CN111123916A - Method and device for power inspection robot - Google Patents
Method and device for power inspection robot Download PDFInfo
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- CN111123916A CN111123916A CN201911235079.5A CN201911235079A CN111123916A CN 111123916 A CN111123916 A CN 111123916A CN 201911235079 A CN201911235079 A CN 201911235079A CN 111123916 A CN111123916 A CN 111123916A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Abstract
The invention discloses a method and a device for a power inspection robot. The method comprises the following steps: the power inspection robot starts to execute inspection tasks according to preset parameters; acquiring a real-time gateway image in the process of executing the inspection task; determining a state of a gateway based on the real-time gateway image; and processing the gateway based on an automatic plug-pull mode identification model when the state of the gateway does not meet the preset parameters. The method and the device for the power inspection robot can automatically inspect and locate the fault of the power equipment through the power inspection robot and can automatically process the fault.
Description
Technical Field
The disclosure relates to the field of computer information processing, in particular to a method and a device for a power inspection robot.
Background
The national power grid aims to accelerate the construction of the smart power grid and to vigorously develop the construction of the high-voltage power grid. Because energy distribution and power demand in China are seriously uncoordinated geographically, the phenomenon of power transmission from west to east and north to south is caused. The ultra-high voltage power grid becomes a main carrier for long-distance power transmission and distribution, so that the guarantee of safe and reliable operation of power grid power transmission is of great importance. The real-time routing inspection of the power transmission line can detect the running state of the power transmission line, find out the problem of the power transmission line in time, detect the running condition of line equipment and provide reliable basis for the maintenance of the power transmission line, so that the real-time routing inspection is very necessary for ensuring the safe and stable running of the power transmission line.
The mode of patrolling and examining that current electric power industry adopted mainly includes: ground visual inspection method: ground workers use equipment such as naked eyes for observation or glasses observation to finish inspection, but the time consumption is long and safety accidents can be caused; aerial surveying method: patrol and examine including the helicopter and patrol and carry detection device such as camera and patrol and examine the line flight, the state of recording line abnormal point, but it is big to receive weather effect, can't use in the bad weather.
The inspection robot realizes power grid inspection through airborne monitoring equipment, can assist and replace an inspection worker to finish line inspection, and realizes bidirectional improvement of work efficiency and inspection precision. However, the power grid has many automatic devices and a complicated system, and how to realize a safe and reliable intelligent inspection mechanism suitable for dispatching automation is a major problem currently facing.
Disclosure of Invention
In view of the above, the present disclosure provides a method and an apparatus for a power inspection robot, which can perform automatic inspection and fault location on power equipment through the power inspection robot, and can also automatically process faults.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a method for a power inspection robot, the method including: the power inspection robot starts to execute inspection tasks according to preset parameters; acquiring a real-time gateway image in the process of executing the inspection task; determining a state of a gateway based on the real-time gateway image; and processing the gateway based on an automatic plug-pull mode identification model when the state of the gateway does not meet the preset parameters.
In an exemplary embodiment of the present disclosure, further comprising: and training the deep learning model through historical data to generate the automatic plug-pull mode recognition model.
In an exemplary embodiment of the present disclosure, the power inspection robot starts to execute an inspection task according to preset parameters, including: the power inspection robot acquires a time parameter from preset parameters; the power inspection robot acquires regional parameters from preset parameters; and executing the inspection task based on the time parameter and the area parameter.
In an exemplary embodiment of the present disclosure, acquiring a real-time gateway image in a process of executing the inspection task includes: determining a gateway position; moving based on the gateway location; and acquiring a real-time gateway image when the mobile terminal moves to the gateway position.
In an exemplary embodiment of the present disclosure, determining a gateway location includes: determining the gateway location based on preset location coordinates; determining the gateway location based on a preset movement path; and automatically determining the gateway position based on the identification of the real-time field image in the polling task process.
In an exemplary embodiment of the present disclosure, determining a status of a gateway based on the real-time gateway image includes: carrying out image recognition on the real-time gateway image, and determining a gateway state display area image; performing character recognition on the gateway state display area image to determine at least one state parameter information; and determining the state of the gateway according to the at least one state parameter information.
In an exemplary embodiment of the present disclosure, processing the gateway based on an automatic plug pattern recognition model includes: determining the type and adaptation of the gateway based on an automatic plug-in mode identification technology; determining a processing method based on the state of the gateway; and performing automatic plugging operation processing on the gateway based on the processing method, the model and the adaptation step.
In an exemplary embodiment of the present disclosure, further comprising: recording the process of the automatic plugging operation processing; and feeding back the process to a processor of the inspection robot so as to update the automatic plugging mode identification model.
In an exemplary embodiment of the present disclosure, training a deep learning model through historical data to generate the automatic plug pattern recognition model includes: generating the historical data by a plurality of gateway images and a plurality of adaptation steps; and training a deep learning model based on the historical data to generate the automatic plugging and unplugging mode recognition model.
According to an aspect of the present disclosure, there is provided a device for a power inspection robot, the device including: the starting module is used for starting and executing the inspection task according to preset parameters; the image module is used for acquiring a real-time gateway image in the process of executing the inspection task; a state module for determining a state of a gateway based on the real-time gateway image; and the processing module is used for processing the gateway based on the automatic plugging and unplugging mode identification model when the state of the gateway does not meet the preset parameters.
Drawings
The present invention is described in detail below with reference to the attached drawings.
Fig. 1 is a system block diagram of a method and apparatus for a power inspection robot of the present invention.
Fig. 2 is a flow chart of a method of the present invention for a power inspection robot.
Fig. 3 is a flow chart of a method of the present invention for a power inspection robot.
Fig. 4 is a flow chart of a method of the present invention for a power inspection robot.
Fig. 5 is a block diagram of an apparatus for a power inspection robot of the present invention.
Detailed Description
Fig. 1 is a system block diagram illustrating a method and apparatus for a power inspection robot in accordance with an exemplary embodiment.
As shown in fig. 1, the system architecture 100 may include inspection robots 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the inspection robots 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the inspection robots 101, 102, 103 to interact with the server 105 through the network 104 to receive or send task messages and the like. The inspection robots 101, 102 and 103 can be installed with various communication client applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like.
The inspection robots 101, 102, 103 may be various electronic inspection devices having a device inspection function and supporting data transmission.
The server 105 may be a server that provides various services, such as a background management server that provides support for tasks processed by the inspection robots 101, 102, 103. The background management server can analyze and process the received related information of the inspection robot, and feed back the processing result (such as automatic updating information and task issuing request) to the inspection robot.
It should be noted that, in order to avoid causing communication failure, the inspection robots 101, 102, and 103 are in an offline working mode when performing tasks, and perform networking work only when data exchange with the server 105 is required after the work is completed or at the initial stage of work start.
The inspection robots 101, 102, 103 may, for example, start to perform inspection tasks according to preset parameters; the inspection robots 101, 102, 103 may, for example, acquire real-time gateway images during the execution of the inspection task; the inspection robots 101, 102, 103 may determine the status of the gateway, for example, based on the real-time gateway images; the inspection robots 101, 102, 103 may process the gateway based on an automatic plug-in mode recognition model, for example, when the state of the gateway does not satisfy preset parameters.
The server 105 may generate the automatic plug pattern recognition model by training a deep learning model, for example, with historical data.
The inspection robots 101, 102, 103 may also generate the automatic plug pattern recognition model by training a deep learning model, for example, with historical data.
The server 105 may be a physical server, or may be composed of a plurality of servers, for example, it should be noted that the method for the power inspection robot provided by the embodiment of the present disclosure may be performed by the inspection robots 101, 102, 103, and accordingly, the devices for the power inspection robot may be disposed in the inspection robots 101, 102, 103.
In one embodiment, the method can include removing the networking characteristic of the original inspection robot, performing offline adaptation on the inspection robot, deploying a multi-gateway automatic plugging and unplugging mode recognition technology based on deep learning in an execution system of the inspection robot, enabling the inspection robot to perform adaptive learning on an actual multi-gateway operation environment, wherein the learning action mainly comprises the steps of multi-gateway positioning in a factory, high-precision image recognition, automatic plugging and unplugging technology action adaptation and the like, finally training to form a reliable deep learning model, and continuously adjusting the logic precision of the model through feedback during operation in the subsequent operation process to complete scene adaptation work of the offline inspection robot.
The method for the power inspection robot in the present disclosure will be described in detail below with reference to specific embodiments.
Fig. 2 is a flow chart illustrating a method for power routing inspection robots in accordance with an exemplary embodiment. The method 20 for power inspection of a robot includes at least steps S202 to S208.
As shown in fig. 2, in S202, the power inspection robot starts to perform an inspection task according to preset parameters. The method comprises the following steps: the power inspection robot acquires a time parameter from preset parameters; the power inspection robot acquires regional parameters from preset parameters; and executing the inspection task based on the time parameter and the area parameter.
The preset parameters may be parameters remotely transmitted to the local power inspection robot, and as described above, the power inspection robot may communicate with the remote server through the network to receive or send messages at the early stage of executing the task and after the task is completed.
Wherein, at the in-process that the robot worked is patrolled and examined to electric power, in order to guarantee the safety of electric wire netting and the robot is patrolled and examined to electric power and not receive external communication interference, it is in the off-line state at the robot working process is patrolled and examined to electric power.
In S204, a real-time gateway image is acquired in the process of executing the inspection task.
In one embodiment, a gateway location may be determined, for example; moving based on the gateway location; and acquiring a real-time gateway image when the mobile terminal moves to the gateway position.
In one embodiment, after determining the gateway location, an optimal path may also be determined based on an offline stored map, and automatic navigation may be performed to move to the gateway location based on the optimal path.
In one embodiment, when moving to the gateway position, the camera angle and the camera parameters may be adjusted according to preset parameters to obtain the real-time gateway image. Furthermore, the electronic inspection robot can automatically adjust the photographing parameters, the focal length parameters and the like according to the current light and environment information.
In S206, the status of the gateway is determined based on the real-time gateway image. Can include the following steps: carrying out image recognition on the real-time gateway image, and determining a gateway state display area image; performing character recognition on the gateway state display area image to determine at least one state parameter information; and determining the state of the gateway according to the at least one state parameter information.
In one embodiment, the real-time gateway image may be subjected to image recognition, and a specific area where the gateway is located may be determined according to the image recognition result, so as to determine the gateway state display area image. The area of the gateway in the upper half of the image can be determined, for example, from the real-time gateway image. And then the upper half area is amplified so as to more accurately shoot and analyze the display screen area of the gateway.
In an embodiment, after performing character recognition on the gateway status display area image, a plurality of character strings are obtained, and the character strings may be compared with preset parameter bit character strings to determine the character strings and corresponding parameter information thereof.
Further, the parameter information is compared with a preset parameter range to determine the state of the gateway. The method can comprise a plurality of parameter information, and the parameter information can be compared one by one to obtain a final comparison result.
In S208, when the state of the gateway does not satisfy the preset parameter, the gateway is processed based on an automatic plug-pull mode recognition model.
In one embodiment, may include: determining the type and adaptation of the gateway based on an automatic plug-in mode identification technology; determining a processing method based on the state of the gateway; and performing automatic plugging operation processing on the gateway based on the processing method, the model and the adaptation step.
According to the method for the power inspection robot, the inspection task is started and executed according to the preset parameters; acquiring a real-time gateway image in the process of executing the inspection task; determining a state of a gateway based on the real-time gateway image; and when the state of the gateway does not meet the preset parameters, the gateway is processed based on the automatic plug-pull mode identification model, the power equipment can be automatically inspected and positioned through the power inspection robot, and the fault can be automatically processed.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a flow chart illustrating a method for power routing inspection robots according to another exemplary embodiment. The flow shown in fig. 3 is a detailed description of "acquiring a real-time gateway image during the process of executing the inspection task" in S204 in the flow shown in fig. 2.
As shown in fig. 3, in S302, a gateway location is determined. Can include the following steps: determining the gateway location based on preset location coordinates; determining the gateway location based on a preset movement path; and automatically determining the gateway position based on the identification of the real-time field image in the polling task process.
Gateway location information may be obtained, for example, in the task information; for example, a task path is acquired from the task information, and the gateway position is determined by moving according to the task path; for example, the gateway image information to be processed is acquired from the task information, the power inspection robot moves randomly, and the identification of the surrounding state is performed to determine the position of the gateway.
In S304, movement is performed based on the gateway location. The method can include automatically planning a route and automatically avoiding risks of obstacles based on the position of the gateway.
Furthermore, the power inspection robot can divide the path planning into global path planning based on prior complete information and local path planning based on sensor information according to the degree of grasp on the environment information. When the power inspection robot carries out global path planning, all environment information needs to be mastered, and path planning is carried out according to all information of an environment map; the local path planning only needs to acquire the environmental information in real time by a sensor, know the environmental map information and then determine the position of the map and the local obstacle distribution condition thereof, so that the optimal path from the current node to a certain sub-target node can be selected.
In S306, a real-time gateway image is acquired when the mobile terminal moves to the gateway location. And the method can also comprise automatically adjusting the photographing parameters according to the current environment information. The real-time gateway image may also be acquired again or multiple times, for example, based on the results of subsequent image recognition.
Fig. 4 is a flow chart illustrating a method for power routing inspection robots according to another exemplary embodiment. The flow shown in fig. 4 is a detailed description of "training the deep learning model by historical data to generate the automatic plug pattern recognition model".
As shown in fig. 4, in S402, the history data is generated through a plurality of gateway images and a plurality of adaptation steps.
In S404, a deep learning model is trained based on the historical data to generate the automatic plug pattern recognition model.
Among them, Deep Learning (DL) is a new research direction in the field of Machine Learning (ML), which is introduced to make Machine Learning closer to the original target.
Deep learning is the intrinsic law and expression level of the learning sample data, and the information obtained in the learning process is very helpful for the interpretation of data such as characters, images and sounds. The final aim of the method is to enable the machine to have the analysis and learning capability like a human, and to recognize data such as characters, images and sounds. Deep learning is a complex machine learning algorithm, and achieves the effect in speech and image recognition far exceeding the prior related art.
The deep learning model in the present disclosure may be an artificial neural network model, which is substantially similar to an abnormally complex network composed of neurons, and is formed by interconnecting individual units, each unit having an input and an output of numerical quantities, and the form may be a real number or a linear combination function. It must first learn with a learning criterion before it can do its job. When the network judges an error, it reduces the possibility of making the same error by learning. The method has strong generalization capability and nonlinear mapping capability, and can perform model processing on a system with small information quantity. The function simulation point of view has parallelism and extremely high information transmission speed
In S406, the process of the automatic plugging operation process is recorded. Recording the whole process of automatic plugging and unplugging processing, and more specifically, the whole process can include the result of each plugging and unplugging, the success or not, the time required by each plugging and unplugging, and the like.
More specifically, the automatic plugging process can be recorded in a video recording mode for subsequent analysis.
In S408, the process is fed back to the processor of the inspection robot to update the automatic plug pattern recognition model.
In a real-time mode, the inspection robot can feed back the case which fails in the automatic plugging and unplugging process to the server side, and the server extracts key actions in the plugging and unplugging process according to the images in the failed case and carries out analysis and processing on the key actions and subsequent feedback.
At present, automation equipment of enterprises is many, systems are complicated, manual inspection and troubleshooting are time-consuming and labor-consuming, and a traditional robot uses a wireless communication mode to bring safety risks to a monitoring system. The situation is further complicated when multiple gateway operations are involved. Based on such practical situations, there is a need for an electric power monitoring robot capable of disengaging from wireless network automation to realize automation of operation and maintenance work. According to the method, by using an automatic plugging gateway mode recognition technology based on deep learning and applying an artificial intelligence cognition and reasoning model which accords with an actual production environment, multi-port automatic recognition and accurate plugging of the intelligent inspection robot are achieved, and finally real-time quick positioning of fault diagnosis is achieved.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 5 is a block diagram illustrating an apparatus for a power inspection robot according to an exemplary embodiment. As shown in fig. 5, the apparatus 50 for the power inspection robot includes: an initiation module 502, an image module 504, a state module 506, and a processing module 508, a model training module 510.
The starting module 502 is used for starting to execute the inspection task according to preset parameters; for example, the starting module 502 obtains a time parameter from preset parameters; the starting module 502 obtains the area parameters from the preset parameters; and the initiating module 502 executes the inspection task based on the time parameter and the area parameter.
The image module 504 is used for acquiring a real-time gateway image in the process of executing the inspection task; the image module 504 may, for example, determine a gateway location; the inspection robot moves based on the gateway position; and the image module 504 obtains real-time gateway images when moving to the gateway location.
Specific examples thereof include: determining the gateway location based on preset location coordinates; determining the gateway location based on a preset movement path; and automatically determining the gateway position based on the identification of the real-time field image in the polling task process.
A status module 506 for determining a status of a gateway based on the real-time gateway image; can include the following steps: carrying out image recognition on the real-time gateway image, and determining a gateway state display area image; performing character recognition on the gateway state display area image to determine at least one state parameter information; and determining the state of the gateway according to the at least one state parameter information.
The processing module 508 is configured to process the gateway based on an automatic plug-pull mode identification model when the state of the gateway does not satisfy a preset parameter. Can include the following steps: determining the type and adaptation of the gateway based on an automatic plug-in mode identification technology; determining a processing method based on the state of the gateway; and performing automatic plugging operation processing on the gateway based on the processing method, the model and the adaptation step.
The model training module 510 is configured to train a deep learning model through historical data to generate the automatic plug and unplug pattern recognition model. The method comprises the following steps: generating the historical data by a plurality of gateway images and a plurality of adaptation steps; and training a deep learning model based on the historical data to generate the automatic plugging and unplugging mode recognition model. The model training module 510 is further configured to record a process of the automatic plugging operation; and feeding back the process to a processor of the inspection robot so as to update the automatic plugging mode identification model.
According to the device for the power inspection robot, the inspection task is started and executed according to the preset parameters; acquiring a real-time gateway image in the process of executing the inspection task; determining a state of a gateway based on the real-time gateway image; and when the state of the gateway does not meet the preset parameters, the gateway is processed based on the automatic plug-pull mode identification model, the power equipment can be automatically inspected and positioned through the power inspection robot, and the fault can be automatically processed.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A method for a power inspection robot, comprising:
the power inspection robot starts to execute inspection tasks according to preset parameters;
acquiring a real-time gateway image in the process of executing the inspection task;
determining a state of a gateway based on the real-time gateway image; and
and when the state of the gateway does not meet the preset parameters, processing the gateway based on an automatic plug-pull mode identification model.
2. The method of claim 1, further comprising:
and training the deep learning model through historical data to generate the automatic plug-pull mode recognition model.
3. The method of claim 1, wherein the power inspection robot initiates execution of inspection tasks according to preset parameters, including:
the power inspection robot acquires a time parameter from preset parameters;
the power inspection robot acquires regional parameters from preset parameters; and
and executing the inspection task based on the time parameter and the area parameter.
4. The method of claim 1, wherein acquiring real-time gateway images during execution of the inspection task comprises:
determining a gateway position;
moving based on the gateway location; and
and when the mobile terminal moves to the gateway position, acquiring a real-time gateway image.
5. The method of claim 4, wherein determining a gateway location comprises:
determining the gateway location based on preset location coordinates;
determining the gateway location based on a preset movement path; and
and automatically determining the gateway position based on the identification of the real-time field image in the polling task process.
6. The method of claim 1, wherein determining the status of the gateway based on the real-time gateway image comprises:
carrying out image recognition on the real-time gateway image, and determining a gateway state display area image;
performing character recognition on the gateway state display area image to determine at least one state parameter information; and
and determining the state of the gateway according to the at least one state parameter message.
7. The method of claim 1, wherein processing the gateway based on an automatic plug pattern recognition model comprises:
determining the type and adaptation of the gateway based on an automatic plug-in mode identification technology;
determining a processing method based on the state of the gateway;
and performing automatic plugging operation processing on the gateway based on the processing method, the model and the adaptation step.
8. The method of claim 7, further comprising:
recording the process of the automatic plugging operation processing; and
and feeding back the process to a processor of the inspection robot so as to update the automatic plugging mode identification model.
9. The method of claim 2, wherein training a deep learning model through historical data generates the automatic plug pattern recognition model, comprising:
generating the historical data by a plurality of gateway images and a plurality of adaptation steps;
and training a deep learning model based on the historical data to generate the automatic plugging and unplugging mode recognition model.
10. A device for electric power inspection robot, its characterized in that includes:
the starting module is used for starting and executing the inspection task according to preset parameters;
the image module is used for acquiring a real-time gateway image in the process of executing the inspection task;
a state module for determining a state of a gateway based on the real-time gateway image; and
and the processing module is used for processing the gateway based on an automatic plugging and unplugging mode identification model when the state of the gateway does not meet the preset parameters.
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