CN111582016A - Intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning - Google Patents
Intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning Download PDFInfo
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Abstract
The invention discloses an intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning, wherein the method comprises the following steps: the sensing equipment collects the monitoring image and gives an alarm according to the alarm information; the edge computing node identifies the monitoring image based on the monitoring image and a power grid abnormity detection model issued by the cloud computing center, and sends alarm information to the sensing equipment when determining that the power grid abnormity exists; transmitting the identified monitoring image to a cloud computing center; the cloud computing center generates a power grid abnormity detection model according to the stored training set image training deep learning model, updates the power grid abnormity detection model according to the recognized monitoring image training, and issues the power grid abnormity detection model to the edge computing node; the invention also discloses a corresponding power grid monitoring system; by adopting the method and the system, the operation burden of the cloud computing center can be reduced.
Description
Technical Field
The invention belongs to the field of power grid safety protection, and particularly relates to an intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning.
Background
The application of intelligent video monitoring in an electric power system is particularly important, the traditional video monitoring is not enough in the aspects of timely processing, evidence obtaining after the fact and the like, massive videos need a large amount of manpower to identify and analyze the abnormity existing in video images, the intelligent monitoring video gradually becomes a development trend along with the development of artificial intelligence, deep learning and the like, the video images are subjected to uninterrupted analysis by adopting an artificial intelligence algorithm, and once abnormity is found, early warning information is immediately sent to a worker, the workload of equipment maintenance of the worker can be reduced, and the efficiency and the effect of the whole monitoring management are improved.
The cloud-based video monitoring technology reduces the construction and maintenance cost of users, and the centralized computing and storing mode of the cloud improves the safety and reliability of video data; however, video data has two characteristics, namely, data generated by video monitoring is mainly unstructured data, and video data has an explosive growth trend, so that the cloud-based video monitoring technology has the following problems:
images obtained by mass video streams are transmitted to a cloud computing center, so that a large amount of network bandwidth is consumed, and further problems of service interruption, network delay and the like can be caused, so that the real-time performance cannot be guaranteed;
the image processing task is centralized in the cloud computing center, a large amount of computing resources are needed, and the operation burden of the cloud computing center is increased.
Disclosure of Invention
The invention aims to solve one of the problems in the prior art, and provides an intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning, so that the operation burden of a cloud computing center is reduced.
In order to achieve the purpose, the intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning is used for a power grid monitoring system, the power grid monitoring system comprises sensing equipment, edge computing nodes and a cloud computing center, and the method comprises the following steps:
the sensing equipment collects a monitoring image;
the edge computing node acquires a monitoring image acquired by the sensing equipment;
the edge computing node identifies the monitoring image to determine whether the power grid abnormality exists or not based on the monitoring image and a power grid abnormality detection model issued by the cloud computing center, and sends alarm information to the sensing equipment when the power grid abnormality exists;
the sensing equipment receives alarm information of the edge computing node and gives an alarm according to the alarm information;
the edge computing node transmits the identified monitoring image to a cloud computing center;
the cloud computing center trains a deep learning model according to the stored training set images to generate a power grid abnormity detection model, and the power grid abnormity detection model is updated according to the recognized monitoring image training transmitted by the edge computing node;
and the cloud computing center issues the power grid abnormity detection model to the edge computing node.
Optionally, the method further includes:
the edge computing node carries out preprocessing operation on the monitoring image;
the edge computing node performs duplicate removal operation on the monitoring images acquired within the interval time;
the monitoring image is identified to determine whether the power grid abnormity exists based on the monitoring image and a power grid abnormity detection model issued by a cloud computing center: and identifying the preprocessed and deduplicated monitoring image according to the power grid abnormality detection model so as to determine whether the power grid abnormality exists in the monitoring image.
Optionally, transmitting the identified monitoring image to the cloud computing center includes:
temporarily storing the identified monitoring image in an edge computing node;
and transmitting the temporarily stored monitoring images to the cloud computing center in a unified manner in a time period when the network bandwidth flow is smaller than a preset value.
Optionally, the generating a power grid anomaly detection model according to the stored training set image training deep learning model includes:
adjusting the training set image to 320 x 320;
constructing a lightweight target detection network ThunderNet as a deep learning model;
and training the deep learning model according to the adjusted training set image to generate a power grid abnormity detection model.
Optionally, the method further includes:
the cloud computing center builds and trains an abnormal flow monitoring model;
the cloud computing center issues the trained abnormal flow monitoring model to the edge computing node;
monitoring the flow of the edge by the edge computing node according to the abnormal flow monitoring model, and if the abnormal flow exists in the edge, sending a flow abnormal signal to the cloud computing center;
and when the cloud computing center receives the flow abnormal signal, the cloud computing center blocks the abnormal flow.
In another aspect of the disclosure, an intelligent maintenance-free power grid monitoring system based on cloud-edge collaborative deep learning comprises a sensing device, an edge computing node and a cloud computing center;
the sensing device includes:
the image acquisition component is used for acquiring a monitoring image;
the alarm device is used for receiving the alarm information of the edge computing node and giving an alarm according to the alarm information;
the edge computing node comprises:
the image processing module is used for acquiring a monitoring image acquired by the sensing equipment;
the image detection module is used for identifying the monitoring image and determining whether the power grid is abnormal or not based on the monitoring image and a power grid abnormity detection model issued by the cloud computing center;
the alarm module is used for sending alarm information to the sensing equipment when the power grid abnormity exists;
the first transmission module is used for transmitting the identified monitoring image to the cloud computing center;
the cloud computing center includes:
the power grid anomaly detection model training module is used for training the deep learning model according to the stored training set images to generate a power grid anomaly detection model, and training and updating the power grid anomaly detection model according to the identified monitoring images transmitted by the edge computing nodes;
and the second transmission module is used for transmitting the power grid abnormity detection model to the edge computing node.
Optionally, the image acquisition assembly is connected to a camera for shooting the power monitoring video, and is used for acquiring the monitoring image from a video stream shot by the camera.
Optionally, the edge computing node further includes a repeat filtering module;
the image processing module is also used for carrying out preprocessing operation on the monitoring image;
the repeated filtering module is used for carrying out duplicate removal operation on the monitoring images collected within the interval time;
the monitoring image is identified and whether the power grid abnormity exists is determined based on the monitoring image and a power grid abnormity detection model issued by a cloud computing center: and identifying the preprocessed and deduplicated monitoring image according to the power grid abnormality detection model so as to determine whether the power grid abnormality exists in the monitoring image.
Optionally, transmitting the identified monitoring image to the cloud computing center includes: and temporarily storing the identified monitoring images in the edge computing nodes, and uniformly transmitting the temporarily stored monitoring images to the cloud computing center in a time period when the network bandwidth flow is less than a preset value.
Optionally, the generating a power grid anomaly detection model according to the stored training set image training deep learning model includes:
adjusting the training set image to 320 x 320;
constructing a lightweight target detection network ThunderNet as a deep learning model;
and training the deep learning model according to the adjusted training set image to generate a power grid abnormity detection model.
The technical scheme of the present disclosure can be implemented to obtain the following beneficial technical effects:
the cloud computing center is used for training and updating the power grid abnormity detection model; the power grid abnormity detection model trained and updated by the edge computing node judges whether power grid abnormity exists or not based on the monitoring image, the power grid abnormity detection model issued by the cloud computing center and the monitoring image acquired by the sensing equipment, sends alarm information to the corresponding sensing equipment when the power grid is abnormal, and alarms by the sensing equipment, so that a worker can know the power grid abnormity in time and take effective measures for the power grid abnormity in time.
The cloud computing center is mainly used for training and updating the power grid abnormity detection model, and the recognition work is distributed to each edge computing node, so that the operation amount of the cloud computing center is greatly reduced, and the operation burden of the cloud computing center is reduced.
Massive video image data are directly uploaded to a cloud computing center, network bandwidth pressure is high, network time delay is often generated, the edge computing nodes process the image data nearby a data source and make judgment in real time, the problems of interaction delay and the like are effectively solved, and bandwidth cost is reduced
Drawings
Fig. 1 is a flowchart of an intelligent maintenance-free power grid monitoring method based on cloud-edge collaborative deep learning in an embodiment of the present disclosure;
fig. 2 is a block diagram of an intelligent maintenance-free power grid monitoring system based on cloud-edge collaborative deep learning according to an embodiment of the present disclosure;
fig. 3 is another block diagram of an intelligent maintenance-free power grid monitoring system based on cloud-edge collaborative deep learning in an embodiment of the present disclosure;
fig. 4 is another block diagram of an intelligent maintenance-free power grid monitoring system based on cloud-edge collaborative deep learning in an embodiment of the present disclosure;
fig. 5 is another block diagram of an intelligent maintenance-free power grid monitoring system based on cloud-edge collaborative deep learning in an embodiment of the present disclosure.
Detailed Description
To facilitate understanding of those skilled in the art, the present invention will be further described with reference to specific examples:
referring to fig. 1 and 2, the intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning is used for a power grid monitoring system, the power grid monitoring system comprises a sensing device 1, an edge computing node 2 and a cloud computing center 3, and the method comprises the following steps:
s1: the sensing equipment collects a monitoring image;
s2: the edge computing node acquires a monitoring image acquired by the sensing equipment;
s3: the edge computing node identifies the monitoring image to determine whether the power grid abnormality exists or not based on the monitoring image and a power grid abnormality detection model issued by the cloud computing center, and sends alarm information to the sensing equipment when the power grid abnormality exists;
s4: the sensing equipment receives alarm information of the edge computing node and gives an alarm according to the alarm information;
s5: the edge computing node transmits the identified monitoring image to a cloud computing center;
s6: the cloud computing center trains a deep learning model according to the stored training set images to generate a power grid abnormity detection model, and the power grid abnormity detection model is updated according to the recognized monitoring image training transmitted by the edge computing node;
s7: and the cloud computing center issues the power grid abnormity detection model to the edge computing node.
It is to be appreciated that the edge computing node 2 may be in communication with a plurality of sensing devices 1, and the cloud computing center 3 may be in communication with a plurality of sensing devices.
Note that the above numbers S1 to S6 do not represent the execution order relationship of the steps S1 to S6.
In the intelligent maintenance-free power grid monitoring method based on cloud-edge collaborative deep learning, a cloud computing center is used for training and updating a power grid abnormity detection model; the power grid abnormity detection model trained and updated by the edge computing node judges whether power grid abnormity exists or not based on the monitoring image, the power grid abnormity detection model issued by the cloud computing center and the monitoring image acquired by the sensing equipment, sends alarm information to the corresponding sensing equipment when the power grid is abnormal, and alarms by the sensing equipment, so that a worker can know the power grid abnormity in time and take effective measures for the power grid abnormity in time.
Massive video image data are directly uploaded to a cloud computing center, network bandwidth pressure is high, network delay is often generated, edge computing nodes process the image data nearby a data source and make judgment in real time, the problems of interaction delay and the like are effectively solved, and bandwidth cost is reduced;
the deep learning calculation is completed by the cooperation of a cloud calculation center and an edge side, the cloud calculation center is responsible for basic model training of historical data with large calculation amount, model optimization is completed based on calculation and storage capacity, the model optimization is issued to the edge side, the inference process of the training model is completed, namely the detection of real-time images, and the intelligent cooperation of the cloud side is realized;
the cloud computing center is mainly used for training and updating the power grid abnormity detection model, and recognition work is distributed to each edge computing node, so that the operation amount of the cloud computing center is greatly reduced, and the operation burden of the cloud computing center is reduced.
The cloud computing center trains and updates the power grid anomaly detection model according to the identified monitoring images uploaded by the edge computing nodes, so that the power grid anomaly detection model is more complete, and the identification rate of the power grid anomaly detection model is improved; when the monitoring image is identified and the power grid is abnormal, the staff can carry out further manual verification, and the verified monitoring image is trained and the power grid abnormality detection model is updated.
In one embodiment, the intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning further includes:
the edge computing node carries out preprocessing operation on the monitoring image;
the edge computing node performs duplicate removal operation on the monitoring images acquired within the interval time;
the monitoring image is identified and whether the power grid abnormity exists is determined based on the monitoring image and a power grid abnormity detection model issued by a cloud computing center: and identifying the preprocessed and de-duplicated monitoring image according to the power grid abnormality detection model so as to determine whether the monitoring image judges whether the power grid abnormality exists.
In the embodiment of the present disclosure, the main purpose of the preprocessing is to eliminate irrelevant information in the image, which includes removing the image of the mashup, and adjusting the size of the image to 320 × 320 pixels, and performing a normalization operation on the image. Because a large number of repeated pictures exist in the interval time due to the fact that images in the video stream are obtained, the identification operation which is unnecessary to repeat can be reduced through the duplication removing operation, and the identification efficiency is greatly improved.
In one embodiment, the grid anomaly detection model may identify and locate grid anomalies.
In one embodiment, when the power grid abnormality is identified, the corresponding label text is used as alarm information; meanwhile, if the images of the continuous frame number in the preset interval time are the same alarm information, the alarm information is sent to the alarm device only once, and frequent repeated alarm is avoided.
In one embodiment, transmitting the identified monitoring image to the cloud computing center comprises:
temporarily storing the identified monitoring image in an edge computing node;
and transmitting the temporarily stored monitoring images to the cloud computing center in a unified manner in a time period when the network bandwidth flow is smaller than a preset value.
By selecting a time period with small network agent flow and sending the monitoring image, the occupation of the loan can be reduced.
In one embodiment, training the deep learning model to generate a grid anomaly detection model from the stored training set images comprises:
resizing the training set images to 320 x 320;
constructing a lightweight target detection network ThunderNet as a deep learning model;
and training the deep learning model according to the adjusted training set image to generate a power grid abnormity detection model.
ThunderNet includes two parts: the system comprises a backbone network and a detection network, wherein the backbone network is SNet, and the detection network adopts a Light-Head R-CNN network architecture. In the process of training the deep learning model according to the adjusted training set image, the AP can be used as a performance evaluation index, and when the accuracy of the model on each class reaches a preset threshold value, the training is stopped; and the cloud computing center optimizes the trained model based on the computing and storage capacity of the application nodes required by the deployment of the model, and generates a thunderNet power grid abnormality detection model required by the edge computing nodes.
When the size of the training set image is adjusted, the image data can be cleaned, redundant images with mixed mashups are removed, and normalization processing is carried out on the images. So as to keep consistent with the preprocessing operation of the monitoring image by the edge computing node.
In one embodiment, the intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning further includes:
the method comprises the steps that a cloud computing center builds and trains an abnormal flow monitoring model DAE-RNN, the built model is trained by using historical flow data stored on the cloud, and the internal difference between normal flow and abnormal flow is mined;
the cloud computing center issues the trained abnormal flow monitoring model to the edge computing node;
monitoring the flow of the edge by the edge computing node according to the abnormal flow monitoring model, and if the abnormal flow exists in the edge, sending a flow abnormal signal to the cloud computing center;
and when the cloud computing center receives the flow abnormal signal, the cloud computing center blocks the abnormal flow.
The computing center provides safety guarantee for the edge side, and when the edge side detects abnormal flow, the cloud computing center is informed to block the abnormal flow, so that the safety cooperation of the cloud edge is realized.
Example 2:
referring to fig. 2 to 5, the intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning includes a sensing device 1, an edge computing node 2 and a cloud computing center 3;
sensing device 1, comprising:
the image acquisition component 11 is used for acquiring monitoring images;
the alarm device 12 is used for receiving alarm information of the edge computing node and giving an alarm according to the alarm information;
an edge compute node 2, comprising:
the image processing module 21 is configured to obtain a monitoring image acquired by the sensing device;
the image detection module 22 is configured to identify the monitoring image and determine whether a power grid abnormality exists based on the monitoring image and a power grid abnormality detection model issued by the cloud computing center;
the warning module 23 is configured to send warning information to the sensing device when there is a power grid abnormality;
a first transmission module 24, configured to transmit the identified monitoring image to the cloud computing center;
the cloud computing center 3 includes:
the model training module 31 is used for training the deep learning model according to the stored training set images to generate a power grid abnormity detection model, and training and updating the power grid abnormity detection model according to the identified monitoring images transmitted by the edge computing nodes;
and the second transmission module 32 is configured to issue the power grid abnormality detection model to the edge computing node.
In the intelligent maintenance-free power grid monitoring system based on cloud-edge collaborative deep learning, a cloud computing center is used for training and updating a power grid abnormity detection model; the power grid abnormity detection model trained and updated by the edge computing node judges whether power grid abnormity exists or not based on the monitoring image, the power grid abnormity detection model issued by the cloud computing center and the monitoring image acquired by the sensing equipment, sends alarm information to the corresponding sensing equipment when the power grid is abnormal, and alarms by the sensing equipment, so that a worker can know the power grid abnormity in time and take effective measures for the power grid abnormity in time.
Massive video image data are directly uploaded to a cloud computing center, network bandwidth pressure is high, network delay is often generated, edge computing nodes process the image data nearby a data source and make judgment in real time, the problems of interaction delay and the like are effectively solved, and bandwidth cost is reduced;
the deep learning calculation is completed by the cooperation of a cloud calculation center and an edge side, the cloud calculation center is responsible for basic model training of historical data with large calculation amount, model optimization is completed based on calculation and storage capacity, the model optimization is issued to the edge side, the inference process of the training model is completed, namely the detection of real-time images, and the intelligent cooperation of the cloud side is realized;
the cloud computing center is mainly used for training and updating the power grid abnormity detection model, and recognition work is distributed to each edge computing node, so that the operation amount of the cloud computing center is greatly reduced, and the operation burden of the cloud computing center is reduced.
The cloud computing center trains and updates the power grid anomaly detection model according to the identified monitoring images uploaded by the edge computing nodes, so that the power grid anomaly detection model is more complete, and the identification rate of the power grid anomaly detection model is improved; when the monitoring image is identified and the power grid is abnormal, the staff can carry out further manual verification, and the verified monitoring image is trained and the power grid abnormality detection model is updated.
In one embodiment, referring to fig. 3, the image capturing assembly 11 is connected to a camera for capturing power surveillance video for obtaining surveillance images from a video stream captured by the camera.
In one embodiment, referring to fig. 4, the edge compute node further includes a repeat filter module 25;
the image processing module 21 is further configured to perform a preprocessing operation on the monitoring image;
the repeated filtering module 25 is used for carrying out duplicate removal operation on the monitoring images collected within the interval time;
based on the monitoring image and a power grid abnormity detection model issued by the cloud computing center, identifying the monitoring image to determine whether the power grid abnormity exists comprises the following steps: and identifying the preprocessed and de-duplicated monitoring image according to the power grid abnormality detection model so as to determine whether the monitoring image judges whether the power grid abnormality exists.
In the embodiment of the present disclosure, the main purpose of the preprocessing is to eliminate irrelevant information in the image, which includes removing the image of the mashup, and adjusting the size of the image to 320 × 320, and performing a normalization operation on the image. Because a large number of repeated pictures exist in the interval time due to the fact that images in the video stream are obtained, the identification operation which is unnecessary to repeat can be reduced through the duplication removing operation, and the identification efficiency is greatly improved.
In one embodiment, when the power grid abnormality is identified, the corresponding label text is used as alarm information; meanwhile, if the images of the continuous frame number in the preset interval time are the same alarm information, the alarm information is sent to the alarm device only once, and frequent repeated alarm is avoided.
In one embodiment, transmitting the identified monitoring image to the cloud computing center comprises: and temporarily storing the identified monitoring images in the edge computing nodes, and uniformly transmitting the temporarily stored monitoring images to the cloud computing center in a time period when the network bandwidth flow is less than a preset value. By selecting a time period with small network agent flow and sending the monitoring image, the occupation of the loan can be reduced.
In one embodiment, training the deep learning model to generate a grid anomaly detection model from the stored training set images comprises:
adjusting the training set image to 320 x 320;
constructing a lightweight target detection network ThunderNet as a deep learning model;
and training the deep learning model according to the adjusted training set image to generate a power grid abnormity detection model.
ThunderNet includes two parts: the system comprises a backbone network and a detection network, wherein the backbone network is SNet, and the detection network adopts a Light-Head R-CNN network architecture. In the process of training the deep learning model according to the adjusted training set image, the AP can be used as a performance evaluation index, and when the accuracy of the model on each class reaches a preset threshold value, the training is stopped; and the cloud computing center optimizes the trained model based on the computing and storage capacity of the application nodes required by the deployment of the model, and generates a thunderNet power grid abnormality detection model required by the edge computing nodes.
When the size of the training set image is adjusted, the image data can be cleaned, redundant images with mixed mashups are removed, and normalization processing is carried out on the images. So as to keep consistent with the preprocessing operation of the monitoring image by the edge computing node.
The intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning further comprises a flow monitoring module, and the flow monitoring module is used for:
building and training an abnormal flow monitoring model DAE-RNN, training the built model by using historical flow data stored on the cloud, and mining the internal difference between normal flow and abnormal flow;
issuing the trained abnormal flow monitoring model to an edge computing node;
and when the flow abnormal signal is received, the abnormal flow is blocked.
The edge computing node is further used for monitoring the edge traffic according to the abnormal traffic monitoring model, and if the edge is found to have abnormal traffic, sending a traffic abnormal signal to the cloud computing center.
The computing center provides safety guarantee for the edge side, and when the edge side detects abnormal flow, the cloud computing center is informed to block the abnormal flow, so that the safety cooperation of the cloud edge is realized.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an embodiment of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications within the technical field of the present invention by those skilled in the art are covered by the claims of the present invention.
Claims (10)
1. The intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning is characterized by being used for a power grid monitoring system, wherein the power grid monitoring system comprises sensing equipment, edge computing nodes and a cloud computing center, and the method comprises the following steps:
the sensing equipment collects a monitoring image;
the edge computing node acquires a monitoring image acquired by the sensing equipment;
the edge computing node identifies the monitoring image to determine whether the power grid abnormality exists or not based on the monitoring image and a power grid abnormality detection model issued by the cloud computing center, and sends alarm information to the sensing equipment when the power grid abnormality exists;
the sensing equipment receives alarm information of the edge computing node and gives an alarm according to the alarm information;
the edge computing node transmits the identified monitoring image to a cloud computing center;
the cloud computing center trains a deep learning model according to the stored training set images to generate a power grid abnormity detection model, and the power grid abnormity detection model is updated according to the recognized monitoring image training transmitted by the edge computing node;
and the cloud computing center issues the power grid abnormity detection model to the edge computing node.
2. The intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning according to claim 1,
the method further comprises the following steps:
the edge computing node carries out preprocessing operation on the monitoring image;
the edge computing node performs duplicate removal operation on the monitoring images acquired within the interval time;
the monitoring image is identified and whether the power grid abnormity exists is determined based on the monitoring image and a power grid abnormity detection model issued by a cloud computing center: and identifying the preprocessed and deduplicated monitoring image according to the power grid abnormality detection model so as to determine whether the power grid abnormality exists in the monitoring image.
3. The intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning according to claim 2, wherein transmitting the identified monitoring image to a cloud computing center comprises:
temporarily storing the identified monitoring image in an edge computing node;
and transmitting the temporarily stored monitoring images to the cloud computing center in a unified manner in a time period when the network bandwidth flow is smaller than a preset value.
4. The intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning as claimed in claim 3, wherein training the deep learning model to generate the power grid anomaly detection model according to the stored training set image comprises:
adjusting the training set image to 320 x 320;
constructing a lightweight target detection network ThunderNet as a deep learning model;
and training the deep learning model according to the adjusted training set image to generate a power grid abnormity detection model.
5. The intelligent maintenance-free power grid monitoring method based on cloud edge collaborative deep learning according to claim 4, wherein the method further comprises:
the cloud computing center builds and trains an abnormal flow monitoring model;
the cloud computing center issues the trained abnormal flow monitoring model to the edge computing node;
monitoring the flow of the edge by the edge computing node according to the abnormal flow monitoring model, and if the abnormal flow exists in the edge, sending a flow abnormal signal to the cloud computing center;
and when the cloud computing center receives the flow abnormal signal, the cloud computing center blocks the abnormal flow.
6. The intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning is characterized by comprising sensing equipment, edge computing nodes and a cloud computing center;
the sensing device includes:
the image acquisition component is used for acquiring a monitoring image;
the alarm device is used for receiving the alarm information of the edge computing node and giving an alarm according to the alarm information;
the edge computing node comprises:
the image processing module is used for acquiring a monitoring image acquired by the sensing equipment;
the image detection module is used for identifying the monitoring image and determining whether the power grid is abnormal or not based on the monitoring image and a power grid abnormity detection model issued by the cloud computing center;
the alarm module is used for sending alarm information to the sensing equipment when the power grid abnormity exists;
the first transmission module is used for transmitting the identified monitoring image to the cloud computing center;
the cloud computing center includes:
the power grid anomaly detection model training module is used for training the deep learning model according to the stored training set images to generate a power grid anomaly detection model, and training and updating the power grid anomaly detection model according to the identified monitoring images transmitted by the edge computing nodes;
and the second transmission module is used for transmitting the power grid abnormity detection model to the edge computing node.
7. The intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning according to claim 6, wherein the image acquisition component is connected with a camera for shooting power monitoring video and is used for acquiring monitoring images from a video stream shot by the camera.
8. The intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning according to claim 7, wherein the edge computing node further comprises a repeat filtering module;
the image processing module is also used for carrying out preprocessing operation on the monitoring image;
the repeated filtering module is used for carrying out duplicate removal operation on the monitoring images collected within the interval time;
the monitoring image is identified and whether the power grid abnormity exists is determined based on the monitoring image and a power grid abnormity detection model issued by a cloud computing center: and identifying the preprocessed and deduplicated monitoring image according to the power grid abnormality detection model so as to determine whether the power grid abnormality exists in the monitoring image.
9. The intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning according to claim 8, wherein transmitting the identified monitoring image to a cloud computing center comprises: and temporarily storing the identified monitoring images in the edge computing nodes, and uniformly transmitting the temporarily stored monitoring images to the cloud computing center in a time period when the network bandwidth flow is less than a preset value.
10. The intelligent maintenance-free power grid monitoring system based on cloud edge collaborative deep learning as claimed in claim 9, wherein training the deep learning model to generate the power grid anomaly detection model according to the stored training set image comprises:
adjusting the training set image to 320 x 320;
constructing a lightweight target detection network ThunderNet as a deep learning model;
and training the deep learning model according to the adjusted training set image to generate a power grid abnormity detection model.
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