CN114202738A - Power grid monitoring method, device and equipment based on edge calculation and artificial intelligence - Google Patents
Power grid monitoring method, device and equipment based on edge calculation and artificial intelligence Download PDFInfo
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Abstract
The invention discloses a power grid monitoring method, a device and equipment based on edge calculation and artificial intelligence, wherein the method comprises the following steps: acquiring a mass of training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel; obtaining a recognition model for recognizing the dangerous phenomenon by utilizing the training image; deploying the recognition model in the front-end equipment; acquiring a monitored image by using front-end equipment, identifying dangerous phenomena of the monitored image by using an identification model in the front-end equipment, and sending the monitored image to a server; when the dangerous phenomenon is identified in the monitoring image, sending alarm information to a server; and displaying the monitoring image in the server in real time in the display equipment. The method can be used for carrying out model training aiming at external broken hidden dangers of foreign matters of the conducting wires, construction machinery, tower cranes and the like which often occur in a power transmission channel, deploying the trained model in front-end equipment, and automatically detecting and identifying the hidden dangers of the foreign matters in the image.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a power grid monitoring method, a device and equipment based on edge calculation and artificial intelligence.
Background
The inspection mode of the power transmission line of the power grid system mainly comprises civil air defense and technical air defense, wherein the civil air defense refers to the regular inspection of an inspector, and the technical air defense refers to 4G video monitoring, but the two schemes have great limitations. The scheme of patrolling personnel's periodic inspection, firstly the cycle of patrolling is long, is difficult to guarantee to give the superordinate person hidden danger information timely transmission, and secondly someone participates in and can the deviation appears in the inevitable, leads to the distortion of information, and above all can't guarantee that personnel really are to the assigned position to patrol, has careless result that causes unforeseen slightly.
Disclosure of Invention
In view of the above technical problems, an object of the present invention is to provide a method, an apparatus and a device for monitoring and shooting a power grid based on edge calculation and artificial intelligence.
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 invention, a power grid monitoring method based on edge calculation and artificial intelligence is provided, and the method comprises the following steps: acquiring a mass of training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel; based on an artificial intelligence deep learning technology, obtaining a recognition model for recognizing dangerous phenomena by utilizing the training image; deploying the recognition model in the front-end device based on an edge computing technique; acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment, and sending the monitored image to a server; when the dangerous phenomenon is identified in the monitoring image, sending alarm information to the server; and displaying the monitoring image in the server in real time in display equipment.
Further, the dangerous phenomenon includes construction machinery, a crane, a tower crane, smoke and fire and foreign matters wound on the power transmission passage.
Further, the obtaining of the monitoring image by using the front-end device specifically includes: and setting the front-end equipment to obtain the monitoring image at regular time, and controlling the front-end equipment to obtain the monitoring image at regular time through the server.
Further, the method further comprises: acquiring a surveillance video, and transmitting the surveillance video to the server, wherein the surveillance video comes from the front-end equipment; acquiring a key frame sequence of the surveillance video; utilizing the identification model in the front-end equipment to identify dangerous phenomena for each frame in the key frame sequence; when the critical frame sequence is identified to have a dangerous phenomenon, sending alarm information to the server; and displaying the monitoring video in the server in real time in display equipment.
Further, after receiving the monitoring image, the server uses the monitoring image as a training sample of the recognition model, dynamically updates the recognition model, and transmits the updated recognition model to the front-end device.
Further, the obtaining of the recognition model for recognizing the dangerous phenomenon based on the artificial intelligence deep learning technology by using the training image specifically includes: inputting a mass of training images into a recognition model; marking dangerous phenomena in the training images; and automatically training the recognition model.
Further, when the monitoring image identifies a dangerous phenomenon, after sending alarm information to the server, the server is further configured to: performing linkage analysis on the alarm information and the alarm information of the monitoring images of other front-end equipment; and upgrading or keeping unchanged the danger level of the alarm information according to the linkage analysis result.
Further, the method further comprises: identifying an external device in wired or wireless connection with the front-end device; receiving a protocol program from the server according to the type of the external equipment; managing the external equipment according to the protocol program; and sending the data transmitted from the external equipment to the front-end equipment to the server.
According to a second aspect of the present disclosure, there is provided a power grid monitoring device based on edge calculation and artificial intelligence, comprising: the acquisition module is used for acquiring massive training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel; the training module is used for obtaining a recognition model for recognizing dangerous phenomena by utilizing the training images based on an artificial intelligence deep learning technology; a deployment module for deploying the recognition model in the front-end device based on an edge computing technique; the identification module is used for acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment and sending the monitored image to a server; the alarm module is used for sending alarm information to the server when the monitoring image identifies a dangerous phenomenon; and the monitoring module is used for displaying the monitoring image in the server in real time in display equipment.
According to a third aspect of the present disclosure, there is provided a power grid monitoring device based on edge calculation and artificial intelligence, comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: acquiring a mass of training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel; based on an artificial intelligence deep learning technology, obtaining a recognition model for recognizing dangerous phenomena by utilizing the training image; deploying the recognition model in the front-end device based on an edge computing technique; acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment, and sending the monitored image to a server; when the dangerous phenomenon is identified in the monitoring image, sending alarm information to the server; and displaying the monitoring image in the server in real time in display equipment.
The technical scheme of the disclosure has the following beneficial effects:
according to the power grid monitoring and shooting method, the device, the equipment and the storage medium based on the edge calculation and the artificial intelligence, model training can be carried out aiming at external broken hidden dangers of conducting wire foreign matters, construction machinery, tower cranes and the like which often occur in a power transmission channel, the trained model is deployed in front-end equipment, the hidden dangers of the foreign matters in the images are automatically detected and recognized, foreign matter invasion in a corridor of power transmission and transformation equipment and abnormal state detection of power transmission and transformation equipment and tower hardware can be carried out without the cooperation of a server, abnormal scene information is reported in real time, and the functions of acquisition of monitoring images of the power transmission and transformation equipment, front-end recognition and hidden danger fault alarm are achieved.
The edge calculation technology adopted by the invention can greatly reduce communication overhead and calculation delay, can utilize the monitoring image collected on line to dynamically adjust the model, parameters and application, can distribute new functions on line, improves the precision and reliability of the detection result, can continuously and dynamically learn, and has real intelligence.
Drawings
Fig. 1 is a flowchart of a power grid monitoring method based on edge calculation and artificial intelligence in an embodiment of the present specification;
fig. 2 is a block diagram of a power grid monitoring device based on edge calculation and artificial intelligence in an embodiment of the present specification;
fig. 3 is a terminal device for implementing a power grid monitoring method based on edge calculation and artificial intelligence in an embodiment of the present specification;
fig. 4 is a computer-readable storage medium for implementing an edge-computing and artificial intelligence-based grid monitoring method in an embodiment of the present specification.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are only schematic illustrations of the present disclosure. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
As shown in fig. 1, an embodiment of the present specification provides a power grid monitoring method based on edge computing and artificial intelligence, where an execution subject of the method may be a terminal device, where the terminal device may be a mobile phone, a tablet computer, a personal computer, and the like. The method may specifically include the following steps S101 to S106:
in step S101, a large number of training images are obtained, where the training images are from front-end equipment and a sample pool, and the front-end equipment is located around a power transmission channel.
Wherein, the front-end equipment can be a monitoring camera, and also can be electronic equipment with a calculating function and a shooting function,
in step S102, based on the artificial intelligence deep learning technique, a recognition model for recognizing a dangerous phenomenon is obtained using the training image.
In the on-line monitoring of the power transmission line, the deep learning algorithm gradually extracts input data by simulating cerebral cortex and adopting a multilayer nonlinear feature processing mode, and gradually establishes mapping from bottom features to high-level abstract features, so that the complex feature extraction work is simplified.
In step S103, the recognition model is deployed in the front-end device based on an edge calculation technique.
The camera or the electronic equipment arranged on the power transmission tower is provided with an edge intelligent calculation function, and the recognition model is stored in the front-end equipment, so that the latest calculation can be realized, the calculation delay is avoided, and the calculation speed is increased.
In step S104, a surveillance image is obtained by using the front-end device, a dangerous phenomenon is identified for the surveillance image by using the identification model in the front-end device, and the surveillance image is sent to a server.
The image and video are analyzed in real time locally according to the recognition model of the deep learning neural network of the preselection training, people, vehicles, cranes, towers, fireworks and other dangerous objects in the image are intelligently recognized, and meanwhile, monitoring data are sent to the server.
In step S105, when the monitoring image identifies a dangerous phenomenon, alarm information is sent to the server.
After the dangerous phenomenon in the monitored image is identified, alarming is carried out, and after the server receives the alarming information, the alarming information can be sent to the client side connected with the server so as to inform a manager of danger elimination.
In step S106, the monitoring image in the server is displayed in real time in a display device. Wherein real-time monitoring can be achieved.
In an embodiment, the dangerous phenomena include construction machinery, cranes, tower cranes, fireworks and foreign objects wound on the power transmission passage.
In an embodiment, the obtaining, by using the front-end device, a surveillance image specifically includes: and setting the front-end equipment to obtain the monitoring image at regular time, and controlling the front-end equipment to obtain the monitoring image at regular time through the server.
The system comprises a display device, a front-end device and an identification model, wherein the display device has timing snapshot and manual snapshot functions, after a manager on the display device finds a suspected dangerous phenomenon, a shooting function instruction can be triggered manually, and after the instruction is sent to the front-end device, the front-end device conducts snapshot so that the identification model in the front-end device conducts further dangerous phenomenon identification on a monitored image.
In an embodiment, the method further comprises: acquiring a surveillance video, and transmitting the surveillance video to the server, wherein the surveillance video comes from the front-end equipment; acquiring a key frame sequence of the surveillance video; utilizing the identification model in the front-end equipment to identify dangerous phenomena for each frame in the key frame sequence; when the critical frame sequence is identified to have a dangerous phenomenon, sending alarm information to the server; and displaying the monitoring video in the server in real time in display equipment.
Meanwhile, the dangerous phenomenon analysis can be carried out by utilizing videos shot by the front-end equipment, so that multiple analysis of monitoring images with high resolution and monitoring videos with low resolution is realized, the monitoring images can be used for specific dangerous phenomenon analysis, and the monitoring videos can be used for trend analysis of dangerous phenomena.
In an embodiment, after receiving the monitoring image, the server uses the monitoring image as a training sample of the recognition model, dynamically updates the recognition model, and transmits the updated recognition model to the front-end device.
The recognition model can be dynamically updated, so that the recognition function is more and more perfect, and the early warning capability is more and more strong.
In an embodiment, the obtaining, based on the artificial intelligence deep learning technique, a recognition model for recognizing a dangerous phenomenon by using the training image specifically includes: inputting a mass of training images into a recognition model; marking dangerous phenomena in the training images; and automatically training the recognition model.
Specifically, an AlexNet network model can be adopted, and the performance of the convolutional neural network is remarkably improved through application of a larger data set, a stronger model and a more optimized fitting technology.
In one embodiment, after sending an alarm message to the server when the monitoring image identifies a dangerous phenomenon, the server is further configured to: performing linkage analysis on the alarm information and the alarm information of the monitoring images of other front-end equipment; and upgrading or keeping unchanged the danger level of the alarm information according to the linkage analysis result.
In an embodiment, the method further comprises: identifying an external device in wired or wireless connection with the front-end device; receiving a protocol program from the server according to the type of the external equipment; managing the external equipment according to the protocol program; and sending the data transmitted from the external equipment to the front-end equipment to the server.
Based on the same idea, the exemplary embodiment of the present disclosure further provides an edge calculation and artificial intelligence based grid monitoring apparatus, as shown in fig. 2, the edge calculation and artificial intelligence based grid monitoring apparatus 200 includes: the acquisition module 201 is configured to acquire a large number of training images, where the training images are from front-end equipment and a sample pool, and the front-end equipment is located around a power transmission channel; the training module 202 is used for obtaining a recognition model for recognizing the dangerous phenomenon by using the training image based on an artificial intelligence deep learning technology; a deployment module 203, configured to deploy the recognition model in the front-end device based on an edge computing technique; the identification module 204 is configured to obtain a monitored image by using the front-end device, identify a dangerous phenomenon in the monitored image by using the identification model in the front-end device, and send the monitored image to a server; the alarm module 205 is configured to send alarm information to the server when the monitoring image identifies a dangerous phenomenon; and the monitoring module 206 is configured to display the monitoring image in the server in a display device in real time.
In an embodiment, the dangerous phenomena in the training module 202 include construction machinery, cranes, tower cranes, fireworks and foreign objects that get entangled on the power transmission channel.
In an embodiment, the identifying module 204 specifically includes: and setting the front-end equipment to obtain the monitoring image at regular time, and controlling the front-end equipment to obtain the monitoring image at regular time through the server.
In an embodiment, the grid monitoring apparatus 200 based on edge calculation and artificial intelligence is further configured to: acquiring a surveillance video, and transmitting the surveillance video to the server, wherein the surveillance video comes from the front-end equipment; acquiring a key frame sequence of the surveillance video; utilizing the identification model in the front-end equipment to identify dangerous phenomena for each frame in the key frame sequence; when the critical frame sequence is identified to have a dangerous phenomenon, sending alarm information to the server; and displaying the monitoring video in the server in real time in display equipment.
In an embodiment, the training module 202 is further configured to, after receiving the monitoring image, the server uses the monitoring image as a training sample of the recognition model, dynamically updates the recognition model, and transmits the updated recognition model to the front-end device.
In one embodiment, the training module 202 specifically includes. Based on artificial intelligence deep learning technique, utilize the training image, obtain the recognition model who is used for discerning dangerous phenomenon, specifically include: inputting a mass of training images into a recognition model; marking dangerous phenomena in the training images; and automatically training the recognition model.
In an embodiment, the alarm module 205 is specifically configured to: performing linkage analysis on the alarm information and the alarm information of the monitoring images of other front-end equipment; and upgrading or keeping unchanged the danger level of the alarm information according to the linkage analysis result.
In an embodiment, the grid monitoring apparatus 200 based on edge calculation and artificial intelligence is further configured to: identifying an external device in wired or wireless connection with the front-end device; receiving a protocol program from the server according to the type of the external equipment; managing the external equipment according to the protocol program; and sending the data transmitted from the external equipment to the front-end equipment to the server.
The embodiment of the specification provides a device is clapped in electric wire netting prison based on edge calculation and artificial intelligence, can carry out model training to outer broken hidden danger such as the wire foreign matter that transmission channel often takes place, construction machinery, tower crane, and deploy the model that the training was accomplished in front end equipment, the foreign matter hidden danger in the automatic detection discernment image, need not the server cooperation and can carry out the invasion of power transmission and transformation equipment corridor foreign matter and transformer equipment, the unusual detection of shaft tower gold utensil state, and report unusual scene information in real time, realize the collection of power transmission and transformation equipment monitoring image, front end discernment, trouble hidden danger alarming function.
The edge calculation technology adopted by the embodiment of the specification can greatly reduce communication overhead and calculation delay, can utilize monitoring images collected on line to dynamically adjust models, parameters and applications, can distribute new functions on line, improves the precision and reliability of detection results, can continuously and dynamically learn, and has real intelligence.
The specific details of each module/unit in the above-mentioned apparatus have been described in detail in the method section, and the details that are not disclosed may refer to the contents of the method section, and thus are not described again.
Based on the same idea, the embodiment of the present specification further provides a power grid monitoring device based on edge calculation and artificial intelligence, as shown in fig. 3.
The grid monitoring device based on edge calculation and artificial intelligence can be a terminal device or a server provided by the above embodiment.
The grid monitoring device based on edge calculation and artificial intelligence can generate larger difference due to different configurations or performances, and can comprise one or more processors 301 and a memory 302, wherein the memory 302 can store one or more stored applications or data. Memory 302 may include, among other things, readable media in the form of volatile memory units, such as random access memory units (RAM) and/or cache memory units, and may further include read-only memory units. The application programs stored in memory 302 may include one or more program modules (not shown), including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment. Still further, the processor 301 may be configured to communicate with the memory 302 to execute a series of computer-executable instructions in the memory 302 on an edge computing and artificial intelligence based grid monitoring device. The edge computing and artificial intelligence based grid monitoring device may also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more I/O interfaces (input output interfaces) 305, one or more external devices 306 (e.g., keyboard, pointing device, bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the device, and/or any device (e.g., router, modem, etc.) that enables the device to communicate with one or more other computing devices. Such communication may occur via I/O interface 305. Also, the device may communicate with one or more networks (e.g., a Local Area Network (LAN)) via a wired or wireless interface 304.
In particular, in the embodiment, the edge-computing and artificial intelligence based grid monitoring apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the edge-computing and artificial intelligence based grid monitoring apparatus, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:
acquiring a mass of training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel; based on an artificial intelligence deep learning technology, obtaining a recognition model for recognizing dangerous phenomena by utilizing the training image; deploying the recognition model in the front-end device based on an edge computing technique; acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment, and sending the monitored image to a server; when the dangerous phenomenon is identified in the monitoring image, sending alarm information to the server; and displaying the monitoring image in the server in real time in display equipment.
The dangerous phenomenon comprises construction machinery, a crane, a tower crane, smoke and fire and foreign matters wound on the power transmission channel.
The obtaining of the monitoring image by using the front-end device specifically includes: and setting the front-end equipment to obtain the monitoring image at regular time, and controlling the front-end equipment to obtain the monitoring image at regular time through the server.
The instructions further include: acquiring a surveillance video, and transmitting the surveillance video to the server, wherein the surveillance video comes from the front-end equipment; acquiring a key frame sequence of the surveillance video; utilizing the identification model in the front-end equipment to identify dangerous phenomena for each frame in the key frame sequence; when the critical frame sequence is identified to have a dangerous phenomenon, sending alarm information to the server; and displaying the monitoring video in the server in real time in display equipment.
And after receiving the monitoring image, the server takes the monitoring image as a training sample of the recognition model, dynamically updates the recognition model, and transmits the updated recognition model to the front-end equipment.
Based on artificial intelligence deep learning technique, utilize the training image, obtain the recognition model who is used for discerning dangerous phenomenon, specifically include: inputting a mass of training images into a recognition model; marking dangerous phenomena in the training images; and automatically training the recognition model.
When the monitoring image is identified to be dangerous, after alarm information is sent to the server, the server is further used for: performing linkage analysis on the alarm information and the alarm information of the monitoring images of other front-end equipment; and upgrading or keeping unchanged the danger level of the alarm information according to the linkage analysis result.
The instructions further include: identifying an external device in wired or wireless connection with the front-end device; receiving a protocol program from the server according to the type of the external equipment; managing the external equipment according to the protocol program; and sending the data transmitted from the external equipment to the front-end equipment to the server.
Based on the same idea, the exemplary embodiments of the present disclosure also provide a computer-readable storage medium on which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the above-mentioned "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 4, a program product 400 for implementing the above method according to an exemplary embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the exemplary embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in 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.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, according to exemplary embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A power grid monitoring method based on edge calculation and artificial intelligence is characterized by comprising the following steps:
acquiring a mass of training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel;
based on an artificial intelligence deep learning technology, obtaining a recognition model for recognizing dangerous phenomena by utilizing the training image;
deploying the recognition model in the front-end device based on an edge computing technique;
acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment, and sending the monitored image to a server;
when the dangerous phenomenon is identified in the monitoring image, sending alarm information to the server;
and displaying the monitoring image in the server in real time in display equipment.
2. The edge-computing and artificial intelligence based grid monitoring method according to claim 1, wherein the dangerous phenomena include construction machinery, cranes, tower cranes, fireworks and foreign objects wound on the transmission channel.
3. The power grid monitoring method based on edge calculation and artificial intelligence as claimed in claim 1, wherein the obtaining of the monitored image by using the front-end device specifically includes:
and setting the front-end equipment to obtain the monitoring image at regular time, and controlling the front-end equipment to obtain the monitoring image at regular time through the server.
4. The grid monitoring method based on edge computing and artificial intelligence of claim 1, wherein the method further comprises:
acquiring a surveillance video, and transmitting the surveillance video to the server, wherein the surveillance video comes from the front-end equipment;
acquiring a key frame sequence of the surveillance video;
utilizing the identification model in the front-end equipment to identify dangerous phenomena for each frame in the key frame sequence;
when the critical frame sequence is identified to have a dangerous phenomenon, sending alarm information to the server;
and displaying the monitoring video in the server in real time in display equipment.
5. The power grid monitoring method based on edge computing and artificial intelligence of claim 1, wherein after receiving the monitoring image, the server takes the monitoring image as a training sample of the recognition model, dynamically updates the recognition model, and transmits the updated recognition model to the front-end device.
6. The power grid monitoring and photographing method based on edge calculation and artificial intelligence of claim 1, wherein the obtaining of the recognition model for recognizing the dangerous phenomenon based on the artificial intelligence deep learning technology by using the training image specifically comprises:
inputting a mass of training images into a recognition model;
marking dangerous phenomena in the training images;
and automatically training the recognition model.
7. The grid monitoring method based on edge computing and artificial intelligence of claim 1, wherein after sending an alarm message to the server when the monitoring image identifies a dangerous phenomenon, the server is further configured to:
performing linkage analysis on the alarm information and the alarm information of the monitoring images of other front-end equipment;
and upgrading or keeping unchanged the danger level of the alarm information according to the linkage analysis result.
8. The grid monitoring method based on edge computing and artificial intelligence of claim 1, wherein the method further comprises:
identifying an external device in wired or wireless connection with the front-end device;
receiving a protocol program from the server according to the type of the external equipment;
managing the external equipment according to the protocol program;
and sending the data transmitted from the external equipment to the front-end equipment to the server.
9. A power grid monitoring device based on edge calculation and artificial intelligence comprises:
the acquisition module is used for acquiring massive training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel;
the training module is used for obtaining a recognition model for recognizing dangerous phenomena by utilizing the training images based on an artificial intelligence deep learning technology;
a deployment module for deploying the recognition model in the front-end device based on an edge computing technique;
the identification module is used for acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment and sending the monitored image to a server;
the alarm module is used for sending alarm information to the server when the monitoring image identifies a dangerous phenomenon;
and the monitoring module is used for displaying the monitoring image in the server in real time in display equipment.
10. An edge calculation and artificial intelligence based power grid monitoring device, comprising:
a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: acquiring a mass of training images, wherein the training images come from front-end equipment and a sample pool, and the front-end equipment is positioned at the periphery of a power transmission channel;
based on an artificial intelligence deep learning technology, obtaining a recognition model for recognizing dangerous phenomena by utilizing the training image;
deploying the recognition model in the front-end device based on an edge computing technique;
acquiring a monitored image by using the front-end equipment, identifying dangerous phenomena of the monitored image by using the identification model in the front-end equipment, and sending the monitored image to a server;
when the dangerous phenomenon is identified in the monitoring image, sending alarm information to the server;
and displaying the monitoring image in the server in real time in display equipment.
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