CN117830815A - Point distribution method and system for researching migration rule of fish - Google Patents
Point distribution method and system for researching migration rule of fish Download PDFInfo
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Abstract
The invention relates to a point distribution method and system for researching fish migration rules, comprising the following steps: the method comprises the steps of using a mapping tool to guide and measure a river basin, measuring the position of a river basin section and basic information of the river basin, using an unmanned plane to conduct laser detection on the river basin section and the river basin topography, constructing a three-dimensional topography model, conducting simulation experiments on the flow rate of the river basin section based on hydrodynamic combination of the basic information of the river basin section and the position of the river basin section, obtaining flow rate positions of points of the river basin section, constructing a river basin flow rate field model through combination of a BP neural network and flow rate position data of points of the river basin section, conducting combination analysis on the three-dimensional topography model and the river basin flow rate field model, obtaining the distribution position of a camera suitable for researching fish migration rules in the river basin, installing the camera in the distribution position, shooting fish migration images and judging the fish migration rules.
Description
Technical Field
The invention relates to the field of underwater equipment installation, in particular to a dotting method and a dotting system for researching a migration rule of fishes.
Background
The fish needs to periodically perform long-distance directional swimming, called migration, due to seasonal changes, food searching, reproduction and other reasons, and the fish needs to replace living water areas in various living periods so as to meet the requirements of different living periods on living conditions and smoothly complete important life activities in the living history. The research on the migration rule of the fishes can know the migration place and migration time of the fishes, improve the water quality of the river basin, protect the reproduction of the fishes, improve the economic benefit, reduce the water quality pollution and the like. The point distribution method for researching the migration rule of the fishes can shoot the migration image of the fishes through the camera to obtain the migration rule of the fishes, and is convenient for fishery operators to work.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a dotting method and a dotting system for researching the migration rule of fishes.
The technical scheme adopted by the invention for achieving the purpose is as follows:
the first aspect of the invention provides a spotting method for researching fish migration rules, which comprises the following steps:
measuring the river basin by using a mapping tool, and calculating the section position and the basic information of the river basin after the elevation data are obtained;
Performing laser detection on the topography of the river basin by using an unmanned plane carrying a laser detector above the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information;
measuring the flow velocity data of the water flow in the river basin by using a Doppler flow velocity profiler, dividing the section position of the river basin into a grid shape, and combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin;
constructing a drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, constructing the drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and carrying out integrated analysis on the flow velocity field model and a three-dimensional topography model to obtain an underwater camera installation position;
the underwater camera is arranged at the installation position of the underwater camera, the camera shoots fish migration images, fish migration data are obtained after the fish migration images are processed, and fish migration rules are judged according to the fish migration data.
Further, in a preferred embodiment of the present invention, the measuring tool is used to measure the river basin, and the section position and the basic information of the river basin are calculated after the elevation data are obtained, which specifically includes:
Firstly, respectively carrying out drainage basin elevation measurement on the upstream and downstream of a drainage basin by using a mapping tool to obtain water surface elevations, normal water storage level elevations and flood limit water level elevations at a plurality of positions of the upstream and downstream of the drainage basin;
measuring the area of the river basin from the upstream to the downstream by using a mapping tool, and calculating to obtain the position of the river basin section and the basic information of the river basin by combining the elevation of the river basin, the area of the river basin and the historical data information;
further, in a preferred embodiment of the present invention, an unmanned aerial vehicle carrying a laser detector is used to perform laser detection on the topography of the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information, wherein the three-dimensional topography model comprises the following specific steps:
the unmanned aerial vehicle is controlled to work above the drainage basin, a laser detector in the unmanned aerial vehicle carries out laser detection on the topography and the topography of the drainage basin, and the following method is:
the unmanned aerial vehicle is provided with a laser detector, the laser detector emits an infrared laser beam and a green laser beam, wherein the infrared laser beam cannot penetrate through the water surface and returns to a laser detector receiver along an incident path; the green laser beam can penetrate through the water surface to reach the bottom of the drainage basin, and then returns to the laser detector receiver along the incident path;
According to the propagation time difference of the infrared laser beam and the green laser beam between the unmanned plane and the water surface, the distance between the water surface refraction point of the infrared laser beam and the underwater refraction point of the corresponding green laser beam is obtained, and the water depth data of the laser scanning point is obtained according to the distance between the water surface refraction point of the infrared laser beam and the underwater refraction point of the corresponding green laser beam;
and the unmanned aerial vehicle performs laser detection on each point in the drainage basin to obtain the water depth data of each laser scanning point in the drainage basin, and constructs a three-dimensional topography model according to the water depth data of each laser scanning point in the drainage basin.
Further, in a preferred embodiment of the present invention, the Doppler flow profiler is used to measure the flow velocity data of the water flow in the river basin, the section position of the river basin is divided into grids, and the flow velocity information of each point of the section of the river basin is obtained by combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data, specifically:
the Doppler flow profiler emits an acoustic pulse signal in the drainage basin, particles with the same flow velocity as the drainage basin exist in water in the drainage basin, the acoustic pulse signal is scattered after passing through the particles, and scattered acoustic pulse signals are received by the Doppler flow profiler transducer to obtain scattered echo frequency;
Doppler frequency shift exists between the scattered echo frequency and the emitted sound wave pulse signal, and calculation is carried out based on the Doppler frequency shift to obtain water flow velocity data in a flow domain;
dividing the section position of the river basin into grids, detecting each grid point by using a thermal imager and an underwater pressure sensor to obtain thermal imaging water temperature data and underwater pressure data of each grid point, and combining the water flow velocity data in the river basin to obtain flow velocity information of each point of the section of the river basin.
Further, in a preferred embodiment of the present invention, a drainage basin flow velocity field model is constructed according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, the drainage basin flow velocity field model is constructed according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and the flow velocity field model and the three-dimensional topography model are integrated and analyzed to obtain the installation position of the underwater camera, specifically:
constructing a training set and a testing set according to the flow velocity information of each point of the river basin section, initializing the connection weight and the threshold value of the BP neural network, importing the training set, carrying out normalization processing, and taking the training set after normalization processing as a final output testing result;
The BP neural network calculates input and output data of each unit of the middle layer, calculates input and output data of each unit of the output layer, calculates generalized errors of each unit of the output layer according to the input and output data of each unit of the output layer, and calculates generalized errors of each unit of the middle layer according to the input and output data of each unit of the middle layer;
adjusting the connection weight between the intermediate layer and the output layer and the threshold value of each unit of the output layer according to the generalized error of each unit of the output layer and the generalized error of each unit of the intermediate layer, and simultaneously adjusting the connection weight between the input layer and the intermediate layer and the threshold value of each unit of the intermediate layer;
updating a learning input mode according to the adjusted connection weight and the threshold value of each unit, continuously training the training set, updating the learning times after the training set is trained, if the training data obtained by the training set is smaller than the specified error or larger than the specified learning times, finishing BP neural network training, if the training data obtained by the training set is not smaller than the specified error or not larger than the specified training times, re-performing BP neural network training, introducing the training data into a test set for testing, and when the test result meets the preset test result, storing test parameters, and constructing a basin flow field model according to the test parameters;
Constructing a three-dimensional coordinate system, importing the drainage basin flow velocity field model and the three-dimensional topography model for data integration to obtain a drainage basin three-dimensional model, and analyzing the drainage basin three-dimensional model to obtain the installation position of the underwater camera.
Further, in a preferred embodiment of the present invention, a camera is installed at the installation position of the underwater camera, the camera captures a fish migration image, the fish migration image is processed to obtain fish migration data, and a fish migration rule is determined according to the fish migration data, which specifically includes:
installing a camera for shooting fish images at the installation position of the underwater camera, wherein the camera shoots fish migration under water to obtain fish migration images;
carrying out gray scale treatment on the fish migration image by a weighted average gray scale treatment method to obtain a fish migration gray scale initial image, and carrying out wavelet transformation treatment and self-adaptive gray scale treatment on the fish migration gray scale initial image to obtain a fish migration gray scale image;
extracting the fish migration gray scale image pixel points to generate fish migration gray scale image pixel point data, importing the fish migration gray scale image pixel point data into the basin three-dimensional model for analysis to generate fish migration data, and judging the fish migration rule according to the fish migration data.
The second aspect of the present invention provides a point distribution system for researching fish migration rules, the point distribution system includes a storage and a processor, the storage includes a point distribution program for researching fish migration rules, and when the point distribution program for researching fish migration rules is executed by the processor, the following steps are implemented:
measuring the river basin by using a mapping tool, and calculating the section position and the basic information of the river basin after the elevation data are obtained;
performing laser detection on the topography of the river basin by using an unmanned plane carrying a laser detector above the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information;
measuring the flow velocity data of the water flow in the river basin by using a Doppler flow velocity profiler, dividing the section position of the river basin into a grid shape, and combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin;
constructing a drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, constructing the drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and carrying out integrated analysis on the flow velocity field model and a three-dimensional topography model to obtain an underwater camera installation position;
The underwater camera is arranged at the installation position of the underwater camera, the camera shoots fish migration images, fish migration data are obtained after the fish migration images are processed, and fish migration rules are judged according to the fish migration data.
Further, in a preferred embodiment of the present invention, the Doppler flow profiler is used to measure the flow velocity data of the water flow in the river basin, the section position of the river basin is divided into grids, and the flow velocity information of each point of the section of the river basin is obtained by combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data, specifically:
the Doppler flow profiler emits an acoustic pulse signal in the drainage basin, particles with the same flow velocity as the drainage basin exist in water in the drainage basin, the acoustic pulse signal is scattered after passing through the particles, and scattered acoustic pulse signals are received by the Doppler flow profiler transducer to obtain scattered echo frequency;
doppler frequency shift exists between the scattered echo frequency and the emitted sound wave pulse signal, and calculation is carried out based on the Doppler frequency shift to obtain water flow velocity data in a flow domain;
dividing the section position of the river basin into grids, detecting each grid point by using a thermal imager and an underwater pressure sensor to obtain thermal imaging water temperature data and underwater pressure data of each grid point, and combining the water flow velocity data in the river basin to obtain flow velocity information of each point of the section of the river basin.
Further, in a preferred embodiment of the present invention, a drainage basin flow velocity field model is constructed according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, the drainage basin flow velocity field model is constructed according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and the flow velocity field model and the three-dimensional topography model are integrated and analyzed to obtain the installation position of the underwater camera, specifically:
constructing a training set and a testing set according to the flow velocity information of each point of the river basin section, initializing the connection weight and the threshold value of the BP neural network, importing the training set, carrying out normalization processing, and taking the training set after normalization processing as a final output testing result;
the BP neural network calculates input and output data of each unit of the middle layer, calculates input and output data of each unit of the output layer, calculates generalized errors of each unit of the output layer according to the input and output data of each unit of the output layer, and calculates generalized errors of each unit of the middle layer according to the input and output data of each unit of the middle layer;
adjusting the connection weight between the intermediate layer and the output layer and the threshold value of each unit of the output layer according to the generalized error of each unit of the output layer and the generalized error of each unit of the intermediate layer, and simultaneously adjusting the connection weight between the input layer and the intermediate layer and the threshold value of each unit of the intermediate layer;
Updating a learning input mode according to the adjusted connection weight and the threshold value of each unit, continuously training the training set, updating the learning times after the training set is trained, if the training data obtained by the training set is smaller than the specified error or larger than the specified learning times, finishing BP neural network training, if the training data obtained by the training set is not smaller than the specified error or not larger than the specified training times, re-performing BP neural network training, introducing the training data into a test set for testing, and when the test result meets the preset test result, storing test parameters, and constructing a basin flow field model according to the test parameters;
constructing a three-dimensional coordinate system, importing the drainage basin flow velocity field model and the three-dimensional topography model for data integration to obtain a drainage basin three-dimensional model, and analyzing the drainage basin three-dimensional model to obtain the installation position of the underwater camera.
The invention solves the technical defects existing in the background technology, and has the following beneficial effects: the method comprises the steps of using a mapping tool to guide and measure a river basin, measuring the position of a river basin section and basic information of the river basin, using an unmanned plane to conduct laser detection on the river basin section and the river basin topography, constructing a three-dimensional topography model, conducting simulation experiments on the flow rate of the river basin section based on hydrodynamic combination of the basic information of the river basin section and the position of the river basin section, obtaining flow rate positions of points of the river basin section, constructing a river basin flow rate field model through combination of a BP neural network and flow rate position data of points of the river basin section, conducting combination analysis on the three-dimensional topography model and the river basin flow rate field model, obtaining the distribution position of a camera suitable for researching fish migration rules in the river basin, installing the camera in the distribution position, shooting fish migration images and judging the fish migration rules. The fish migration rule can be obtained, the development work of fishery operators is facilitated, and the fishing economic benefit is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a spotting method for studying fish migration laws;
FIG. 2 is a flow chart of constructing a basin flow field model;
fig. 3 is a flow chart of a spotting system for studying fish migration laws.
As shown in fig. 1, the first aspect of the present invention provides a flowchart of a spotting method for studying a migration rule of fish, comprising the steps of:
s102: measuring the river basin by using a mapping tool, and calculating the section position and the basic information of the river basin after the elevation data are obtained;
s104: performing laser detection on the topography of the river basin by using an unmanned plane carrying a laser detector above the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information;
S106: measuring the flow velocity data of the water flow in the river basin by using a Doppler flow velocity profiler, dividing the section position of the river basin into a grid shape, and combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin;
s108: constructing a drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, constructing the drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and carrying out integrated analysis on the flow velocity field model and a three-dimensional topography model to obtain an underwater camera installation position;
s110: the underwater camera is arranged at the installation position of the underwater camera, the camera shoots fish migration images, fish migration data are obtained after the fish migration images are processed, and fish migration rules are judged according to the fish migration data.
The river basin is a water area for researching fish migration rules in a preset mode. The fish migration rule can be obtained, the development work of fishery operators is facilitated, and the fishing economic benefit is improved.
Further, in a preferred embodiment of the present invention, the measuring tool is used to measure the river basin, and the section position and the basic information of the river basin are calculated after the elevation data are obtained, which specifically includes:
Firstly, respectively carrying out drainage basin elevation measurement on the upstream and downstream of a drainage basin by using a mapping tool to obtain water surface elevations, normal water storage level elevations and flood limit water level elevations at a plurality of positions of the upstream and downstream of the drainage basin;
measuring the area of the river basin from the upstream to the downstream by using a mapping tool, and calculating to obtain the position of the river basin section and the basic information of the river basin by combining the elevation of the river basin, the area of the river basin and the historical data information;
the water level elevation refers to the distance from the current water surface to the underwater rock layer part, the normal water storage level elevation refers to the distance from the water surface of the river basin to the underwater rock layer part, the flood limit water level elevation is the highest water level limited by the flood period of the river basin, and the basic information of the river basin and the section position of the river basin can be obtained through calculation according to the river basin elevation, the river basin control area and the historical data information. The river basin section is divided into a cross section and a vertical section, and the basic river basin information comprises river basin water flow, river basin soil conditions, river basin water quality and the like. The method can obtain the position of the river basin section and the basic information of the river basin, and provides a data basis for three-dimensional topography modeling.
Further, in a preferred embodiment of the present invention, an unmanned aerial vehicle carrying a laser detector is used to perform laser detection on the topography of the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information, wherein the three-dimensional topography model comprises the following specific steps:
The unmanned aerial vehicle is controlled to work above the drainage basin, a laser detector in the unmanned aerial vehicle carries out laser detection on the topography and the topography of the drainage basin, and the following method is:
the unmanned aerial vehicle is provided with a laser detector, the laser detector emits an infrared laser beam and a green laser beam, wherein the infrared laser beam cannot penetrate through the water surface and returns to a laser detector receiver along an incident path; the green laser beam can penetrate through the water surface to reach the bottom of the drainage basin, and then returns to the laser detector receiver along the incident path;
according to the propagation time difference of the infrared laser beam and the green laser beam between the unmanned plane and the water surface, the distance between the water surface refraction point of the infrared laser beam and the underwater refraction point of the corresponding green laser beam is obtained, and the water depth data of the laser scanning point is obtained according to the distance between the water surface refraction point of the infrared laser beam and the underwater refraction point of the corresponding green laser beam;
and the unmanned aerial vehicle performs laser detection on each point in the drainage basin to obtain the water depth data of each laser scanning point in the drainage basin, and constructs a three-dimensional topography model according to the water depth data of each laser scanning point in the drainage basin.
It should be noted that, the pulse width of the infrared laser beam and the green laser beam are both 5ns pulse width and contain 2mJ energy, the infrared laser beam and the green laser beam are collectively referred to as laser pulse, actually the laser pulse is not completely vertically injected into the water surface in the process of being injected into the water surface, a scanning angle exists between the laser pulse and the water surface, and the unmanned plane is not stable in the flight process due to the influence of air flow, so that the incidence position of the laser pulse on the water surface is indirectly influenced, and the water depth data measured at the same place has deviation. The unmanned plane needs to measure the water depth data for many times at the same place, and the average value is taken, so that the measurement accuracy is improved; in addition, install GPS signal receiver on unmanned aerial vehicle laser detector, carry out accurate location to the position in same place, adjust laser pulse incident angle according to incident place, reduce the water depth data error value. According to the invention, the water depth data can be obtained by calculating the time difference between the laser pulses, so that a three-dimensional topography model is constructed according to the water depth data.
Further, in a preferred embodiment of the present invention, a camera is installed at the installation position of the underwater camera, the camera photographs a fish migration image, fish migration data is obtained after the fish migration image is processed, and a fish migration rule is determined according to the fish migration data, specifically:
Installing a camera for shooting fish images at the installation position of the underwater camera, wherein the camera shoots fish migration under water to obtain fish migration images;
carrying out gray scale treatment on the fish migration image by a weighted average gray scale treatment method to obtain a fish migration gray scale initial image, and carrying out wavelet transformation treatment and self-adaptive gray scale treatment on the fish migration gray scale initial image to obtain a fish migration gray scale image;
extracting the fish migration gray scale image pixel points to generate fish migration gray scale image pixel point data, importing the fish migration gray scale image pixel point data into the basin three-dimensional model for analysis to generate fish migration data, and judging the fish migration rule according to the fish migration data.
The fish migration grey-scale image is processed by wavelet transformation processing and a self-adaptive grey-scale compensation method, so that granularity of the fish migration grey-scale image can be reduced, and image definition can be improved; in addition, the image graying processing can adopt a binarization image processing mode, so that the memory occupied by the image is further reduced, and the operation speed is improved. And extracting pixel point data of the fish migration gray-scale image, and introducing the pixel point data into a three-dimensional flow domain model for analysis to obtain fish migration data, and judging a fish migration rule according to the fish migration data. The invention can judge the fish migration rule by shooting the fish migration image.
As shown in fig. 2, fig. 2 shows a flow chart of constructing a basin flow field model, comprising the steps of:
s202: measuring flow velocity data of water flow in the flow field by using a Doppler flow velocity profiler;
s204: dividing the section position of the river basin into grids to obtain flow velocity information of each grid point of the section of the river basin;
s206: constructing a drainage basin flow velocity field model according to the flow velocity information of each grid point of the drainage basin section and the BP neural network;
s208: and integrating and analyzing the drainage basin flow velocity field model and the three-dimensional topography model.
Further, in a preferred embodiment of the present invention, the flow velocity data in the flow field is measured by using a doppler flow velocity profiler, specifically:
the Doppler flow profiler emits an acoustic pulse signal in the drainage basin, particles with the same flow velocity as the drainage basin exist in water in the drainage basin, the acoustic pulse signal is scattered after passing through the particles, and scattered acoustic pulse signals are received by the Doppler flow profiler transducer to obtain scattered echo frequency;
and Doppler frequency shift exists between the scattered echo frequency and the transmitted sound wave pulse signal, and calculation is performed based on the Doppler frequency shift to obtain the water flow velocity data in the river basin.
The Doppler frequency shift exists between the acoustic pulse signal and the scattered echo frequency, the change of the Doppler frequency shift depends on the movement speed of the reflector, the relative speed of the Doppler flow profiler and the scatterer can be calculated by measuring the Doppler frequency shift, and the relative speed of the Doppler flow profiler and the scatterer is converted to obtain the water flow velocity data in the river basin. The invention can calculate the water flow velocity data in the river basin through the Doppler flow velocity profiler.
Further, in a preferred embodiment of the present invention, the section position of the river basin is divided into grids to obtain flow velocity information of each grid point of the section of the river basin, specifically:
dividing the section position of the river basin into grids, detecting each grid point by using a thermal imager and an underwater pressure sensor to obtain thermal imaging water temperature data and underwater pressure data of each grid point, and combining the water flow velocity data in the river basin to obtain flow velocity information of each point of the section of the river basin.
The thermal imaging hydrologic data is detected by a thermal imager, the flow rate of water is fast along with the temperature rise based on a hydrodynamic Bernoulli equation and a thermodynamic first law, the water temperature data of each grid point can be obtained by the thermal imager, and the flow rate of water is judged; the underwater pressure data are detected by an underwater pressure sensor, the flow rate of water is related to the intensity of the underwater pressure based on a Bernoulli equation of fluid mechanics and a first thermodynamic law, the flow rate of water is higher as the pressure is smaller, the pressure intensity of each grid point can be obtained through the underwater pressure sensor, and the flow rate of single water is judged; the flow velocity information of each grid point of the river basin section can be obtained by combining the thermal imaging hydrological data of each grid point, the underwater pressure data and the flow velocity data of the water flow in the river basin. The invention can obtain the flow velocity information of each point of the river basin section.
Further, in a preferred embodiment of the present invention, a drainage basin flow velocity field model is constructed by combining BP neural network according to flow velocity information of each grid point of a drainage basin section, specifically:
constructing a training set and a testing set according to the flow velocity information of each point of the river basin section, initializing the connection weight and the threshold value of the BP neural network, importing the training set, carrying out normalization processing, and taking the training set after normalization processing as a final output testing result;
the BP neural network calculates input and output data of each unit of the middle layer, calculates input and output data of each unit of the output layer, calculates generalized errors of each unit of the output layer according to the input and output data of each unit of the output layer, and calculates generalized errors of each unit of the middle layer according to the input and output data of each unit of the middle layer;
adjusting the connection weight between the intermediate layer and the output layer and the threshold value of each unit of the output layer according to the generalized error of each unit of the output layer and the generalized error of each unit of the intermediate layer, and simultaneously adjusting the connection weight between the input layer and the intermediate layer and the threshold value of each unit of the intermediate layer;
updating a learning input mode according to the adjusted connection weight and the threshold value of each unit, continuously training the training set, updating the learning times after the training set is trained, if the training data obtained by the training set is smaller than the specified error or larger than the specified learning times, finishing BP neural network training, if the training data obtained by the training set is not smaller than the specified error or not larger than the specified training times, re-performing BP neural network training, introducing the training data into a test set for testing, and when the test result meets the preset test result, storing test parameters, and constructing a basin flow field model according to the test parameters;
It should be noted that, the purpose of normalizing the training set is that the training set data after normalization is preset test result data, in order to minimize the error convergence between the actual result obtained by training the neural network and the preset test result, parameter back propagation training may be performed by using a cross entropy function, the error is back propagated to each layer in the network by using a chained method, the weight and bias of each neuron are updated by using the error, the parameters of the neural network are continuously optimized until the error converges to the preset value, and finally, a drainage basin flow velocity field model is constructed according to the test result. The flow velocity of each grid point changes along with the changes of time, temperature and pressure, the flow velocity field model of the river basin is more accurate and is obtained by measuring according to the period. The invention can construct a drainage basin flow velocity field model through the BP neural network.
Further, in a preferred embodiment of the present invention, the integration analysis is performed on the basin flow velocity field model and the three-dimensional topography model, specifically:
constructing a three-dimensional coordinate system, and importing the drainage basin flow velocity field model and the three-dimensional topography model for data integration to obtain a drainage basin three-dimensional model;
and analyzing the three-dimensional model of the river basin to obtain the installation position of the underwater camera.
It should be noted that, the underwater environment is more complex, the water flow speed and the underwater obstacles can block the camera from working, a three-dimensional model of the watershed needs to be constructed, the three-dimensional model of the watershed is analyzed to obtain the installation position of the underwater camera, and the camera is installed at the installation position of the underwater camera and used for shooting the migration image of fish. The installation position of the underwater camera is required to be fixed on the rock and installed on the zone with smaller water flow speed without barriers or shielding. According to the method, the underwater camera mounting position can be obtained by constructing and analyzing the watershed three-dimensional model.
In addition, the dotting method for researching the migration rule of the fish further comprises the following steps:
the method comprises the steps of using an underwater camera installation position as a center point, calibrating the center point as a planning origin, calibrating a preset range area of the planning origin as an underwater camera installation area, and determining plants in each underwater camera installation area in a three-dimensional model of a river basin;
acquiring the growth rate of plants in each underwater camera installation area based on a big data network, determining the installation and working time of the underwater cameras, and obtaining the growth state of the plants in each underwater camera installation area based on the growth rate of the plants in each underwater camera installation area and the installation and working time of the underwater cameras;
Judging whether plant growth states in each underwater camera installation area interfere each underwater camera in the installation and working time of the underwater cameras, if so, eliminating the installation position of the underwater camera in the underwater camera installation area, and retrieving new installation points of the underwater camera again;
and coating nontoxic and pungent dyes on the underwater camera bodies.
In the installation process of the underwater camera, plants temporarily do not influence the operation of the underwater camera, but the underwater camera has longer operation time and faster plant growth rate, so that the plants can easily shield the lens of the underwater camera, and the underwater camera is easily damaged in the plant growth process, thereby being not beneficial to researching the migration rule of fish; the underwater camera body is smeared with nontoxic and pungent odor dye, so that the fish is prevented from being damaged by approaching the underwater camera. The invention can obtain the optimal installation position of the underwater camera.
As shown in fig. 3, the second aspect of the present invention provides a point distribution system for researching fish migration rules, the point distribution system includes a storage 31 and a processor 32, the storage 31 includes a point distribution program for researching fish migration rules, and when the point distribution program for researching fish migration rules is executed by the processor 32, the following steps are implemented:
Measuring the river basin by using a mapping tool, and calculating the section position of the river basin and the basic information of the river basin after the elevation data are obtained;
performing laser detection on the topography of the river basin by using an unmanned plane carrying a laser detector above the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information;
measuring the flow velocity data of the water flow in the river basin by using a Doppler flow velocity profiler, dividing the section position of the river basin into a grid shape, and combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin;
constructing a drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, constructing the drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and carrying out integrated analysis on the flow velocity field model and the three-dimensional topography model to obtain the installation position of the underwater camera;
the underwater camera is arranged at the installation position of the underwater camera, the camera shoots fish migration images, fish migration data are obtained after the fish migration images are processed, and fish migration rules are judged according to the fish migration data.
Further, in a preferred embodiment of the present invention, the Doppler flow profiler is used to measure the flow velocity data of the water flow in the river basin, the section position of the river basin is divided into grids, and the flow velocity information of each point of the section of the river basin is obtained by combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data, specifically:
the Doppler flow profiler emits an acoustic pulse signal in the drainage basin, particles with the same flow velocity as the drainage basin exist in water in the drainage basin, the acoustic pulse signal is scattered after passing through the particles, and scattered acoustic pulse signals are received by the Doppler flow profiler transducer to obtain scattered echo frequency;
doppler frequency shift exists between the scattered echo frequency and the emitted sound wave pulse signal, and calculation is carried out based on the Doppler frequency shift to obtain water flow velocity data in a flow domain;
dividing the section position of the river basin into grids, detecting each grid section by using a thermal imager and an underwater pressure sensor to obtain thermal imaging water temperature data and underwater pressure data of each grid section, and combining the water flow velocity data in the river basin to obtain flow velocity information of each point of the river basin section.
Further, in a preferred embodiment of the present invention, a drainage basin flow velocity field model is constructed according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, the drainage basin flow velocity field model is constructed according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and the flow velocity field model and the three-dimensional topography model are integrated and analyzed to obtain the installation position of the underwater camera, specifically:
constructing a training set and a testing set according to the flow velocity information of each point of the river basin section, initializing the connection weight and the threshold value of the BP neural network, importing the training set and carrying out normalization processing;
the BP neural network calculates input and output data of each unit of the middle layer, calculates input and output data of each unit of the output layer, calculates generalized errors of each unit of the output layer according to the input and output data of each unit of the output layer, and calculates generalized errors of each unit of the middle layer according to the input and output data of each unit of the middle layer;
adjusting the connection weight between the intermediate layer and the output layer and the threshold value of each unit of the output layer according to the generalized error of each unit of the output layer and the generalized error of each unit of the intermediate layer, and simultaneously adjusting the connection weight between the input layer and the intermediate layer and the threshold value of each unit of the intermediate layer;
Updating a learning input mode according to the adjusted connection weight and the threshold value of each unit, continuously training the training set, updating the learning times after the training set is trained, if the training data obtained by the training set is smaller than the specified error or larger than the specified learning times, finishing BP neural network training, if the training data obtained by the training set is not smaller than the specified error or not larger than the specified training times, re-performing BP neural network training, introducing the training data into a test set for testing, and when the test result meets the preset test result, storing test parameters, and constructing a basin flow field model according to the test parameters;
constructing a three-dimensional coordinate system, importing the drainage basin flow velocity field model and the three-dimensional topography model for data integration to obtain a drainage basin three-dimensional model, and analyzing the drainage basin three-dimensional model to obtain the installation position of the underwater camera.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (9)
1. The dotting method for researching the migration rule of the fish is characterized by comprising the following steps of:
measuring the river basin by using a mapping tool, and calculating the section position of the river basin and the basic information of the river basin after the elevation data are obtained;
performing laser detection on the topography of the river basin by using an unmanned plane carrying a laser detector above the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information;
measuring the flow velocity data of the water flow in the river basin by using a Doppler flow velocity profiler, dividing the section position of the river basin into a grid shape, and combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin;
constructing a drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, constructing the drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and carrying out integrated analysis on the flow velocity field model and the three-dimensional topography model to obtain the installation position of the underwater camera;
the underwater camera is arranged at the installation position of the underwater camera, the camera shoots fish migration images, fish migration data are obtained after the fish migration images are processed, and fish migration rules are judged according to the fish migration data.
2. The method for studying fish migration law according to claim 1, wherein the measuring tool is used for measuring the river basin, and the section position and the basic information of the river basin are calculated after the elevation data are obtained, and specifically:
firstly, respectively carrying out drainage basin elevation measurement on the upstream and downstream of a drainage basin by using a mapping tool to obtain water surface elevations, normal water storage level elevations and flood limit water level elevations at a plurality of positions of the upstream and downstream of the drainage basin;
and measuring the river basin control area from the upstream to the downstream of the river basin by using a mapping tool, and calculating the river basin section position and the river basin basic information by combining the river basin elevation, the river basin control area and the historical data information.
3. The method for studying fish migration laws of claim 1, wherein the unmanned aerial vehicle with the laser detector is used for carrying out laser detection on the topography of the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information, wherein the three-dimensional topography model comprises the following specific steps:
the unmanned aerial vehicle is controlled to work above the drainage basin, a laser detector in the unmanned aerial vehicle carries out laser detection on the topography and the topography of the drainage basin, and the following method is:
The unmanned aerial vehicle is provided with a laser detector, the laser detector emits an infrared laser beam and a green laser beam, wherein the infrared laser beam cannot penetrate through the water surface and returns to a laser detector receiver along an incident path; the green laser beam can penetrate through the water surface to reach the bottom of the drainage basin, and then returns to the laser detector receiver along the incident path;
according to the propagation time difference of the infrared laser beam and the green laser beam between the unmanned plane and the water surface, the distance between the water surface refraction point of the infrared laser beam and the underwater refraction point of the corresponding green laser beam is obtained, and the water depth data of the laser scanning point is obtained according to the distance between the water surface refraction point of the infrared laser beam and the underwater refraction point of the corresponding green laser beam;
and the unmanned aerial vehicle performs laser detection on each point in the drainage basin to obtain the water depth data of each laser scanning point in the drainage basin, and constructs a three-dimensional topography model according to the water depth data of each laser scanning point in the drainage basin.
4. The method for studying fish migration law according to claim 1, wherein the measuring of the water flow velocity data in the river basin by using the doppler flow velocity profiler divides the section position of the river basin into a grid shape, and the flow velocity information of each point of the section of the river basin is obtained by combining the water flow velocity data in the river basin, the thermal imaging water temperature data and the underwater pressure data, specifically:
The Doppler flow profiler emits an acoustic pulse signal in the drainage basin, particles with the same flow velocity as the drainage basin exist in water in the drainage basin, the acoustic pulse signal is scattered after passing through the particles, and scattered acoustic pulse signals are received by the Doppler flow profiler transducer to obtain scattered echo frequency;
doppler frequency shift exists between the scattered echo frequency and the emitted sound wave pulse signal, and calculation is carried out based on the Doppler frequency shift to obtain water flow velocity data in a flow domain;
dividing the section position of the river basin into grids, detecting each grid point by using a thermal imager and an underwater pressure sensor to obtain thermal imaging water temperature data and underwater pressure data of each grid point, and combining the water flow velocity data in the river basin to obtain flow velocity information of each point of the section of the river basin.
5. The method for studying fish migration law according to claim 1, wherein the constructing a basin flow velocity field model according to the flow velocity information of each point of the basin section by combining with a BP neural network, and the integrating analysis of the flow velocity field model and the three-dimensional topography model to obtain the installation position of the underwater camera specifically comprises:
Constructing a training set and a testing set according to the flow velocity information of each point of the river basin section, initializing the connection weight and the threshold value of the BP neural network, importing the training set, carrying out normalization processing, and taking the training set after normalization processing as a final output testing result;
the BP neural network calculates input and output data of each unit of the middle layer, calculates input and output data of each unit of the output layer, calculates generalized errors of each unit of the output layer according to the input and output data of each unit of the output layer, and calculates generalized errors of each unit of the middle layer according to the input and output data of each unit of the middle layer;
adjusting the connection weight between the intermediate layer and the output layer and the threshold value of each unit of the output layer according to the generalized error of each unit of the output layer and the generalized error of each unit of the intermediate layer, and simultaneously adjusting the connection weight between the input layer and the intermediate layer and the threshold value of each unit of the intermediate layer;
updating a learning input mode according to the adjusted connection weight and the threshold value of each unit, continuously training the training set, updating the learning times after the training set is trained, and ending the BP neural network training if the training data obtained by the training set is smaller than the specified error or larger than the specified learning times;
If the training data obtained by the training set is not smaller than the specified error or not larger than the specified training times, the BP neural network training is conducted again, the training data is imported into a testing set for testing, when the testing result meets the preset testing result, testing parameters are saved, and a drainage basin flow velocity field model is built according to the testing parameters;
constructing a three-dimensional coordinate system, importing the drainage basin flow velocity field model and the three-dimensional topography model for data integration to obtain a drainage basin three-dimensional model, and analyzing the drainage basin three-dimensional model to obtain the installation position of the underwater camera.
6. The method for studying fish migration law according to claim 1, wherein a camera is installed at the underwater camera installation position, the camera captures a fish migration image, the fish migration image is processed to obtain fish migration data, and the fish migration law is determined according to the fish migration data, which is specifically as follows:
installing a camera for shooting fish images at the installation position of the underwater camera, wherein the camera shoots fish migration under water to obtain fish migration images;
carrying out gray scale treatment on the fish migration image by a weighted average gray scale treatment method to obtain a fish migration gray scale initial image, and carrying out wavelet transformation treatment and self-adaptive gray scale treatment on the fish migration gray scale initial image to obtain a fish migration gray scale image;
Extracting the fish migration gray scale image pixel points to generate fish migration gray scale image pixel point data, importing the fish migration gray scale image pixel point data into the basin three-dimensional model for analysis to generate fish migration data, and judging the fish migration rule according to the fish migration data.
7. The utility model provides a point distribution system for studying fish migration law, its characterized in that, point distribution system includes memory and treater, the memory includes studying fish migration law point distribution program, when studying fish migration law point distribution program is executed by the treater, realizes following steps:
measuring the river basin by using a mapping tool, and calculating the section position and the basic information of the river basin after the elevation data are obtained;
performing laser detection on the topography of the river basin by using an unmanned plane carrying a laser detector above the river basin to obtain detection data; constructing a three-dimensional topography model according to the detection data and the basin basic information;
measuring the flow velocity data of the water flow in the river basin by using a Doppler flow velocity profiler, dividing the section position of the river basin into a grid shape, and combining the flow velocity data of the water flow in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin;
Constructing a drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with a BP neural network, constructing the drainage basin flow velocity field model according to the flow velocity information of each point of the drainage basin section in combination with the BP neural network, and carrying out integrated analysis on the flow velocity field model and a three-dimensional topography model to obtain an underwater camera installation position;
the underwater camera is arranged at the installation position of the underwater camera, the camera shoots fish migration images, fish migration data are obtained after the fish migration images are processed, and fish migration rules are judged according to the fish migration data.
8. The system for studying fish migration laws of claim 7, wherein the measuring of the water flow velocity data in the river basin by using the doppler flow velocity profiler divides the section position of the river basin into a grid shape, and combines the water flow velocity data in the river basin, the thermal imaging water temperature data and the underwater pressure data to obtain the flow velocity information of each point of the section of the river basin, specifically:
the Doppler flow profiler emits an acoustic pulse signal in the drainage basin, particles with the same flow velocity as the drainage basin exist in water in the drainage basin, the acoustic pulse signal is scattered after passing through the particles, and scattered acoustic pulse signals are received by the Doppler flow profiler transducer to obtain scattered echo frequency;
Doppler frequency shift exists between the scattered echo frequency and the emitted sound wave pulse signal, and calculation is carried out based on the Doppler frequency shift to obtain water flow velocity data in a flow domain;
dividing the section position of the river basin into grids, detecting each grid point by using a thermal imager and an underwater pressure sensor to obtain thermal imaging water temperature data and underwater pressure data of each grid point, and combining the water flow velocity data in the river basin to obtain flow velocity information of each point of the section of the river basin.
9. The method for studying fish migration law according to claim 7, wherein the constructing a basin flow velocity field model according to the flow velocity information of each point of the basin section by combining with a BP neural network, and the integrating analysis of the flow velocity field model and the three-dimensional topography model to obtain the installation position of the underwater camera specifically comprises:
constructing a training set and a testing set according to the flow velocity information of each point of the river basin section, initializing the connection weight and the threshold value of the BP neural network, importing the training set, carrying out normalization processing, and taking the training set after normalization processing as a final output testing result;
The BP neural network calculates input and output data of each unit of the middle layer, calculates input and output data of each unit of the output layer, calculates generalized errors of each unit of the output layer according to the input and output data of each unit of the output layer, and calculates generalized errors of each unit of the middle layer according to the input and output data of each unit of the middle layer;
adjusting the connection weight between the intermediate layer and the output layer and the threshold value of each unit of the output layer according to the generalized error of each unit of the output layer and the generalized error of each unit of the intermediate layer, and simultaneously adjusting the connection weight between the input layer and the intermediate layer and the threshold value of each unit of the intermediate layer;
updating a learning input mode according to the adjusted connection weight and the threshold value of each unit, continuously training the training set, updating the learning times after the training set is trained, if the training data obtained by the training set is smaller than the specified error or larger than the specified learning times, finishing BP neural network training, if the training data obtained by the training set is not smaller than the specified error or not larger than the specified training times, re-performing BP neural network training, introducing the training data into a test set for testing, and when the test result meets the preset test result, storing test parameters, and constructing a basin flow field model according to the test parameters;
Constructing a three-dimensional coordinate system, importing the drainage basin flow velocity field model and the three-dimensional topography model for data integration to obtain a drainage basin three-dimensional model, and analyzing the drainage basin three-dimensional model to obtain the installation position of the underwater camera.
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