CN115052132A - Fishing electric shock prevention early warning method and system based on artificial intelligence - Google Patents
Fishing electric shock prevention early warning method and system based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to an artificial intelligence-based fishing electric shock prevention early warning method and system, and belongs to the technical field of power protection. The method comprises the following steps: the method comprises the steps that monitoring equipment obtains images of a monitoring area in real time and transmits the images to a background server; the background server identifies the image by using a convolutional neural network and judges whether fishing gear is identified or not; if the fishing gear is identified, the background server determines the position parameter of the fisherman, sends the position parameter to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment; and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher. The method can realize automatic identification of the fisherman, does not need monitoring personnel to observe the monitoring picture in real time, and saves human resources.
Description
Technical Field
The invention belongs to the technical field of power protection, and particularly relates to an artificial intelligence-based fishing electric shock prevention early warning method and system.
Background
In recent years, as the number of fishing people increases, the number of electric shock events caused by fishing increases, and this is because fishing gear used by a fisher, a fishing rod made of carbon fiber, which is a material that is excellent in elasticity and hard but electrically conductive, is used by the fisher, and the fisher burns or even dies people by throwing a fishing line or a fishing rod on a high-voltage line near a water area such as a pond or a river to cause an electric shock.
Because personnel's safety propaganda and the mode of posting warning sign of prohibiting fishing come to the strong and bad people of result that the phenomenon of fishing reaches, still there is the fishing electric shock accident frequently to take place, so can adopt monitored control system control lake surface and peripheral region now, observe the control picture in real time by the control personnel and through going to the mode of scene or speech transmission when the person of fishing appears and forbid the activity of fishing, but this mode needs the maintenance that personnel do not stop to observe the operation, occupies manpower resources.
Disclosure of Invention
The invention mainly aims to overcome the defects of the prior art and provide an artificial intelligence-based fishing electric shock prevention early warning method and system, which can realize automatic identification of a fisherman, do not need monitoring personnel to observe a monitoring picture in real time and save human resources.
According to one aspect of the invention, the invention provides an artificial intelligence-based fishing electric shock prevention early warning method, which comprises the following steps:
s1: the method comprises the steps that monitoring equipment obtains images of a monitoring area in real time and transmits the images to a background server;
s2: the background server identifies the image by using a convolutional neural network and judges whether fishing gear is identified or not;
s3: if the fishing gear is identified, the background server determines the position parameter of the fisherman, sends the position parameter to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment;
s4: and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher.
Preferably, the background server recognizing the image by using a convolutional neural network and judging whether the fishing gear is recognized comprises:
the background server collects an image sample of the fishing gear and establishes a fishing gear sample library;
the background server extracts the characteristics of the fishing gear by using a convolutional neural network, matches the characteristics with the fishing gear sample library and judges whether the fishing gear is identified; the characteristics of the fishing gear include the shape and color of the fishing gear.
Preferably, the convolutional neural network comprises a convolutional layer, a downsampling layer and a full-connection layer;
the convolutional layer: extracting the characteristics of the fishing gear image through convolution operation, and enhancing the characteristics of the fishing gear;
the down-sampling layer: down-sampling the image to generate a feature map;
the full connection layer: and (4) obtaining an activation value by adopting softmax full connection, namely fishing gear characteristics extracted by the convolutional neural network.
Preferably, the background server determining the position parameters of the fisherman comprises:
and the background server acquires a plan view of the lake and the surrounding area by using the satellite map, and draws a grid coordinate axis on the plan view.
Preferably, the ratio of the side length to the actual length of each grid in the grid coordinate axes is 1: 100; and associating the coordinate parameters of the four corners of each grid in the grid coordinate axes with the corresponding positions in the actual area, and storing the associated coordinate parameters and the images of the actual positions in the background server.
According to another aspect of the invention, the invention also provides an artificial intelligence-based fishing electric shock prevention early warning system, which comprises monitoring equipment and a background server;
the monitoring equipment acquires an image of a monitored area in real time and transmits the image to the background server;
the background server identifies the image by using a convolutional neural network and judges whether fishing gear is identified or not;
if the fishing gear is identified, the background server determines the position parameter of the fisherman, sends the position parameter to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment;
and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher.
Preferably, the background server recognizing the image by using a convolutional neural network and judging whether the fishing gear is recognized comprises:
the background server collects an image sample of the fishing gear and establishes a fishing gear sample library;
the background server extracts the characteristics of the fishing gear by using a convolutional neural network, matches the characteristics with the fishing gear sample library and judges whether the fishing gear is identified; the characteristics of the fishing gear include the shape and color of the fishing gear.
Preferably, the convolutional neural network comprises a convolutional layer, a downsampling layer and a full-connection layer;
the convolutional layer: extracting the characteristics of the fishing gear image through convolution operation, and enhancing the characteristics of the fishing gear;
the down-sampling layer: down-sampling the image to generate a feature map;
the full connection layer: and (4) adopting softmax full connection to obtain an activation value, namely the fishing gear characteristics extracted by the convolutional neural network.
Preferably, the background server determining the position parameters of the fisherman comprises:
and the background server acquires a plan view of the lake and the surrounding area by using the satellite map, and draws a grid coordinate axis on the plan view.
Preferably, the ratio of the side length to the actual length of each grid in the grid coordinate axes is 1: 100; and associating the coordinate parameters of the four corners of each grid in the grid coordinate axes with the corresponding positions in the actual area, and storing the associated coordinate parameters and the images of the actual positions in the background server.
Has the advantages that: according to the invention, the monitoring equipment is arranged in the lake region, various fishing gear features are extracted and stored by using the convolutional cloud neural network, the content of a monitoring picture is intelligently identified, and the monitoring equipment can warn inspectors and persons who want to fish in time when the fishing gear appears so as to play a role of early warning, the monitoring personnel do not need to observe the monitoring picture in real time, and the monitoring equipment can send the coordinate parameters of the positions of the fishers to the inspectors when finding the fishers by acquiring the plane diagram of the lake region, drawing the grid coordinate axes and associating the coordinate parameters with the actual positions, so that the monitoring equipment can conveniently enable the inspectors to reach the positions of the fishers quickly.
The features and advantages of the present invention will become apparent by reference to the following drawings and detailed description of specific embodiments of the invention.
Drawings
FIG. 1 is a flow chart of an artificial intelligence-based fishing electric shock prevention early warning method;
FIG. 2 is a plan view illustration;
FIG. 3 is a schematic diagram of grid coordinate axes;
FIG. 4 is a schematic diagram of an electric shock early warning system for fishing based on artificial intelligence.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the 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.
Example 1
FIG. 1 is a flow chart of an artificial intelligence-based fishing electric shock prevention early warning method. As shown in fig. 1, the embodiment provides an electric shock early warning method for fishing based on artificial intelligence, which includes the following steps:
s1: the monitoring equipment acquires images of a monitoring area in real time and transmits the images to the background server.
Specifically, monitoring equipment is built, no monitoring dead angle exists among the monitoring equipment, and the monitoring range of the monitoring equipment covers the whole plan view; after the monitoring equipment acquires the image of the monitoring area in real time, the image can be transmitted to the background server in a wired and wireless mode, wherein the wireless mode comprises but is not limited to wifi, zigbee and the like.
S2: and the background server identifies the image by using a convolutional neural network and judges whether the fishing gear is identified.
Preferably, the background server recognizing the image by using a convolutional neural network and judging whether the fishing gear is recognized comprises:
the background server collects an image sample of the fishing gear and establishes a fishing gear sample library;
the background server extracts the characteristics of the fishing gear by using a convolutional neural network, matches the characteristics with the fishing gear sample library and judges whether the fishing gear is identified; the characteristics of the fishing gear include the shape and color of the fishing gear.
Preferably, the convolutional neural network comprises a convolutional layer, a downsampling layer and a full-connection layer;
the convolutional layer: extracting the characteristics of the fishing gear image through convolution operation, and enhancing the characteristics of the fishing gear;
the down-sampling layer: down-sampling the image to generate a feature map;
the full connection layer: and (4) obtaining an activation value by adopting softmax full connection, namely fishing gear characteristics extracted by the convolutional neural network.
Specifically, utilize convolution cloud neural network to realize the intelligent recognition to the fishing tackle, convolution cloud neural network includes convolution layer, down sampling layer and full connection layer, wherein:
a convolutional layer: extracting features of the fishing gear image through convolution operation, enhancing the features of the fishing gear through convolution operation, deconvolving an input image by using a trainable filter fx, wherein the input image is obtained in the first stage, the convolution feature map is obtained in the later stage, and then a bias bx is added to obtain a convolution layer Cx, and the features of the fishing gear comprise the shape and the color of the fishing gear;
down-sampling layer: because the image is downsampled, the data processing amount can be reduced while useful information is kept, the sampling can confuse the specific positions of the features, because the position of a certain feature is unimportant after the feature is found, only the relative position of the feature and other features is needed, the change of the same kind of objects caused by deformation and distortion can be coped with, four pixels in each neighborhood in the image are summed to form one pixel, then the pixel is weighted by a scalar Wx +1, the bias bx +1 is added, and then a sigmoid activation function is used for generating a feature mapping map Sx +1 which is approximately reduced by four times;
full connection layer: and (4) fully connecting by adopting softmax, and obtaining an activation value, namely the picture characteristics extracted by the convolutional neural network.
Preferably, the background server determining the position parameters of the fisher comprises:
and the background server acquires a plan view of the lake and the surrounding area by using the satellite map, and draws a grid coordinate axis on the plan view.
Specifically, referring to fig. 2, a plan view of the lake and the surrounding area is obtained using a satellite map, and grid coordinate axes are plotted on the plan view, the plan view having a range of radiating an area of 10 to 20 meters to the periphery with the lake area as the center.
Preferably, the ratio of the side length to the actual length of each grid in the grid coordinate axes is 1: 100; and associating the coordinate parameters of the four corners of each grid in the grid coordinate axes with the corresponding positions in the actual area, and storing the associated coordinate parameters and the images of the actual positions in the background server.
Specifically, referring to fig. 3, the ratio of the side length to the actual length of each grid in the grid coordinate axes is 1 cm: 100 cm, the coordinate parameters of the four corners of each grid in the grid coordinate axes are also associated with the corresponding positions in the actual area, and the coordinate parameters are stored in the server after being associated with the image of the actual position.
S3: and if the fishing gear is identified, the background server determines the position parameters of the fisherman, sends the position parameters to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment.
Specifically, a fisher can be brought into a monitoring range after arriving at a lake and pond area, at the moment, the background server intelligently identifies whether a fishing gear exists or not based on a monitoring picture, if the fishing gear is identified, the fisher is judged to be ready for fishing activities, at the moment, the position parameter of the fisher is determined according to a plane graph and network coordinates, meanwhile, an image based on the position of the fisher is associated with a corresponding coordinate parameter in a grid coordinate axis, the position parameter is sent to a terminal device of an inspector, the inspector quickly arrives at the position of the fisher to dissuade the fisher, and electric shock accidents caused by fishing are prevented.
S4: and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher.
The monitoring equipment sends out the voice prompt of forbidding fishing to the fisherman by utilizing the integrated voice broadcasting function of the monitoring equipment, and broadcasts in a circulating mode until the fisherman leaves and stops broadcasting.
This embodiment utilizes the convolutional cloud neural network to extract all kinds of fishing tackle characteristics and store through laying supervisory equipment in the pond region to carry out intelligent recognition to the content of control picture simultaneously, in order in time to patrol personnel and the personnel of will fishing in advance warn when the fishing tackle appears, in order to play the early warning effect, need not the control personnel and survey the control picture in real time, saved manpower resources. And by acquiring a plan view of the pond area, drawing a grid coordinate axis and associating the coordinate parameter with the actual position, the monitoring equipment sends the coordinate parameter of the position of the fisherman to the patrol officer when finding the fisherman, so that the patrol officer can quickly arrive at the position of the fisherman.
Example 2
Fig. 4 is a schematic diagram of an artificial intelligence-based fishing electric shock prevention early warning system. As shown in fig. 4, the embodiment further provides an artificial intelligence-based fishing electric shock prevention early warning system, which includes a monitoring device and a background server;
the monitoring equipment acquires an image of a monitored area in real time and transmits the image to the background server;
the background server identifies the image by using a convolutional neural network and judges whether fishing gear is identified or not;
if the fishing gear is identified, the background server determines the position parameter of the fisherman, sends the position parameter to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment;
and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher.
Preferably, the background server recognizing the image by using a convolutional neural network and judging whether the fishing gear is recognized comprises:
the background server collects an image sample of the fishing gear and establishes a fishing gear sample library;
the background server extracts the characteristics of the fishing gear by using a convolutional neural network, matches the characteristics with the fishing gear sample library and judges whether the fishing gear is identified; the characteristics of the fishing gear include the shape and color of the fishing gear.
Preferably, the convolutional neural network comprises a convolutional layer, a downsampling layer and a full-connection layer;
the convolutional layer: extracting the characteristics of the fishing gear image through convolution operation, and enhancing the characteristics of the fishing gear;
the down-sampling layer: down-sampling the image to generate a feature map;
the full connection layer: and (4) adopting softmax full connection to obtain an activation value, namely the fishing gear characteristics extracted by the convolutional neural network.
Preferably, the background server determining the position parameters of the fisherman comprises:
and the background server acquires a plan view of the lake and the surrounding area by using the satellite map, and draws a grid coordinate axis on the plan view.
Preferably, the ratio of the side length to the actual length of each grid in the grid coordinate axes is 1: 100; and associating the coordinate parameters of the four corners of each grid in the grid coordinate axes with the corresponding positions in the actual area, and storing the associated coordinate parameters and the images of the actual positions in the background server.
The specific implementation process of the functions implemented by the monitoring device and the background server in this embodiment 2 is the same as that in embodiment 1, and is not described herein again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An artificial intelligence-based fishing electric shock prevention early warning method is characterized by comprising the following steps:
s1: the method comprises the steps that monitoring equipment obtains images of a monitoring area in real time and transmits the images to a background server;
s2: the background server identifies the image by using a convolutional neural network and judges whether fishing gear is identified or not;
s3: if the fishing gear is identified, the background server determines the position parameter of the fisherman, sends the position parameter to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment;
s4: and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher.
2. The method of claim 1, wherein the background server identifies the image using a convolutional neural network, and determining whether fishing gear is identified comprises:
the background server collects an image sample of the fishing gear and establishes a fishing gear sample library;
the background server extracts the characteristics of the fishing gear by using the convolutional neural network, matches the characteristics with the fishing gear sample library and judges whether the fishing gear is identified or not; the characteristics of the fishing gear include the shape and color of the fishing gear.
3. The method of claim 2, wherein the convolutional neural network comprises a convolutional layer, a downsampling layer, and a fully-connected layer;
the convolutional layer: extracting the characteristics of the fishing gear image through convolution operation, and enhancing the characteristics of the fishing gear;
the down-sampling layer: down-sampling the image to generate a feature map;
the full connection layer: and (4) adopting softmax full connection to obtain an activation value, namely the fishing gear characteristics extracted by the convolutional neural network.
4. The method of claim 1, wherein the background server determining the location parameters of the fisherman comprises:
and the background server acquires a plan view of the lake and the surrounding area by using the satellite map, and draws a grid coordinate axis on the plan view.
5. The method of claim 4, wherein each of the grid coordinate axes has a side length to actual length ratio of 1: 100; and associating the coordinate parameters of the four corners of each grid in the grid coordinate axes with the corresponding positions in the actual area, and storing the associated coordinate parameters and the images of the actual positions in the background server.
6. An artificial intelligence-based fishing electric shock prevention early warning system is characterized by comprising monitoring equipment and a background server;
the monitoring equipment acquires an image of a monitored area in real time and transmits the image to the background server;
the background server identifies the image by using a convolutional neural network and judges whether fishing gear is identified or not;
if the fishing gear is identified, the background server determines the position parameter of the fisherman, sends the position parameter to patrol personnel terminal equipment and sends an early warning instruction to the monitoring equipment;
and after receiving the early warning instruction, the monitoring equipment sends a voice prompt for prohibiting fishing to the fisher.
7. The system of claim 6, wherein the background server identifies the image using a convolutional neural network, and determining whether fishing gear is identified comprises:
the background server collects an image sample of the fishing gear and establishes a fishing gear sample library;
the background server extracts the characteristics of the fishing gear by using a convolutional neural network, matches the characteristics with the fishing gear sample library and judges whether the fishing gear is identified; the characteristics of the fishing gear include the shape and color of the fishing gear.
8. The system of claim 7, wherein the convolutional neural network comprises a convolutional layer, a downsampling layer, and a fully-connected layer;
the convolutional layer: extracting the characteristics of the fishing gear image through convolution operation, and enhancing the characteristics of the fishing gear;
the down-sampling layer: down-sampling the image to generate a feature map;
the full connection layer: and (4) adopting softmax full connection to obtain an activation value, namely the fishing gear characteristics extracted by the convolutional neural network.
9. The system of claim 6, wherein the background server determining the fisher's location parameters comprises:
and the background server acquires a plan view of the lake and the surrounding area by using the satellite map, and draws a grid coordinate axis on the plan view.
10. The system of claim 9, wherein each of the grid coordinate axes has a side length to actual length ratio of 1: 100; and associating the coordinate parameters of the four corners of each grid in the grid coordinate axes with the corresponding positions in the actual area, and storing the associated coordinate parameters and the images of the actual positions in the background server.
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