CN112101212A - Method for judging positions of personnel in electric power safety control complex scene - Google Patents
Method for judging positions of personnel in electric power safety control complex scene Download PDFInfo
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Abstract
The invention relates to the technical field of personnel management and control, in particular to a method for judging the position of a person in a complex scene of electric power safety management and control. By combining target detection and position judgment post-processing algorithms, image features can be more effectively extracted, accuracy is guaranteed, and detection speed is increased. The target object can be accurately detected through the constructed network model, and the position of the person can be effectively judged through a position post-processing algorithm. When the system is actually used, the positions of personnel on the electric power construction site can be effectively judged, automatic management and control in the electric power safety production process are realized, and the management and control efficiency of the electric power safety production is effectively improved.
Description
Technical Field
The invention relates to the technical field of personnel management and control, in particular to a method for judging the position of a person in a complex scene of electric power safety management and control.
Background
With the continuous development of economy in China, the dependence degree of people on electric power is continuously improved, and the safety problem of electric power production is more and more concerned by people. The safety consciousness of workers is not strong in power safety production management, and illegal operation is easy to occur, so that potential safety hazards are buried in power safety production management work. At present, most of the electric power safety production is supervised by manpower, and efficient and accurate management and control cannot be realized. With the emergence of technologies such as artificial intelligence, deep learning, the automation of electric power safety management and control becomes possible.
The position judgment of the electric power production site constructors is the premise of judging whether the constructors violate rules or not, and if the height workers need to wear safety belts, the judgment of the positions of the constructors is the basis of effective management and control of electric power safety production. At present, related technologies related to target detection exist, but a simple target detection technology cannot complete position judgment of personnel, post-processing is needed, but the existing detection technology is complex in network structure, long in post-processing time consumption and high in requirements for hardware.
Disclosure of Invention
The invention aims to provide a method for judging the position of a person in a complex scene of electric power safety management and control, so as to solve the problems in the prior art in the background technology.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: provides a method for judging the position of a person under a complex scene of electric power safety control,
on the basis of the technical scheme, the method mainly comprises the following steps:
firstly, collecting pictures of an electric power safety construction site by using a collecting device;
step two, preprocessing the picture collected in the step one to obtain an original data set;
step three, constructing a personnel detection network model, and training a personnel data set row model in the original data set obtained in the step two by adopting the personnel detection network model;
step four, constructing an equipment detection network model, and performing model training on the equipment data set in the original data set obtained in the step two by adopting the equipment detection network model;
and step five, sending the results generated in the step three and the step four into a position judgment post-processing algorithm to judge the position of the personnel.
On the basis of the technical scheme, the preprocessing in the step one mainly comprises zooming and fuzzy operation.
On the basis of the technical scheme, the construction method of the personnel detection network model in the second step comprises the following steps:
the personnel detection network model carries out convolution operation by constructing a convolution group and increasing the width of the convolution group, and outputs a final result; the final classification and regression operations are performed after the position sensitive convolution operation.
On the basis of the technical scheme, the convolution group is mainly constructed by a convolution layer, an activation layer, a pooling layer and a batch normalization layer.
On the basis of the technical scheme, each convolution group uses a plurality of convolution kernels, and then the feature maps generated by each convolution kernel are superposed and then subjected to convolution operation, so that the detection time can be effectively reduced.
On the basis of the technical scheme, the equipment detection network model and the personnel detection network model have the same structure.
The technical scheme provided by the invention has the beneficial effects that:
when the system is actually used, the positions of personnel on the electric power construction site can be effectively judged, automatic management and control in the electric power safety production process are realized, and the management and control efficiency of electric power safety production is effectively improved; meanwhile, the detection rate and the use precision of a post-processing algorithm can be effectively improved by constructing a personnel detection network model and an equipment detection network model.
Drawings
FIG. 1 is a flow chart of a method for determining the position of a person in a complex power safety management and control scenario according to the present invention;
FIG. 2 is a network structure diagram of a method for determining a position of a person in a complex power safety management and control scenario according to the present invention;
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, it is to be understood that the terms "left", "right", "front", "back", "top", "bottom", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
As shown in fig. 1 and fig. 2, a method for determining a position of a person in a complex power safety management and control scenario mainly includes the following steps:
firstly, collecting pictures of an electric power safety construction site by using a collecting device;
step two, preprocessing the picture collected in the step one to obtain an original data set;
step three, constructing a personnel detection network model, and training a personnel data set row model in the original data set obtained in the step two by adopting the personnel detection network model;
step four, constructing an equipment detection network model, and performing model training on the equipment data set in the original data set obtained in the step two by adopting the equipment detection network model;
and step five, sending the results generated in the step three and the step four into a position judgment post-processing algorithm to judge the position of the personnel.
On the basis of the technical scheme, the preprocessing in the step one mainly comprises zooming and fuzzy operation.
On the basis of the technical scheme, the construction method of the personnel detection network model in the second step comprises the following steps:
the personnel detection network model carries out convolution operation by constructing a convolution group and increasing the width of the convolution group, and outputs a final result; the final classification and regression operations are performed after the position sensitive convolution operation.
On the basis of the technical scheme, the convolution group is mainly constructed by a convolution layer, an activation layer, a pooling layer and a batch normalization layer.
On the basis of the technical scheme, each convolution group uses a plurality of convolution kernels, and then the feature maps generated by each convolution kernel are superposed and then subjected to convolution operation, so that the detection time can be effectively reduced.
On the basis of the technical scheme, the equipment detection network model and the personnel detection network model have the same structure.
Specifically, in the method for judging the position of a person in a complex scene of electric power safety management and control, in the specific operation process, the person detection is carried out on a training person detection network model after the original data set is manufactured; if the existence of the personnel is detected, using an equipment detection network model to carry out equipment detection, if no relevant equipment is detected, defaulting the personnel to be on the ground, and if the existence of the equipment is detected, transmitting the information of the personnel detection position into a post-processing algorithm to judge the personnel position; the post-processing algorithm can judge whether the personnel works aloft or not by comparing the position relationship of the personnel and the equipment.
The invention adopts deep learning technology to construct two network models of personnel detection and equipment detection, which are respectively used for personnel detection and equipment detection related to high-altitude operation, such as a power transmission line, a power transmission tower, a single ladder and the like. By combining target detection and position judgment post-processing algorithms, image features can be more effectively extracted, accuracy is guaranteed, and detection speed is increased. The target object can be accurately detected through the constructed network model, and the position of the person can be effectively judged through a position post-processing algorithm. The specific position of the constructor, such as on the ground, on a single ladder, on a transmission tower, etc., is obtained by a position judgment post-processing algorithm, i.e., a series of logical judgments are made on the detected coordinates of the personnel and equipment objects.
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. A method for judging the position of a person in a complex scene of electric power safety management and control is characterized by mainly comprising the following steps:
firstly, collecting pictures of an electric power safety construction site by using a collecting device;
step two, preprocessing the picture collected in the step one to obtain an original data set;
step three, constructing a personnel detection network model, and training a personnel data set row model in the original data set obtained in the step two by adopting the personnel detection network model;
step four, constructing an equipment detection network model, and performing model training on the equipment data set in the original data set obtained in the step two by adopting the equipment detection network model;
and step five, sending the results generated in the step three and the step four into a position judgment post-processing algorithm to judge the position of the personnel.
2. The method according to claim 1, wherein the preprocessing in the first step mainly includes scaling and fuzzy operation.
3. The method for judging the position of the person under the complex electric power safety management and control scene according to claim 1, wherein the method for constructing the person detection network model in the second step is as follows:
the personnel detection network model carries out convolution operation by constructing a convolution group and increasing the width of the convolution group, and outputs a final result; the final classification and regression operations are performed after the position sensitive convolution operation.
4. The method according to claim 3, wherein the convolution group is mainly constructed by a convolution layer, an activation layer, a pooling layer and a batch normalization layer.
5. The method according to claim 3, wherein a plurality of convolution kernels are used for each convolution group, and then feature maps generated by each convolution kernel are overlapped and then subjected to convolution operation, so that detection time can be effectively reduced.
6. The method according to claim 1, wherein the device detection network model and the personnel detection network model have the same structure.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569729A (en) * | 2021-07-27 | 2021-10-29 | 广联达科技股份有限公司 | High-altitude operation scene detection method and device and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711320A (en) * | 2018-12-24 | 2019-05-03 | 兴唐通信科技有限公司 | A kind of operator on duty's unlawful practice detection method and system |
| CN109993031A (en) * | 2017-12-29 | 2019-07-09 | 杭州海康威视数字技术股份有限公司 | A kind of animal-drawn vehicle target is driven against traffic regulations behavioral value method, apparatus and camera |
| CN110348376A (en) * | 2019-07-09 | 2019-10-18 | 华南理工大学 | A kind of pedestrian's real-time detection method neural network based |
| CN110852241A (en) * | 2019-11-06 | 2020-02-28 | 西安交通大学 | Small target detection method applied to nursing robot |
| CN111160451A (en) * | 2019-12-27 | 2020-05-15 | 中山德著智能科技有限公司 | Flexible material detection method and storage medium thereof |
| CN111310815A (en) * | 2020-02-07 | 2020-06-19 | 北京字节跳动网络技术有限公司 | Image recognition method and device, electronic equipment and storage medium |
| CN111414873A (en) * | 2020-03-26 | 2020-07-14 | 广州粤建三和软件股份有限公司 | Alarm prompting method, device and alarm system based on wearing state of safety helmet |
-
2020
- 2020-09-15 CN CN202010966388.6A patent/CN112101212A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109993031A (en) * | 2017-12-29 | 2019-07-09 | 杭州海康威视数字技术股份有限公司 | A kind of animal-drawn vehicle target is driven against traffic regulations behavioral value method, apparatus and camera |
| CN109711320A (en) * | 2018-12-24 | 2019-05-03 | 兴唐通信科技有限公司 | A kind of operator on duty's unlawful practice detection method and system |
| CN110348376A (en) * | 2019-07-09 | 2019-10-18 | 华南理工大学 | A kind of pedestrian's real-time detection method neural network based |
| CN110852241A (en) * | 2019-11-06 | 2020-02-28 | 西安交通大学 | Small target detection method applied to nursing robot |
| CN111160451A (en) * | 2019-12-27 | 2020-05-15 | 中山德著智能科技有限公司 | Flexible material detection method and storage medium thereof |
| CN111310815A (en) * | 2020-02-07 | 2020-06-19 | 北京字节跳动网络技术有限公司 | Image recognition method and device, electronic equipment and storage medium |
| CN111414873A (en) * | 2020-03-26 | 2020-07-14 | 广州粤建三和软件股份有限公司 | Alarm prompting method, device and alarm system based on wearing state of safety helmet |
Non-Patent Citations (1)
| Title |
|---|
| 刘思雨;薛劲松;景栋盛;: "基于分阶段深度神经网络的施工违章识别", 软件工程, no. 09, 5 September 2020 (2020-09-05) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569729A (en) * | 2021-07-27 | 2021-10-29 | 广联达科技股份有限公司 | High-altitude operation scene detection method and device and electronic equipment |
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