CN116562467B - Marine fish target species identification and distribution prediction method and system - Google Patents
Marine fish target species identification and distribution prediction method and system Download PDFInfo
- Publication number
- CN116562467B CN116562467B CN202310831241.XA CN202310831241A CN116562467B CN 116562467 B CN116562467 B CN 116562467B CN 202310831241 A CN202310831241 A CN 202310831241A CN 116562467 B CN116562467 B CN 116562467B
- Authority
- CN
- China
- Prior art keywords
- fish
- information
- marine
- target
- distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 241000251468 Actinopterygii Species 0.000 title claims abstract description 521
- 238000009826 distribution Methods 0.000 title claims abstract description 204
- 238000000034 method Methods 0.000 title claims abstract description 32
- 241000894007 species Species 0.000 title claims description 37
- 238000004458 analytical method Methods 0.000 claims abstract description 44
- 230000005012 migration Effects 0.000 claims description 42
- 238000013508 migration Methods 0.000 claims description 42
- 230000008859 change Effects 0.000 claims description 28
- 238000010586 diagram Methods 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 14
- 238000004140 cleaning Methods 0.000 claims description 12
- 230000007613 environmental effect Effects 0.000 claims description 12
- 244000062645 predators Species 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 8
- 230000009467 reduction Effects 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 230000001502 supplementing effect Effects 0.000 claims 1
- 238000011161 development Methods 0.000 abstract description 3
- 238000011835 investigation Methods 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 15
- 238000012545 processing Methods 0.000 description 6
- 238000001514 detection method Methods 0.000 description 4
- 239000000523 sample Substances 0.000 description 4
- 238000013077 scoring method Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 241000143060 Americamysis bahia Species 0.000 description 2
- 241000238424 Crustacea Species 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 238000007477 logistic regression Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000013535 sea water Substances 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/05—Underwater scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
- G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/80—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in fisheries management
- Y02A40/81—Aquaculture, e.g. of fish
Abstract
The invention relates to the technical field of fish identification distribution prediction, and discloses a marine fish target type identification and distribution prediction method and a marine fish target type identification and distribution prediction system, wherein marine information database is constructed, marine target fish data are retrieved from the Internet and are imported into the marine information database; collecting marine fish information in real time, and determining a marine fish area; collecting marine environment characteristic information and marine fish information; constructing a fishery analysis module, and analyzing to obtain fish type identification results; and constructing a distribution prediction model, analyzing to obtain a prediction result, and displaying the prediction result on a preset display. The method can identify marine fishes and predict the distribution of the marine fishes, is beneficial to improving the accuracy of fishery resource investigation and realizes sustainable fishery development.
Description
Technical Field
The invention relates to the technical field of fishery resource investigation and prediction, in particular to a marine fish target species identification and distribution prediction method and system.
Background
Marine fish target species identification and distribution prediction techniques have been widely used. The traditional marine fish target type identification and distribution prediction technology mainly relies on manual observation and experience judgment, and has the problems of low efficiency, poor reliability and the like. In recent years, with the development of computer technology, sensor technology and artificial intelligence technology, marine fish target species identification and distribution prediction technology has been rapidly developed.
Some existing marine fish target species identification and distribution prediction technologies mainly depend on simple shooting identification technologies. However, these techniques typically require extensive data acquisition and processing, are costly, and are not accurate enough, and often the identified and predicted results deviate significantly from reality. Therefore, there is a need for an efficient marine fish target species identification and distribution prediction method.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present invention provides a marine fish target species identification and distribution prediction method and system.
The first aspect of the invention provides a marine fish target species identification and distribution prediction method, comprising the following steps:
constructing a marine information database, retrieving marine target fish data from the Internet based on the target fish name, and importing the marine target fish data into the marine information database;
Acquiring first image information, depth information and acoustic information of marine fishes in real time, and determining a marine fish area based on historical target fish distribution positions through an information analysis module;
collecting marine environmental characteristic information, second image information, depth information and acoustic information of marine fishes;
constructing a fishery analysis module, and comparing the collected first and second image information, depth information and acoustic information of the marine fishes with marine target fish data in the marine information database to obtain a fish type identification result;
a distribution prediction model is built, the fish type identification result is input into the distribution prediction model, the distribution position and the distribution quantity of the fish type are predicted, the variation condition of the fish type quantity in different seasons is predicted, and the prediction result is displayed on a preset display.
In this scheme, the construction of the ocean information database, based on the name of the target fish, retrieves ocean target fish data from the internet, and imports the ocean target fish data into the ocean information database, specifically:
acquiring marine environment characteristic information of a marine fish area, wherein the marine environment characteristic information of the marine fish area comprises water temperature, salinity, flow velocity, depth, brightness and wave information; searching the target fish through the fish information big data to obtain marine target fish data, wherein the marine target fish data comprises a fish name, a growable length, a color, an appearance shape and a living environment;
Constructing a marine information database, importing the marine environment characteristic information and the marine target fish data into the marine information database, and storing the marine environment characteristic information and the marine target fish data into the marine information database.
In this scheme, gather marine fish's first image information, degree of depth information and acoustic information in real time, through information analysis module, confirm marine fish region based on historical target fish distribution position, specifically do:
determining a center point based on a marine fish area, constructing a rectangle by the center point, dividing the constructed rectangle into 16 small rectangles, and sequentially detecting the 16 small rectangles by a fish finder to obtain first image information, depth information and acoustic information of the marine fish;
and analyzing and determining longitude and latitude information, depth information and moving speed of the fish based on the first image information, the depth information and the acoustic information.
In this scheme, collect marine environment characteristic information, marine fish's second image information, degree of depth information and acoustic information, specifically do:
acquiring marine environment characteristic information by using an underwater robot, and preprocessing the collected marine environment characteristic information, wherein the preprocessing comprises the steps of data cleaning, noise filtering and data standardization;
Second image information, depth information and acoustic information of marine fish are collected by the underwater robot.
In this scheme, construct fishery analysis module, with first, second image information, the degree of depth information of ocean fish that gathers, acoustic information with ocean target fish data in the ocean information database compares, obtain fish kind recognition result, specifically do:
based on a fishery analysis module, storing the first and second image information, the depth information and the acoustic information of the marine fish into a marine information database, and classifying and marking the marine fish;
noise reduction pretreatment is carried out on the first image information and the second image information of the marine fish; extracting key characteristics of ocean fish from the pretreated information, wherein the key characteristics comprise size characteristics, outline characteristics, color characteristics, speckle characteristics, mouth shape characteristics, tail fin shape and dorsal fin shape;
extracting target fish characteristics from marine target fish data in a marine information database to obtain key characteristic information of target fish;
and comparing and matching the key features of the marine fish with the key features of the target fish to obtain a fish type identification result.
In this scheme, the construction of a distribution prediction model, inputting the fish species identification result into the distribution prediction model, predicting the fish species distribution position, distribution quantity and the variation condition of the fish quantity in different seasons, and displaying the prediction result on a preset display, specifically:
constructing a distribution prediction model based on machine learning, and importing historical marine fish distribution data and environmental characteristic data into the distribution prediction model for training and optimizing;
inputting the fish type identification result into a distribution prediction model, predicting the distribution position and the quantity of the target fish in different seasons by using the model, and obtaining a prediction result, wherein the prediction result comprises the spatial distribution condition of the fish in a marine fish area, the quantity of the fish and the distribution condition change trend information thereof;
according to the prediction result, a distribution map and a quantity change map of marine fish are generated in a graphical mode, the quantity change map comprises a distribution thermodynamic diagram, a line diagram and a histogram, and the prediction result and the distribution map are displayed on a preset display.
The second aspect of the present invention also provides a marine fish target species identification and distribution prediction system, comprising: the marine fish target species identification and distribution prediction method comprises a memory and a processor, wherein the memory comprises a marine fish target species identification and distribution prediction method program, and when the marine fish target species identification and distribution prediction method program is executed by the processor, the following steps are realized:
Constructing a marine information database, retrieving marine target fish data from the Internet based on the target fish name, and importing the marine target fish data into the marine information database;
acquiring first image information, depth information and acoustic information of marine fishes in real time, and determining a marine fish area based on historical target fish distribution positions through an information analysis module;
collecting marine environmental characteristic information, second image information, depth information and acoustic information of marine fishes;
constructing a fishery analysis module, and comparing the collected first and second image information, depth information and acoustic information of the marine fishes with marine target fish data in the marine information database to obtain a fish type identification result;
a distribution prediction model is built, the fish type identification result is input into the distribution prediction model, the distribution position and the distribution quantity of the fish type are predicted, the variation condition of the fish type quantity in different seasons is predicted, and the prediction result is displayed on a preset display.
In this scheme, gather marine fish's first image information, degree of depth information and acoustic information in real time, through information analysis module, confirm marine fish region based on historical target fish distribution position, specifically do:
Determining a center point based on a marine fish area, constructing a rectangle by the center point, dividing the constructed rectangle into 16 small rectangles, and sequentially detecting the 16 small rectangles by a fish finder to obtain first image information, depth information and acoustic information of the marine fish;
and analyzing and determining longitude and latitude information, depth information and moving speed of the fish based on the first image information, the depth information and the acoustic information.
In this scheme, construct fishery analysis module, with first, second image information, the degree of depth information of ocean fish that gathers, acoustic information with ocean target fish data in the ocean information database compares, obtain fish kind recognition result, specifically do:
based on a fishery analysis module, storing the first and second image information, the depth information and the acoustic information of the marine fish into a marine information database, and classifying and marking the marine fish;
noise reduction pretreatment is carried out on the first image information and the second image information of the marine fish; extracting key characteristics of ocean fish from the pretreated information, wherein the key characteristics comprise size characteristics, outline characteristics, color characteristics, speckle characteristics, mouth shape characteristics, tail fin shape and dorsal fin shape;
Extracting target fish characteristics from marine target fish data in a marine information database to obtain key characteristic information of target fish;
and comparing and matching the key features of the marine fish with the key features of the target fish to obtain a fish type identification result.
In this scheme, the construction of a distribution prediction model, inputting the fish species identification result into the distribution prediction model, predicting the fish species distribution position, distribution quantity and the variation condition of the fish quantity in different seasons, and displaying the prediction result on a preset display, specifically:
constructing a distribution prediction model based on machine learning, and importing historical marine fish distribution data and environmental characteristic data into the distribution prediction model for training and optimizing;
inputting the fish type identification result into a distribution prediction model, predicting the distribution position and the quantity of the target fish in different seasons by using the model, and obtaining a prediction result, wherein the prediction result comprises the spatial distribution condition of the fish in a marine fish area, the quantity of the fish and the distribution condition change trend information thereof;
according to the prediction result, a distribution map and a quantity change map of marine fish are generated in a graphical mode, the quantity change map comprises a distribution thermodynamic diagram, a line diagram and a histogram, and the prediction result and the distribution map are displayed on a preset display.
Drawings
FIG. 1 is a flow chart showing a marine fish target species identification and distribution prediction method of the present invention
FIG. 2 is a flow chart showing the result of fish species identification according to the present invention
FIG. 3 is a flow chart illustrating the method for obtaining a distributed prediction result according to the present invention
FIG. 4 shows a block diagram of a marine fish target species identification and distribution prediction system
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a marine fish target species identification and distribution prediction method.
As shown in fig. 1, the first aspect of the present invention provides a marine fish target species identification and distribution prediction method, which includes:
S102, constructing a marine information database, retrieving marine target fish data from the Internet based on the target fish name, and importing the marine target fish data into the marine information database;
s104, acquiring first image information, depth information and acoustic information of marine fishes in real time, and determining marine fish areas based on historical target fish distribution positions through an information analysis module;
s106, collecting marine environment characteristic information, second image information, depth information and acoustic information of marine fishes;
s108, constructing a fishery analysis module, and comparing the collected first and second image information, depth information and acoustic information of the marine fishes with marine target fish data in the marine information database to obtain a fish type identification result;
s110, constructing a distribution prediction model, inputting the fish type identification result into the distribution prediction model, predicting the fish type distribution position, the distribution quantity and the variation condition of the fish quantity in different seasons, and displaying the prediction result on a preset display.
The fishery analysis module and the distribution prediction model are constructed, and finally input information is compared with the ocean information database through a computer to obtain relevant recognition and prediction results; the first image information, the depth information and the acoustic information are obtained by common equipment such as a mobile phone camera, physical depth measuring equipment and an acoustic sensor, data with lower precision are preliminarily and rapidly obtained, and the approximate area where marine fishes are located is rapidly determined; the second image information, the depth information and the acoustic information are high-precision image information, depth information and acoustic information which are acquired by utilizing high-precision equipment in the approximate area on the basis of determining the approximate area where the marine fish is located, so that the fish type can be accurately identified.
According to the embodiment of the invention, the ocean information database is constructed, ocean target fish data is retrieved from the Internet based on the target fish name, and the ocean target fish data is imported into the ocean information database, specifically:
acquiring marine environment characteristic information of a marine fish area, wherein the marine environment characteristic information of the marine fish area comprises water temperature, salinity, flow velocity, depth, brightness and wave information; searching the target fish through the fish information big data to obtain marine target fish data, wherein the marine target fish data comprises a fish name, a growable length, a color, an appearance shape and a living environment;
constructing a marine information database, importing the marine environment characteristic information and the marine target fish data into the marine information database, and storing the marine environment characteristic information and the marine target fish data into the marine information database.
The marine environment characteristic information of the marine fish area is obtained, and the wide and comprehensive marine environment characteristic information can be obtained by searching the China marine environment website data in the Internet; the target fish is searched through the fish information big data: the Chinese biological website retrieves marine fish data including fish names, growable lengths, colors, appearance shapes, living environments and the like, which provide basic descriptions and features about various fish species, and are helpful for identifying and distinguishing different species of fish. The constructed ocean information database becomes the basis of subsequent analysis and prediction, and the centralized management and rapid access of ocean environment and fish information can be realized by combining and storing the environment characteristic data and the fish data in the database.
According to the embodiment of the invention, the first image information, the depth information and the acoustic information of the marine fish are acquired in real time, and the marine fish area is determined based on the historical target fish distribution position through the information analysis module, specifically:
determining a center point based on a marine fish area, constructing a rectangle by the center point, dividing the constructed rectangle into 16 small rectangles, and sequentially detecting the 16 small rectangles by a fish finder to obtain first image information, depth information and acoustic information of the marine fish;
and analyzing and determining longitude and latitude information, depth information and moving speed of the fish based on the first image information, the depth information and the acoustic information.
The fish finder is manufactured by using a sailing type fishery acoustic detection technology, the fish finder can accurately identify the positions of fishes, the fish finder is required to be installed in an offshore vehicle, and the offshore vehicle comprises a fishing boat, a yacht, a mail boat and a motorboat; when the constructed 16 small rectangles are all required to be detected and rapid detection is required, arranging a plurality of fish probes to detect simultaneously, and finally obtaining first image information, depth information and acoustic information of marine fish in the detected marine fish area; the marine fish information is acquired by determining longitude and latitude information, depth information and moving speed of the fish by using a remote sensing technology, satellite information and a radar technology; the first image information, the depth information and the acoustic information are primarily acquired marine fish data.
According to the embodiment of the invention, the collecting of the marine environmental characteristic information, the second image information, the depth information and the acoustic information of the marine fish is specifically:
acquiring marine environment characteristic information by using an underwater robot, and preprocessing the collected marine environment characteristic information, wherein the preprocessing comprises the steps of data cleaning, noise filtering and data standardization;
second image information, depth information and acoustic information of marine fish are collected by the underwater robot.
The second image information, the depth information and the acoustic information of the marine fish are exact and detailed information acquired by high-precision equipment; the underwater robot is provided with a sensor, a probe, a sonar system and high-definition camera equipment, so that the marine environment data of the seawater temperature, the salinity and the fish can be accurately acquired; the high-precision equipment is high-definition camera equipment and a sonar system.
Fig. 2 shows a flowchart for obtaining fish species identification results according to the present invention.
According to the embodiment of the invention, the fishery analysis module is configured to compare the collected first and second image information, depth information and acoustic information of the marine fish with marine target fish data in the marine information database to obtain a fish type identification result, which specifically includes:
S202, storing the first and second image information, the depth information and the acoustic information of the marine fish into a marine information database based on a fishery analysis module, and classifying and marking the marine fish;
s204, performing noise reduction pretreatment on the first and second image information of the marine fish; extracting key characteristics of ocean fish from the pretreated information, wherein the key characteristics comprise size characteristics, outline characteristics, color characteristics, speckle characteristics, mouth shape characteristics, tail fin shape and dorsal fin shape;
s206, extracting target fish characteristics from marine target fish data in a marine information database to obtain key characteristic information of target fish;
s208, comparing and matching the key features of the marine fish with the key features of the target fish to obtain a fish type identification result.
The image processing algorithm comprises Dijkstra algorithm, bellman-Ford algorithm and Floyd-Warshall algorithm, and characteristic points, contours and color key characteristics of fish can be extracted through the image processing algorithm; in the comparison and matching process, the extracted characteristic points, outlines and color key characteristics of the fish are compared with marine fish information in a marine database to obtain the similarity percentage, and three kinds of fish information with the highest similarity percentage are used as identification results; the shape feature is an appearance integral feature, and the tail fin shape and the dorsal fin shape are detail features; the key features of the target fish include size features, contour features, color features, speckle features, mouth shape features, tail fin shape, dorsal fin shape, collectively referred to as target fish key features.
FIG. 3 shows a flow chart for obtaining a distribution prediction result according to the present invention.
According to the embodiment of the invention, a distribution prediction model is constructed, the fish species identification result is input into the distribution prediction model, the distribution position and the distribution quantity of the fish species and the variation condition of the fish quantity in different seasons are predicted, and the prediction result is displayed on a preset display, specifically:
s302, a distribution prediction model based on machine learning is constructed, and historical marine fish distribution data and environmental characteristic data are imported into the distribution prediction model for training and optimization;
s304, inputting the fish type identification result into a distribution prediction model, predicting the distribution position and the quantity of the target fish in different seasons by using the model, and obtaining a prediction result, wherein the prediction result comprises the spatial distribution condition of the fish in a marine fish area, the quantity of the fish and the distribution condition change trend information thereof;
and S306, generating a distribution map and a quantity change map of marine fishes in a graphical mode according to the prediction result, wherein the quantity change map comprises a distribution thermodynamic diagram, a line diagram and a histogram, and displaying the prediction result and the distribution map on a preset display.
The machine learning is to train and optimize a distribution model by using a naive Bayesian algorithm and a logistic regression algorithm so as to realize accurate prediction of the distribution positions and the quantity of the target fish in different seasons; the preset display can intuitively display the distribution map and intuitively display the distribution and number change conditions of the fishes.
According to an embodiment of the present invention, further comprising:
collecting historical distribution data of target fishes in recent ten years, wherein the historical distribution data comprise fish types, fishing records, geographical positions, seasons and occurrence times of the fishes, and cleaning and arranging the historical distribution data of the target fishes;
counting and analyzing the occurrence times of the target fish in different seasons according to the historical distribution data of the target fish to obtain distribution rules of the target fish in different seasons and fish movement activity information;
establishing a probability model;
acquiring frequency information of occurrence of the same season in the same area in the historical data;
leading the frequency information, the distribution rule, the fish motion activity information, the target season and the target area into a probability model for prediction to obtain the predicted probability information of the target fish in the target season and the target area;
And making a first fish catching plan according to the prediction probability information.
The history distribution data is obtained fromNational data official network of the statistical office of the people's republic of China and union The national grains and agricultural organization officials net,the distribution prediction model comprises a probability model; cleaning and arranging the related data of the target fish, wherein the cleaning and arranging comprises the steps of removing abnormal values, filling missing data, and unifying time and geographical position representation; the designated first fishing plan is provided for fishermen to refer to; the probability model comprises an NLP algorithm; the geographic position data of the target fish is matched with the regional boundary, and different regions of the target fish are divided based on a geographic information system technology.
According to an embodiment of the present invention, further comprising:
obtaining a preset predatory organism species of the target fish;
based on a marine organism migration monitoring system, monitoring the position information of the preset predator species in real time, analyzing migration trend based on the position information, and obtaining a preset predator species migration area;
monitoring quantity change information of target fish before and after the preset predator species migration area;
constructing a target fish migration analysis module, importing the quantity change information into the target fish migration analysis module, and analyzing the migration trend information of the target fish;
Creating a second fishing plan based on the migration trend information;
and integrating the first fishing plan and the second fishing plan to obtain an integral fishing plan.
And acquiring the prediction probability information and the migration trend information, scoring the prediction probability information and the migration trend information, and judging the area with the highest score as the optimal selection area of the target fish.
The predatory organism category includes crustaceans, planktons and shrimps; the marine organism migration monitoring system comprises a fish finder and an underwater robot; the method is summarized as a scoring method, different preset areas are scored by the scoring method for multiple times, fish optimal areas are obtained, the fish optimal areas are the areas with highest probability of existence of target fish, the number of fishing vessels is increased, and accuracy of target fish distribution prediction and fishery fishing benefit are improved.
FIG. 4 shows a block diagram of a marine fish target species identification and distribution prediction system
The second aspect of the present invention also provides a marine fish target species identification and distribution prediction system 4, comprising: the memory 41 and the processor 42, wherein the memory includes marine fish target species identification and distribution prediction method programs, and when the marine fish target species identification and distribution prediction method programs are executed by the processor, the following steps are realized:
constructing a marine information database, retrieving marine target fish data from the Internet based on the target fish name, and importing the marine target fish data into the marine information database;
acquiring first image information, depth information and acoustic information of marine fishes in real time, and determining a marine fish area based on historical target fish distribution positions through an information analysis module;
collecting marine environmental characteristic information, second image information, depth information and acoustic information of marine fishes;
constructing a fishery analysis module, and comparing the collected first and second image information, depth information and acoustic information of the marine fishes with marine target fish data in the marine information database to obtain a fish type identification result;
A distribution prediction model is built, the fish type identification result is input into the distribution prediction model, the distribution position and the distribution quantity of the fish type are predicted, the variation condition of the fish type quantity in different seasons is predicted, and the prediction result is displayed on a preset display.
The fishery analysis module and the distribution prediction model are constructed by comparing the input information with the ocean information database through a computer to obtain related recognition and prediction results.
According to the embodiment of the invention, the ocean information database is constructed, ocean target fish data is retrieved from the Internet based on the target fish name, and the ocean target fish data is imported into the ocean information database, specifically:
acquiring marine environment characteristic information of a marine fish area, wherein the marine environment characteristic information of the marine fish area comprises water temperature, salinity, flow velocity, depth, brightness and wave information; searching the target fish through the fish information big data to obtain marine target fish data, wherein the marine target fish data comprises a fish name, a growable length, a color, an appearance shape and a living environment;
Constructing a marine information database, importing the marine environment characteristic information and the marine target fish data into the marine information database, and storing the marine environment characteristic information and the marine target fish data into the marine information database.
The marine environment characteristic information of the marine fish area is obtained, and the wide and comprehensive marine environment characteristic information can be obtained by searching the China marine environment website data in the Internet; the target fish is searched through the fish information big data: the Chinese biological website retrieves marine fish data including fish names, growable lengths, colors, appearance shapes, living environments and the like, which provide basic descriptions and features about various fish species, and are helpful for identifying and distinguishing different species of fish. The constructed ocean information database becomes the basis of subsequent analysis and prediction, and the centralized management and rapid access of ocean environment and fish information can be realized by combining and storing the environment characteristic data and the fish data in the database.
According to the embodiment of the invention, the first image information, the depth information and the acoustic information of the marine fish are acquired in real time, and the marine fish area is determined based on the historical target fish distribution position through the information analysis module, specifically:
Determining a center point based on a marine fish area, constructing a rectangle by the center point, dividing the constructed rectangle into 16 small rectangles, and sequentially detecting the 16 small rectangles by a fish finder to obtain first image information, depth information and acoustic information of the marine fish;
and analyzing and determining longitude and latitude information, depth information and moving speed of the fish based on the first image information, the depth information and the acoustic information.
The fish finder is manufactured by using a sailing type fishery acoustic detection technology, the fish finder can accurately identify the positions of fishes, the fish finder is required to be installed in an offshore vehicle, and the offshore vehicle comprises a fishing boat, a yacht, a mail boat and a motorboat; when the constructed 16 small rectangles are all required to be detected and rapid detection is required, arranging a plurality of fish probes to detect simultaneously, and finally obtaining first image information, depth information and acoustic information of marine fish in the detected marine fish area; the marine fish information is acquired by determining longitude and latitude information, depth information and moving speed of the fish by using a remote sensing technology, satellite information and a radar technology; the first image information, the depth information and the acoustic information are primarily acquired marine fish data.
According to the embodiment of the invention, the collecting of the marine environmental characteristic information, the second image information, the depth information and the acoustic information of the marine fish is specifically:
acquiring marine environment characteristic information by using an underwater robot, and preprocessing the collected marine environment characteristic information, wherein the preprocessing comprises the steps of data cleaning, noise filtering and data standardization;
second image information, depth information and acoustic information of marine fish are collected by the underwater robot.
The second image information, the depth information and the acoustic information of the marine fish are exact and detailed information acquired by high-precision equipment; the underwater robot is provided with a sensor, a probe, a sonar system and high-definition camera equipment, so that the marine environment data of the seawater temperature, the salinity and the fish can be accurately acquired; the high-precision equipment is high-definition camera equipment and a sonar system.
According to the embodiment of the invention, the fishery analysis module is configured to compare the collected first and second image information, depth information and acoustic information of the marine fish with marine target fish data in the marine information database to obtain a fish type identification result, which specifically includes:
Based on a fishery analysis module, storing the first and second image information, the depth information and the acoustic information of the marine fish into a marine information database, and classifying and marking the marine fish;
noise reduction pretreatment is carried out on the first image information and the second image information of the marine fish; extracting key characteristics of ocean fish from the pretreated information, wherein the key characteristics comprise size characteristics, outline characteristics, color characteristics, speckle characteristics, mouth shape characteristics, tail fin shape and dorsal fin shape;
extracting target fish characteristics from marine target fish data in a marine information database to obtain key characteristic information of target fish;
and comparing and matching the key features of the marine fish with the key features of the target fish to obtain a fish type identification result.
The image processing algorithm comprises Dijkstra algorithm, bellman-Ford algorithm and Floyd-Warshall algorithm, and characteristic points, contours and color key characteristics of fish can be extracted through the image processing algorithm; in the comparison and matching process, the extracted characteristic points, outlines and color key characteristics of the fish are compared with marine fish information in a marine database to obtain the similarity percentage, and three kinds of fish information with the highest similarity percentage are used as identification results; the shape feature is an appearance integral feature, and the tail fin shape and the dorsal fin shape are detail features; the key features of the target fish include size features, contour features, color features, speckle features, mouth shape features, tail fin shape, dorsal fin shape, collectively referred to as target fish key features.
According to the embodiment of the invention, a distribution prediction model is constructed, the fish species identification result is input into the distribution prediction model, the distribution position and the distribution quantity of the fish species and the variation condition of the fish quantity in different seasons are predicted, and the prediction result is displayed on a preset display, specifically:
constructing a distribution prediction model based on machine learning, and importing historical marine fish distribution data and environmental characteristic data into the distribution prediction model for training and optimizing;
inputting the fish type identification result into a distribution prediction model, predicting the distribution position and the quantity of the target fish in different seasons by using the model, and obtaining a prediction result, wherein the prediction result comprises the spatial distribution condition of the fish in a marine fish area, the quantity of the fish and the distribution condition change trend information thereof;
according to the prediction result, a distribution map and a quantity change map of marine fish are generated in a graphical mode, the quantity change map comprises a distribution thermodynamic diagram, a line diagram and a histogram, and the prediction result and the distribution map are displayed on a preset display.
The machine learning is to train and optimize a distribution model by using a naive Bayesian algorithm and a logistic regression algorithm so as to realize accurate prediction of the distribution positions and the quantity of the target fish in different seasons; the preset display can intuitively display the distribution map and intuitively display the distribution and number change conditions of the fishes.
According to an embodiment of the present invention, further comprising:
collecting historical distribution data of target fishes in recent ten years, wherein the historical distribution data comprise fish types, fishing records, geographical positions, seasons and occurrence times of the fishes, and cleaning and arranging the historical distribution data of the target fishes;
counting and analyzing the occurrence times of the target fish in different seasons according to the historical distribution data of the target fish to obtain distribution rules of the target fish in different seasons and fish movement activity information;
establishing a probability model;
acquiring frequency information of occurrence of the same season in the same area in the historical data;
leading the frequency information, the distribution rule, the fish motion activity information, the target season and the target area into a probability model for prediction to obtain the predicted probability information of the target fish in the target season and the target area;
and making a first fish catching plan according to the prediction probability information.
The history distribution data is obtained fromNational data official network of the statistical office of the people's republic of China and union The national grains and agricultural organization officials net,the distribution prediction model comprises a probability model; cleaning and arranging the related data of the target fish, wherein the cleaning and arranging comprises the steps of removing abnormal values, filling missing data, and unifying time and geographical position representation; the designated first fishing plan is provided for fishermen to refer to; the probability model comprises an NLP algorithm; the geographic position data of the target fish is matched with the regional boundary, and different regions of the target fish are divided based on a geographic information system technology.
According to an embodiment of the present invention, further comprising:
obtaining a preset predatory organism species of the target fish;
based on a marine organism migration monitoring system, monitoring the position information of the preset predator species in real time, analyzing migration trend based on the position information, and obtaining a preset predator species migration area;
monitoring quantity change information of target fish before and after the preset predator species migration area;
constructing a target fish migration analysis module, importing the quantity change information into the target fish migration analysis module, and analyzing the migration trend information of the target fish;
creating a second fishing plan based on the migration trend information;
and integrating the first fishing plan and the second fishing plan to obtain an integral fishing plan.
And acquiring the prediction probability information and the migration trend information, scoring the prediction probability information and the migration trend information, and judging the area with the highest score as the optimal selection area of the target fish.
The predatory organism category includes crustaceans, planktons and shrimps; the marine organism migration monitoring system comprises a fish finder and an underwater robot; the method is summarized as a scoring method, different preset areas are scored by the scoring method for multiple times, fish optimal areas are obtained, the fish optimal areas are the areas with highest probability of existence of target fish, the number of fishing vessels is increased, and accuracy of target fish distribution prediction and fishery fishing benefit are improved.
The invention relates to the technical field of fish identification distribution prediction, and discloses a marine fish target type identification and distribution prediction method and a marine fish target type identification and distribution prediction system, wherein marine fish data known in the Internet are searched and imported into a marine information database by constructing the marine information database; collecting marine fish information in real time, and determining a marine fish area; collecting marine environment characteristic information and marine fish information; constructing a fishery analysis module to obtain a fish type identification result; and constructing a distribution prediction model, obtaining a prediction result, and displaying the prediction result on a preset display. The marine fish identification and marine fish distribution prediction method can be used for identifying marine fish and predicting marine fish distribution, is beneficial to improving the accuracy of fishery resource investigation, and realizes sustainable fishery development.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. The marine fish target species identification and distribution prediction method is characterized by comprising the following steps of:
constructing a marine information database, retrieving marine target fish data from the Internet based on the target fish name, and importing the marine target fish data into the marine information database;
acquiring first image information, first depth information and first acoustic information of marine fishes in real time, and determining an approximate region of the marine fishes based on the historical target fish distribution positions through an information analysis module;
collecting marine environment characteristic information, second image information, second depth information and second acoustic information of marine fishes, wherein the second image information, the second depth information and the second acoustic information are high-precision image information, depth information and acoustic information which are acquired by high-precision equipment in a rough area on the basis that data with lower precision are initially and quickly acquired by common equipment and rough areas where the marine fishes are positioned are quickly determined;
constructing a fishery analysis module, and comparing the collected first and second image information, first and second depth information, first and second acoustic information of the marine fish with marine target fish data in the marine information database to obtain a fish type identification result;
Constructing a distribution prediction model, inputting the fish type identification result into the distribution prediction model, predicting the fish type distribution position, the distribution quantity and the variation condition of the fish quantity in different seasons, and displaying the prediction result on a preset display;
constructing a fishery analysis module, comparing the collected first and second image information, first and second depth information, first and second acoustic information of the marine fish with marine target fish data in the marine information database to obtain a fish type identification result, wherein the fish type identification result specifically comprises:
based on a fishery analysis module, storing the first and second image information, the first and second depth information and the first and second acoustic information of the marine fish into a marine information database, and classifying and marking the marine fish;
noise reduction pretreatment is carried out on the first image information and the second image information of the marine fish; extracting key characteristics of ocean fish from the pretreated information, wherein the key characteristics comprise size characteristics, outline characteristics, color characteristics, speckle characteristics, mouth shape characteristics, tail fin shape and dorsal fin shape;
extracting target fish characteristics from marine target fish data in a marine information database to obtain key characteristic information of target fish;
Comparing and matching the key features of the marine fish with the key features of the target fish to obtain a fish type identification result;
wherein, still include:
collecting historical distribution data of target fishes in recent ten years, wherein the historical distribution data comprise fish types, fishing records, geographical positions, seasons and occurrence times of the fishes, and cleaning and arranging the historical distribution data of the target fishes;
counting and analyzing the occurrence times of the target fish in different seasons according to the historical distribution data of the target fish to obtain distribution rules of the target fish in different seasons and fish movement activity information;
establishing a probability model;
acquiring frequency information of occurrence of the same season in the same area in the historical data;
leading the target season, the target area, the frequency information, the distribution rule and the fish motion activity information into a probability model for prediction to obtain the prediction probability information of the target fish in the target season and the target area;
a first fish catching plan is established according to the prediction probability information;
wherein, still include:
obtaining a preset predatory organism species of the target fish;
based on a marine organism migration monitoring system, monitoring the position information of the preset predator species in real time, analyzing migration trend based on the position information, and obtaining a preset predator species migration area;
Monitoring quantity change information of target fish before and after the preset predator species migration area;
constructing a target fish migration analysis module, importing the quantity change information into the target fish migration analysis module, and analyzing the migration trend information of the target fish;
a second fishing plan is formulated based on the migration trend information;
integrating the first fishing plan and the second fishing plan to obtain an integral fishing plan;
and acquiring the prediction probability information and the migration trend information, scoring the prediction probability information and the migration trend information, and judging the area with the highest score as the optimal selection area of the target fish.
2. The marine fish target species identification and distribution prediction method according to claim 1, wherein a marine information database is constructed, marine target fish data is retrieved from the internet based on the target fish name, and the marine target fish data is imported into the marine information database, specifically:
acquiring marine environment characteristic information of a marine fish area, wherein the marine environment characteristic information of the marine fish area comprises water temperature, salinity, flow velocity, depth, brightness and wave information; searching the target fish through the fish information big data to obtain marine target fish data, wherein the marine target fish data comprises a fish name, a growable length, a color, an appearance shape and a living environment;
Constructing a marine information database, importing the marine environment characteristic information and the marine target fish data into the marine information database, and storing the marine environment characteristic information and the marine target fish data into the marine information database.
3. The marine fish target species identification and distribution prediction method according to claim 1, wherein the first image information, the first depth information and the first acoustic information of the marine fish are collected in real time, and the approximate region of the marine fish is determined based on the historical target fish distribution position through the information analysis module, specifically:
determining a center point based on a marine fish area, constructing a rectangle by the center point, dividing the constructed rectangle into 16 small rectangles, and sequentially detecting the 16 small rectangles by a fish finder to obtain first image information, depth information and acoustic information of the marine fish;
and analyzing and determining longitude and latitude information, depth information and moving speed of the fish based on the first image information, the depth information and the acoustic information.
4. The marine fish target species identification and distribution prediction method according to claim 1, wherein marine environmental characteristic information, marine fish second image information, second depth information and second acoustic information are collected, specifically:
Acquiring marine environment characteristic information by using an underwater robot, and preprocessing the collected marine environment characteristic information, wherein the preprocessing comprises the steps of data cleaning, noise filtering and data standardization;
second image information, depth information and acoustic information of marine fish are collected by the underwater robot.
5. The marine fish target species identification and distribution prediction method according to claim 1, wherein a distribution prediction model is constructed, the fish species identification result is input into the distribution prediction model, the fish species distribution position, the distribution quantity and the variation condition of the fish quantity in different seasons are predicted, and the prediction result is displayed on a preset display, specifically:
constructing a distribution prediction model based on machine learning, and importing historical marine fish distribution data and environmental characteristic data into the distribution prediction model for training and optimizing;
inputting the fish type identification result into a distribution prediction model, predicting the distribution position and the quantity of the target fish in different seasons by using the model, and obtaining a prediction result, wherein the prediction result comprises the spatial distribution condition of the fish in a marine fish area, the quantity of the fish and the distribution condition change trend information thereof;
According to the prediction result, a distribution map and a quantity change map of marine fish are generated in a graphical mode, the quantity change map comprises a distribution thermodynamic diagram, a line diagram and a histogram, and the prediction result and the distribution map are displayed on a preset display.
6. The marine fish target species identification and distribution prediction system is characterized by comprising a storage and a processor, wherein the storage comprises a marine fish target species identification and distribution prediction method program, and the marine fish target species identification and distribution prediction method program realizes the following steps when being executed by the processor:
constructing a marine information database, retrieving marine target fish data from the Internet based on the target fish name, and importing the marine target fish data into the marine information database;
acquiring first image information, first depth information and first acoustic information of marine fishes in real time, and determining an approximate region of the marine fishes based on the historical target fish distribution positions through an information analysis module;
collecting marine environment characteristic information, second image information, second depth information and second acoustic information of marine fishes, wherein the second image information, the second depth information and the second acoustic information are high-precision image information, depth information and acoustic information which are acquired by high-precision equipment in a rough area on the basis that data with lower precision are initially and quickly acquired by common equipment and rough areas where the marine fishes are positioned are quickly determined;
Constructing a fishery analysis module, and comparing the collected first and second image information, first and second depth information, first and second acoustic information of the marine fish with marine target fish data in the marine information database to obtain a fish type identification result;
constructing a distribution prediction model, inputting the fish type identification result into the distribution prediction model, predicting the fish type distribution position, the distribution quantity and the variation condition of the fish quantity in different seasons, and displaying the prediction result on a preset display;
constructing a fishery analysis module, comparing the collected first and second image information, first and second depth information, first and second acoustic information of the marine fish with marine target fish data in the marine information database to obtain a fish type identification result, wherein the fish type identification result specifically comprises:
based on a fishery analysis module, storing the first and second image information, the first and second depth information and the first and second acoustic information of the marine fish into a marine information database, and classifying and marking the marine fish;
noise reduction pretreatment is carried out on the first image information and the second image information of the marine fish; extracting key characteristics of ocean fish from the pretreated information, wherein the key characteristics comprise size characteristics, outline characteristics, color characteristics, speckle characteristics, mouth shape characteristics, tail fin shape and dorsal fin shape;
Extracting target fish characteristics from marine target fish data in a marine information database to obtain key characteristic information of target fish;
comparing and matching the key features of the marine fish with the key features of the target fish to obtain a fish type identification result;
wherein, still include:
collecting historical distribution data of target fishes in recent ten years, wherein the historical distribution data comprise fish types, fishing records, geographical positions, seasons and occurrence times of the fishes, and cleaning and arranging the historical distribution data of the target fishes;
counting and analyzing the occurrence times of the target fish in different seasons according to the historical distribution data of the target fish to obtain distribution rules of the target fish in different seasons and fish movement activity information;
establishing a probability model;
acquiring frequency information of occurrence of the same season in the same area in the historical data;
leading the target season, the target area, the frequency information, the distribution rule and the fish motion activity information into a probability model for prediction to obtain the prediction probability information of the target fish in the target season and the target area;
a first fish catching plan is established according to the prediction probability information;
Wherein, still include:
obtaining a preset predatory organism species of the target fish;
based on a marine organism migration monitoring system, monitoring the position information of the preset predator species in real time, analyzing migration trend based on the position information, and obtaining a preset predator species migration area;
monitoring quantity change information of target fish before and after the preset predator species migration area;
constructing a target fish migration analysis module, importing the quantity change information into the target fish migration analysis module, and analyzing the migration trend information of the target fish;
a second fishing plan is formulated based on the migration trend information;
integrating the first fishing plan and the second fishing plan to obtain an integral fishing plan;
obtaining the prediction probability information and the migration trend information, scoring the prediction probability information and the migration trend information, and judging the area with the highest score as a target fish optimal selection area;
the target fish optimal selection area is used for further optimizing and supplementing the prediction result, so that a fish prediction area with higher precision is obtained.
7. The marine fish target species identification and distribution prediction system according to claim 6, wherein the first image information, the first depth information and the first acoustic information of the marine fish are collected in real time, and the approximate region of the marine fish is determined based on the historical target fish distribution position by the information analysis module, specifically:
Determining a center point based on a marine fish area, constructing a rectangle by the center point, dividing the constructed rectangle into 16 small rectangles, and sequentially detecting the 16 small rectangles by a fish finder to obtain first image information, depth information and acoustic information of the marine fish;
and analyzing and determining longitude and latitude information, depth information and moving speed of the fish based on the first image information, the depth information and the acoustic information.
8. The marine fish target species identification and distribution prediction system according to claim 6, wherein a distribution prediction model is constructed, the fish species identification result is input into the distribution prediction model, the fish species distribution position, the distribution quantity and the variation condition of the fish quantity in different seasons are predicted, and the prediction result is displayed on a preset display, specifically:
constructing a distribution prediction model based on machine learning, and importing historical marine fish distribution data and environmental characteristic data into the distribution prediction model for training and optimizing;
inputting the fish type identification result into a distribution prediction model, predicting the distribution position and the quantity of the target fish in different seasons by using the model, and obtaining a prediction result, wherein the prediction result comprises the spatial distribution condition of the fish in a marine fish area, the quantity of the fish and the distribution condition change trend information thereof;
According to the prediction result, a distribution map and a quantity change map of marine fish are generated in a graphical mode, the quantity change map comprises a distribution thermodynamic diagram, a line diagram and a histogram, and the prediction result and the distribution map are displayed on a preset display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310831241.XA CN116562467B (en) | 2023-07-07 | 2023-07-07 | Marine fish target species identification and distribution prediction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310831241.XA CN116562467B (en) | 2023-07-07 | 2023-07-07 | Marine fish target species identification and distribution prediction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116562467A CN116562467A (en) | 2023-08-08 |
| CN116562467B true CN116562467B (en) | 2024-02-20 |
Family
ID=87486521
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310831241.XA Active CN116562467B (en) | 2023-07-07 | 2023-07-07 | Marine fish target species identification and distribution prediction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116562467B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117077004B (en) * | 2023-08-18 | 2024-02-23 | 中国科学院华南植物园 | Species identification method, system, device and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114444819A (en) * | 2022-04-11 | 2022-05-06 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Fishery resource prediction method and device, storage medium and electronic equipment |
| CN114494837A (en) * | 2022-01-06 | 2022-05-13 | 中国水产科学研究院南海水产研究所 | Intelligent density identification method and system for fishery resources |
| CN114994691A (en) * | 2022-05-31 | 2022-09-02 | 中国水产科学研究院东海水产研究所 | Visualization-based fishery resource cluster distribution analysis method |
| CN115100512A (en) * | 2022-05-16 | 2022-09-23 | 中国水产科学研究院南海水产研究所 | Monitoring, identifying and catching method and system for marine economic species and storage medium |
| CN115293658A (en) * | 2022-10-08 | 2022-11-04 | 中国水产科学研究院南海水产研究所 | Fishery resource planning method and system based on big data |
| CN115359386A (en) * | 2022-08-03 | 2022-11-18 | 中国水产科学研究院南海水产研究所 | Safe fishing method, system and medium for oceanic fishery based on Internet of things |
| 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 |
| CN116229248A (en) * | 2023-03-10 | 2023-06-06 | 中科曙光国际信息产业有限公司 | Ocean species distribution prediction method, device, equipment and storage medium |
| CN116386090A (en) * | 2023-06-07 | 2023-07-04 | 中国水产科学研究院南海水产研究所 | Plankton identification method, system and medium based on scanning atlas |
-
2023
- 2023-07-07 CN CN202310831241.XA patent/CN116562467B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114494837A (en) * | 2022-01-06 | 2022-05-13 | 中国水产科学研究院南海水产研究所 | Intelligent density identification method and system for fishery resources |
| CN114444819A (en) * | 2022-04-11 | 2022-05-06 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Fishery resource prediction method and device, storage medium and electronic equipment |
| CN115100512A (en) * | 2022-05-16 | 2022-09-23 | 中国水产科学研究院南海水产研究所 | Monitoring, identifying and catching method and system for marine economic species and storage medium |
| CN114994691A (en) * | 2022-05-31 | 2022-09-02 | 中国水产科学研究院东海水产研究所 | Visualization-based fishery resource cluster distribution analysis method |
| CN115359386A (en) * | 2022-08-03 | 2022-11-18 | 中国水产科学研究院南海水产研究所 | Safe fishing method, system and medium for oceanic fishery based on Internet of things |
| CN115293658A (en) * | 2022-10-08 | 2022-11-04 | 中国水产科学研究院南海水产研究所 | Fishery resource planning method and system based on big data |
| 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 |
| CN116229248A (en) * | 2023-03-10 | 2023-06-06 | 中科曙光国际信息产业有限公司 | Ocean species distribution prediction method, device, equipment and storage medium |
| CN116386090A (en) * | 2023-06-07 | 2023-07-04 | 中国水产科学研究院南海水产研究所 | Plankton identification method, system and medium based on scanning atlas |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116562467A (en) | 2023-08-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110310264A (en) | A kind of large scale object detection method, device based on DCNN | |
| CN108920580A (en) | Image matching method, device, storage medium and terminal | |
| CN110348304A (en) | A kind of maritime affairs distress personnel search system being equipped on unmanned plane and target identification method | |
| CN116562467B (en) | Marine fish target species identification and distribution prediction method and system | |
| CN114494837B (en) | Intelligent density identification method and system for fishery resources | |
| CN115100512A (en) | Monitoring, identifying and catching method and system for marine economic species and storage medium | |
| CN112926541B (en) | Sleeping post detection method and device and related equipment | |
| CN110647802A (en) | Remote sensing image ship target detection method based on deep learning | |
| CN113065474B (en) | Behavior recognition method and device and computer equipment | |
| CN110610165A (en) | Ship behavior analysis method based on YOLO model | |
| Huettmann et al. | Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic | |
| CN111222526A (en) | Fishing boat real-time fishing behavior recognition method, device, equipment and storage medium | |
| CN113591592B (en) | Overwater target identification method and device, terminal equipment and storage medium | |
| CN113378785A (en) | Forest type identification method and device | |
| CN115456490B (en) | Fishery resource data analysis method and system based on geographic information | |
| Alburshaid et al. | Palm trees detection using the integration between gis and deep learning | |
| CN110991300B (en) | Automatic identification method for abnormal swelling state of dorking abdomen | |
| CN113588571B (en) | Hyperspectral imaging-based method and system for identifying fishy smell of aquatic product | |
| CN114782519A (en) | Method, device and medium for positioning spherical or quasi-spherical object based on point cloud information | |
| CN103136439B (en) | Plant illness discrimination method and system thereof | |
| CN117557789B (en) | Intelligent detection method and system for offshore targets | |
| Tong et al. | Automatic single fish detection with a commercial echosounder using YOLO v5 and its application for echosounder calibration | |
| CN116562472B (en) | Method and system for identifying and predicting target species of middle-upper marine organisms | |
| CN115376023A (en) | Cultivation area detection method based on deformation convolutional network, unmanned aerial vehicle and medium | |
| Lin et al. | Intelligent Preprocessing Selection for Pavement Crack Detection based on Deep Reinforcement Learning. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |