CN112257500A - An intelligent image recognition system and recognition method for power equipment based on cloud-edge collaboration technology - Google Patents

An intelligent image recognition system and recognition method for power equipment based on cloud-edge collaboration technology Download PDF

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CN112257500A
CN112257500A CN202010972865.XA CN202010972865A CN112257500A CN 112257500 A CN112257500 A CN 112257500A CN 202010972865 A CN202010972865 A CN 202010972865A CN 112257500 A CN112257500 A CN 112257500A
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power equipment
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栾奇麒
官国飞
王昕平
宋庆武
李春鹏
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Jiangsu Fangtian Power Technology Co Ltd
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Abstract

本发明公开了一种基于云边协同技术的电力设备智能图像识别系统及识别方法,所述电力设备智能图像识别系统包括:图像采集设备、边缘设备和云平台,所述图像采集设备上运行有嵌入式目标检测算法;所述边缘设备上运行有嵌入式目标检测算法且设置有ZEUS AI智能计算硬件平台;所述云平台上运行有嵌入式目标检测算法;所述图像采集设备、边缘设备和云平台彼此之间电性连接且通过网络传输数据。本技术方案选择ZEUS AI智能计算硬件平台与CAFFE算法框架,采用人工智能深度学习完成电力配网的安全帽检测、人员倒地和轨迹跟踪等目标检测的算法训练、性能调优。

Figure 202010972865

The invention discloses a power equipment intelligent image recognition system and a recognition method based on cloud-edge collaboration technology. The power equipment intelligent image recognition system includes: image acquisition equipment, edge equipment and cloud platform, and the image acquisition equipment runs on Embedded target detection algorithm; embedded target detection algorithm runs on the edge device and a ZEUS AI intelligent computing hardware platform is provided; embedded target detection algorithm runs on the cloud platform; the image acquisition device, edge device and The cloud platforms are electrically connected to each other and transmit data through the network. This technical solution selects the ZEUS AI intelligent computing hardware platform and the CAFFE algorithm framework, and uses artificial intelligence deep learning to complete the algorithm training and performance tuning of target detection such as helmet detection, personnel downing and trajectory tracking in the power distribution network.

Figure 202010972865

Description

Intelligent image recognition system and method for power equipment based on cloud edge cooperation technology
Technical Field
The invention relates to the field of electrical engineering science, in particular to an intelligent image recognition system and method for power equipment based on a cloud edge cooperation technology.
Background
Along with the development of related artificial intelligence at home and abroad, except that the human face recognition technology and the license plate recognition technology tend to be mature and start to be commercially used in a large scale in each subdivision field, other related technologies are slowly developed due to various factors, particularly countless subdivision application scenes exist in countless subdivision industry fields, and the development speed of the technology is greatly limited by the customization difficulty and the large-scale market effect.
With the deep development of the power internet of things technology, tens of thousands of power devices generate massive data, especially in the field of computer vision, the amount of pictures and video data generated by front-end devices is huge, if all the data are gathered to a cloud for intelligent analysis, infinite pressure is brought to the bandwidth requirement, the real-time requirement and the like of communication, especially in some scenes with high requirements on real-time performance and privacy safety, for example, it is unrealistic to transmit a large amount of video data to the cloud for processing and then return decision results, so that we are required to provide edge intelligent services nearby on a device node side or a network edge side close to a data source, and move AI calculation power (inference power) from the cloud to the edge side gradually. The cloud is focused on the training of the non-real-time and large-data-volume deep learning algorithm, optimizes the algorithm model, and provides services such as algorithm scheduling for the edge. The technology combining edge computing and cloud computing is more and more emphasized, and the edge computing and the cloud computing are widely applied to various application scenes in various AI + IOT scenes.
According to the safety standard of the electric power construction site, all constructors must wear safety helmets and patrol according to the specified lines. In order to supervise whether all constructors meet the requirements of safe construction, the current methods generally include: firstly, a manual monitoring method: transmitting the monitoring video of the personnel in the construction site to a supervision center through a camera installed on site, and manually checking the video; secondly, a monitoring method based on cloud intelligence comprises the following steps: the method comprises the steps of installing a camera on site, transmitting a monitoring video of a construction site to a supervision center, deploying a computing server in the supervision center, processing the received video through an AI algorithm deployed on the server, analyzing whether a constructor meets the safety management requirement and giving an alarm to the supervisors.
The first method completely does not adopt an intelligent means, and completely depends on manual identification, so that the first method has the following defects: 1) manual inspection is carried out, and the efficiency is low; 2) when a plurality of construction sites are simultaneously carved, monitoring omission or inspection by more supervision personnel is possibly caused, so that the cost is high and the effect is poor; 3) the construction site and the monitoring center generally adopt wireless transmission real-time video, the bandwidth requirement is high, the video quality is difficult to guarantee, and meanwhile, a large amount of redundant video causes bandwidth waste. In the second method, artificial intelligence recognition processing is added on the basis of the first method, so that efficiency is greatly improved, but the cost is high due to the adoption of a cloud server, and meanwhile, a complete video needs to be transmitted to a cloud in real time on a construction site, so that the defects of long time delay, waste of bandwidth and the like exist.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent image recognition system and method for power equipment based on a cloud edge cooperation technology.
In order to achieve the object of the present invention, the present invention provides an intelligent image recognition system for an electrical device based on a cloud-edge coordination technology, comprising: the system comprises image acquisition equipment, edge equipment and a cloud platform, wherein a ZEUS AI intelligent computing hardware platform and an embedded target detection algorithm software platform are arranged on the edge equipment, and the image acquisition equipment, the edge equipment and the cloud platform transmit data through a network.
Further, the invention provides an intelligent image recognition method for power equipment based on a cloud edge coordination technology, which comprises the following steps:
s1: the image acquisition equipment acquires and analyzes image sample data of the power equipment;
s2: selecting a detection recognition model, and training the model;
s3: summarizing a monitoring range of the operation and maintenance of the power equipment according to the analysis of the application scene;
s4: calibrating the model training result according to the acquired image sample, and performing deep learning on a ZEUS AI intelligent computing hardware platform of the edge device and the embedded target detection algorithm software platform;
s5: carrying out model training again in S2 according to the deep learning to obtain a model training result;
s6: and adjusting parameters according to the model training result to complete the simulation test.
Further, the implementation process of the embedded target detection algorithm includes:
extracting an input video stream, and selecting a frame of data to perform preprocessing operation;
inputting the data after the preprocessing operation into a pre-trained classification network, and fixing corresponding network parameters;
and performing the following operation on the obtained feature map through the last convolutional layer of the pre-training classification network:
a. performing region generation network operation on the feature map and obtaining a corresponding region of interest;
b. acquiring a plurality of position sensitivity score mapping graphs with K X K (C +1) dimensions on the feature graph and carrying out classification operation;
c. acquiring a plurality of position sensitivity score maps with dimensions of 4 x K on the feature map and performing regression operation;
d. and respectively executing position sensitive ROI pooling operation on the K-by-K (C +1) -dimensional position sensitive score mapping graph and the 4-by-K-dimensional position sensitive score mapping graph, and acquiring corresponding category and position information, wherein K is the size of the sub-region of the region of interest, and C is the number of the identified target categories.
Further, K is 3 or more and 5 or less; c is not less than 2 and not more than 3.
Compared with the prior art, the invention has the following beneficial technical effects:
based on a high-performance ZEUS AI intelligent computing hardware platform and a CAFFE algorithm framework (an embedded target detection algorithm framework), the invention completes image acquisition, image processing, target detection, deep learning, target key feature extraction, structured video extraction and other processing at an equipment end through an advanced embedded target detection algorithm running at the equipment end, completes safety helmet detection, personnel falling down and track tracking at the equipment end and a cloud platform in real time according to the acquired video at the site, starts alarm linkage in real time for the situation that the safety management is not met, transmits an identification result and a structured alarm video to the cloud platform for display processing, and supervisors only pay attention to the alarm result and the alarm segment without checking a large amount of redundant videos, thereby greatly improving the management efficiency. The artificial intelligence video detection method has the advantages of intelligence, high efficiency, real time, low occupied transmission bandwidth and the like, is very convenient to deploy, and is particularly suitable for mobile or fixed deployment in various power construction places.
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Fig. 1 is a schematic flowchart of an intelligent image recognition method for an electrical device based on a cloud edge coordination technology according to an embodiment;
FIG. 2 is a schematic diagram of an embedded target detection algorithm framework of an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention provides an intelligent image recognition system of power equipment based on a cloud edge coordination technology, which comprises the following components: the system comprises image acquisition equipment, edge equipment and a cloud platform, wherein a ZEUS AI intelligent computing hardware platform and an embedded target detection algorithm software platform are arranged on the edge equipment, and the image acquisition equipment, the edge equipment and the cloud platform transmit data through a network.
In one embodiment, an intelligent image recognition method for an electric power device based on a cloud edge coordination technology is provided, as shown in fig. 1, including the following steps:
s1: the image acquisition equipment acquires and analyzes image sample data of the power equipment;
s2: selecting a detection recognition model, and training the model;
s3: summarizing a monitoring range of the operation and maintenance of the power equipment according to the analysis of the application scene, thereby determining the activity range of the monitored operation and maintenance personnel;
s4: calibrating the model training result according to the acquired image sample, and performing deep learning on a ZEUS AI intelligent computing hardware platform of the edge device and the embedded target detection algorithm software platform;
s5: carrying out model training again in S2 according to the deep learning to obtain a model training result;
s6: and adjusting parameters according to the model training result to complete the simulation test.
In one embodiment, as shown in fig. 2, the implementation process of the embedded target detection algorithm includes:
extracting an input video stream, and selecting a frame of data to perform preprocessing operation;
inputting the data after the preprocessing operation into a pre-trained classification network, and fixing corresponding network parameters;
and performing the following operation on the obtained feature map through the last convolutional layer of the pre-training classification network:
a. performing region generation network operation on the feature map and obtaining a corresponding region of interest;
b. acquiring a plurality of position sensitivity score mapping graphs with K X K (C +1) dimensions on the feature graph and carrying out classification operation;
c. acquiring a plurality of position sensitivity score maps with dimensions of 4 x K on the feature map and performing regression operation;
d. and respectively executing position sensitive ROI pooling operation on the K-by-K (C +1) -dimensional position sensitive score mapping graph and the 4-by-K-dimensional position sensitive score mapping graph, and acquiring corresponding category and position information, wherein K is the size of the sub-region of the region of interest, and C is the number of the identified target categories.
The embedded target detection algorithm is a full convolution neural network framework algorithm based on a region, when the algorithm is identified, firstly, image features are extracted and a feature map is generated, then, the region is used for generating a network RPN, regions of interest with different proportions are generated on the feature map, meanwhile, a position sensitivity score mapping map is generated by using the feature map, and the algorithm identifies different types of targets according to the position sensitivity score mapping map.
In one embodiment, according to the characteristics of an electric power application scene, the value range of K is set to be 3-5, and the value range of C is set to be 2-3.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
It should be noted that the terms "first \ second \ third" referred to in the embodiments of the present application merely distinguish similar objects, and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may exchange a specific order or sequence when allowed. It should be understood that "first \ second \ third" distinct objects may be interchanged under appropriate circumstances such that the embodiments of the application described herein may be implemented in an order other than those illustrated or described herein.
The terms "comprising" and "having" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, product, or device.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (4)

1.一种基于云边协同技术的电力设备智能图像识别系统,其特征在于,包括:图像采集设备、边缘设备和云平台,所述边缘设备上设置有ZEUS AI智能计算硬件平台和嵌入式目标检测算法软件平台,所述图像采集设备、边缘设备和云平台彼此之间通过网络传输数据。1. a power equipment intelligent image recognition system based on cloud-edge collaboration technology, is characterized in that, comprises: image acquisition equipment, edge equipment and cloud platform, described edge equipment is provided with ZEUS AI intelligent computing hardware platform and embedded target The detection algorithm software platform, the image acquisition device, the edge device and the cloud platform transmit data between each other through the network. 2.一种基于云边协同技术的电力设备智能图像识别方法,其特征在于,包括如下步骤:2. A power equipment intelligent image recognition method based on cloud-edge collaboration technology, is characterized in that, comprises the following steps: S1:所述图像采集设备采集电力设备的图像样本数据并进行分析;S1: The image acquisition device collects and analyzes image sample data of the power equipment; S2:选择检测识别模型,进行模型的训练;S2: Select the detection and recognition model to train the model; S3:根据对应用场景的分析,汇总出电力设备运维的监测范围;S3: According to the analysis of application scenarios, summarize the monitoring scope of power equipment operation and maintenance; S4:根据采集的所述图像样本标定所述模型训练的结果,并在所述边缘设备的ZEUS AI智能计算硬件平台和所述嵌入式目标检测算法软件平台上进行深度学习;S4: calibrate the model training result according to the collected image samples, and perform deep learning on the ZEUS AI intelligent computing hardware platform of the edge device and the embedded target detection algorithm software platform; S5:根据所述深度学习再次进行S2中所述的模型训练并得到模型训练结果;S5: Carry out the model training described in S2 again according to the deep learning and obtain the model training result; S6:根据所述模型训练结果进行参数调整,完成模拟测试。S6: Perform parameter adjustment according to the model training result to complete the simulation test. 3.根据权利要求2所述的基于云边协同技术的电力设备智能图像识别方法,其特征在于,所述嵌入式目标检测算法的实现过程包括:3. The intelligent image recognition method for power equipment based on cloud-edge collaboration technology according to claim 2, wherein the implementation process of the embedded target detection algorithm comprises: 提取输入的视频流,选择一帧数据进行预处理操作;Extract the input video stream and select a frame of data for preprocessing; 将经过所述预处理操作后的数据输入经过预训练的分类网络中,并固定其对应的网络参数;Input the data after the preprocessing operation into the pretrained classification network, and fix its corresponding network parameters; 经过所述预训练分类网络的最后一个卷积层对获得的特征图进行如下操作:After the last convolutional layer of the pre-trained classification network, the following operations are performed on the obtained feature map: a.在所述特征图上进行区域生成网络操作并获得相应的感兴趣区域;a. Perform a region generation network operation on the feature map and obtain the corresponding region of interest; b.在所述特征图上获取若干K*K*(C+1)维的位置敏感得分映射图并进行分类操作;b. Obtain several K*K*(C+1)-dimensional position-sensitive score maps on the feature map and perform a classification operation; c.在所述特征图上获取若干4*K*K维的位置敏感得分映射图并进行回归操作;c. Obtain several 4*K*K-dimensional position-sensitive score maps on the feature map and perform a regression operation; d.在所述K*K*(C+1)维的位置敏感得分映射图和所述4*K*K维的位置敏感得分映射图上面分别执行位置敏感的ROI池化操作,并获取对应的类别和位置信息,所述K为感兴趣区域子区域的大小,所述C为识别的目标类别数量。d. Perform a position-sensitive ROI pooling operation on the K*K*(C+1)-dimensional position-sensitive score map and the 4*K*K-dimensional position-sensitive score map, respectively, and obtain the corresponding The category and location information of , the K is the size of the sub-region of the region of interest, and the C is the number of identified target categories. 4.根据权利要求3所述的基于云边协同技术的电力设备智能图像识别方法,其特征在于:所述K大于等于3且小于等于5;所述C大于等于2且小于等于3。4 . The intelligent image recognition method for power equipment based on cloud-edge collaboration technology according to claim 3 , wherein the K is greater than or equal to 3 and less than or equal to 5; the C is greater than or equal to 2 and less than or equal to 3. 5 .
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113011297A (en) * 2021-03-09 2021-06-22 全球能源互联网研究院有限公司 Power equipment detection method, device, equipment and server based on edge cloud cooperation
CN113242271A (en) * 2021-03-17 2021-08-10 北京大学 Digital retina-based end, edge and cloud cooperation system, method and equipment
CN114067235A (en) * 2021-10-22 2022-02-18 广西中科曙光云计算有限公司 Cloud-based data processing system and method
CN114140447A (en) * 2021-12-06 2022-03-04 国网新疆电力有限公司信息通信公司 A method and system for image recognition of power equipment based on cloud-edge collaboration technology
CN114359781A (en) * 2021-12-02 2022-04-15 国家石油天然气管网集团有限公司 An Intelligent Recognition System Based on Cloud-Edge Collaborative Autonomous Learning
CN114500536A (en) * 2022-01-27 2022-05-13 京东方科技集团股份有限公司 Cloud edge cooperation method, system, device, cloud platform, equipment and medium
CN114612825A (en) * 2022-03-09 2022-06-10 云南大学 Target detection method based on edge equipment

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113011297A (en) * 2021-03-09 2021-06-22 全球能源互联网研究院有限公司 Power equipment detection method, device, equipment and server based on edge cloud cooperation
CN113242271A (en) * 2021-03-17 2021-08-10 北京大学 Digital retina-based end, edge and cloud cooperation system, method and equipment
CN114067235A (en) * 2021-10-22 2022-02-18 广西中科曙光云计算有限公司 Cloud-based data processing system and method
CN114359781A (en) * 2021-12-02 2022-04-15 国家石油天然气管网集团有限公司 An Intelligent Recognition System Based on Cloud-Edge Collaborative Autonomous Learning
CN114140447A (en) * 2021-12-06 2022-03-04 国网新疆电力有限公司信息通信公司 A method and system for image recognition of power equipment based on cloud-edge collaboration technology
CN114500536A (en) * 2022-01-27 2022-05-13 京东方科技集团股份有限公司 Cloud edge cooperation method, system, device, cloud platform, equipment and medium
CN114500536B (en) * 2022-01-27 2024-03-01 京东方科技集团股份有限公司 Cloud edge cooperation method, cloud edge cooperation system, cloud device, cloud platform equipment and cloud medium
CN114612825A (en) * 2022-03-09 2022-06-10 云南大学 Target detection method based on edge equipment
CN114612825B (en) * 2022-03-09 2024-03-19 云南大学 Target detection method based on edge equipment

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Application publication date: 20210122