CN113537051A - Marine personnel safety operation monitoring algorithm under complex large scene - Google Patents
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Abstract
The invention relates to safety operation monitoring, in particular to a marine personnel safety operation monitoring algorithm under a complex large scene, which is characterized in that a risk monitoring model based on marine accident behaviors is established, continuous frame images about operating personnel are obtained, human skeleton point information is obtained from each frame image, behavior analysis is carried out on the operating personnel based on the human skeleton point information in the continuous frame images, a behavior analysis result is input into the risk monitoring model, and the behavior of the operating personnel is judged by the risk monitoring model; the technical scheme provided by the invention can effectively overcome the defect that the operation behavior of the offshore operation personnel cannot be effectively monitored and judged in the prior art.
Description
Technical Field
The invention relates to safety operation monitoring, in particular to a marine personnel safety operation monitoring algorithm under a complex large scene.
Background
Under the severe situation that the existing stone energy sources such as coal, petroleum and the like are increasingly deficient and the emission of greenhouse gases threatens the living environment of human beings, wind energy is taken as a green energy source which can be continuously regenerated and sustainably utilized in nature, and is increasingly valued by countries in the world due to the advantages of huge reserves, wide distribution, no pollution and the like.
Since the first world offshore wind farms were built in Denmark in 1991, offshore wind power has become a key area of world renewable energy development. After more than 20 years of development, the offshore wind power technology is mature day by day and enters a large-scale development stage. By the end of 2014, 84 offshore wind farms were built in 11 countries in Europe, with a total installed capacity of 11,027 MW. A100 MW offshore wind power plant of an east-sea bridge for grid-connected power generation in 2010 of China is the first large offshore wind power project in Asia, and the total installed capacity of offshore wind power in China reaches 428.58MW by 2013. The wind energy reserve on land which can be developed and utilized by China is reported to be 2.53 hundred million kilowatts, the wind energy reserve on offshore is reported to be 7.5 hundred million kilowatts, and the wind energy reserve on offshore is far greater than that on land, so that the development space is wide. Meanwhile, the coastal areas of the east of China are developed economically and are in short supply of energy, and the development of abundant wind energy resources can effectively improve the energy supply structure. Therefore, although offshore wind power projects start later in China, the development potential is huge.
At present, the environment of an offshore operation field is severe, and workers often need to go to an offshore operation platform in 10-80 seas to expand operation projects. On one hand, the complex conditions of sea waves, tides and the like and on the other hand, the lack of safety operation consciousness of offshore operators can bring serious life threats to the offshore operators. At present, the operation behavior of offshore operators cannot be effectively monitored and judged, and the safety requirement of offshore production operation cannot be met.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a marine worker safety operation monitoring algorithm under a complex large scene, which can effectively overcome the defect that the prior art can not effectively monitor and judge the operation behaviors of marine workers.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a marine personnel safety operation monitoring algorithm under a complex large scene comprises the following steps:
s1, establishing a risk monitoring model based on marine accident behaviors;
s2, acquiring continuous frame images of the operator, and acquiring human skeleton point information from each frame image;
s3, performing behavior analysis on the operator based on the human skeleton point information in the continuous frame images;
and S4, inputting the behavior analysis result into the risk monitoring model, and judging the behavior of the operator by the risk monitoring model.
Preferably, in S1, a risk monitoring model based on marine accident behavior is established, including:
s11, analyzing each influence factor in the typical marine accident behavior, and establishing a risk influence system based on the influence factors of the marine accident behavior;
S12, determining boundary conditions of the risk monitoring model according to the risk influence system, and setting parameters of the risk monitoring model;
and S13, fitting the risk monitoring model by using simulation software of the marine accident behavior influence factors.
Preferably, the step S2 of acquiring successive frame images of the operator, and obtaining the human skeleton point information from each frame image includes:
preprocessing the continuous frame image, and performing multi-region feature extraction on the preprocessed continuous frame image to acquire positioning information of each region of the human body of an operator;
and performing multi-scale feature extraction based on the positioning information of each region of the human body, and outputting the information of each part of the skeleton point of the operator.
Preferably, the preprocessed continuous frame images are subjected to multi-region feature extraction through a depth residual error network, and positioning information of each region of the human body of the operator is obtained.
Preferably, the performing multi-scale feature extraction based on the positioning information of each region of the human body and outputting information of skeleton points of each part of the operator includes:
inputting the positioning information of each region of the human body into a stacked hourglass network structure, performing multi-scale feature extraction, and performing inverse coordinate mapping on the information of each part of the skeleton point of an operator by using a space transfer network.
Preferably, the step S2 of acquiring successive frame images of the operator, and obtaining the human skeleton point information from each frame image includes:
carrying out posture monitoring on the operator in the continuous frame images by using a human body posture estimation algorithm to obtain skeleton information of behavior and action of the operator;
and constructing an undirected graph containing behavior and actions of the operator based on the skeleton information, and obtaining skeleton point information of each part of the operator according to the undirected graph.
Preferably, in S3, based on the human skeleton point information in the continuous frame images, performing behavior analysis on the operator, including:
carrying out frame-by-frame convolution on human body skeleton point information in the continuous frame images by utilizing a space-time image convolution network to obtain a motion picture of each skeleton point of an operator;
and extracting a posture behavior characteristic diagram of the operator according to the motion graphs of all the skeleton points, and taking the posture behavior characteristic diagram as a behavior analysis result.
Preferably, the performing frame-by-frame convolution on the human skeleton point information in the continuous frame images by using a space-time image convolution network to obtain the motion picture of each skeleton point of the operator includes:
and transforming the human skeleton point information in the continuous frame images into a spectral domain, and performing filtering processing.
Preferably, the step of inputting the behavior analysis result into the risk monitoring model in S4, and the determining the behavior of the operator by the risk monitoring model includes:
Inputting the attitude behavior characteristic diagrams into a risk monitoring model, converting all the attitude behavior characteristic diagrams into a one-dimensional matrix by the risk monitoring model, identifying and matching the attitude behavior characteristic diagrams through the one-dimensional matrix, judging the marine accident behavior influence factors, and quantitatively evaluating the risk level of the current operator behavior.
(III) advantageous effects
Compared with the prior art, the marine worker safety operation monitoring algorithm under the complex large scene can accurately obtain the human skeleton point information from the continuous frame images, conduct behavior analysis on the workers based on the human skeleton point information in the continuous frame images, input the behavior analysis result into the risk monitoring model, and then quickly and accurately judge the behaviors of the workers through the risk monitoring model, so that the marine worker safety operation is monitored in real time under the complex large scene, and the life safety of the marine workers is guaranteed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A monitoring algorithm for marine personnel safety operation in a complex large scene is shown in figure 1, and comprises the following steps:
s1, establishing a risk monitoring model based on marine accident behaviors;
s2, acquiring continuous frame images of the operator, and acquiring human skeleton point information from each frame image;
s3, performing behavior analysis on the operator based on the human skeleton point information in the continuous frame images;
and S4, inputting the behavior analysis result into the risk monitoring model, and judging the behavior of the operator by the risk monitoring model.
Establishing a risk monitoring model based on marine accident behaviors, comprising the following steps:
S11, analyzing each influence factor in the typical marine accident behavior, and establishing a risk influence system based on the influence factors of the marine accident behavior;
s12, determining boundary conditions of the risk monitoring model according to the risk influence system, and setting parameters of the risk monitoring model;
and S13, fitting the risk monitoring model by using simulation software of the marine accident behavior influence factors.
According to the technical scheme, the risk monitoring model based on the marine accident behaviors is established, and data support can be provided for the follow-up risk monitoring model to quickly and accurately judge the behaviors of the operators.
According to the technical scheme, the method for acquiring the human skeleton point information comprises two methods for acquiring continuous frame images of an operator and acquiring the human skeleton point information from each frame image.
The first method comprises the following steps:
preprocessing the continuous frame images, and performing multi-region feature extraction on the preprocessed continuous frame images through a depth residual error network to obtain positioning information of each region of the human body of an operator;
and performing multi-scale feature extraction based on the positioning information of each region of the human body, and outputting the information of each part of the skeleton point of the operator.
Wherein, based on each regional locating information of human body carries out the multi-scale feature extraction, each part skeleton point information of output operation personnel includes:
Inputting the positioning information of each region of the human body into a stacked hourglass network structure, performing multi-scale feature extraction, and performing inverse coordinate mapping on the information of each part of the skeleton point of an operator by using a space transfer network.
The second method comprises the following steps:
carrying out posture monitoring on the operator in the continuous frame images by using a human body posture estimation algorithm to obtain skeleton information of behavior and action of the operator;
and constructing an undirected graph containing behavior and actions of the operator based on the skeleton information, and obtaining skeleton point information of each part of the operator according to the undirected graph.
Based on the human skeleton point information in the continuous frame images, the behavior analysis is carried out on the operating personnel, and the behavior analysis comprises the following steps:
carrying out frame-by-frame convolution on human body skeleton point information in the continuous frame images by utilizing a space-time image convolution network to obtain a motion picture of each skeleton point of an operator;
and extracting a posture behavior characteristic diagram of the operator according to the motion graphs of all the skeleton points, and taking the posture behavior characteristic diagram as a behavior analysis result.
The method for obtaining the motion picture of each bone point of an operator by performing frame-by-frame convolution on human bone point information in continuous frame images by utilizing a space-time image convolution network comprises the following steps:
and transforming the human skeleton point information in the continuous frame images into a spectral domain, and performing filtering processing.
Inputting the behavior analysis result into a risk monitoring model, and judging the behavior of the operating personnel by the risk monitoring model, wherein the method comprises the following steps:
inputting the attitude behavior characteristic diagrams into a risk monitoring model, converting all the attitude behavior characteristic diagrams into a one-dimensional matrix by the risk monitoring model, identifying and matching the attitude behavior characteristic diagrams through the one-dimensional matrix, judging the marine accident behavior influence factors, and quantitatively evaluating the risk level of the current operator behavior.
According to the technical scheme, the human skeleton point information can be accurately obtained from the continuous frame images, the behavior of the operating personnel is analyzed based on the human skeleton point information in the continuous frame images, the behavior of the operating personnel is quickly and accurately judged by the risk monitoring model after the behavior analysis result is input into the risk monitoring model, and therefore the real-time monitoring on the operation safety of the marine personnel in a complex large scene is achieved, and the life safety of the marine operating personnel is guaranteed.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A marine personnel safety operation monitoring algorithm under a complex large scene is characterized in that: the method comprises the following steps:
s1, establishing a risk monitoring model based on marine accident behaviors;
s2, acquiring continuous frame images of the operator, and acquiring human skeleton point information from each frame image;
s3, performing behavior analysis on the operator based on the human skeleton point information in the continuous frame images;
and S4, inputting the behavior analysis result into the risk monitoring model, and judging the behavior of the operator by the risk monitoring model.
2. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 1, characterized in that: and S1, establishing a risk monitoring model based on marine accident behaviors, wherein the risk monitoring model comprises the following steps:
s11, analyzing each influence factor in the typical marine accident behavior, and establishing a risk influence system based on the influence factors of the marine accident behavior;
s12, determining boundary conditions of the risk monitoring model according to the risk influence system, and setting parameters of the risk monitoring model;
and S13, fitting the risk monitoring model by using simulation software of the marine accident behavior influence factors.
3. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 2, characterized in that: in S2, acquiring successive frame images of the operator, and obtaining human skeleton point information from each frame image, the method includes:
Preprocessing the continuous frame image, and performing multi-region feature extraction on the preprocessed continuous frame image to acquire positioning information of each region of the human body of an operator;
and performing multi-scale feature extraction based on the positioning information of each region of the human body, and outputting the information of each part of the skeleton point of the operator.
4. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 3, characterized in that: and performing multi-region feature extraction on the preprocessed continuous frame images through a depth residual error network to acquire positioning information of each region of the human body of the operator.
5. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 4, characterized in that: the method for extracting the multi-scale features based on the positioning information of each region of the human body and outputting the information of each part of the skeleton point of the operator comprises the following steps:
inputting the positioning information of each region of the human body into a stacked hourglass network structure, performing multi-scale feature extraction, and performing inverse coordinate mapping on the information of each part of the skeleton point of an operator by using a space transfer network.
6. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 2, characterized in that: in S2, acquiring successive frame images of the operator, and obtaining human skeleton point information from each frame image, the method includes:
Carrying out posture monitoring on the operator in the continuous frame images by using a human body posture estimation algorithm to obtain skeleton information of behavior and action of the operator;
and constructing an undirected graph containing behavior and actions of the operator based on the skeleton information, and obtaining skeleton point information of each part of the operator according to the undirected graph.
7. The marine personnel safety operation monitoring algorithm under the complex large scene according to the claim 3 or 6, characterized in that: in S3, based on the human skeleton point information in the continuous frame images, performing behavior analysis on the operator, including:
carrying out frame-by-frame convolution on human body skeleton point information in the continuous frame images by utilizing a space-time image convolution network to obtain a motion picture of each skeleton point of an operator;
and extracting a posture behavior characteristic diagram of the operator according to the motion graphs of all the skeleton points, and taking the posture behavior characteristic diagram as a behavior analysis result.
8. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 7, characterized in that: the method for obtaining the motion picture of each bone point of the operator by performing frame-by-frame convolution on the human bone point information in the continuous frame images by utilizing the space-time image convolution network comprises the following steps:
and transforming the human skeleton point information in the continuous frame images into a spectral domain, and performing filtering processing.
9. The marine personnel safety operation monitoring algorithm under the complex large scene according to claim 7, characterized in that: and S4, inputting the behavior analysis result into a risk monitoring model, and judging the behavior of the operator by the risk monitoring model, wherein the method comprises the following steps:
inputting the attitude behavior characteristic diagrams into a risk monitoring model, converting all the attitude behavior characteristic diagrams into a one-dimensional matrix by the risk monitoring model, identifying and matching the attitude behavior characteristic diagrams through the one-dimensional matrix, judging the marine accident behavior influence factors, and quantitatively evaluating the risk level of the current operator behavior.
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