A method and system for improving hatching management efficiency of brooding shrimp

Abstract

The present invention relates to the technical field of aquaculture management, and in particular to a method and system for improving the hatching management efficiency of brooding shrimp. Feature extraction and denoising are performed on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; the feature point set corresponding to each morphological feature image is registered to obtain a feature point three-dimensional model; a morphological feature three-dimensional model diagram of the target red claw crayfish is constructed based on the feature point three-dimensional model; the morphological feature three-dimensional model diagram of the target red claw crayfish is imported into a knowledge base to estimate the egg carrying capacity of the target red claw crayfish to obtain the egg carrying capacity of the target red claw crayfish; and the target red claw crayfish are classified and screened based on the egg carrying capacity of the target red claw crayfish. This method can effectively improve the accuracy and efficiency of estimating the egg carrying capacity of the target red claw crayfish, while rationally utilizing resources, saving management costs, and improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture management, and in particular to a method and system for improving hatching management efficiency of brooding shrimp. Background Art

[0002] The egg carrying capacity of the parents is a key indicator for measuring the reproductive performance of red claw crayfish. In the early stage of red claw crayfish breeding, the egg carrying capacity of shrimps with different egg carrying capacity has different requirements for nutrients such as oxygen, trace elements and protein. Red claw crayfish can hatch and produce shrimplets after about 6-7 weeks after carrying eggs. In this process, if the differences in nutritional needs of the parents are ignored, the management efficiency during the hatching of the seedlings will be low. For example, it is impossible to increase or decrease the amount of feed for individuals with different egg carrying capacity, and blind feeding will also cause a certain amount of feed waste. Therefore, it is necessary to measure the egg carrying capacity of parent shrimps during the breeding and seedling raising process, and centrally manage parent shrimps with smaller differences in egg carrying capacity. While making rational use of resources, it can also save management costs and improve management efficiency. However, the accuracy and efficiency of the existing methods for measuring the egg carrying capacity of parent shrimps are low, which affects management efficiency. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for improving hatching management efficiency of brooding shrimp.

[0004] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention discloses a method for improving hatching management efficiency of brooding shrimp, comprising the following steps:

[0006] Acquiring regional images of the target area at multiple angles, and performing image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles;

[0007] Perform feature extraction and denoising on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; perform registration on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model;

[0008] Constructing a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish according to the three-dimensional model of the characteristic points of the target red claw crayfish;

[0009] A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish to obtain the egg carrying capacity of the target red claw crayfish;

[0010] The target red claw crayfish are classified, screened and processed according to their egg carrying capacity, so that parents with different egg carrying capacity can be raised separately.

[0011] More specifically, regional images of the target area are acquired at multiple angles, and image segmentation processing is performed on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles, specifically:

[0012] Acquire regional images of the target area at multiple angles, convert the regional images into grayscale images, extract each pixel in the grayscale image and convert it into feature information, so as to represent each pixel in the grayscale image as a feature vector;

[0013] Randomly initialize several cluster centers, calculate the Euclidean distance between each pixel in the grayscale image and each cluster center, and assign it to the cluster with the cluster center with the closest Euclidean distance;

[0014] Calculate the feature average of all pixels in each cluster based on the feature vector of each pixel in each cluster and use it as the new cluster center;

[0015] Repeat the above steps until the cluster center of each cluster no longer changes, and output the clustering results;

[0016] According to the clustering results, the pixels belonging to the background cluster are marked as background, and the pixels of other clusters are marked as foreground. A binary image is generated based on the marked pixels; the background is black and the foreground is white;

[0017] The binary image is segmented based on a watershed algorithm to remove the background of the regional image and obtain a morphological feature image of the target red claw crayfish.

[0018] More specifically, feature extraction and denoising are performed on each morphological feature image to obtain a feature point set corresponding to each morphological feature image, specifically:

[0019] The SURF feature point detection algorithm is introduced to perform feature extraction processing on each morphological feature image to obtain several feature points of each morphological feature image;

[0020] According to the several feature points of each morphological feature image, a set of unvisited feature points is created, and each feature point is marked as unvisited;

[0021] The DBSCAN algorithm is introduced, and the neighborhood radius and minimum number of neighbors are preset. The feature points in each feature point set are visited one by one, and the number of neighbors of each visited feature point within the preset radius is counted;

[0022] Compare the number of neighbors of each visited feature point within a preset radius with the minimum number of neighbors;

[0023] If the number of neighbors of a certain visited feature point within the preset radius is greater than or equal to the minimum number of neighbors, the visited feature point is marked as a key feature point;

[0024] If the number of neighbors of a certain visited feature point within the preset radius is less than the minimum number of neighbors, the visited feature point is marked as a noise feature point;

[0025] Repeat the above steps until all feature points in each feature point set are visited; filter out the feature points marked as noise feature points in each feature point set to obtain the feature point set corresponding to each morphological feature image.

[0026] More specifically, the feature point sets corresponding to each morphological feature image are registered to obtain a feature point three-dimensional model, specifically:

[0027] Obtain a feature descriptor for each feature point in each feature point set, and calculate the cosine similarity between any two feature points in each feature point set based on the feature descriptor of each feature point;

[0028] Mark two feature points whose cosine similarity is greater than the preset similarity threshold as a matching feature point pair, and so on, to obtain several pairs of matching feature point pairs;

[0029] Constructing a spatial three-dimensional coordinate system, importing each feature point set into the spatial three-dimensional coordinate system, and performing pairing processing on each feature point set based on matching feature point pairs to obtain an initial feature point three-dimensional model;

[0030] Dividing the initial feature point three-dimensional model into a plurality of sub-regions, and counting the total number of feature points in each sub-region;

[0031] If the total number of feature points in a sub-region is less than the preset threshold, the sub-region is marked as a low-density region; if the total number of feature points in a sub-region is greater than the preset threshold, the sub-region is marked as a high-density region;

[0032] If a subregion is marked as a low-density region, the relative coordinate values ​​of the feature points in the subregion in the three-dimensional spatial coordinate system are obtained, and the median coordinate points between any two feature points in the subregion are obtained according to the relative coordinate values, and each obtained median coordinate point is set as the new feature point of the subregion to convert the subregion into a high-density region;

[0033] Repeat the above steps until all areas marked as low-density areas are converted into high-density areas, and obtain the three-dimensional model of the feature points of the target red claw crayfish.

[0034] More specifically, a 3D model diagram of the morphological characteristics of the target red claw crayfish is constructed based on the 3D model of the characteristic points of the target red claw crayfish, specifically:

[0035] Obtaining a three-dimensional model of characteristic points of a target red claw crayfish, randomly selecting a characteristic point in the three-dimensional model as a coordinate origin, constructing a Cartesian coordinate system based on the coordinate origin, and determining the direction and unit of the coordinate axis;

[0036] Obtaining the relative positional relationship between the feature points in the Cartesian coordinate system, and connecting the feature points using line segments based on the relative positional relationship between the feature points;

[0037] Obtaining information about connecting lines between characteristic points, performing surface fitting processing on the connecting lines between characteristic points according to a surface fitting algorithm, and generating a surface model of the target red claw crayfish;

[0038] The surface model of the target red claw crayfish is smoothed and patched to obtain a three-dimensional model of the morphological characteristics of the target red claw crayfish.

[0039] More specifically, a knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish, thereby obtaining the egg carrying capacity of the target red claw crayfish, specifically:

[0040] collecting characteristic image data of red claw crayfish in various brood size ranges through a big data network, constructing a knowledge base, and dividing the knowledge base into a plurality of sub-storage spaces; and assigning a label to each sub-storage space based on the various brood size ranges;

[0041] constructing characteristic three-dimensional models of red claw crayfish in various egg-carrying ranges based on characteristic image data of red claw crayfish in various egg-carrying ranges; and storing the characteristic three-dimensional models of red claw crayfish in various egg-carrying ranges in sub-storage spaces assigned corresponding labels.

[0042] Obtaining a morphological feature three-dimensional model image of a target red claw crayfish, and calculating the degree of overlap between the morphological feature three-dimensional model image and the feature three-dimensional model images in each sub-storage space based on geometric hashing to obtain a plurality of overlap degrees;

[0043] Extracting a maximum degree of overlap from a plurality of degrees of overlap, marking a sub-storage space corresponding to the maximum degree of overlap, and extracting a label of the marked sub-storage space;

[0044] The egg carrying capacity of the target red claw crayfish is estimated according to the labels of the marked sub-storage spaces.

[0045] A second aspect of the present invention discloses a system for improving the hatching management efficiency of brooding shrimp. The system includes a memory and a processor. The memory stores a method program for improving the hatching management efficiency of brooding shrimp. When the method program for improving the hatching management efficiency of brooding shrimp is executed by the processor, the following steps are implemented:

[0046] Acquiring regional images of the target area at multiple angles, and performing image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles;

[0047] Perform feature extraction and denoising on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; perform registration on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model;

[0048] Constructing a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish according to the three-dimensional model of the characteristic points of the target red claw crayfish;

[0049] A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish to obtain the egg carrying capacity of the target red claw crayfish;

[0050] The target red claw crayfish are classified, screened and processed according to their egg carrying capacity, so that parents with different egg carrying capacity can be raised separately.

[0051] The present invention solves the technical deficiencies in the prior art and has the following beneficial effects: acquiring regional images of a target area at multiple angles, performing image segmentation processing on the regional images, and obtaining morphological feature images of the target red claw crayfish at multiple angles; performing feature extraction and denoising processing on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; performing registration processing on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model; constructing a morphological feature three-dimensional model diagram of the target red claw crayfish based on the feature point three-dimensional model of the target red claw crayfish; constructing a knowledge base in advance, and importing the morphological feature three-dimensional model diagram of the target red claw crayfish into the knowledge base to estimate the egg carrying capacity of the target red claw crayfish, thereby obtaining the egg carrying capacity of the target red claw crayfish; and classifying and screening the target red claw crayfish based on the egg carrying capacity of the target red claw crayfish, so that parents with different egg carrying capacities can be reared separately. This method not only improves the accuracy and efficiency of red claw crayfish egg carrying capacity estimation, but also greatly saves the time and effort of manual analysis, and has high practical value and application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0053] Figure 1A flow chart of an overall method for improving hatchery management efficiency of brooding shrimp;

[0054] Figure 2 A partial flow chart of a method for improving hatchery management efficiency of brooding shrimp;

[0055] Figure 3 The following is a system block diagram of a system for improving the hatching management efficiency of brooded shrimp. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0058] like Figure 1 As shown, the first aspect of the present invention discloses a method for improving the hatching management efficiency of brooding shrimp, comprising the following steps:

[0059] S102: acquiring regional images of the target area at multiple angles, and performing image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles;

[0060] S104: performing feature extraction and denoising on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; performing registration on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model;

[0061] S106: constructing a morphological feature three-dimensional model diagram of the target red claw crayfish according to the feature point three-dimensional model of the target red claw crayfish;

[0062] S108: constructing a knowledge base in advance, and importing the three-dimensional model of the morphological characteristics of the target red claw crayfish into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish, thereby obtaining the egg carrying capacity of the target red claw crayfish;

[0063] S110: Classifying and screening the target red claw crayfish according to their egg carrying capacity, so as to raise parents with different egg carrying capacity separately.

[0064] It should be noted that, first, images of the target area are acquired at multiple angles and segmented to obtain morphological images of the target red claw crayfish at different angles. Next, these morphological images are subjected to feature extraction and denoising to obtain a clear and accurate set of feature points. Through registration, these feature points are integrated into a 3D model, thereby constructing a 3D model of the target red claw crayfish's morphological characteristics. To improve the accuracy of egg-carrying capacity determination, a knowledge base is pre-established and the 3D model of the target red claw crayfish's morphological characteristics is imported into it. Using this knowledge base, the system can estimate the egg-carrying capacity of the target red claw crayfish, quickly and accurately obtaining a relatively accurate egg-carrying capacity value. Finally, based on the egg-carrying capacity of the target red claw crayfish, the system can perform classification and screening, separating parents with different egg-carrying capacities to optimize breeding management and improve aquaculture efficiency and yield. The entire process demonstrates a complete technical chain from image acquisition, processing, analysis, to knowledge application, achieving automated and precise egg-carrying capacity determination and management. The application of artificial intelligence technology not only improves the efficiency and accuracy of measurement, but also provides scientific decision-making support for the aquaculture industry, helps to realize intelligent aquaculture management, and promotes the sustainable development of the red claw crayfish aquaculture industry.

[0065] More specifically, if Figure 2 As shown, regional images of the target area are obtained at multiple angles, and image segmentation processing is performed on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles, specifically:

[0066] S202: Acquire regional images of the target area at multiple angles, convert the regional images into grayscale images, extract each pixel in the grayscale image and convert it into feature information, so as to represent each pixel in the grayscale image as a feature vector;

[0067] S204: randomly initialize several cluster centers, calculate the Euclidean distance between each pixel in the grayscale image and each cluster center, and assign the pixel to the cluster with the cluster center closest to the Euclidean distance;

[0068] S206: Calculate the feature average of all pixels in each cluster based on the feature vector of each pixel in each cluster, and use it as the new cluster center;

[0069] S208: Repeat the above steps until the cluster center of each cluster no longer changes, and output the clustering result;

[0070] S210: Marking pixels belonging to the background cluster as background according to the clustering result, marking pixels of other clusters as foreground, and generating a binary image based on the marked pixels; wherein the background is black and the foreground is white;

[0071] S212: Segmenting the binary image based on a watershed algorithm to remove the background of the regional image and obtain a morphological feature image of the target red claw crayfish.

[0072] It should be noted that first, the regional image is converted into a grayscale image, and each pixel is converted into a feature vector. Then, by randomly initializing the cluster center and calculating the Euclidean distance between each pixel and the cluster center, the pixel is assigned to the cluster with the nearest cluster center. Next, a new cluster center is calculated based on the feature vector of the pixels in each cluster, and this process is repeated until the cluster center no longer changes. Finally, based on the clustering results, the pixels are labeled as background or foreground, generating a binary image. The binary image is then segmented using the watershed algorithm to remove the background and obtain the morphological feature image of the target red claw crayfish. This method can effectively remove background interference and extract the morphological features of the target red claw crayfish, providing a basis for subsequent analysis and processing.

[0073] More specifically, feature extraction and denoising are performed on each morphological feature image to obtain a feature point set corresponding to each morphological feature image, specifically:

[0074] The SURF feature point detection algorithm is introduced to perform feature extraction processing on each morphological feature image to obtain several feature points of each morphological feature image;

[0075] According to the several feature points of each morphological feature image, a set of unvisited feature points is created, and each feature point is marked as unvisited;

[0076] The DBSCAN algorithm is introduced, and the neighborhood radius and minimum number of neighbors are preset. The feature points in each feature point set are visited one by one, and the number of neighbors of each visited feature point within the preset radius is counted;

[0077] Compare the number of neighbors of each visited feature point within a preset radius with the minimum number of neighbors;

[0078] If the number of neighbors of a certain visited feature point within the preset radius is greater than or equal to the minimum number of neighbors, the visited feature point is marked as a key feature point;

[0079] If the number of neighbors of a certain visited feature point within the preset radius is less than the minimum number of neighbors, the visited feature point is marked as a noise feature point;

[0080] Repeat the above steps until all feature points in each feature point set are visited; filter out the feature points marked as noise feature points in each feature point set to obtain the feature point set corresponding to each morphological feature image.

[0081] It should be noted that image feature extraction and noise removal are performed based on the SURF feature point detection algorithm and the DBSCAN clustering algorithm. Specifically, the SURF algorithm is used to extract key feature points from each morphological feature image. These feature points typically represent significant features in the image, such as edges and corners. In this way, each morphological feature image is converted into a set of feature points describing its important structure. Next, an unvisited state list is created for this set of feature points to ensure that the same feature point is not visited repeatedly during processing, ensuring the algorithm's efficiency and accuracy. The DBSCAN algorithm is then applied to the set of feature points, visiting and analyzing them using a preset neighborhood radius and a minimum number of neighbors as parameters. During this process, each feature point is counted for the number of neighbors within its preset radius, which forms the basis for the DBSCAN algorithm to identify clusters. By comparing the number of neighbors of each visited feature point with the minimum number of neighbors, the algorithm identifies those with sufficient neighboring feature points as "key feature points" and labels those with insufficient neighboring feature points as "noise feature points." Key feature points are usually located on important structures or features of the image, while noise feature points may be non-typical feature points caused by image noise, edge blur, etc. Ultimately, by traversing all feature point sets and marking noise feature points, effective screening of feature points of each morphological feature image is achieved. After removing noise feature points, the remaining key feature point set retains the core information of the morphological feature image, providing a high-quality data foundation for subsequent image analysis, recognition, matching and other tasks. This method achieves efficient extraction of key features and accurate removal of noise in complex images; improves the stability of feature points and reduces the problem of feature instability caused by image changes (such as lighting, angle changes, etc.); provides high-quality feature descriptors for subsequent image processing tasks, thereby improving the robustness of the system.

[0082] More specifically, the feature point sets corresponding to each morphological feature image are registered to obtain a feature point three-dimensional model, specifically:

[0083] Obtain a feature descriptor for each feature point in each feature point set, and calculate the cosine similarity between any two feature points in each feature point set based on the feature descriptor of each feature point;

[0084] Mark two feature points whose cosine similarity is greater than the preset similarity threshold as a matching feature point pair, and so on, to obtain several pairs of matching feature point pairs;

[0085] Constructing a spatial three-dimensional coordinate system, importing each feature point set into the spatial three-dimensional coordinate system, and performing pairing processing on each feature point set based on matching feature point pairs to obtain an initial feature point three-dimensional model;

[0086] Dividing the initial feature point three-dimensional model into a plurality of sub-regions, and counting the total number of feature points in each sub-region;

[0087] If the total number of feature points in a sub-region is less than the preset threshold, the sub-region is marked as a low-density region; if the total number of feature points in a sub-region is greater than the preset threshold, the sub-region is marked as a high-density region;

[0088] If a subregion is marked as a low-density region, the relative coordinate values ​​of the feature points in the subregion in the three-dimensional spatial coordinate system are obtained, and the median coordinate points between any two feature points in the subregion are obtained according to the relative coordinate values, and each obtained median coordinate point is set as the new feature point of the subregion to convert the subregion into a high-density region;

[0089] Repeat the above steps until all areas marked as low-density areas are converted into high-density areas, and obtain the three-dimensional model of the feature points of the target red claw crayfish.

[0090] It should be noted that, first, feature descriptors for the feature points in each feature point set are obtained, and the cosine similarity between any two feature points is calculated. Feature points with a similarity greater than a preset threshold are marked as matching feature point pairs. Then, each feature point set is imported into a three-dimensional spatial coordinate system, and pairing is performed based on the matching feature point pairs to obtain an initial feature point 3D model. Next, the initial feature point 3D model is divided into several subregions, and the total number of feature points in each subregion is counted. Based on the comparison of the total number of feature points with a preset threshold, the subregions are marked as low-density or high-density. For low-density regions, the relative coordinates and median coordinates of the feature points are calculated, and the median coordinate point is set as a new feature point, thereby converting the low-density region into a high-density region. This process is repeated until all low-density regions are converted to high-density regions, ultimately obtaining a feature point 3D model of the target red claw crayfish. Feature point matching and density analysis enable more accurate 3D model construction, reducing the impact of noise and mismatches. By converting low-density regions into high-density regions, the model's detail and accuracy are increased, better reflecting the shape and structure of the target object. Matching and density analysis of feature points can improve the model's robustness to varying viewing angles, lighting, and noise. This method can effectively construct a three-dimensional model of the target red claw crayfish's feature points, improving the model's accuracy, detail, and robustness, effectively increasing the accuracy of measuring the crayfish's egg load.

[0091] More specifically, a 3D model diagram of the morphological characteristics of the target red claw crayfish is constructed based on the 3D model of the characteristic points of the target red claw crayfish, specifically:

[0092] Obtaining a three-dimensional model of characteristic points of a target red claw crayfish, randomly selecting a characteristic point in the three-dimensional model as a coordinate origin, constructing a Cartesian coordinate system based on the coordinate origin, and determining the direction and unit of the coordinate axis;

[0093] Obtaining the relative positional relationship between the feature points in the Cartesian coordinate system, and connecting the feature points using line segments based on the relative positional relationship between the feature points;

[0094] Obtaining information about connecting lines between characteristic points, performing surface fitting processing on the connecting lines between characteristic points according to a surface fitting algorithm, and generating a surface model of the target red claw crayfish;

[0095] The surface model of the target red claw crayfish is smoothed and patched to obtain a three-dimensional model of the morphological characteristics of the target red claw crayfish.

[0096] It should be noted that a feature point is randomly selected from the three-dimensional model of the target red claw crayfish's characteristic points as the coordinate origin, and a Cartesian coordinate system is established, determining the directions and units of the coordinate axes. Then, the relative positional relationships between the feature points are obtained in the Cartesian coordinate system, and they are connected using line segments. Next, information about the connecting lines is obtained and surface fitting is performed on the connecting lines using a surface fitting algorithm to generate a surface model of the target red claw crayfish. Finally, the surface model is smoothed and patched to obtain the final three-dimensional model of the morphological characteristics. By establishing a Cartesian coordinate system and connecting the feature points, the morphological characteristics of the target red claw crayfish, including its shape, size, and structure, can be accurately represented. The surface fitting algorithm can generate a smooth surface model based on the information about the connecting lines, improving the model's accuracy and realism. This method can effectively construct a three-dimensional model of the target red claw crayfish's morphological characteristics.

[0097] More specifically, a knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish, thereby obtaining the egg carrying capacity of the target red claw crayfish, specifically:

[0098] collecting characteristic image data of red claw crayfish in various brood size ranges through a big data network, constructing a knowledge base, and dividing the knowledge base into a plurality of sub-storage spaces; and assigning a label to each sub-storage space based on the various brood size ranges;

[0099] constructing characteristic three-dimensional models of red claw crayfish in various egg-carrying ranges based on characteristic image data of red claw crayfish in various egg-carrying ranges; and storing the characteristic three-dimensional models of red claw crayfish in various egg-carrying ranges in sub-storage spaces assigned corresponding labels.

[0100] Obtaining a morphological feature three-dimensional model image of a target red claw crayfish, and calculating the degree of overlap between the morphological feature three-dimensional model image and the feature three-dimensional model images in each sub-storage space based on geometric hashing to obtain a plurality of overlap degrees;

[0101] Extracting a maximum degree of overlap from a plurality of degrees of overlap, marking a sub-storage space corresponding to the maximum degree of overlap, and extracting a label of the marked sub-storage space;

[0102] The egg carrying capacity of the target red claw crayfish is estimated according to the labels of the marked sub-storage spaces.

[0103] First, a large amount of characteristic image data within the brooding range of red claw crayfish was collected through a big data network, and a knowledge base was constructed. This knowledge base was divided into several sub-storage spaces, each labeled with a specific brooding range. This facilitates subsequent retrieval and analysis, allowing the model to quickly locate the brooding range that most closely matches the target red claw crayfish. Next, using the characteristic image data, three-dimensional models of red claw crayfish with various brooding ranges were constructed and stored in corresponding sub-storage spaces according to their corresponding brooding ranges. This organization enables the model to efficiently access the brooding range model that most closely resembles the target red claw crayfish. When measuring and estimating the brooding range of a target red claw crayfish, a three-dimensional model of its morphological characteristics is first obtained. A geometric hashing algorithm is then used to calculate the degree of overlap between the target model and the models within each sub-storage space to identify the brooding range that best matches the target model. The higher the overlap, the more similar the target model is to the model for that brooding range. Among all calculated overlaps, the maximum overlap is selected and the corresponding sub-storage space is labeled. This sub-storage space is then labeled as its egg-carrying capacity range. Finally, based on this egg-carrying capacity range, the approximate egg-carrying capacity of the target red claw crayfish can be estimated. By building a knowledge base and utilizing machine learning techniques, it is possible to efficiently manage and rapidly retrieve large amounts of red claw crayfish image data. By calculating the overlap, the egg-carrying capacity model that most closely resembles the target red claw crayfish can be accurately found, allowing for precise egg-carrying capacity estimation. This method not only improves the accuracy and efficiency of red claw crayfish egg-carrying capacity estimation but also significantly reduces the time and effort required for manual analysis. While rationally utilizing resources, it also reduces management costs and improves efficiency, demonstrating its high practical value and potential for application.

[0104] In addition, the method further comprises the following steps:

[0105] Obtaining preset incubation plans for parents with different egg gravidities, and determining various preset incubation parameters for each parent incubation device when incubating the parents with different egg gravidities based on the preset incubation plans; the incubation parameters include water temperature, salinity, pH value, and oxygen concentration;

[0106] Acquiring various real-time incubation parameters of each parent incubation device, calculating the difference between the various real-time incubation parameters of each parent incubation device and the corresponding preset incubation parameters, and obtaining the deviation value of the various real-time incubation parameters of each parent incubation device;

[0107] If the deviation value of a certain real-time incubation parameter of a parent incubation device is greater than a preset deviation value, the parent incubation device is marked as an abnormal parent incubation device, and the real-time incubation parameter of the abnormal parent incubation device with a deviation value greater than the preset deviation value is obtained;

[0108] Obtaining hatching function characteristic information of each sub-hatching module in the abnormal parent hatching device, and analyzing the correlation between the real-time hatching parameters of the abnormal parent hatching device having a deviation value greater than a preset deviation value and the hatching function characteristic information of each sub-hatching module in the abnormal parent hatching device based on a grey correlation analysis method;

[0109] Obtaining a sub-hatching module with a correlation greater than a preset correlation, which is defined as a correlation sub-hatching module; obtaining real-time operating parameters of the correlation sub-hatching module within a preset time period; the operating parameters include current, voltage, and temperature;

[0110] The real-time working parameters of the relevant sub-incubation module within a preset time period are imported into the support vector machine model for fault diagnosis analysis to obtain the failure probability of the relevant sub-incubation module. If the failure probability is greater than a preset threshold, the cause of the failure is obtained and sent to the user end.

[0111] It should be noted that the incubation parameters of parents with different egg loads are first determined, and then the deviation between the real-time incubation parameters and the preset parameters is calculated. The abnormal equipment is marked and the real-time incubation parameters with larger deviation values ​​are obtained. Next, the correlation between the abnormal parameters and the sub-incubation modules is analyzed by gray correlation analysis to obtain the sub-incubation modules with larger correlations and their working parameters. Finally, the support vector machine model is used to perform fault diagnosis on the relevant sub-incubation modules. If the fault probability is greater than the preset threshold, the cause of the fault is obtained and sent to the user end. This method can realize real-time monitoring and fault diagnosis of parent incubation equipment, improve the reliability and stability of the incubation equipment, reduce the probability of faults, and thus improve incubation efficiency and incubation quality. At the same time, this method can also help users to promptly discover and solve fault problems, reduce maintenance costs and downtime, and improve management efficiency.

[0112] like Figure 3As shown, the second aspect of the present invention discloses a system for improving the hatching management efficiency of brooding shrimp. The system for improving the hatching management efficiency of brooding shrimp includes a memory 41 and a processor 62. The memory 41 stores a method program for improving the hatching management efficiency of brooding shrimp. When the method program for improving the hatching management efficiency of brooding shrimp is executed by the processor 62, the following steps are implemented:

[0113] Acquiring regional images of the target area at multiple angles, and performing image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles;

[0114] Perform feature extraction and denoising on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; perform registration on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model;

[0115] Constructing a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish according to the three-dimensional model of the characteristic points of the target red claw crayfish;

[0116] A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish to obtain the egg carrying capacity of the target red claw crayfish;

[0117] The target red claw crayfish are classified, screened and processed according to their egg carrying capacity, so that parents with different egg carrying capacity can be raised separately.

[0118] In the several embodiments provided in 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 schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: 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, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0119] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0120] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0121] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0122] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, 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 a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0123] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for improving the hatching management efficiency of brooding shrimp, characterized in that: The following steps are involved: Acquiring regional images of the target area at multiple angles, and performing image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles; Perform feature extraction and denoising on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; perform registration on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model; Constructing a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish according to the three-dimensional model of the characteristic points of the target red claw crayfish; A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish to obtain the egg carrying capacity of the target red claw crayfish; The target red claw crayfish are classified and screened according to their egg carrying capacity, so that parents with different egg carrying capacity can be reared separately; A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to estimate the egg carrying capacity of the target red claw crayfish, and the egg carrying capacity of the target red claw crayfish is obtained, specifically: collecting characteristic image data of red claw crayfish in various brood size ranges through a big data network, constructing a knowledge base, and dividing the knowledge base into a plurality of sub-storage spaces; and assigning a label to each sub-storage space based on the various brood size ranges; According to the characteristic image data of red claw crayfish in various egg carrying ranges, characteristic three-dimensional model diagrams of red claw crayfish in various egg carrying ranges are constructed; The characteristic three-dimensional model images of red claw crayfish with various egg carrying capacity ranges are stored in the sub-storage spaces assigned with corresponding labels; Obtaining a morphological feature three-dimensional model image of a target red claw crayfish, and calculating the degree of overlap between the morphological feature three-dimensional model image and the feature three-dimensional model images in each sub-storage space based on geometric hashing to obtain a plurality of overlap degrees; Extracting a maximum degree of overlap from a plurality of degrees of overlap, marking a sub-storage space corresponding to the maximum degree of overlap, and extracting a label of the marked sub-storage space; The egg carrying capacity of the target red claw crayfish is estimated according to the labels of the marked sub-storage spaces.

2. A method for improving hatching management efficiency of brooding shrimp according to claim 1, characterized in that, Acquire regional images of the target area at multiple angles, and perform image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles, specifically: Acquire regional images of the target area at multiple angles, convert the regional images into grayscale images, extract each pixel in the grayscale image and convert it into feature information, so as to represent each pixel in the grayscale image as a feature vector; Randomly initialize several cluster centers, calculate the Euclidean distance between each pixel in the grayscale image and each cluster center, and assign it to the cluster with the cluster center with the closest Euclidean distance; Calculate the feature average of all pixels in each cluster based on the feature vector of each pixel in each cluster and use it as the new cluster center; Repeat the above steps until the cluster center of each cluster no longer changes, and output the clustering results; According to the clustering results, the pixels belonging to the background cluster are marked as background, and the pixels of other clusters are marked as foreground. A binary image is generated based on the marked pixels; the background is black and the foreground is white; The binary image is segmented based on a watershed algorithm to remove the background of the regional image and obtain a morphological feature image of the target red claw crayfish.

3. A method for improving hatching management efficiency of brooding shrimp according to claim 1, characterized in that, Perform feature extraction and denoising on each morphological feature image to obtain the feature point set corresponding to each morphological feature image, specifically: The SURF feature point detection algorithm is introduced to perform feature extraction processing on each morphological feature image to obtain several feature points of each morphological feature image; According to the several feature points of each morphological feature image, a set of unvisited feature points is created, and each feature point is marked as unvisited; The DBSCAN algorithm is introduced, and the neighborhood radius and minimum number of neighbors are preset. The feature points in each feature point set are visited one by one, and the number of neighbors of each visited feature point within the preset radius is counted; Compare the number of neighbors of each visited feature point within a preset radius with the minimum number of neighbors; If the number of neighbors of a certain visited feature point within the preset radius is greater than or equal to the minimum number of neighbors, the visited feature point is marked as a key feature point; If the number of neighbors of a certain visited feature point within the preset radius is less than the minimum number of neighbors, the visited feature point is marked as a noise feature point; Repeat the above steps until all feature points in each feature point set are visited; filter out the feature points marked as noise feature points in each feature point set to obtain the feature point set corresponding to each morphological feature image.

4. A method for improving hatching management efficiency of brooding shrimp according to claim 1, characterized in that, The feature point sets corresponding to each morphological feature image are registered to obtain a three-dimensional model of feature points, specifically: Obtain a feature descriptor for each feature point in each feature point set, and calculate the cosine similarity between any two feature points in each feature point set based on the feature descriptor of each feature point; Mark two feature points whose cosine similarity is greater than the preset similarity threshold as a matching feature point pair, and so on, to obtain several pairs of matching feature point pairs; Constructing a spatial three-dimensional coordinate system, importing each feature point set into the spatial three-dimensional coordinate system, and performing pairing processing on each feature point set based on matching feature point pairs to obtain an initial feature point three-dimensional model; Dividing the initial feature point three-dimensional model into a plurality of sub-regions, and counting the total number of feature points in each sub-region; If the total number of feature points in a sub-region is less than the preset threshold, the sub-region is marked as a low-density region; if the total number of feature points in a sub-region is greater than the preset threshold, the sub-region is marked as a high-density region; If a subregion is marked as a low-density region, the relative coordinate values ​​of the feature points in the subregion in the three-dimensional spatial coordinate system are obtained, and the median coordinate points between any two feature points in the subregion are obtained according to the relative coordinate values, and each obtained median coordinate point is set as the new feature point of the subregion to convert the subregion into a high-density region; Repeat the above steps until all areas marked as low-density areas are converted into high-density areas, and obtain the three-dimensional model of the feature points of the target red claw crayfish.

5. A method for improving hatching management efficiency of brooding shrimp according to claim 1, characterized in that, A three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is constructed based on the three-dimensional model of the characteristic points of the target red claw crayfish, specifically: Obtaining a three-dimensional model of characteristic points of a target red claw crayfish, randomly selecting a characteristic point in the three-dimensional model as a coordinate origin, constructing a Cartesian coordinate system based on the coordinate origin, and determining the direction and unit of the coordinate axis; Obtaining the relative positional relationship between the feature points in the Cartesian coordinate system, and connecting the feature points using line segments based on the relative positional relationship between the feature points; Obtaining information about connecting lines between characteristic points, performing surface fitting processing on the connecting lines between characteristic points according to a surface fitting algorithm, and generating a surface model of the target red claw crayfish; The surface model of the target red claw crayfish is smoothed and patched to obtain a three-dimensional model of the morphological characteristics of the target red claw crayfish.

6. A system for improving hatching management efficiency of brooding shrimp, characterized in that: The system for improving hatching management efficiency of brooded shrimp includes a memory and a processor. The memory stores a method program for improving hatching management efficiency of brooded shrimp. When the method program for improving hatching management efficiency of brooded shrimp is executed by the processor, the following steps are implemented: Acquiring regional images of the target area at multiple angles, and performing image segmentation processing on the regional images to obtain morphological feature images of the target red claw crayfish at multiple angles; Perform feature extraction and denoising on each morphological feature image to obtain a feature point set corresponding to each morphological feature image; perform registration on the feature point set corresponding to each morphological feature image to obtain a feature point three-dimensional model; Constructing a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish according to the three-dimensional model of the characteristic points of the target red claw crayfish; A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to perform an estimation process on the egg carrying capacity of the target red claw crayfish to obtain the egg carrying capacity of the target red claw crayfish; The target red claw crayfish are classified and screened according to their egg carrying capacity, so that parents with different egg carrying capacity can be reared separately; A knowledge base is constructed in advance, and a three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is imported into the knowledge base to estimate the egg carrying capacity of the target red claw crayfish, and the egg carrying capacity of the target red claw crayfish is obtained, specifically: collecting characteristic image data of red claw crayfish in various brood size ranges through a big data network, constructing a knowledge base, and dividing the knowledge base into a plurality of sub-storage spaces; and assigning a label to each sub-storage space based on the various brood size ranges; According to the characteristic image data of red claw crayfish in various egg carrying ranges, characteristic three-dimensional model diagrams of red claw crayfish in various egg carrying ranges are constructed; The characteristic three-dimensional model images of red claw crayfish with various egg carrying capacity ranges are stored in the sub-storage spaces assigned with corresponding labels; Obtaining a morphological feature three-dimensional model image of a target red claw crayfish, and calculating the degree of overlap between the morphological feature three-dimensional model image and the feature three-dimensional model images in each sub-storage space based on geometric hashing to obtain a plurality of overlap degrees; Extracting a maximum degree of overlap from a plurality of degrees of overlap, marking a sub-storage space corresponding to the maximum degree of overlap, and extracting a label of the marked sub-storage space; The egg carrying capacity of the target red claw crayfish is estimated according to the labels of the marked sub-storage spaces.

7. A system for improving hatching management efficiency of brooding shrimp according to claim 6, characterized in that: The feature point sets corresponding to each morphological feature image are registered to obtain a three-dimensional model of feature points, specifically: Obtain a feature descriptor for each feature point in each feature point set, and calculate the cosine similarity between any two feature points in each feature point set based on the feature descriptor of each feature point; Mark two feature points whose cosine similarity is greater than the preset similarity threshold as a matching feature point pair, and so on, to obtain several pairs of matching feature point pairs; Constructing a spatial three-dimensional coordinate system, importing each feature point set into the spatial three-dimensional coordinate system, and performing pairing processing on each feature point set based on matching feature point pairs to obtain an initial feature point three-dimensional model; Dividing the initial feature point three-dimensional model into a plurality of sub-regions, and counting the total number of feature points in each sub-region; If the total number of feature points in a sub-region is less than the preset threshold, the sub-region is marked as a low-density region; if the total number of feature points in a sub-region is greater than the preset threshold, the sub-region is marked as a high-density region; If a subregion is marked as a low-density region, the relative coordinate values ​​of the feature points in the subregion in the three-dimensional spatial coordinate system are obtained, and the median coordinate points between any two feature points in the subregion are obtained according to the relative coordinate values, and each obtained median coordinate point is set as the new feature point of the subregion to convert the subregion into a high-density region; Repeat the above steps until all areas marked as low-density areas are converted into high-density areas, and obtain the three-dimensional model of the feature points of the target red claw crayfish.

8. The system for improving hatching management efficiency of brooding shrimp according to claim 6, characterized in that: A three-dimensional model diagram of the morphological characteristics of the target red claw crayfish is constructed based on the three-dimensional model of the characteristic points of the target red claw crayfish, specifically: Obtaining a three-dimensional model of characteristic points of a target red claw crayfish, randomly selecting a characteristic point in the three-dimensional model as a coordinate origin, constructing a Cartesian coordinate system based on the coordinate origin, and determining the direction and unit of the coordinate axis; Obtaining the relative positional relationship between the feature points in the Cartesian coordinate system, and connecting the feature points using line segments based on the relative positional relationship between the feature points; Obtaining information about connecting lines between characteristic points, performing surface fitting processing on the connecting lines between characteristic points according to a surface fitting algorithm, and generating a surface model of the target red claw crayfish; The surface model of the target red claw crayfish is smoothed and patched to obtain a three-dimensional model of the morphological characteristics of the target red claw crayfish.

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