CN114994691A - Visualization-based fishery resource cluster distribution analysis method - Google Patents
Visualization-based fishery resource cluster distribution analysis method Download PDFInfo
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Abstract
The invention relates to a fishery resource cluster distribution analysis method based on visualization, which comprises the following steps: establishing a three-dimensional imaging sonar system and a visual monitoring system through a detection fleet; establishing a fish school image and sonar database; dividing the detection fleet into detection ships of different levels through the flow direction of a fishery water area; carrying out primary detection; predicting the flow direction of the fish school according to the water flow and the temperature; and scanning and comparing for many times at the predicted position, and screening and comparing the fish shoals flowing through the predicted position again to obtain target fish shoal fishery resource cluster distribution data. The invention can effectively improve the positioning tracking and judgment of the fishery resource cluster and provide effective data support for the analysis of fishery resources.
Description
Technical Field
The invention relates to the technical field of fishery resource monitoring, in particular to a fishery resource cluster distribution analysis method based on visualization.
Background
Fishery resources refer to the totality of economic animals and plants such as fish, crustaceans, shellfish, algae and sea beasts with utilization value in natural waters, and in order to take fishing and breeding plans for sustainable economic development, fishery resource clusters in sea areas generally need to be explored, the reproduction capability of fishery resources is maintained, the optimal fishery harvest amount is obtained, the quantity and quality of aquatic resources are regulated, and the aim is to ensure the maximum continuous utilization of ecological systems and biological species by humans.
Present fishery resource monitoring is scan through the sonar more, only can realize the crowd observation to fish, lack the visual location observation to the shoal of fish, simultaneously when tracing the shoal of fish, the use of high-power sonar is more, the normal life of fish in the detection range can be influenced, and survey a large amount of ship work and can influence the shoal of fish and move towards, influence the analysis that fishery resource distributes, visual observation needs of satisfying that can not be fine, and simultaneously, present sonar technique lacks the discernment ability to the fingerling, still need catch the affirmation, the detection and analysis efficiency of satisfying that can not be fine.
Disclosure of Invention
The invention aims to solve the technical problem of providing a fishery resource cluster distribution analysis method based on visualization, which can effectively improve the positioning tracking and judgment of fishery resource clusters and provide effective data support for fishery resource analysis.
The technical scheme adopted by the invention for solving the technical problems is as follows: the fishery resource cluster distribution analysis method based on visualization comprises the following steps:
(1) establishing a three-dimensional imaging sonar system and a visual monitoring system through a detection fleet, wherein the three-dimensional sonar system is used for constructing regional fish school space distribution, and the visual monitoring system is used for acquiring visual images;
(2) performing feature storage on the representation of the fish school swimming data habit image in the water area, and establishing a fish school image and sonar database;
(3) dividing the detection fleet into detection ships of different levels according to the flow direction of a fishery water area, wherein each detection ship has a respective detection area position, establishing nodes of each detection ship through the drainage tonnage and the sonar intensity of the detection ship, and realizing communication interaction among the nodes of each detection ship through an Internet of things system;
(4) each level of detection ship node sends a three-dimensional sonar detection signal to a nearby position, simultaneously shoots an underwater picture of the position through a visual monitoring system, and summarizes collected data to the center of a detection ship body;
(5) taking water flow and temperature as factors for judging the trend of fish swarm nodes, adopting an ant colony algorithm to carry out fish swarm convergence prejudgment to obtain prejudged positions, sending three-dimensional sonar data obtained by three-dimensional sonar detection signals to the prejudged positions by nodes of each level of detection ship again, obtaining image data of the prejudged positions by adopting a visual monitoring system again, and carrying out gray level image measurement and calculation on the obtained three-dimensional sonar data and the image data;
(6) and scanning and comparing for many times at the pre-judging position, screening and comparing the fish swarm flowing through the pre-judging position again, and obtaining the fishery resource cluster distribution data of the target fish swarm when the target passes through the fish swarm.
The camera of the visual monitoring system in the step (1) is designed to be waterproof and sealed, the camera is lifted under the action of the mooring rope, and the diving depth of the camera is 50-200 m.
And (2) arranging a sonar device in the three-dimensional imaging sonar system in the step (1) at an underwater position 1-2 meters away from the water surface.
And (4) when the detection ships at all levels are divided in the step (3), determining the grade number of the detection ships according to the area of the detection water area.
The ant colony algorithm in (5) is to regard the fish colony that scans as current ant colony, regard current waters temperature and rivers flow direction as the pheromone of current ant colony, current ant colony passes through current ant colony pheromone makes the decision-making, obtains current ant colony trend is judged, after training through the training set, obtains to the fish colony prediction model that flows, can obtain the fish colony direction of flow according to current waters temperature and rivers flow direction.
The gray level image measurement and calculation performed on the three-dimensional sonar data in the step (5) specifically comprises the following steps: carrying out gray processing on image information and three-dimensional sonar data information obtained by a visual monitoring system, carrying out gray value adaptive compensation to obtain a clear gray-scale fish school visual image, comparing the gray-scale fish school visual image with images in a water area fish school library through image comparison software, and judging the habit type of the fish school.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
according to the invention, the detection area can be effectively expanded by dividing the node area of the detection fleet, the overlapping of sonar detection ranges is avoided, the tracking judgment of a target fish school is obtained by matching the ant colony algorithm model with the hull divided by the area, the sonar and the visual image are subjected to single scanning contrast according to the predicted position, the energy consumption of repeated and comprehensive detection scanning of the sonar and the influence on aquatic organisms are avoided, the screening contrast accuracy and efficiency of the fish school are improved by matching the visual image with the gray image algorithm, the positioning tracking and judgment of the fishery resource cluster are effectively improved by matching the three-dimensional sonar and other detection systems on the basis of the regional detection fleet, and effective data support is provided for the analysis of fishery resources.
According to the invention, by establishing the fishery resource cluster distribution analysis system, fish data collection is realized, the overall distribution uniformity of a detection fleet is ensured, the adaptability of fishery resource cluster distribution detection analysis operation is improved, and the detection and acquisition of data are realized through the three-dimensional imaging sonar system and the visual detection system.
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FIG. 1 is a flow chart of an embodiment of the present invention;
fig. 2 is a schematic diagram of an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a fishery resource cluster distribution analysis method based on visualization, which comprises the following steps as shown in figures 1 and 2:
(1) system establishment: the three-dimensional imaging sonar system and the visual monitoring system are established through the exploration fleet, the regional space distribution can be constructed through the three-dimensional sonar system, and the visual image can be obtained through the visual monitoring system.
(2) Establishing a fish image and sonar database: the characteristic storage is carried out on the representation of the fish school swimming data habit image in the water area, so that the fish school can be conveniently identified and the trend can be conveniently predicted;
(3) area division: the detection fleet is divided into detection ships at different levels through the fishery water area flow direction, the detection ships at different levels have respective detection area positions, detection ship nodes at different levels are established through the drainage tonnage and sonar intensity construction of the detection ships, the sensors are connected into the signal generating device, and meanwhile communication interaction among the detection ship nodes is established through the Internet of things system. The stage number of the detection ship node can be adjusted according to the area of the detected water area, the adjustable stage number is 2-4, when the area of the water area is small, two-stage detection can be adopted, and when the area of the water area is large, four-stage detection can be adopted.
(4) Primary detection: three-dimensional sonar detection signals are sent to nearby positions through three-dimensional sonar systems of respective detection ship nodes, meanwhile, visual pictures of underwater relative positions are shot through a visual monitoring system, and collected data are transmitted to the center of a detection ship body in a mode that the detection ship nodes upload and gather to nearby high-level nodes step by step;
(5) prediction analysis of fish flow direction: collecting and prejudging by an ant colony algorithm by taking water flow, temperature and the like as factors for judging the moving direction of the fish school nodes to obtain prejudged positions, sending three-dimensional sonar data obtained by three-dimensional sonar detection signals to the prejudged positions by all levels of detection ship nodes again, obtaining image data of the prejudged positions by adopting a visual monitoring system again, and carrying out gray image measurement and calculation on the obtained three-dimensional sonar data and the image data of the prejudged positions so as to realize verification of the prediction of the flow direction of the fish school and analysis of the next step;
(6) measurement and comparison: and after carrying out gray image measurement and calculation on the obtained three-dimensional sonar data and image data of a pre-judging position, carrying out scanning comparison for multiple times at the pre-judging position, carrying out screening comparison again on fish groups flowing through the predicted node position, and when judging that the target passes through the fish groups, obtaining target fish group fishery resource cluster distribution data.
In this embodiment, the grayscale image measurement and calculation specifically includes: gray value processing is carried out on a scanned image (namely three-dimensional sonar data acquired by a three-dimensional sonar system and image data acquired by a visual monitoring system), gray value self-adaptive compensation is carried out after the average gray value of the scanned image is obtained to obtain a clear gray fish school visual picture, and the gray fish school visual picture is compared with a water fish school library through picture comparison software to judge the habit type of the fish school.
The detection fleet in the embodiment is registered by a plurality of detection ship bodies, wherein the detection ship bodies are connected with a visual monitoring system and a three-dimensional imaging sonar system and are respectively used for acquiring image data and three-dimensional sonar data of a target fish school, the detection fleet is connected with a fish school image library and a sonar database and is used for comparing fish school images and sonar data, the input end of the fish school image and sonar database is connected with a gray image measuring and calculating module and is used for reducing the image volume, and the input end of the gray image measuring and calculating module is connected with the output end of the visual detection system.
The camera of the visual monitoring system adopts a diving sealing design, the focal length of the camera is adjustable, the camera is provided with a cable to lift and construct, the diving depth of the camera supports 50-200 m, and the sonar device of the three-dimensional imaging sonar system can be arranged at corresponding positions in two sides of a depth ship body, the bottom of the ship body and a water area which are 1-2m away from the water surface through a small boat, an underwater excavator, a measuring ship, an underwater robot and the like. Can also set up the movable detection support that is used for placing sonar device on surveying the tow-cable of ship bottom in this embodiment, drag sonar device through the tow-cable that detects the ship and carry out sound and static combination measurement.
The ant colony algorithm in this embodiment specifically includes taking a current scanned fish colony as an ant colony, taking a current water area temperature and a current water flow direction as ant colony pheromones, making a decision by the current ant colony through the ant colony pheromones, obtaining ant colony trend judgment, obtaining a fish colony flow prediction model after training of a training set, and obtaining a fish colony flow direction according to the current water area temperature and the current water flow direction.
In the prediction analysis of the fish school flow direction, the influence of the temperature and the water flow direction on the fish school swimming can be analyzed by methods such as an analysis of variance method or a multiple regression analysis method, the variance analysis method is selected to separate relevant factors and obtain the effect of the factors on the total variation, the correlation between the temperature change and the fish school swimming change and the correlation between the change of the water flow direction and the fish school swimming change can be obtained, and a reliable result can be obtained by prediction on the basis.
The ant colony algorithm principle is that ants release pheromones on a path, randomly select a path to go when meeting intersections which are not passed yet, and release pheromones related to the path length, wherein the concentration of the pheromones is inversely proportional to the path length. When the following ants touch the intersection again, the paths with higher pheromone concentration are selected, the pheromone concentration on the optimal paths is increased, and finally the ant colony finds the optimal food seeking path, so that after the pheromone concentration of the fish-colony water flow is judged through the data set, the optimal solution of the fish-colony water flow passing pheromone, namely the water flow information flow, can be obtained by using the ant colony algorithm model, and the water flow information comprises the tide flow direction, the water temperature zone and the water layer.
The embodiment is implemented specifically as follows: firstly, the lowest-level detection ship body collects information of respective responsible areas. Because the fish school judging ability of R, G, B elements in the image is often lack of support when the fish school is shot in water, more information storage capacity is occupied, the influence of R, G, B pigments on system energy consumption in shooting is reduced through processing gray levels, and the contrast precision and efficiency are effectively improved. After a series of preprocessing operations such as gray level processing and resolution reduction are carried out on the obtained picture information and sonar information, the information storage capacity is reduced, the transmission speed is increased, the picture information and the sonar information are uploaded to a superior node in real time until the superior node is reached, a detection ship of the superior node carries out contrastive analysis on the obtained picture information and the sonar information, a fish swarm image library and a sonar database, when the fish breed of the collected information is identified to be a target fish breed, the energy consumption and the water area temperature of the current sea area are combined, the fish swarm position is predicted and tracked by using an ant colony algorithm, single scanning contrast of sonar and a visual image is carried out according to the predicted position, the influence of repeated and comprehensive detection scanning of sonar on aquatic organisms is avoided, and effective data support is provided for the analysis of fishery resources.
The invention can effectively expand the detection area by dividing the node area of the detection fleet to avoid the overlapping of sonar detection ranges, simultaneously obtains the tracking judgment of the detected fish school by matching the regionalized divided ship body with an ant colony algorithm model, carries out single scanning contrast of sonar and a visual image according to the predicted position, avoids the scanning energy consumption of repeated detection and the influence on aquatic resources, and simultaneously improves the screening contrast efficiency of the fish school by matching the visual image with a gray image for optimization.
Claims (6)
1. A fishery resource cluster distribution analysis method based on visualization is characterized by comprising the following steps:
(1) establishing a three-dimensional imaging sonar system and a visual monitoring system through a detection fleet, wherein the three-dimensional sonar system is used for constructing regional fish school spatial distribution, and the visual monitoring system is used for acquiring visual images;
(2) performing feature storage on the representation of the fish school swimming data habit image in the water area, and establishing a fish school image and sonar database;
(3) dividing the detection fleet into detection ships of different levels according to the flow direction of a fishery water area, wherein each detection ship of different levels has a respective detection area position, establishing detection ship nodes of different levels according to the drainage tonnage and the sonar intensity of the detection ships, and realizing communication interaction among the detection ship nodes of different levels through an Internet of things system;
(4) each level of detection ship node sends a three-dimensional sonar detection signal to a nearby position, simultaneously shoots an underwater picture of the position through a visual monitoring system, and summarizes collected data to the center of a detection ship body;
(5) taking water flow and temperature as factors for judging the trend of fish nodes, adopting an ant colony algorithm to perform fish aggregation pre-judgment to obtain pre-judgment positions, sending three-dimensional sonar data obtained by three-dimensional sonar detection signals to the pre-judgment positions by nodes of each level of detection ship, obtaining image data of the pre-judgment positions by adopting a visual monitoring system again, and performing gray image measurement and calculation on the obtained three-dimensional sonar data and the image data;
(6) and scanning and comparing for many times at the pre-judging position, screening and comparing the fish swarm flowing through the pre-judging position again, and obtaining the fishery resource cluster distribution data of the target fish swarm when the target passes through the fish swarm.
2. The visualization-based fishery resource cluster distribution analysis method according to claim 1, wherein the camera of the visualization monitoring system in the step (1) is designed to be waterproof and sealed, the camera is lifted and lowered under the action of a cable, and the diving depth of the camera is 50-200 m.
3. The visualization-based fishery resource cluster distribution analysis method according to claim 1, wherein the sonar device in the three-dimensional imaging sonar system in step (1) is arranged at an underwater position 1-2 meters away from the water surface.
4. The visualization-based fishery resource cluster distribution analysis method according to claim 1, wherein when the detection ships are classified in the step (3), the number of the detection ships is determined according to the area of the detection water area.
5. The visual fishery resource cluster distribution analysis method based on the claim 1, wherein the ant colony algorithm in (5) is to use the scanned fish colony as the current ant colony, use the current water area temperature and the current water flow direction as the pheromone of the current ant colony, make a decision by the current ant colony pheromone to obtain the current ant colony trend judgment, obtain a fish colony flow prediction model after training of the training set, and obtain the fish colony flow direction according to the current water area temperature and the current water flow direction.
6. The visualization-based fishery resource cluster distribution analysis method according to claim 1, wherein the grayscale image measurement of the three-dimensional sonar data in the step (5) is specifically as follows: carrying out gray processing on image information and three-dimensional sonar data information obtained by a visual monitoring system, carrying out gray value self-adaptive compensation to obtain a clear gray-scale fish school visual image, comparing the gray-scale fish school visual image with an image in a water area fish school bank through image comparison software, and judging the habit type of the fish school.
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Cited By (6)
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| CN115456490A (en) * | 2022-11-14 | 2022-12-09 | 中国水产科学研究院南海水产研究所 | Fishery resource data analysis method and system based on geographic information |
| CN116542405A (en) * | 2023-07-07 | 2023-08-04 | 中国水产科学研究院南海水产研究所 | Image processing-based blue-round-clad resource distribution prediction method and system |
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| CN115456490A (en) * | 2022-11-14 | 2022-12-09 | 中国水产科学研究院南海水产研究所 | Fishery resource data analysis method and system based on geographic information |
| CN116596163A (en) * | 2023-07-05 | 2023-08-15 | 中国水产科学研究院南海水产研究所 | Image detection-based white croaker resource distribution prediction system and method |
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| CN116562472A (en) * | 2023-07-10 | 2023-08-08 | 中国水产科学研究院南海水产研究所 | Method and system for identifying and predicting target species of middle-upper marine organisms |
| CN116562472B (en) * | 2023-07-10 | 2024-01-09 | 中国水产科学研究院南海水产研究所 | Method and system for identifying and predicting target species of middle-upper marine organisms |
| CN117176756A (en) * | 2023-09-07 | 2023-12-05 | 青岛海洋地质研究所 | Visual biological trawl system based on underwater vehicle |
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