in a high-altitude patrol mode;
the inspection robot is used for carrying the thermal imaging sensor to take pictures of the electrical equipment in a land patrol mode;
the fixed camera position is used for fixedly shooting the picture of the power equipment;
the learning module is used for learning an equipment initialization model;
the electric power operation and inspection image library is used for troubleshooting and storing an initialization model;
the maintenance APP is used for the maintenance personnel to receive maintenance information;
the remote dimension system is used for the dimension personnel to consult the expert personnel to remotely assist the dimension and the transportation.
Preferably, the learning module includes an AI core processing chip and a scene/parameter extraction module, and the scene/parameter extraction module includes an image receiving module, a parameter acquisition module, an environment analysis module, a feature extraction module, an algorithm storage module, a reinforcement learning module, and an output module.
Preferably, the learning module specifically processes the following steps:
the method comprises the following steps: the AI core processing chip processes, calculates and executes the commands of the whole learning module;
step two: during learning, the image receiving module and the parameter receiving module respectively receive images and parameters of the electrical equipment;
step three: after receiving the image and the parameters, the environment analysis module and the feature extraction module analyze the image environment and extract initial features of the power equipment;
step four: the extracted features are learned by matching the algorithms stored in the algorithm storage module with the reinforcement learning module;
step five: the learned initialization model is output by an output module.
Preferably, the feature extraction module extracts a range including: normal operation parameters, appearance, normal operation temperature, opening and closing degrees/positions of various valves and switches of various power equipment and fault phenomena possibly generated by three-phase short circuit faults of various equipment.
Preferably, the algorithm program stored in the algorithm storage module includes: one or more of an unbalanced data learning algorithm, a cost sensitive learning algorithm, an ensemble learning algorithm and the like.
Preferably, the electric power operation and inspection image library comprises a circuit operation and inspection module and an initial model repository, and the electric power operation and inspection module comprises an image receiving module, a parameter receiving module, a flash memory module and a feature matching module.
Preferably, the specific processing steps of the electric power operation and inspection image library are as follows:
step 1: the initial model of the power equipment output by the output module is stored by an initial model repository;
step 2: the image receiving module and the parameter receiving module respectively receive the influence of the power equipment and the collected operation parameters;
and step 3: each item of data after receiving is stored by the flash memory module, and the model of flash memory is matchd with the initial model of storing in the initial model repository in real time to the characteristic matching module, and when the mismatch phenomenon appears in the matching, send dimension fortune information to dimension fortune APP.
Preferably, the maintenance APP comprises an information receiving module, an information processing module, a voice interaction module and a navigation package, when the information receiving module receives the maintenance information, the information processing module provides the position where the fault information is sent to the maintenance personnel through the navigation package, and meanwhile, the maintenance personnel perform voice interaction through the voice interaction module.
Preferably, the remote maintenance and transportation system is composed of an intelligent wearable device camera and a tablet, a mobile phone or a PC terminal, the intelligent wearable device camera is worn on a maintenance and transportation person, a shot picture is transmitted to the tablet, the mobile phone or the PC terminal of an expert person through the Internet, and the expert person and the maintenance and transportation person communicate with each other through a voice interaction and an augmented reality electronic whiteboard to conduct remote guidance and maintenance and transportation.
Compared with the prior art, the invention has the following advantages:
the environmental information is analyzed by utilizing deep learning, the characteristics are extracted from the environmental information, the characteristics are further analyzed and extracted based on the characteristics by means of reinforcement learning, corresponding actions are selected, target return is realized, an AI deep learning function is achieved, and manual input work is reduced;
by setting the electric power operation and inspection image library, an AI deep learning algorithm and the electric power operation and inspection image library are integrated, and after an equipment image and operation parameters are acquired, the equipment image and the operation parameters can be quickly matched, so that faults can be eliminated, and the fault elimination speed is greatly improved;
through the navigation package and the voice interaction module, when the maintenance APP receives the work order message, the navigation package directly provides navigation information for the maintenance personnel, so that the maintenance personnel can quickly arrive at the site for maintenance, the best maintenance opportunity is avoided being missed, voice interaction can be carried out through the voice interaction module in the whole process, and both hands are liberated, so that the working efficiency is improved;
through the remote operation and maintenance system, for difficult operation and maintenance of complex scenes, operation and maintenance personnel can directly transmit the scene to a remote expert through a camera of the intelligent wearable equipment at a first visual angle, the remote expert can directly superimpose digital information on an operation object in the field of the operation and maintenance personnel through voice and an augmented reality electronic whiteboard in a visual mode, the troublesome problem is handled just like the guidance of the field expert, communication and communication cost is greatly reduced, and fault diagnosis and repair capacity is improved.
Drawings
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a system diagram of the learning module and circuit audit exposure library of the present invention;
FIG. 3 is a system diagram of a maintenance APP of the present invention;
FIG. 4 is a system diagram of the remote maintenance system of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
As shown in fig. 1 to 4, the present embodiment provides a technical solution: a low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research comprises an unmanned aerial vehicle, an inspection robot, a fixed camera position, a learning module, an electric operation and inspection image library, an operation and maintenance APP and a remote operation and maintenance system;
the unmanned aerial vehicle is used for shooting pictures of the power equipment in a high-altitude patrol mode;
the inspection robot is used for carrying the thermal imaging sensor to take pictures of the electrical equipment in a land patrol mode;
the fixed camera position is used for fixedly shooting the picture of the power equipment;
the learning module is used for learning an equipment initialization model;
the electric power operation and inspection image library is used for troubleshooting and storing the initialization model;
the maintenance APP is used for the maintenance personnel to receive the maintenance information;
the remote dimension system is used for the dimension personnel to consult the expert personnel to remotely assist the dimension and the transportation.
The learning module comprises an AI core processing chip and a scene/parameter extraction module, wherein the scene/parameter extraction module comprises an image receiving module, a parameter acquisition module, an environment analysis module, a feature extraction module, an algorithm storage module, a reinforcement learning module and an output module.
The learning module comprises the following specific processing steps:
the method comprises the following steps: the AI core processing chip processes, calculates and executes the commands of the whole learning module;
step two: during learning, the image receiving module and the parameter receiving module respectively receive images and parameters of the electrical equipment;
step three: after receiving the image and the parameters, the environment analysis module and the feature extraction module analyze the image environment and extract initial features of the power equipment;
step four: the extracted features are learned by matching the algorithms stored in the algorithm storage module with the reinforcement learning module;
step five: the learned initialization model is output by an output module.
The extraction range of the feature extraction module comprises: normal operation parameters, appearance, normal operation temperature, opening and closing degrees/positions of various valves and switches of various power equipment and fault phenomena possibly generated by three-phase short circuit faults of various equipment.
The algorithm program stored in the algorithm storage module comprises: one or more of an unbalanced data learning algorithm, a cost sensitive learning algorithm, an ensemble learning algorithm and the like.
The electric power operation and inspection image library comprises a circuit operation and inspection module and an initial model repository, and the electric power operation and inspection module comprises an image receiving module, a parameter receiving module, a flash memory module and a characteristic matching module.
The specific processing steps of the electric power operation and inspection image library are as follows:
step 1: the initial model of the power equipment output by the output module is stored by an initial model repository;
step 2: the image receiving module and the parameter receiving module respectively receive the influence of the power equipment and the collected operation parameters;
and step 3: each item of data after receiving is stored by the flash memory module, and the model of flash memory is matchd with the initial model of storing in the initial model repository in real time to the characteristic matching module, and when the mismatch phenomenon appears in the matching, send dimension fortune information to dimension fortune APP.
The maintenance APP comprises an information receiving module, an information processing module, a voice interaction module and a navigation package, when the information receiving module receives maintenance information, the information processing module provides a position where fault information is sent to a maintenance person through the navigation package, and the maintenance person performs voice interaction through the voice interaction module;
the remote maintenance and transportation system is composed of an intelligent wearable device camera and a tablet, a mobile phone or a PC terminal, the intelligent wearable device camera is worn on a maintenance and transportation person, a shooting picture is transmitted to the tablet, the mobile phone or the PC terminal of an expert person through the Internet, and the expert person and the maintenance and transportation person communicate with each other through a voice interaction and an augmented reality electronic whiteboard to conduct remote guidance maintenance and transportation.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. A low-voltage centralized reading operation and maintenance simulation device based on AI deep learning algorithm research is characterized by comprising an unmanned aerial vehicle, an inspection robot, a fixed camera position, a learning module, an electric operation and inspection image library, an operation and maintenance APP and a remote operation and maintenance system;
the unmanned aerial vehicle is used for shooting pictures of the power equipment in a high-altitude patrol mode;
the inspection robot is used for carrying the thermal imaging sensor to take pictures of the electrical equipment in a land patrol mode;
the fixed camera position is used for fixedly shooting the picture of the power equipment;
the learning module is used for learning an equipment initialization model;
the electric power operation and inspection image library is used for troubleshooting and storing an initialization model;
the maintenance APP is used for the maintenance personnel to receive maintenance information;
the remote dimension system is used for the dimension personnel to consult the expert personnel to remotely assist the dimension and the transportation.
2. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 1, wherein: the learning module comprises an AI core processing chip and a scene/parameter extraction module, wherein the scene/parameter extraction module comprises an image receiving module, a parameter acquisition module, an environment analysis module, a feature extraction module, an algorithm storage module, a reinforcement learning module and an output module.
3. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 2, wherein: the learning module comprises the following specific processing steps:
the method comprises the following steps: the AI core processing chip processes, calculates and executes the commands of the whole learning module;
step two: during learning, the image receiving module and the parameter receiving module respectively receive images and parameters of the electrical equipment;
step three: after receiving the image and the parameters, the environment analysis module and the feature extraction module analyze the image environment and extract initial features of the power equipment;
step four: the extracted features are learned by matching the algorithms stored in the algorithm storage module with the reinforcement learning module;
step five: the learned initialization model is output by an output module.
4. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 2, wherein: the feature extraction module extracts a range including: normal operation parameters, appearance, normal operation temperature, opening and closing degrees/positions of various valves and switches of various power equipment and fault phenomena possibly generated by three-phase short circuit faults of various equipment.
5. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 2, wherein: the algorithm program stored in the algorithm storage module comprises: one or more of an unbalanced data learning algorithm, a cost sensitive learning algorithm, an ensemble learning algorithm and the like.
6. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 1, wherein: the electric power operation and inspection image library comprises a circuit operation and inspection module and an initial model repository, and the electric power operation and inspection module comprises an image receiving module, a parameter receiving module, a flash memory module and a feature matching module.
7. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 6, wherein: the specific processing steps of the electric power operation and inspection image library are as follows:
step 1: the initial model of the power equipment output by the output module is stored by an initial model repository;
step 2: the image receiving module and the parameter receiving module respectively receive the influence of the power equipment and the collected operation parameters;
and step 3: each item of data after receiving is stored by the flash memory module, and the model of flash memory is matchd with the initial model of storing in the initial model repository in real time to the characteristic matching module, and when the mismatch phenomenon appears in the matching, send dimension fortune information to dimension fortune APP.
8. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 1, wherein: the maintenance APP comprises an information receiving module, an information processing module, a voice interaction module and a navigation package, when the information receiving module receives maintenance information, the information processing module provides a position where fault information is sent to a maintenance person through the navigation package, and meanwhile the maintenance person carries out voice interaction through the voice interaction module.
9. The low-voltage set reading, operation and maintenance simulation device based on AI deep learning algorithm research according to claim 1, wherein: the remote maintenance and transportation system is composed of an intelligent wearable device camera and a tablet, a mobile phone or a PC terminal, the intelligent wearable device camera is worn on a maintenance and transportation person, a shooting picture is transmitted to the tablet, the mobile phone or the PC terminal of an expert through the Internet, the expert and the maintenance and transportation person communicate through voice interaction and an augmented reality electronic whiteboard, and remote guidance and maintenance are conducted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110423538.3A CN113132689A (en) | 2021-04-20 | 2021-04-20 | Low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110423538.3A CN113132689A (en) | 2021-04-20 | 2021-04-20 | Low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113132689A true CN113132689A (en) | 2021-07-16 |
Family
ID=76777863
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110423538.3A Pending CN113132689A (en) | 2021-04-20 | 2021-04-20 | Low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113132689A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114102618A (en) * | 2021-11-16 | 2022-03-01 | 中国电信集团系统集成有限责任公司 | Operation and maintenance robot, and machine room operation and maintenance method and system based on operation and maintenance robot |
| CN115173558A (en) * | 2022-07-15 | 2022-10-11 | 国网江苏省电力有限公司 | New forms of energy station yard on-line measuring system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110728670A (en) * | 2019-10-14 | 2020-01-24 | 贵州电网有限责任公司 | Low-voltage equipment operation and maintenance method based on AR technology |
| CN111985561A (en) * | 2020-08-19 | 2020-11-24 | 安徽蓝杰鑫信息科技有限公司 | Fault diagnosis method and system for intelligent electric meter and electronic device |
| CN112018892A (en) * | 2020-09-04 | 2020-12-01 | 南京太司德智能电气有限公司 | Electric power operation and maintenance remote guidance system |
| CN112287915A (en) * | 2020-12-28 | 2021-01-29 | 北京蒙帕信创科技有限公司 | Equipment fault warning method and system based on deep learning |
-
2021
- 2021-04-20 CN CN202110423538.3A patent/CN113132689A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110728670A (en) * | 2019-10-14 | 2020-01-24 | 贵州电网有限责任公司 | Low-voltage equipment operation and maintenance method based on AR technology |
| CN111985561A (en) * | 2020-08-19 | 2020-11-24 | 安徽蓝杰鑫信息科技有限公司 | Fault diagnosis method and system for intelligent electric meter and electronic device |
| CN112018892A (en) * | 2020-09-04 | 2020-12-01 | 南京太司德智能电气有限公司 | Electric power operation and maintenance remote guidance system |
| CN112287915A (en) * | 2020-12-28 | 2021-01-29 | 北京蒙帕信创科技有限公司 | Equipment fault warning method and system based on deep learning |
Non-Patent Citations (1)
| Title |
|---|
| 张秋雁等: "基于增强现实与人工智能融合的低压集抄运维技术研究", 《电力设备管理》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114102618A (en) * | 2021-11-16 | 2022-03-01 | 中国电信集团系统集成有限责任公司 | Operation and maintenance robot, and machine room operation and maintenance method and system based on operation and maintenance robot |
| CN114102618B (en) * | 2021-11-16 | 2023-10-10 | 中电信数智科技有限公司 | Operation and maintenance robot, machine room operation and maintenance method and system based on operation and maintenance robot |
| CN115173558A (en) * | 2022-07-15 | 2022-10-11 | 国网江苏省电力有限公司 | New forms of energy station yard on-line measuring system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110033453B (en) | Power transmission and transformation line insulator aerial image fault detection method based on improved YOLOv3 | |
| CN112633535B (en) | Intelligent inspection method and system for photovoltaic power station based on unmanned aerial vehicle image | |
| WO2022188379A1 (en) | Artificial intelligence system and method serving electric power robot | |
| CN102176161B (en) | Flight simulation system facing to power line polling | |
| CN109800697B (en) | Transformer target detection and appearance defect identification method based on VGG-net style migration | |
| CN113132689A (en) | Low-voltage centralized reading, operation and maintenance simulation device based on AI deep learning algorithm research | |
| CN108269322B (en) | Inspection method | |
| CN106682592A (en) | Automatic image recognition system and method based on neural network method | |
| CN111797725A (en) | Equipment state detection method, device and system | |
| CN104977856B (en) | Substation monitoring analogue means, system and method | |
| CN113485433A (en) | Inspection method and device for power transmission line | |
| CN113159334A (en) | Electrical equipment infrared image real-time detection and diagnosis method based on light-weight deep learning | |
| Sruthi et al. | YOLOv5 based open-source UAV for human detection during search and rescue (SAR) | |
| CN115935637A (en) | GIS digital twin-based transformer substation visual operation and maintenance system | |
| CN112614130A (en) | Unmanned aerial vehicle power transmission line insulator fault detection method based on 5G transmission and YOLOv3 | |
| CN114202819B (en) | Substation inspection method, system and computer based on robot | |
| CN109002633B (en) | Device network modeling method based on separate space | |
| CN115082813A (en) | Detection method, unmanned aerial vehicle, detection system and medium | |
| CN113359829B (en) | Unmanned aerial vehicle power plant intelligent inspection method based on big data | |
| CN114092624A (en) | Environment inspection system based on virtual technology | |
| WO2022082660A1 (en) | Power station inspection system and power station inspection method | |
| Yan et al. | Intergrating UAV development technology with augmented reality toward landscape tele-simulation | |
| CN115442739B (en) | Temporary communication processing method and device for subway construction, electronic equipment and medium | |
| CN115147591A (en) | Transformer equipment infrared image voltage heating type defect diagnosis method and system | |
| CN113256839B (en) | AI-based intelligent inspection system for power distribution 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210716 |
|
| RJ01 | Rejection of invention patent application after publication |