Grass carp culture adaptability improvement method and system in saline-alkali environment

Abstract

The invention discloses a grass carp culture adaptability improvement method and system in a saline-alkali environment. According to the method, the adaptability of the grass carp in the saline-alkali environment is improved through the following steps: firstly, putting the grass carp at different growth stages in the saline-alkali environment for breeding, and acquiring activity behavior data of the grass carp by using an image recognition technology; then, constructing a grass carp adaptability analysis model, evaluating the saline-alkaline environment adaptability of the grass carp in different growth stages, and determining an optimal improved growth stage; thirdly, adaptively breeding the grass carps at the optimal growth stage, and constructing a saline-alkaline environment adaptability control system through water quality data analysis; finally, adaptive changes of the grass carps are monitored in real time, and parameters of the control system are adjusted to optimize the breeding environment. According to the method, through comprehensive evaluation and dynamic regulation and control, the growth and survival ability of the grass carps in the saline-alkali environment is improved, and a scientific technical scheme is provided for saline-alkali soil aquaculture.

Description

Technical Field

[0001] The invention relates to the technical field of aquaculture, and in particular to a method and a system for improving the adaptability of grass carp farming in a saline-alkali environment. Background Art

[0002] With the intensification of environmental pollution and the impact of climate change, many aquaculture areas are facing problems such as water pollution and salinization, which have affected the growth and reproduction of grass carp to varying degrees, reducing the breeding efficiency and economic benefits. Especially in saline-alkali environments, grass carp has poor growth adaptability, resulting in unsatisfactory breeding results.

[0003] At present, most of the research on aquaculture in saline-alkali environments focuses on water quality control and optimization of aquaculture facilities, but there are few studies on the growth process, behavioral characteristics and adaptive changes of grass carp in saline-alkali environments. In particular, how to accurately evaluate the adaptability of grass carp in saline-alkali environments by real-time monitoring of its activity and behavioral characteristics, combined with image recognition and intelligent analysis technology, and improve aquaculture through scientific control methods to improve the aquaculture efficiency and growth health level of grass carp is still a technical problem that needs to be solved urgently.

[0004] In addition, the existing environmental control system is usually unable to achieve dynamic and precise adjustment, and can only set fixed parameters based on experience, which makes it difficult to cope with the challenges of complex and changeable saline-alkali environment to the adaptability of grass carp. Therefore, developing an intelligent breeding control system based on grass carp behavioral characteristics and environmental adaptability analysis, which can monitor and adjust the adaptability parameters of saline-alkali environment in real time, is of great significance to improving the stability and economic benefits of grass carp breeding.

[0005] In summary, the existing technology still has great limitations in improving the adaptability of grass carp farming in saline-alkali environments, and an innovative technical solution is urgently needed to achieve healthy growth and efficient farming of grass carp in saline-alkali environments through adaptability evaluation of grass carp at different growth stages and intelligent regulation of the farming environment. Therefore, the present invention proposes a method and system for improving the adaptability of grass carp farming in saline-alkali environments based on grass carp activity behavior analysis and environmental adaptability control, aiming to solve the deficiencies in the existing technology and improve the growth adaptability and farming efficiency of grass carp in saline-alkali environments. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a method and system for improving the adaptability of grass carp farming in saline-alkali environments.

[0007] The first aspect of the present invention provides a method for improving the adaptability of grass carp farming in a saline-alkali environment, comprising:

[0008] Obtain grass carp at different growth stages and place them in a preset saline-alkali environment for breeding. Obtain the video data of the grass carp's activity behavior during the breeding process, and based on the image recognition algorithm, identify the activity behavior characteristics of the grass carp during the breeding process;

[0009] Construct a grass carp adaptability analysis model, import the activity behavior characteristics into the grass carp adaptability analysis model to evaluate the saline-alkali environment adaptability of grass carp at different growth stages, and determine the most suitable growth stage for the grass carp to improve its saline-alkali environment adaptability according to the saline-alkali environment adaptability;

[0010] Carry out saline-alkali environment adaptive breeding for the grass carp at the most suitable growth stage, obtain the water quality condition data of the breeding environment, analyze the water quality condition data, and construct a grass carp saline-alkali environment adaptive control system;

[0011] Real-time monitor the adaptability changes of the grass carp in the breeding saline-alkali environment, and determine the parameter adjustment strategy of the saline-alkali environment adaptive control system according to the adaptability changes.

[0012] In this solution, the obtaining of grass carp at different growth stages and placing them in a preset saline-alkali environment for breeding, obtaining the video data of the grass carp's activity behavior during the breeding process, and based on the image recognition algorithm to identify the activity behavior characteristics of the grass carp during the breeding process are specifically as follows:

[0013] Obtain the saline-alkali degree data of the suitable growth environment of the grass carp and the target saline-alkali degree data for the improvement of the grass carp's saline-alkali environment adaptability, and determine the initial adapted saline-alkali degree of the grass carp according to the saline-alkali degree data of the suitable growth environment and the target saline-alkali degree data;

[0014] Adjust the saline-alkali degree of the preset saline-alkali environment according to the initial adapted saline-alkali degree, obtain grass carp at different growth stages and carry out breeding operations in the preset saline-alkali environment, and obtain the video data of the grass carp's activity behavior during the breeding process;

[0015] Obtain the appearance feature data of the grass carp, and based on the convolutional neural network, perform training operations on the appearance feature data to construct a grass carp feature extraction model;

[0016] Introduce the SSD target detection algorithm to convert the activity behavior video data into video frame images, import the video frame images into the grass carp feature extraction model to extract the appearance features of the grass carp, and generate a multi-scale feature map of the grass carp according to the extracted appearance features of the grass carp;

[0017] Generate default boxes for the multi-scale feature map, predict the offset of the default boxes through the convolutional layer in the default boxes, and correct the boundaries of the default boxes according to the offset to obtain the bounding boxes of each grass carp individual in the video frame image;

[0018] Obtain the coordinate information of the bounding box, construct a state transition matrix based on the coordinate position information of the grass carp individual bounding box in each video frame image according to the SORT algorithm, analyze the state transition matrix according to the SORT algorithm, identify the movement positions of grass carp at different growth stages in the activity behavior video data over time, and draw the movement trajectory of the grass carp according to the movement positions over time;

[0019] Identify the activity behavior characteristics in the grass carp breeding process according to the grass carp movement trajectory, including jumping out of the water, swimming speed characteristics, turning behavior characteristics, group aggregation characteristics, activity area characteristics, and movement frequency characteristics.

[0020] In this solution, the grass carp adaptability analysis model is constructed, and the activity behavior characteristics are imported into the grass carp adaptability analysis model to evaluate the adaptability of grass carp at different growth stages to the saline-alkali environment. According to the adaptability to the saline-alkali environment, the most suitable growth stage for the grass carp to carry out saline-alkali environment adaptability improvement is determined. Specifically:

[0021] Obtain the historical adaptability evaluation data for evaluating the environmental adaptability of grass carp based on the historical activity behavior characteristics of grass carp;

[0022] Analyze the historical adaptability evaluation data based on the fuzzy logic algorithm to construct a membership function of each activity behavior characteristic for the environmental adaptability of grass carp;

[0023] Obtain the influence weight data of each activity behavior characteristic on the environmental adaptability of grass carp. According to the membership function and the influence weight data, construct a grass carp adaptability analysis model. Import the activity behavior characteristics into the grass carp adaptability analysis model to calculate the environmental adaptability membership degree of each behavior characteristic, and construct a membership matrix with the environmental adaptability membership degrees;

[0024] Perform a weighted scoring operation on the activity behavior characteristics according to the membership matrix and the influence weight data to obtain the environmental adaptability scores of grass carp at different growth stages. Evaluate the adaptability of grass carp at different growth stages to the saline-alkali environment in a preset saline-alkali environment according to the environmental adaptability scores;

[0025] Determine the most suitable growth stage for the grass carp to carry out saline-alkali environment adaptability improvement according to the adaptability to the saline-alkali environment.

[0026] In this solution, for the grass carp at the most suitable growth stage, carry out saline-alkali environment adaptability breeding, obtain the water quality condition data of the breeding environment, analyze the water quality condition data, and construct a grass carp saline-alkali environment adaptability control system. Specifically:

[0027] Place the grass carp at the optimal growth stage in the target aquaculture pond for saline-alkali environment adaptation aquaculture, construct a three-dimensional model of the target aquaculture pond, and divide the three-dimensional model into N small regions according to a preset size;

[0028] Obtain the water quality condition data of each small region in the target aquaculture pond after a preset period of grass carp aquaculture;

[0029] Introduce the spectral clustering algorithm, calculate the similarity between the water quality condition data of each small region according to the Gaussian kernel function, and construct a similarity matrix according to the similarity;

[0030] Calculate the degree matrix according to the similarity matrix, calculate the Laplacian matrix of the graph according to the degree matrix, perform eigenvalue decomposition on the Laplacian matrix, and calculate the eigenvalues and eigenvectors of the Laplacian matrix;

[0031] Take the first k eigenvectors as the representation of new data points, with each row representing the eigenvector of a data point in the water quality condition data, and perform clustering operations on the k-dimensional eigenvectors based on the K-means clustering algorithm to obtain the clustering results of the water quality condition data of each small region in the target aquaculture pond;

[0032] Identify the distribution area of the water quality conditions in the target aquaculture pond according to the clustering results, and determine the layout positions of the equipment of the grass carp saline-alkali environment adaptation control system according to the distribution area of the water quality conditions. The control system equipment includes saline-alkali adjustment equipment, inlet and outlet positions, temperature control equipment, and water quality adjustment equipment;

[0033] Perform control system layout according to the layout positions of the control system equipment to construct a grass carp saline-alkali environment adaptation control system.

[0034] In this solution, the adaptability change of grass carp in the aquaculture saline-alkali environment is monitored in real time, and the parameter adjustment strategy of the saline-alkali environment adaptation control system is determined according to the adaptability change. Specifically:

[0035] Calculate the difference between the suitable environment salinity data of grass carp and the target salinity data for the improvement of grass carp saline-alkali environment adaptability, and divide the difference according to a preset step size to obtain the salinity gradient elevation table for the improvement of grass carp saline-alkali environment adaptability;

[0036] Perform control operations on the saline-alkali environment adaptation control system according to the minimum salinity value of the salinity gradient elevation table, obtain the periodic video image data of grass carp in the minimum salinity aquaculture environment at a preset time period, and extract the periodic activity behavior characteristics of grass carp in each period;

[0037] Import the periodic activity behavior characteristics into the grass carp adaptability analysis model to evaluate the adaptability of grass carp to the saline-alkali environment in each cycle, and construct adaptability time series data for the adaptability of grass carp in each cycle;

[0038] Construct an adaptability prediction model according to the XGBoost algorithm, construct the adaptability time series data into lag feature data, divide the lag feature data into a training set and a prediction set according to a preset ratio, and import the training set into the adaptability prediction model for training operations;

[0039] Import the prediction set into the trained adaptability prediction model to predict the adaptability change of grass carp in a preset future time period, and obtain an adaptability prediction result;

[0040] Determine the adjustment time of the next saline-alkali gradient in the saline-alkali gradient elevation table according to the adaptability prediction result, adjust the saline-alkali parameters of the saline-alkali environment adaptability control system according to the adjustment time, and re-monitor the adaptability of grass carp in each time cycle after the saline-alkali parameter adjustment to obtain the second adaptability time series data;

[0041] Re-train the adaptability prediction model according to the second adaptability time series data and predict the adjustment time of the next saline-alkali gradient to obtain the parameter adjustment strategy of the saline-alkali environment adaptability control system;

[0042] Adjust the saline-alkali environment adaptability control system according to the parameter adjustment strategy, and monitor the adaptation consistency of grass carp in real time. If the adaptation consistency is less than the preset value, optimize the parameter adjustment strategy to construct an optimized improvement plan for the adaptability of grass carp to the saline-alkali environment.

[0043] In this solution, the real-time monitoring of the adaptation consistency of grass carp, if the adaptation consistency is less than the preset value, optimize the parameter adjustment strategy to obtain an optimized improvement plan for the adaptability of grass carp, specifically:

[0044] Obtain the saline-alkali environment adaptability data of all grass carp in the target aquaculture pond after adjusting the saline-alkali environment adaptability control system according to the parameter adjustment strategy;

[0045] Map the saline-alkali environment adaptability data to a preset adaptability value interval, and determine the proportion information of the number of grass carp in each preset adaptability value interval;

[0046] Evaluate the adaptation consistency of the grass carp in the target aquaculture pond according to the proportion information of the number of grass carp in each preset adaptability value interval to obtain the evaluation result of the saline-alkali environment adaptation consistency;

[0047] According to the evaluation result of the consistency of the saline-alkali environment, if the adaptation consistency is lower than the preset value, the grass carps in the target breeding pond with adaptability greater than or equal to the first preset value are designated as first-class adaptable grass carps, the grass carps with adaptability greater than or equal to the second preset value and less than the first preset value are designated as second-class adaptable grass carps, and the grass carps less than the second preset value are designated as third-class adaptable grass carps;

[0048] The first-class adaptable grass carps are improved in adaptability according to the current parameter adjustment strategy;

[0049] Obtain a preset monitoring quantity of second-class adaptable grass carps and place them in a preset test breeding environment. Obtain the current control parameters of the saline-alkali environment adaptability control system controlled according to the parameter adjustment strategy and the current salinity of the target breeding pond. Analyze the current control parameters according to the genetic algorithm to construct a breeding test parameter group;

[0050] Control the environmental condition parameters of the preset test breeding environment according to the breeding test parameter group, record in real time the improvement of the environmental adaptability of the second-class adaptable grass carps during the test breeding process, and identify the optimal control parameters of the saline-alkali environment adaptability control system under the current salinity according to the improvement of the environmental adaptability;

[0051] Perform a breeding zoning operation on the target breeding pond, place the second-class adaptable grass carps in the same zone, and optimize the breeding environment parameters of the zone where the second-class adaptable grass carps are located according to the optimal control parameters;

[0052] Perform an operation to remove the saline-alkali environment adaptability improvement of the third-class adaptable grass carps to obtain an optimized plan for the saline-alkali environment adaptability improvement of grass carps.

[0053] The second aspect of the present invention also provides a system for improving the adaptability of grass carp breeding in a saline-alkali environment. The system includes: a memory and a processor. The memory includes a program for the method of improving the adaptability of grass carp breeding in a saline-alkali environment. When the program for the method of improving the adaptability of grass carp breeding in a saline-alkali environment is executed by the processor, the following steps are implemented:

[0054] Obtain grass carps at different growth stages and breed them in a preset saline-alkali environment. Obtain the video data of the activity behaviors of the grass carps during the breeding process, and identify the activity behavior characteristics during the breeding process of the grass carps based on the image recognition algorithm;

[0055] Construct a grass carp adaptability analysis model, import the activity behavior characteristics into the grass carp adaptability analysis model to evaluate the saline-alkali environment adaptability ability of grass carps at different growth stages, and determine the most suitable growth stage for the grass carps to carry out the saline-alkali environment adaptability improvement according to the saline-alkali environment adaptability ability;

[0056] Perform adaptive cultivation of grass carp in a saline-alkali environment at the optimal growth stage, obtain water quality condition data of the cultivation environment, analyze the water quality condition data, and construct an adaptive control system for grass carp in a saline-alkali environment;

[0057] Monitor the adaptive changes of grass carp in the saline-alkali cultivation environment in real time, and determine the parameter adjustment strategy of the adaptive control system for the saline-alkali environment according to the adaptive changes.

[0058] The present invention discloses a method and system for improving the adaptability of grass carp cultivation in a saline-alkali environment. The present invention realizes the improvement of the adaptability of grass carp in a saline-alkali environment through the following steps: First, place grass carp at different growth stages in a saline-alkali environment and use image recognition technology to obtain the activity behavior data of grass carp; Then, construct an adaptive analysis model of grass carp to evaluate the adaptability of grass carp at different growth stages in a saline-alkali environment and determine the optimal improvement growth stage; Next, perform adaptive cultivation on the grass carp at the optimal growth stage, and construct an adaptive control system for the saline-alkali environment through water quality data analysis; Finally, monitor the adaptive changes of grass carp in real time and adjust the control system parameters to optimize the cultivation environment. The present invention improves the growth and survival ability of grass carp in a saline-alkali environment through comprehensive evaluation and dynamic regulation, and provides a scientific technical solution for aquaculture in saline-alkali land. Brief Description of the Drawings

[0059] Figure 1 Shows a flowchart of a method for improving the adaptability of grass carp cultivation in a saline-alkali environment according to the present invention;

[0060] Figure 2 Shows a flowchart of determining the optimal growth stage for grass carp to improve its adaptability in a saline-alkali environment according to the present invention;

[0061] Figure 3 Shows a flowchart of constructing an adaptive control system for grass carp in a saline-alkali environment according to the present invention;

[0062] Figure 4 Shows a block diagram of a system for improving the adaptability of grass carp cultivation in a saline-alkali environment according to the present invention. Detailed Description of the Invention

[0063] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0064] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0065] Figure 1 The flowchart of a method for improving the adaptability of grass carp farming in a saline-alkali environment according to the present invention is shown.

[0066] As Figure 1 shown, in the first aspect of the present invention, a method for improving the adaptability of grass carp farming in a saline-alkali environment is provided, including:

[0067] S102, obtaining grass carp at different growth stages and placing them in a preset saline-alkali environment for farming, obtaining video data of the activity behaviors of the grass carp during the farming process, and identifying the activity behavior characteristics of the grass carp during the farming process based on an image recognition algorithm;

[0068] S104, constructing an adaptability analysis model for grass carp, importing the activity behavior characteristics into the adaptability analysis model for grass carp to evaluate the adaptability of grass carp at different growth stages to the saline-alkali environment, and determining the optimal growth stage for grass carp to improve its adaptability to the saline-alkali environment according to the adaptability;

[0069] S106, performing saline-alkali environment adaptability farming on the grass carp at the optimal growth stage, obtaining water quality condition data of the farming environment, analyzing the water quality condition data, and constructing a control system for the adaptability of grass carp to the saline-alkali environment;

[0070] S108, monitoring in real time the adaptability changes of the grass carp in the saline-alkali farming environment, and determining a parameter adjustment strategy for the control system for the adaptability of the saline-alkali environment according to the adaptability changes.

[0071] It should be noted that by obtaining the video data of the activity behaviors of grass carp in the saline-alkali environment at different growth stages and using image recognition algorithms to accurately extract the activity behavior characteristics of grass carp, the behavior changes of grass carp can be monitored in real time, and its adaptability characteristics at different growth stages can be identified; by establishing a grass carp adaptability analysis model and importing the activity behavior characteristics of grass carp, the adaptability ability of grass carp in the saline-alkali environment can be quantitatively evaluated. This step can accurately identify the differences in the adaptability of grass carp to the saline-alkali environment at different growth stages, thereby helping to determine the growth stage most suitable for improving the adaptability to the saline-alkali environment; implementing saline-alkali environment adaptive farming at the optimal growth stage and combining with the analysis of water quality condition data can monitor the impact of water quality parameters on the adaptability of grass carp in real time. By establishing a grass carp saline-alkali environment adaptability control system, the accurate regulation of water quality conditions can be realized to ensure that the water quality is within an appropriate range. By monitoring the adaptability changes of grass carp in the saline-alkali environment in real time, the reaction of grass carp to environmental changes can be dynamically captured, and adaptability problems can be discovered in time. According to the adaptability change data, the parameters of the grass carp saline-alkali environment adaptability control system (such as salinity, temperature, water quality, etc.) are intelligently adjusted to ensure that the breeding environment of grass carp always maintains the state most beneficial to growth. This real-time regulation ability greatly improves the adaptability management level of the breeding environment, makes the breeding system more intelligent and self-adaptive, and improves the breeding efficiency and the healthy growth of grass carp.

[0072] According to an embodiment of the present invention, placing grass carp at different growth stages in a preset saline-alkali environment for breeding, obtaining the video data of the activity behaviors of grass carp during the breeding process, and identifying the activity behavior characteristics of grass carp during the breeding process based on an image recognition algorithm specifically include:

[0073] Obtaining the saline-alkali degree data of the suitable growth environment of grass carp and the target saline-alkali degree data for the improvement of the saline-alkali environment adaptability of grass carp, and determining the initial adapted saline-alkali degree of grass carp according to the saline-alkali degree data of the suitable growth environment and the target saline-alkali degree data;

[0074] Adjusting the saline-alkali degree of the preset saline-alkali environment according to the initial adapted saline-alkali degree, placing grass carp at different growth stages in the preset saline-alkali environment for breeding operations, and obtaining the video data of the activity behaviors of grass carp during the breeding process;

[0075] Obtaining the appearance feature data of grass carp, and constructing a grass carp feature extraction model by performing training operations on the appearance feature data based on a convolutional neural network;

[0076] Introducing the SSD target detection algorithm to convert the activity behavior video data into video frame images, importing the video frame images into the grass carp feature extraction model for extracting the appearance features of grass carp, and generating a multi-scale feature map of grass carp according to the extracted appearance features of grass carp;

[0077] Generate default boxes for the multi-scale feature maps, predict the offsets of the default boxes through a convolutional layer in the default boxes, and correct the boundaries of the default boxes according to the offsets to obtain the bounding boxes of each grass carp individual in the video frame image;

[0078] Obtain the coordinate information of the bounding boxes, construct a state transition matrix based on the coordinate position information of the grass carp individual bounding boxes in each video frame image using the SORT algorithm, analyze the state transition matrix according to the SORT algorithm, identify the movement positions of grass carp at different growth stages over time in the activity behavior video data, and draw the movement trajectories of grass carp according to the movement positions over time;

[0079] Identify the activity behavior characteristics in the grass carp breeding process based on the grass carp movement trajectories, including jumping out of the water, swimming speed characteristics, turning behavior characteristics, group aggregation characteristics, activity area characteristics, and movement frequency characteristics.

[0080] It should be noted that by introducing the SSD (Single Shot MultiBox Detector) object detection algorithm, the position and boundary of grass carp can be identified in real time in video frame images. This technology can effectively handle the dynamic performance of grass carp in the aquaculture environment and avoid traditional manual or inefficient object detection methods. The SSD algorithm processes grass carp images through a convolutional neural network, and can complete the detection and positioning of multiple targets simultaneously in a single forward pass, achieving high-precision detection of grass carp targets. Especially in saline-alkali aquaculture waters with complex environments and dynamically changing backgrounds, it ensures the stability and accuracy of object detection. Using the SSD algorithm to extract multi-scale feature maps of grass carp and correcting the bounding boxes of grass carp individuals through default boxes and offsets can accurately locate the appearance features of each grass carp and eliminate the influence of changes in lighting, posture, and angle. Grass carp at different growth stages may have different body shapes and movement characteristics, and SSD can adapt to these changes through multi-scale feature extraction to ensure accurate identification of grass carp. By inputting the coordinate information of the bounding boxes of grass carp targets in video frame images into the SORT (Simple Online and Real Time Tracking) algorithm, the movement trajectories of grass carp during aquaculture can be tracked in real time. The SORT algorithm combines Kalman filtering and the Hungarian algorithm to efficiently and accurately match and construct trajectories for grass carp targets. Through this method, each individual grass carp can be tracked in the video frame sequence, avoiding problems such as target loss or mis-matching, and accurately capturing the movement changes of grass carp. Through the dynamic changes in the movement trajectories of grass carp, the behavioral characteristics of grass carp, such as jumping out of the water, swimming speed, turning behavior, group aggregation, activity area characteristics, and movement frequency, can be further analyzed. This step provides an important basis for the adaptability assessment of grass carp in saline-alkali environments. For example, when grass carp adapts to a poor saline-alkali environment, it may exhibit excessive behavior of jumping out of the water, abnormal swimming speed, or group dispersion. These behavioral characteristics can be used as indicators of grass carp adaptability problems, thus providing warning signals for aquaculture managers; the appearance feature data includes length, color, texture, fin shape, and contour features; the default box is a rectangular box of grass carp initially selected, and the bounding box is the edge selection of grass carp.

[0081] Figure 2 The flowchart shows the determination of the optimal growth stage for grass carp to adapt to saline-alkali environment improvement according to the present invention.

[0082] According to an embodiment of the present invention, in constructing the grass carp adaptability analysis model, importing the activity behavior characteristics into the grass carp adaptability analysis model to evaluate the saline-alkali environment adaptability of grass carp at different growth stages, and determining the optimal growth stage for grass carp to adapt to saline-alkali environment improvement according to the saline-alkali environment adaptability, specifically:

[0083] S202, obtain historical adaptability evaluation data for evaluating the environmental adaptability of grass carp based on the historical activity behavior characteristics of grass carp;

[0084] S204, analyze the historical adaptability evaluation data based on the fuzzy logic algorithm, and construct a membership function for each activity behavior characteristic to the environmental adaptability of grass carp;

[0085] S206, obtain the influence weight data of each activity behavior characteristic on the environmental adaptability of grass carp, construct a grass carp adaptability analysis model according to the membership function and the influence weight data, import the activity behavior characteristics into the grass carp adaptability analysis model to calculate the environmental adaptability membership degree of each behavior characteristic, and construct a membership degree matrix with the environmental adaptability membership degree;

[0086] S208, perform a weighted scoring operation on the activity behavior characteristics according to the membership degree matrix and the influence weight data to obtain the environmental adaptability scores of grass carp at different growth stages, and evaluate the saline-alkali environmental adaptability ability of grass carp at different growth stages in a preset saline-alkali environment;

[0087] S210, determine the most suitable growth stage for grass carp to improve its saline-alkali environmental adaptability according to the saline-alkali environmental adaptability ability.

[0088] It should be noted that the fuzzy logic algorithm is used to analyze the historical adaptability evaluation data of grass carp and construct a membership function for each activity behavior characteristic and environmental adaptability. Fuzzy logic can handle complex, fuzzy, and non-linear data relationships. It can not only effectively reduce the errors caused by data uncertainty, but also fully consider the changes in the adaptability of grass carp under different environmental conditions, so as to achieve more accurate adaptability evaluation. By establishing a grass carp adaptability analysis model, the activity behavior characteristics of grass carp can be imported into the model for environmental adaptability analysis, and an adaptability membership degree matrix for different grass carp behavior characteristics can be generated. This model can quantify the adaptability of grass carp under different environmental conditions. By weighted scoring the membership degree matrix of each activity behavior characteristic and combining the influence weight data, the environmental adaptability scores of grass carp at different growth stages can be obtained. Through this quantitative scoring method, the adaptability of grass carp in the saline-alkali environment can be accurately evaluated, and it can be identified which growth stages of grass carp are most suitable for environmental adaptability improvement. This process makes the judgment of the adaptability of grass carp growth stages more accurate, thus optimizing the adaptability improvement plan.

[0089] Figure 3 The flowchart of constructing the saline-alkali environmental adaptability control system of grass carp in the present invention is shown.

[0090] According to an embodiment of the present invention, the grass carp at the optimal growth stage is subjected to saline-alkali environment adaptation breeding, water quality condition data of the breeding environment is obtained, and the water quality condition data is analyzed to construct a grass carp saline-alkali environment adaptation control system, specifically as follows:

[0091] S302, Place the grass carp at the optimal growth stage in a target breeding pond for saline-alkali environment adaptation breeding, construct a three-dimensional model of the target breeding pond, and divide the three-dimensional model into N small regions according to a preset size;

[0092] S304, Obtain the water quality condition data of each small region in the target breeding pond after a preset breeding period of the grass carp;

[0093] S306, Introduce the spectral clustering algorithm, calculate the similarity between the water quality condition data of each small region according to the Gaussian kernel function, and construct a similarity matrix according to the similarity;

[0094] S308, Calculate the degree matrix according to the similarity matrix, calculate the Laplacian matrix of the graph according to the degree matrix, perform eigenvalue decomposition on the Laplacian matrix, and calculate the eigenvalues and eigenvectors of the Laplacian matrix;

[0095] S310, Use the first k eigenvectors as new data point representations, where each row represents the eigenvector of a data point in the water quality condition data, and perform clustering operations on the k-dimensional eigenvectors based on the K-means clustering algorithm to obtain the clustering results of the water quality condition data of each small region in the target breeding pond;

[0096] S312, Identify the distribution regions of the water quality conditions in the target breeding pond according to the clustering results, and determine the layout positions of the equipment of the grass carp saline-alkali environment adaptation control system according to the distribution regions of the water quality conditions. The control system equipment includes saline-alkali adjustment equipment, inlet and outlet positions, temperature control equipment, and water quality adjustment equipment;

[0097] S314, Perform control system layout according to the layout positions of the control system equipment to construct a grass carp saline-alkali environment adaptation control system.

[0098] It should be noted that the spectral clustering algorithm can effectively classify the water quality in different small areas in the aquaculture pond according to the similarity of water quality conditions. By calculating the similarity between each small area through the Gaussian kernel function and constructing a similarity matrix, it can accurately identify which areas have similar water quality conditions and which areas have significant differences in water quality. Thus, the aquaculture pond can be divided into multiple water quality distribution areas, revealing the change patterns of water quality in different areas. These changes may be related to the distribution of key factors such as temperature, salinity, and dissolved oxygen, and can help identify the water quality inhomogeneity that affects the adaptability of grass carp. For example, there may be areas where the water quality has deteriorated too much, while other areas may have relatively ideal water quality. Through this identification, potential water quality problems can be discovered and corrected in a timely manner to ensure that grass carp can grow in the most suitable environment. The identification of water quality distribution can help design the optimal layout of control system equipment. Specifically, through the spectral clustering results, the water quality conditions of different areas can be determined, and then the control system equipment (such as salinity adjustment equipment, inlet and outlet positions, temperature control equipment, etc.) can be reasonably arranged according to the characteristics of these areas. This precise layout can better meet the growth needs of grass carp in each area and improve the efficiency of water quality regulation in the entire aquaculture pond. It can avoid wasting resources by setting up too many devices in areas with good water quality conditions, and at the same time ensure that areas with poor water quality receive sufficient adjustment support; the water quality condition data includes water body salinity, dissolved oxygen content, turbidity, and organic matter concentration; the degree matrix is the diagonal matrix of the similarity matrix; the clustering result is to cluster areas with similar water quality condition data and similar positions into one category; according to the distribution area of the water quality conditions, the layout position of the grass carp salinity environment adaptability control system equipment is determined. For example, for areas with high salinity or high saline-alkali concentration, salinity adjustment equipment (such as fresh water introduction system, salt removal system, or water softening equipment) needs to be configured. For areas with low dissolved oxygen content, oxygenation equipment (such as oxygenation pumps, air pumps, or bubble generators) needs to be configured. By constructing an environment adaptability control system, the water quality conditions at different positions in the target aquaculture pond can be prevented from showing differences, and the consistency of the water quality conditions in the target aquaculture pond can be maintained.

[0099] According to an embodiment of the present invention, the adaptability change of grass carp in the aquaculture saline-alkali environment is monitored in real time, and the parameter adjustment strategy of the saline-alkali environment adaptability control system is determined according to the adaptability change, specifically:

[0100] Calculate the difference between the suitable environment salinity data of grass carp and the target salinity data for the improvement of grass carp's saline-alkali environment adaptability, and divide the difference according to a preset step size to obtain a salinity gradient elevation table for the improvement of grass carp's saline-alkali environment adaptability;

[0101] Control the salinity environment adaptability control system according to the minimum salinity value of the salinity gradient elevation table, obtain the periodic video image data of grass carp in the minimum salinity aquaculture environment at a preset time period, and extract the periodic activity behavior characteristics of grass carp in each period;

[0102] Import the periodic activity behavior characteristics into the grass carp adaptability analysis model to evaluate the salinity environment adaptability of grass carp in each period, and construct adaptability time series data for the salinity environment adaptability of grass carp in each period;

[0103] Construct an adaptability prediction model according to the XGBoost algorithm, construct the adaptability time series data into lag feature data, divide the lag feature data into a training set and a prediction set according to a preset ratio, and import the training set into the adaptability prediction model for training operations;

[0104] Import the prediction set into the trained adaptability prediction model to predict the adaptability change of grass carp in a preset future time period, and obtain an adaptability prediction result;

[0105] Determine the adjustment time of the next salinity gradient in the salinity gradient elevation table according to the adaptability prediction result, adjust the salinity parameters of the salinity environment adaptability control system according to the adjustment time, and re-monitor the adaptability of grass carp in each time period after the salinity parameter adjustment to obtain the second adaptability time series data;

[0106] Re-train the adaptability prediction model according to the second adaptability time series data and predict the adjustment time of the next salinity gradient to obtain the parameter adjustment strategy of the salinity environment adaptability control system;

[0107] Adjust the salinity environment adaptability control system according to the parameter adjustment strategy, monitor the adaptation consistency of grass carp in real time, and if the adaptation consistency is less than the preset value, optimize the parameter adjustment strategy to construct an improved optimization plan for the salinity environment adaptability of grass carp.

[0108] It should be noted that by calculating the difference between the suitable salinity data of grass carp and the target salinity data, and obtaining the salinity gradient increase table divided according to the preset step size, the salinity in the saline-alkali environment can be adjusted gradually and progressively, rather than directly changing it significantly. This progressive change in salinity helps grass carp gradually adapt to environmental changes, avoiding stress reactions or poor adaptation caused by excessive or rapid salinity fluctuations, and reducing risks during the breeding process. By analyzing the adaptive time series data of grass carp through an adaptive prediction model (based on the XGBoost algorithm), the adaptive changes of grass carp within a preset future time period can be predicted. Based on the prediction results, the control system can calculate and arrange the best timing for the next salinity gradient adjustment in advance, avoiding over-regulation or untimely adjustment of the environment, and preventing the salinity from being increased before grass carp have adapted to the saline-alkali environment. This way of predicting and adjusting in advance can ensure that the salinity changes within the most suitable range, reduce the negative impact of salinity fluctuations on the health of grass carp, and improve the breeding efficiency of the saline-alkali environment. After each salinity adjustment, the adaptive state of grass carp may change. Updating the prediction model based on these new adaptive data can make the model better reflect the response of grass carp in the actual environment. By dynamically updating the prediction model, the prediction accuracy of the model for the adaptability of grass carp can be gradually improved, reducing model bias, and ensuring the accuracy and reliability of the prediction results.

[0109] According to an embodiment of the present invention, the adaptation consistency of grass carp is monitored in real time. If the adaptation consistency is less than a preset value, the parameter adjustment strategy is optimized to obtain an optimized solution for improving the adaptability of grass carp, specifically:

[0110] Obtain the saline-alkali environment adaptation data of all grass carp in the target breeding pond after adjusting the saline-alkali environment adaptation control system according to the parameter adjustment strategy;

[0111] Map the saline-alkali environment adaptation data into a preset adaptation numerical interval, and determine the proportion information of the number of grass carp in each preset adaptation numerical interval;

[0112] Evaluate the adaptation consistency of the grass carp in the target breeding pond according to the proportion information of the number of grass carp in each preset adaptation numerical interval, and obtain the evaluation result of the saline-alkali environment adaptation consistency;

[0113] According to the evaluation result of the saline-alkali environment consistency, if the adaptation consistency is lower than the preset value, the grass carp with adaptability greater than or equal to the first preset value in the target breeding pond is designated as first-class adaptable grass carp, the grass carp with adaptability greater than or equal to the second preset value and less than the first preset value is designated as second-class adaptable grass carp, and the grass carp with adaptability less than the second preset value is designated as third-class adaptable grass carp;

[0114] Improve the adaptability of the first-class adaptable grass carp according to the current parameter adjustment strategy;

[0115] Obtain a preset monitoring quantity of type-II adaptable grass carps and place them in a preset test aquaculture environment. Obtain the current control parameters of the saline-alkali environment adaptability control system controlled according to the parameter adjustment strategy and the current salinity of the target aquaculture pond. Analyze the current control parameters according to the genetic algorithm to construct an aquaculture test parameter group;

[0116] Control the environmental condition parameters of the preset test aquaculture environment according to the aquaculture test parameter group, record in real time the improvement of the environmental adaptability of the type-II adaptable grass carps during the test aquaculture process, and identify the optimal control parameters of the saline-alkali environment adaptability control system under the current salinity according to the improvement of the environmental adaptability;

[0117] Perform an aquaculture zoning operation on the target aquaculture pond, place the type-II adaptable grass carps in the same zone, and optimize the aquaculture environmental parameters of the zone where the type-II adaptable grass carps are located according to the optimal control parameters;

[0118] Perform an operation to improve the saline-alkali environment adaptability on the type-III adaptable grass carps to obtain an optimized plan for improving the saline-alkali environment adaptability of grass carps.

[0119] It should be noted that when culturing grass carps in a saline-alkali environment, due to the differences in the adaptability of different grass carps in the saline-alkali environment, the phenomenon of inconsistent adaptability within the grass carp population may occur. Therefore, by real-time monitoring and evaluating the adaptability consistency of grass carps, individuals with poor adaptability in the aquaculture pond can be identified. By classifying grass carps according to adaptability into different categories (such as type-I, type-II, and type-III adaptable grass carps), precise zoning management can be carried out. For example, for grass carps with better adaptability (designated as type-I adaptable grass carps), they can be continuously cultured according to the current salinity; while grass carps with poor adaptability (such as type-III adaptable grass carps) can be removed or treated separately to avoid their impact on the overall adaptability of the population. By analyzing and optimizing the current salinity and control parameters of type-II adaptable grass carps according to the genetic algorithm, the system can help find the saline-alkali environment control parameters most suitable for the growth of grass carps in the test environment. Perform a zoning operation on the target aquaculture pond according to the adaptability classification results, and adjust the environmental parameters of each zone targeted, especially for the zone where the type-II adaptable grass carps are located, and optimize its salinity and other environmental conditions. By continuously adjusting and optimizing the control parameters of the saline-alkali environment adaptability control system and combining with the adaptability evaluation results of grass carps, the adaptability improvement plan of grass carps can be dynamically improved. With the real-time monitoring and feedback of the adaptability of grass carps during the aquaculture process, the control system can more precisely adjust the saline-alkali environment parameters, maximize the adaptability of grass carps in the saline-alkali environment, and thus improve the aquaculture efficiency.

[0120] Figure 4 The block diagram of a system for improving the aquaculture adaptability of grass carps in a saline-alkali environment according to the present invention is shown.

[0121] In a second aspect of the present invention, a system 4 for improving the adaptability of grass carp farming in saline-alkali environments is also provided. The system includes: a memory 41 and a processor 42. The memory includes a program for a method for improving the adaptability of grass carp farming in saline-alkali environments. When the program for the method for improving the adaptability of grass carp farming in saline-alkali environments is executed by the processor, the following steps are implemented:

[0122] Obtain grass carp at different growth stages and place them in a preset saline-alkali environment for farming. Obtain video data of the activity behaviors of the grass carp during the farming process, and identify the activity behavior characteristics of the grass carp during the farming process based on an image recognition algorithm;

[0123] Construct an adaptability analysis model for grass carp, import the activity behavior characteristics into the adaptability analysis model for grass carp to evaluate the adaptability of grass carp at different growth stages to the saline-alkali environment, and determine the optimal growth stage for improving the adaptability of grass carp to the saline-alkali environment according to the adaptability;

[0124] Carry out saline-alkali environment adaptability farming on the grass carp at the optimal growth stage, obtain water quality condition data of the farming environment, analyze the water quality condition data, and construct a saline-alkali environment adaptability control system for grass carp;

[0125] Real-time monitor the adaptability changes of grass carp in the saline-alkali farming environment, and determine a parameter adjustment strategy for the saline-alkali environment adaptability control system according to the adaptability changes.

[0126] The present invention discloses a method and a system for improving the adaptability of grass carp farming in saline-alkali environments. The present invention realizes the improvement of the adaptability of grass carp in saline-alkali environments through the following steps: First, place grass carp at different growth stages in a saline-alkali environment for farming, and use image recognition technology to obtain the activity behavior data of the grass carp; Then, construct an adaptability analysis model for grass carp, evaluate the adaptability of grass carp at different growth stages to the saline-alkali environment, and determine the optimal growth stage for improvement; Next, carry out adaptability farming on the grass carp at the optimal growth stage, and construct a saline-alkali environment adaptability control system through water quality data analysis; Finally, real-time monitor the adaptability changes of grass carp and adjust the control system parameters to optimize the farming environment. The present invention improves the growth and survival ability of grass carp in saline-alkali environments through comprehensive evaluation and dynamic regulation, and provides a scientific technical solution for aquaculture in saline-alkali land.

[0127] In several embodiments provided by this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0128] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit. The above integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0130] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0131] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0132] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. A method for improving the adaptability of grass carp farming in saline-alkali environments, characterized in that, It includes the following steps: Obtain grass carps at different growth stages and place them in a preset saline-alkali environment for breeding. Obtain the video data of the grass carps' activity behaviors during the breeding process, and identify the activity behavior characteristics of the grass carps during the breeding process based on an image recognition algorithm; Construct a grass carp adaptability analysis model, import the activity behavior characteristics into the grass carp adaptability analysis model to evaluate the saline-alkali environment adaptability of grass carps at different growth stages, and determine the optimal growth stage for the grass carps to carry out saline-alkali environment adaptability improvement according to the saline-alkali environment adaptability; Carry out saline-alkali environment adaptability breeding on the grass carps at the optimal growth stage, obtain the water quality condition data of the breeding environment, analyze the water quality condition data, and construct a grass carp saline-alkali environment adaptability control system; Monitor the adaptability changes of the grass carps in the breeding saline-alkali environment in real time, and determine the parameter adjustment strategy of the saline-alkali environment adaptability control system according to the adaptability changes.

2. The method for improving the adaptability of grass carp farming in saline-alkali environment according to claim 1, wherein, The step of obtaining grass carps at different growth stages and placing them in a preset saline-alkali environment for breeding, obtaining the video data of the grass carps' activity behaviors during the breeding process, and identifying the activity behavior characteristics of the grass carps during the breeding process based on an image recognition algorithm is specifically as follows: Obtain the saline-alkali degree data of the suitable growth environment of the grass carps and the target saline-alkali degree data for the saline-alkali environment adaptability improvement of the grass carps, and determine the initial adapted saline-alkali degree of the grass carps according to the saline-alkali degree data of the suitable growth environment and the target saline-alkali degree data; Adjust the saline-alkali degree of the preset saline-alkali environment according to the initial adapted saline-alkali degree, obtain grass carps at different growth stages and place them in the preset saline-alkali environment for breeding operations, and obtain the video data of the grass carps' activity behaviors during the breeding process; Obtain the appearance feature data of the grass carps, and construct a grass carp feature extraction model by training the appearance feature data based on a convolutional neural network; Introduce the SSD target detection algorithm to convert the activity behavior video data into video frame images, import the video frame images into the grass carp feature extraction model for grass carp appearance feature extraction, and generate a multi-scale feature map of the grass carps according to the extracted grass carp appearance features; Generate default boxes for the multi-scale feature map, predict the offset of the default boxes through a convolutional layer in the default boxes, and correct the boundaries of the default boxes according to the offset to obtain the bounding boxes of each grass carp individual in the video frame images; Obtain the coordinate information of the bounding boxes, construct a state transition matrix based on the coordinate position information of the grass carp individual bounding boxes in each video frame image by the SORT algorithm, analyze the state transition matrix according to the SORT algorithm, identify the moving positions of the grass carps at different growth stages in the activity behavior video data over time, and draw the movement trajectories of the grass carps according to the moving positions over time; Identify the activity behavior characteristics of the grass carps during the breeding process according to the grass carp movement trajectories, including jumping out of the water, swimming speed characteristics, turning behavior characteristics, group aggregation characteristics, activity area characteristics, and movement frequency characteristics.

3. The method for improving the adaptability of grass carp farming in saline-alkali environment according to claim 1, wherein, Construct the grass carp adaptability analysis model, import the activity behavior characteristics into the grass carp adaptability analysis model to evaluate the adaptability of grass carp to saline-alkali environments at different growth stages, and determine the optimal growth stage for grass carp to improve its adaptability to saline-alkali environments according to the adaptability to saline-alkali environments. Specifically: Obtain historical adaptability evaluation data for evaluating the environmental adaptability of grass carp based on the historical activity behavior characteristics of grass carp. Analyze the historical adaptability evaluation data based on the fuzzy logic algorithm to construct a membership function for each activity behavior characteristic's impact on the environmental adaptability of grass carp. Obtain the influence weight data of each activity behavior characteristic on the environmental adaptability of grass carp, construct a grass carp adaptability analysis model according to the membership function and the influence weight data, import the activity behavior characteristics into the grass carp adaptability analysis model to calculate the environmental adaptability membership degree of each behavior characteristic, and construct a membership degree matrix from the environmental adaptability membership degrees. Perform a weighted scoring operation on the activity behavior characteristics according to the membership degree matrix and the influence weight data to obtain the environmental adaptability scores of grass carp at different growth stages, and evaluate the adaptability of grass carp at different growth stages to saline-alkali environments in a preset saline-alkali environment according to the environmental adaptability scores. Determine the optimal growth stage for grass carp to improve its adaptability to saline-alkali environments according to the adaptability to saline-alkali environments.

4. A method for improving the adaptability of grass carp farming in saline-alkali environments according to claim 1, characterized in that, Carry out saline-alkali environment adaptive breeding for grass carp at the optimal growth stage, obtain water quality condition data of the breeding environment, analyze the water quality condition data, and construct a grass carp saline-alkali environment adaptability control system. Specifically: Place the grass carp at the optimal growth stage in a target breeding pond for saline-alkali environment adaptive breeding, construct a three-dimensional model of the target breeding pond, and divide the three-dimensional model into N small regions according to a preset size. Obtain the water quality condition data of each small region in the target breeding pond after a preset breeding period for the grass carp. Introduce the spectral clustering algorithm, calculate the similarity between the water quality condition data of each small region according to the Gaussian kernel function, and construct a similarity matrix according to the similarity. Calculate the degree matrix according to the similarity matrix, calculate the Laplacian matrix of the graph according to the degree matrix, perform eigenvalue decomposition on the Laplacian matrix, and calculate the eigenvalues and eigenvectors of the Laplacian matrix. Use the first k eigenvectors as new data point representations, with each row representing the eigenvector of a data point in the water quality condition data, and perform a clustering operation on the k-dimensional eigenvectors based on the K-means clustering algorithm to obtain the clustering results of the water quality condition data of each small region in the target breeding pond. Identify the distribution regions of the water quality conditions in the target breeding pond according to the clustering results, and determine the installation positions of the equipment of the grass carp saline-alkali environment adaptability control system according to the distribution regions of the water quality conditions. The control system equipment includes saline-alkali adjustment equipment, inlet and outlet positions, temperature control equipment, and water quality adjustment equipment. Conduct control system installation according to the installation positions of the control system equipment to construct a grass carp saline-alkali environment adaptability control system.

5. The method for improving the adaptability of grass carp farming in saline-alkali environment according to claim 1, characterized in that, Real-time monitor the adaptive changes of grass carp in the saline-alkali aquaculture environment, and determine the parameter adjustment strategy of the saline-alkali environment adaptive control system according to the adaptive changes, specifically as follows: Calculate the difference between the suitable saline-alkali degree data of grass carp and the target saline-alkali degree data for the improvement of the saline-alkali environment adaptability of grass carp, and divide the difference according to a preset step size to obtain the saline-alkali degree gradient elevation table for the improvement of the saline-alkali environment adaptability of grass carp; Control the saline-alkali environment adaptive control system according to the minimum saline-alkali degree value of the saline-alkali degree gradient elevation table, obtain the periodic video image data of grass carp in the minimum saline-alkali degree aquaculture environment at a preset time period, and extract the periodic activity behavior characteristics of grass carp in each period; Import the periodic activity behavior characteristics into the grass carp adaptability analysis model to evaluate the saline-alkali environment adaptability of grass carp in each period, and construct adaptability time series data for the saline-alkali environment adaptability of grass carp in each period; Construct an adaptability prediction model according to the XGBoost algorithm, construct the adaptability time series data into lag feature data, divide the lag feature data into a training set and a prediction set according to a preset ratio, and import the training set into the adaptability prediction model for training operations; Import the prediction set into the trained adaptability prediction model to predict the adaptive changes of grass carp in a preset future time period, and obtain an adaptability prediction result; Determine the adjustment time of the next saline-alkali gradient in the saline-alkali degree gradient elevation table according to the adaptability prediction result, adjust the saline-alkali parameters of the saline-alkali environment adaptive control system according to the adjustment time, and re-monitor the adaptability of grass carp in each time period after the saline-alkali parameter adjustment to obtain the second adaptability time series data; Re-train the adaptability prediction model according to the second adaptability time series data and predict the adjustment time of the next saline-alkali gradient to obtain the parameter adjustment strategy of the saline-alkali environment adaptive control system; Adjust the saline-alkali environment adaptive control system according to the parameter adjustment strategy, and real-time monitor the adaptation consistency of grass carp. If the adaptation consistency is less than a preset value, optimize the parameter adjustment strategy to construct an optimized scheme for the improvement of the saline-alkali environment adaptability of grass carp.

6. A method for improving the adaptability of grass carp farming in saline-alkali environments according to claim 5, characterized in that, Real-time monitor the adaptation consistency of grass carp. If the adaptation consistency is less than a preset value, optimize the parameter adjustment strategy to obtain an optimized scheme for the improvement of grass carp adaptability, specifically as follows: Obtain the saline-alkali environment adaptability data of all grass carp in the target aquaculture pond after adjusting the saline-alkali environment adaptive control system according to the parameter adjustment strategy; Map the saline-alkali environment adaptability data into a preset adaptability numerical interval, and determine the proportion information of the number of grass carp in each preset adaptability numerical interval; Evaluate the adaptation consistency of grass carp in the target aquaculture pond according to the proportion information of the number of grass carp in each preset adaptability numerical interval to obtain the evaluation result of the saline-alkali environment adaptation consistency; According to the evaluation result of the salinity-alkalinity environment consistency, if the adaptation consistency is lower than the preset value, the grass carps in the target breeding pond with adaptability greater than or equal to the first preset value are designated as first-class adaptable grass carps, the grass carps with adaptability greater than or equal to the second preset value and less than the first preset value are designated as second-class adaptable grass carps, and the grass carps less than the second preset value are designated as third-class adaptable grass carps; The first-class adaptable grass carps are adaptively improved according to the current parameter adjustment strategy; Obtain a preset monitoring quantity of second-class adaptable grass carps and place them in a preset test breeding environment. Obtain the current control parameters of the salinity-alkalinity environment adaptability control system controlled according to the parameter adjustment strategy and the current salinity-alkalinity of the target breeding pond. Analyze the current control parameters according to the genetic algorithm to construct a breeding test parameter group; Control the environmental condition parameters of the preset test breeding environment according to the breeding test parameter group, record in real time the improvement of the environmental adaptability of the second-class adaptable grass carps during the test breeding process, and identify the optimal control parameters of the salinity-alkalinity environment adaptability control system under the current salinity-alkalinity according to the improvement of the environmental adaptability; Perform a breeding zoning operation on the target breeding pond, place the second-class adaptable grass carps in the same zone, and optimize the breeding environment parameters of the zone where the second-class adaptable grass carps are located according to the optimal control parameters; Perform an operation to remove the salinity-alkalinity environment adaptability improvement on the third-class adaptable grass carps to obtain an optimized plan for the salinity-alkalinity environment adaptability improvement of grass carps.

7. A system for improving the adaptability of grass carp farming in saline-alkali environments, characterized in that, The grass carp breeding adaptability improvement system for the salinity-alkalinity environment includes a memory and a processor. The memory includes a program for the grass carp breeding adaptability improvement method in the salinity-alkalinity environment. When the program for the grass carp breeding adaptability improvement method in the salinity-alkalinity environment is executed by the processor, the following steps are implemented: Obtain grass carps at different growth stages and breed them in a preset salinity-alkalinity environment. Obtain the video data of the activity behaviors of the grass carps during the breeding process, and identify the activity behavior characteristics of the grass carps during the breeding process based on the image recognition algorithm; Construct a grass carp adaptability analysis model, import the activity behavior characteristics into the grass carp adaptability analysis model to evaluate the salinity-alkalinity environment adaptability of grass carps at different growth stages, and determine the most suitable growth stage for the grass carps to carry out the salinity-alkalinity environment adaptability improvement according to the salinity-alkalinity environment adaptability; Carry out the salinity-alkalinity environment adaptability breeding on the grass carps at the most suitable growth stage, obtain the water quality condition data of the breeding environment, analyze the water quality condition data, and construct a grass carp salinity-alkalinity environment adaptability control system; Monitor in real time the adaptability changes of the grass carps in the breeding salinity-alkalinity environment, and determine the parameter adjustment strategy of the salinity-alkalinity environment adaptability control system according to the adaptability changes.

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