An insect autonomous flight monitoring system and analysis method

Abstract

The invention relates to the technical field of flight monitoring and data analysis, and provides an insect autonomous flight monitoring system and an analysis method; the system comprises a monitoring device for accommodating experimental insects, and an environmental control module for controlling environmental parameters in the monitoring device; a data acquisition module acquires flight behavior information and gene information of the insects in the monitoring device, and sends the information to a data processing module, the gene information is sent to a data analysis module, and the data processing module processes image data; the data analysis module is connected with the data processing module and the data acquisition module, and performs in-depth analysis according to the transmitted data; the invention provides a rich data source through the data acquisition module, the data processing module obtains a clear insect edge image, and the analysis method calculates the correlation degree between the flight behavior and the gene and environmental changes, which is helpful to reveal the complexity and diversity of the insect flight behavior, and provides an important reference and basis for entomological research and related fields.

Description

Technical Field

[0001] The invention relates to the technical field of flight monitoring and data analysis, in particular to an insect autonomous flight monitoring system and an analysis method. Background Art

[0002] Insects are one of the most abundant and diverse biological groups on Earth. Insect flight behavior is an important part of insect ecology and insect behavior research, and is of great significance for understanding insect living habits, migration patterns, and interactions with environmental factors. Some insects have developed unique flight mechanisms and strategies during a long evolutionary process. Autonomous flight monitoring systems and analysis methods for insects can help improve our understanding of the insect world, and can also bring inspiration and breakthroughs to other fields such as robotics and micro-aircraft design; in the agricultural field, more accurate monitoring of pest migration, spread, and other dynamics can be achieved, so as to develop more effective prevention and control strategies and reduce agricultural losses; in the field of ecosystems, it can enable us to comprehensively assess the health status and changing trends of ecosystems, so as to take timely protection and restoration measures.

[0003] Traditional methods for studying insect flight behavior mostly rely on manual observation and recording. Although there are some insect flight behavior monitoring devices that can achieve basic video recording or image capture functions, they cannot conduct in-depth analysis of insect flight behavior. Existing analysis methods can achieve a simple description of flight trajectories, but lack in-depth exploration and interpretation of flight behavior patterns.

[0004] There are still some shortcomings in the existing insect autonomous flight monitoring systems and analysis methods:

[0005] 1. Existing systems have limitations in environmental simulation. Although some systems can control environmental factors within the monitoring hood, they can often only simulate a few fixed conditions and cannot achieve continuous and dynamic environmental changes. This limits researchers' ability to conduct in-depth research on the flight behavior of insects under different environmental conditions.

[0006] 2. Existing systems are not intelligent enough in data collection and processing. When monitoring the autonomous flight behavior of insects, due to the small size of insects, high flight speed, and often accompanied by complex flight trajectories and changes, some existing monitoring equipment often has problems such as blurred, missing or incorrect images when capturing high-speed flying insects, resulting in reduced data quality and affecting the accuracy of subsequent analysis results.

[0007] 3. Existing analysis methods still need to be improved in mining flight behavior patterns. Although traditional analysis methods can record insect flight behavior, they often require a lot of manual operations to process and analyze these data, which is not only inefficient but also easily interfered by human factors. Moreover, they only focus on simple flight trajectory descriptions and fail to deeply explore the complex patterns and mechanisms behind insect flight behavior, which makes the understanding of insect flight behavior remain at a superficial level. Summary of the invention

[0008] The invention provides an insect autonomous flight monitoring system and an analysis method to solve the problems in the prior art.

[0009] The technical solution of the present invention is as follows:

[0010] An insect autonomous flight monitoring system includes a monitoring device, an environment control module, a data acquisition module, a data processing module and a data analysis module;

[0011] The monitoring device is made of transparent material and is used to accommodate experimental insects and provide conditions for the environmental control module to control changes in environmental parameters;

[0012] The data output terminal of the environment control module is connected to the data input terminal of the monitoring device, and is used to control the environmental state parameters in the monitoring device;

[0013] The data acquisition module is placed outside the monitoring device and is used to collect autonomous flight behavior information and insect gene information of the insects in the monitoring device, and send the collected image information to the data processing module, and send the gene information to the data analysis module;

[0014] The data input end of the data processing module is connected to the data output end of the data acquisition module, and is used to analyze the image file transmitted by the data acquisition module, process the image data and identify the autonomous flight image of the insect;

[0015] The data input end of the data analysis module is connected to the data output end of the data processing module and the data acquisition module, and is used to analyze the data transmitted by the data acquisition module and the data processing module to explore the relationship between insect genes and flight ability.

[0016] Preferably, the environmental control module includes an environmental control unit and an operating unit. The environmental control unit is used to simulate different experimental environments in the monitoring device, support dynamic adjustments during the monitoring process, and change environmental parameters at any time according to the instructions input by the operating unit to stimulate insects to produce new flight behaviors.

[0017] Preferably, the data acquisition module includes an image acquisition device and an insect gene data acquisition device; the image acquisition device is used to acquire the image information of the insect flight behavior in the monitoring device in real time, and the insect gene data acquisition device is used to obtain the insect gene information after the insect flight behavior monitoring is completed.

[0018] Preferably, the data acquisition module first grayscales the input image to reduce the amount of calculation; then represents the image as a two-dimensional grid, each grid point represents the position of a search agent, and the search agent is a credential for confirming the edge of the insect image;

[0019] The data acquisition module generates search agents at various locations in the image; uses a random number generator to determine the initial horizontal and vertical coordinates of the search agent, and then assigns the same initial pheromone value of 1 to each pixel in the image;

[0020] Update the pheromone concentration based on the search agent's movement path on the image and the edge detection results;

[0021] For edge information detection, a gradient operator is used to calculate the gradient magnitude and gradient direction of each pixel in the image; the gradient magnitude represents the rate of change of the grayscale value of the pixel, and the gradient direction represents the direction in which the grayscale value changes fastest;

[0022] Horizontal gradient operator: ;

[0023] Vertical gradient operator: ;

[0024] For a pixel point (x, y) in an image, its gradient magnitude G is: ;

[0025] Further calculation ;

[0026] Gradient direction calculate: , where f represents the pixel value of the input image pixel;

[0027] At the edge, the pheromone concentration increases; at the non-edge, the pheromone concentration decreases;

[0028] The search agent selects the direction of movement based on the pheromone concentration and heuristic information; the heuristic information is the gradient magnitude and gradient direction of the image pixel points;

[0029] Let the search agent search and update the pheromone on the image until the gradient amplitude G no longer changes; the pheromone update formula is: ;

[0030] in, represents the pheromone concentration from position k to position j at time r; is the pheromone volatilization rate; is the increment of pheromone, calculated based on the search agent’s path and edge detection results;

[0031] According to the final pheromone distribution, the edge of the image is determined;

[0032] Non-maximum suppression is performed on the pixels marked as edge points to remove false edge points. The gradient amplitudes of each pixel and its adjacent pixels are compared. If the gradient amplitude of a pixel is smaller than that of its adjacent pixels, it is marked as a non-maximum point and its grayscale value is set to 0; if the gradient amplitude of a pixel is larger than that of its adjacent pixels, it is retained. False edge points in the insect image are removed, leaving the real edge points to form a clear insect edge image.

[0033] Preferably, the data analysis module analyzes the deep correlation between flight behavior and genes based on the insect's flight behavior patterns, responses to environmental factors, and genes of the insect.

[0034] An analysis method for an insect autonomous flight monitoring system, the method comprising the following steps:

[0035] Step 1. Start the insect autonomous flight monitoring system and set the experimental environment parameters, temperature, humidity and light, as well as the simulated environment, and place the experimental insects in the monitoring device to ensure that the insects can fly freely in it;

[0036] Step 2. The environmental control module dynamically adjusts the environmental state in the monitoring device to simulate different experimental conditions. At the same time, the data acquisition module starts working and the image acquisition device collects image information of insect flight behavior in real time.

[0037] Step 3. The collected video files are processed by the data processing module, and the data analysis module obtains the relevant parameters of the insect flight behavior from the processed images, including but not limited to the flight altitude, flight distance, speed and acceleration;

[0038] Step 4. Analyze the relationship between the insect flight behavior pattern and the environment, time and genes based on the acquired insect flight behavior related parameters and the insect genes collected by the data acquisition module.

[0039] Preferably, in the environmental control module in step 2, relevant researchers dynamically adjust the monitoring environment in the operating unit according to the monitored changes, and the monitoring environment includes the foraging environment and the living environment; the environmental parameters that can be adjusted include light, temperature, humidity and gas concentration.

[0040] Preferably, in step 3, relevant parameters are calculated for the identified insect flight behavior image to calculate the insect flight altitude, flight distance, speed and acceleration within a certain period of time;

[0041] The vertical position coordinates of the insects at different times are obtained through the data processing module , i represents different moments; the ascending height is calculated by calculating the vertical position difference between adjacent moments; the time of the insect's flight ascending is evenly divided into n equal time periods , within a certain period of time T, the average flight height change of the insect is calculated as:

[0042] in , is the vertical position at the final moment, is the vertical position at the initial moment;

[0043] The horizontal position coordinates of the insect at different times are: and ; m is the number of measurements;

[0044] The flight distance is obtained by calculating the displacement of the horizontal position at adjacent moments and adding these displacements;

[0045] To calculate acceleration, first calculate the speed of the insect at different times;

[0046] The position coordinates of the insect at different times , and ; Velocity is derived from the time derivative of position:

[0047] , horizontal direction; , vertical direction; , close direction;

[0048] The acceleration, D, is calculated from the time derivative of the velocity.

[0049] Preferably, in step 4, the association between flight behavior and genetic and environmental changes is calculated based on the records and analysis of the flight trajectory, speed and altitude obtained in step 3 and the gene pool information of the insect;

[0050] Define a correlation function Q to describe the relationship between flight behavior and genes and environment:

[0051] ;

[0052] in, , , , , is the weight coefficient, which is used to measure the relative influence of each factor on flight behavior; the normal equation is used to solve the weight coefficient: L represents the genetic information of the insect, E represents the environmental change information, V represents the flight speed, D represents the flight acceleration, and H represents the flight altitude;

[0053] The weight coefficient is calculated as follows: there are p samples, each sample contains gene information L, environmental change information E, flight speed V, flight acceleration D, and flight altitude H:

[0054] matrix for: ;

[0055] vector for: ;

[0056] A collection of weight coefficients It is directly calculated by the following normal equation: ;

[0057] in, Meaning matrix The transposed matrix is ​​the new matrix obtained by swapping the rows and columns of the original matrix; find the weight coefficient set w and substitute it into , function Q represents the relationship between insect flight behavior and genes and environment.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. Through the environmental control module and the data acquisition module, the system of the present invention can simulate different environmental conditions and collect image information of insect flight behavior, providing a rich and accurate data source for subsequent data processing and analysis, which not only improves the efficiency and accuracy of the research, but also helps to reveal the complexity and diversity of insect flight behavior.

[0060] 2. The present invention obtains the edge point related data of the insect image through the data processing module search agent calculation, and removes the false edge points through the non-maximum suppression technology, thereby obtaining a clear insect edge image; significantly improving the quality and clarity of the image, providing a better basis for subsequent behavior analysis. At the same time, it effectively reduces the complexity and calculation amount of data processing and improves the performance of the entire system.

[0061] 3. The present invention uses data analysis modules and analysis methods, combined with the genetic information of insects, to deeply analyze the deep correlation between insect flight behavior and genes and the environment. It records and analyzes parameters such as insect flight trajectory, speed, altitude, etc., combined with the insect genetic information, and systematically calculates the degree of correlation between flight behavior and genetic and environmental changes, providing important reference and basis for entomological research and related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flowchart of the system work flow of the present invention.

[0063] Figure 2 It is a flow chart of image data processing by the data processing module of the present invention.

[0064] Figure 3 It is a schematic diagram of the steps of the analysis method of the present invention. DETAILED DESCRIPTION

[0065] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0066] like Figure 1-3 As shown, the present invention provides an insect autonomous flight monitoring system, including a monitoring device, an environment control module, a data acquisition module, a data processing module and a data analysis module;

[0067] The monitoring device is made of transparent material and is used to accommodate experimental insects and provide conditions for the environmental control module to control changes in environmental parameters;

[0068] The data output terminal of the environment control module is connected to the data input terminal of the monitoring device, and is used to control the environmental state parameters in the monitoring device;

[0069] The data acquisition module is placed outside the monitoring device and is used to collect autonomous flight behavior information and insect gene information of the insects in the monitoring device, and send the collected image information to the data processing module, and send the gene information to the data analysis module;

[0070] The data input end of the data processing module is connected to the data output end of the data acquisition module, and is used to analyze the image file transmitted by the data acquisition module, process the image data and identify the autonomous flight image of the insect;

[0071] The data input end of the data analysis module is connected to the data output end of the data processing module and the data acquisition module, and is used to analyze the data transmitted by the data acquisition module and the data processing module to explore the relationship between insect genes and flight ability.

[0072] Embodiment 1: Figure 1-3 As shown, in this embodiment, an insect autonomous flight monitoring system monitors the autonomous flight behavior of an insect; and monitors the gene-controlled flight behavior of two insects in the same living environment.

[0073] The environmental control module includes an environmental control unit and an operating unit. The environmental control unit is used to simulate different experimental environments in the monitoring device, support dynamic adjustments during the monitoring process, and change environmental parameters at any time according to the instructions input by the operating unit to stimulate insects to produce new flight behaviors.

[0074] By simulating different environmental conditions and collecting image information of insect flight behavior, it provides a rich and accurate data source for subsequent data processing and analysis, improves the efficiency and accuracy of research, and helps to reveal the complexity and diversity of insect flight behavior.

[0075] The data acquisition module includes an image acquisition device and an insect gene data acquisition device; the image acquisition device is used to collect insect flight behavior image information in the monitoring device in real time, and the insect gene data acquisition device is used to obtain insect gene information after the insect flight behavior monitoring is completed.

[0076] The data acquisition module first grayscales the input image to reduce the amount of calculation; then represents the image as a two-dimensional grid, where each grid point represents the position of a search agent, which is the credential for confirming the edge of the insect image;

[0077] The data acquisition module generates search agents at various locations in the image; uses a random number generator to determine the initial horizontal and vertical coordinates of the search agent, and then assigns the same initial pheromone value of 1 to each pixel in the image; updates the pheromone concentration based on the search agent's movement path on the image and the edge detection results;

[0078] For edge information detection, a gradient operator is used to calculate the gradient magnitude and gradient direction of each pixel in the image; the gradient magnitude represents the rate of change of the grayscale value of the pixel, and the gradient direction represents the direction in which the grayscale value changes fastest;

[0079] Horizontal gradient operator: ;

[0080] Vertical gradient operator: ;

[0081] For a pixel point (x, y) in an image, its gradient magnitude G is: ;

[0082] Further calculation ;

[0083] Gradient direction calculate: , where f represents the pixel value of the input image pixel;

[0084] At the edge, the pheromone concentration increases; at the non-edge, the pheromone concentration decreases;

[0085] The search agent selects the direction of movement based on the pheromone concentration and heuristic information; the heuristic information is the gradient magnitude and gradient direction of the image pixel points;

[0086] Let the search agent search and update the pheromone on the image until the gradient amplitude G no longer changes; the pheromone update formula is: ;

[0087] in, represents the pheromone concentration from position k to position j at time r; is the pheromone volatilization rate; is the increment of pheromone, calculated based on the search agent’s path and edge detection results;

[0088] According to the final pheromone distribution, the edge of the image is determined;

[0089] Non-maximum suppression is performed on the pixels marked as edge points to remove false edge points. The gradient amplitudes of each pixel and its adjacent pixels are compared. If the gradient amplitude of a pixel is smaller than that of its adjacent pixels, it is marked as a non-maximum point and its grayscale value is set to 0; if the gradient amplitude of a pixel is larger than that of its adjacent pixels, it is retained. False edge points in the insect image are removed, leaving the real edge points to form a clear insect edge image.

[0090] The data processing module searches for the agent to calculate the edge point related data of the insect image, and the non-maximum suppression technology removes the false edge points to obtain a clear insect edge image; the image quality and clarity are significantly improved, providing a better basis for subsequent behavior analysis. At the same time, the complexity and calculation amount of data processing are effectively reduced, and the performance of the entire system is improved.

[0091] The data analysis module analyzes the flight behavior patterns of insects, their responses to environmental factors, and the genes of insects. The flight behavior is deeply correlated with the genes.

[0092] Embodiment 2: Figure 1-3 As shown, in this embodiment, an analysis method of an insect autonomous flight monitoring system analyzes the flight behavior of an insect; and analyzes the relationship between the changes in the flight behaviors controlled by genes of two insects in the same living environment.

[0093] Step 1. Start the insect autonomous flight monitoring system and set the experimental environment parameters, temperature, humidity and light, as well as the simulated environment, and place the experimental insects in the monitoring device to ensure that the insects can fly freely in it;

[0094] Step 2. The environmental control module dynamically adjusts the environmental status within the monitoring device to simulate different experimental conditions. At the same time, the data acquisition module starts working, and the image acquisition device collects image information of insect flight behavior in real time. Relevant researchers dynamically adjust the monitoring environment in the operating unit according to the changes in monitoring. The monitoring environment includes the foraging environment and the living environment. The environmental parameters that can be adjusted include light, temperature, humidity and gas concentration.

[0095] Step 3. The collected video files are processed by the data processing module, and the data analysis module obtains the relevant parameters of the insect flight behavior in the processed images; the relevant parameters of the identified insect flight behavior images are calculated to calculate the insect flight altitude, flight distance, speed and acceleration within a certain period of time;

[0096] The vertical position coordinates of the insects at different times are obtained through the data processing module , i represents different moments; the ascending height is calculated by calculating the vertical position difference between adjacent moments; the time of the insect's flight ascending is evenly divided into n equal time periods , within a certain period of time T, the average flight height change of the insect is calculated as:

[0097] in , is the vertical position at the final moment, is the vertical position at the initial moment;

[0098] The horizontal position coordinates of the insect at different times are: and ; m is the number of measurements;

[0099] The flight distance is obtained by calculating the displacement of the horizontal position at adjacent moments and adding these displacements;

[0100] To calculate acceleration, first calculate the speed of the insect at different times;

[0101] The position coordinates of the insect at different times , and ; Velocity is derived from the time derivative of position:

[0102] , horizontal direction; , vertical direction; , close direction;

[0103] The acceleration, D, is calculated from the time derivative of the velocity.

[0104] Step 4. Analyze the relationship between the insect flight behavior pattern and the environment, time and genes based on the acquired insect flight behavior related parameters and the insect genes collected by the data acquisition module.

[0105] By recording and analyzing the flight trajectory, speed and altitude obtained in step 3, the association between flight behavior and genetic and environmental changes is calculated based on the gene pool information of the insect;

[0106] Define a correlation function Q to describe the relationship between flight behavior and genes and environment:

[0107] ;

[0108] in, , , , , is the weight coefficient, which is used to measure the relative influence of each factor on flight behavior; the normal equation is used to solve the weight coefficient: L represents the genetic information of the insect, E represents the environmental change information, V represents the flight speed, D represents the flight acceleration, and H represents the flight altitude;

[0109] The weight coefficient is calculated as follows: there are p samples, each sample contains gene information L, environmental change information E, flight speed V, flight acceleration D, and flight altitude H:

[0110] matrix for: ;

[0111] vector for: ;

[0112] A collection of weight coefficients It is directly calculated by the following normal equation: ;

[0113] in, Meaning matrix The transposed matrix is ​​the new matrix obtained by swapping the rows and columns of the original matrix; find the weight coefficient set w and substitute it into , function Q represents the relationship between insect flight behavior and genes and environment.

[0114] Through data analysis modules and analysis methods, combined with the genetic information of insects, we have deeply analyzed the deep correlation between insect flight behavior and genes and environment. The recording and analysis of insect flight trajectory, speed, altitude and other parameters, combined with insect genetic information, no longer requires a large amount of manual operation to process and analyze these data. We not only focus on simple flight trajectory descriptions, but also deeply explore the correlation between insect flight behavior and genetic and environmental changes, providing important reference and basis for entomology research and related fields.

[0115] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An analysis method for an insect autonomous flight monitoring system, It is characterized in that The method includes: Step 1: Start the insect autonomous flight monitoring system and set the experimental environment parameters, temperature, humidity and light, as well as the simulated environment, and place the experimental insects in the monitoring device to ensure that the insects can fly freely in it; Step 2: The environmental control module dynamically adjusts the environmental state in the monitoring device to simulate different experimental conditions. At the same time, the data acquisition module starts working, and the image acquisition device collects image information of insect flight behavior in real time; Step 3: The collected video files are processed by the data processing module, and the data analysis module obtains the relevant parameters of the insect flight behavior from the processed images, including but not limited to the flight altitude, flight distance, speed and acceleration; Step 4: Analyze the relationship between the insect flight behavior pattern and the environment, time and genes based on the acquired insect flight behavior related parameters and the insect genes collected by the data acquisition module; Based on the records and analysis of flight trajectory, speed and altitude obtained in step 3, the association between flight behavior and genetic and environmental changes is calculated according to the gene pool information of the insect; Define a correlation function Q to describe the relationship between flight behavior and genes and environment: ； in, , , , , is the weight coefficient, which is used to measure the relative influence of each factor on flight behavior; the normal equation is used to solve the weight coefficient: L represents the genetic information of the insect, E represents the environmental change information, V represents the flight speed, D represents the flight acceleration, and H represents the flight altitude; The weight coefficient is calculated as follows: there are p samples, each sample contains gene information L, environmental change information E, flight speed V, flight acceleration D, and flight altitude H: matrix for: ; vector for: ; A collection of weight coefficients It is directly calculated by the following normal equation: ; in, Meaning matrix The transposed matrix is ​​the new matrix obtained by swapping the rows and columns of the original matrix; find the weight coefficient set w and substitute it into , function Q represents the relationship between insect flight behavior and genes and environment.

2. The analysis method of an insect autonomous flight monitoring system as claimed in claim 1, Features: In the environmental control module in step 2, relevant researchers dynamically adjust the monitoring environment in the operating unit according to the monitored changes. The monitoring environment includes the foraging environment and the living environment; the environmental parameters that can be adjusted include light, temperature, humidity and gas concentration.

3. The analysis method of an insect autonomous flight monitoring system as claimed in claim 1, Features: In step 3, the data analysis module calculates relevant parameters of the identified insect flight behavior image to calculate the insect flight altitude, flight distance, speed and acceleration within a specified time; The vertical position coordinates of the insects at different times are obtained through the data processing module , i represents different moments; the ascending height is calculated by calculating the vertical position difference between adjacent moments; the time of the insect's flight ascending is evenly divided into n equal time periods , within a certain period of time T, the average flight height change of the insect is calculated as: in , is the vertical position at the final moment, is the vertical position at the initial moment; The horizontal position coordinates of the insect at different times are: and ; m is the number of measurements; The flight distance is obtained by calculating the displacement of the horizontal position at adjacent moments and adding these displacements; To calculate acceleration, first calculate the speed of the insect at different times; The position coordinates of the insect at different times , and ; Velocity is derived from the time derivative of position: , horizontal direction; , vertical direction; , close direction; The acceleration, D, is calculated from the time derivative of the velocity.

4. An insect autonomous flight monitoring system, used to implement the analysis method of an insect autonomous flight monitoring system according to any one of claims 1 to 3, It is characterized in that It includes a monitoring device, an environmental control module, a data acquisition module, a data processing module and a data analysis module; The monitoring device is made of transparent material and is used to accommodate experimental insects and provide conditions for the environmental control module to control changes in environmental parameters; The data output terminal of the environment control module is connected to the data input terminal of the monitoring device, and is used to control the environmental state parameters in the monitoring device; The data acquisition module is placed outside the monitoring device and is used to collect autonomous flight behavior information and insect gene information of the insects in the monitoring device, and send the collected image information to the data processing module, and send the gene information to the data analysis module; The data input end of the data processing module is connected to the data output end of the data acquisition module, and is used to analyze the image file transmitted by the data acquisition module, process the image data and identify the autonomous flight image of the insect; The data input end of the data analysis module is connected to the data output end of the data processing module and the data acquisition module, and is used to analyze the data transmitted by the data acquisition module and the data processing module to explore the relationship between insect genes and flight ability.

5. An insect autonomous flight monitoring system as claimed in claim 4, Features: The environmental control module includes an environmental control unit and an operating unit. The environmental control unit is used to simulate different experimental environments in the monitoring device, support dynamic adjustments during the monitoring process, and change environmental parameters at any time according to the instructions input by the operating unit to stimulate insects to produce new flight behaviors.

6. An insect autonomous flight monitoring system as claimed in claim 4, Features: The data acquisition module includes an image acquisition device and an insect gene data acquisition device; the image acquisition device is used to acquire insect flight behavior image information in the monitoring device in real time, and the insect gene data acquisition device is used to obtain insect gene information after the insect flight behavior monitoring is completed.

7. An insect autonomous flight monitoring system as claimed in claim 4, Features: The data acquisition module first grayscales the input image to reduce the amount of calculation; then represents the image as a two-dimensional grid, where each grid point represents the position of a search agent, which is the credential for confirming the edge of the insect image; The data acquisition module generates search agents at various locations in the image; Use a random number generator to determine the initial horizontal and vertical coordinates of the search agent, and then assign the same initial pheromone value of 1 to each pixel in the image; Update the pheromone concentration based on the search agent's movement path on the image and the edge detection results; For edge information detection, a gradient operator is used to calculate the gradient magnitude and gradient direction of each pixel in the image; the gradient magnitude represents the rate of change of the grayscale value of the pixel, and the gradient direction represents the direction in which the grayscale value changes fastest; Horizontal gradient operator: ; Vertical gradient operator: ; For a pixel point (x, y) in an image, its gradient magnitude G is: ; Further calculation ; Gradient direction calculate: , where f represents the pixel value of the input image pixel; At the edge, the pheromone concentration increases; at the non-edge, the pheromone concentration decreases; The search agent selects the direction of movement based on the pheromone concentration and heuristic information; the heuristic information is the gradient magnitude and gradient direction of the image pixel points; Let the search agent search and update the pheromone on the image until the gradient amplitude G no longer changes; the pheromone update formula is: ; in, represents the pheromone concentration from position k to position j at time r; is the pheromone volatilization rate; is the increment of pheromone, calculated based on the search agent’s path and edge detection results; According to the final pheromone concentration distribution, the edge of the image is determined; Non-maximum suppression is performed on the pixels marked as edge points to remove false edge points. The gradient amplitudes of each pixel and its adjacent pixels are compared. If the gradient amplitude of a pixel is smaller than that of its adjacent pixels, it is marked as a non-maximum point and its grayscale value is set to 0; if the gradient amplitude of a pixel is larger than that of its adjacent pixels, it is retained. False edge points in the insect image are removed, leaving the real edge points to form a clear insect edge image.

8. An insect autonomous flight monitoring system as claimed in claim 4, Features: The data analysis module analyzes the deep correlation between flight behavior and genes based on the insect's flight behavior patterns, responses to environmental factors, and genes of the insect.

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