Integrated device and method for intelligently transferring sea cucumbers into pool and monitoring and predicting growth of sea cucumbers

Abstract

The invention provides a sea cucumber intelligent pool pouring and growth monitoring and prediction integrated device and a sea cucumber intelligent pool pouring and growth monitoring and prediction integrated method, a sea cucumber pool pouring process and sea cucumber growth monitoring are organically integrated, in the sea cucumber pool pouring process, a conveyor belt arranged between an original pool and a new pool is used for conveying sea cucumbers, and an image acquisition module is arranged on the conveyor belt to obtain sea cucumber images; a sea cucumber is identified by using a pre-constructed visual identification model through an image identification module, an identification frame is drawn by taking the longest position and the widest position of the edge of the sea cucumber as critical points, and the actual size of the identification frame is calculated by a data processing module; the prediction model detects and predicts the sea cucumber growth character data based on the relationship between the actual size of the identification frame and the sea cucumber growth character, selects a plurality of fitting models with the highest confidence coefficient which is greater than a threshold value, and calculates the average value of the fitting models to obtain the growth monitoring data of the sea cucumber size and the prediction data of the wet weight and the dry weight; and intelligent monitoring of the growth state of the sea cucumbers is realized.

Description

Technical Field

[0001] The invention relates to the technical field of sea cucumber breeding, and provides a device and method for integrating intelligent pond inversion, growth monitoring and prediction of sea cucumbers. Background Art

[0002] Sea cucumbers have become a valuable fishery resource due to their unique nutritional value and market demand. In the future, with economic development and increased acceptance, the demand for sea cucumber products among Chinese residents will further increase. During the sea cucumber farming process, sea cucumbers are farmed in breeding ponds and need to be regularly drained. The main purpose is to clean the sediments at the bottom of the pond, improve the breeding environment, prevent the occurrence of diseases, and provide a healthier and cleaner growth environment for sea cucumbers. The time interval for draining sea cucumber ponds is usually controlled by factors such as the water temperature, feed feeding, and the amount of food and excretion of sea cucumbers. During the normal feeding period of sea cucumbers, the ponds usually need to be drained once every 1-2 weeks. If the sea cucumbers eat a lot or the water quality deteriorates, the draining cycle needs to be further shortened.

[0003] During the sea cucumber farming process, the growth status of sea cucumbers needs to be monitored regularly to determine whether their growth conditions are in line with expectations. If abnormal conditions such as growth stagnation are found, timely measures can be taken such as adjusting feed formulas, improving water quality, etc. to promote the normal growth of sea cucumbers.

[0004] Currently, monitoring the growth status of sea cucumbers usually requires staff to detect with the naked eye, which has a low degree of intelligence and a large workload. Regular pond emptying is a necessary part of the sea cucumber farming process. If the sea cucumber pond emptying process can be combined with the sea cucumber growth status detection process, the workload of sea cucumber farming can be reduced. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the first object of the present invention is to provide a sea cucumber intelligent pond inversion and growth monitoring and prediction integrated device, which combines the sea cucumber pond inversion process with the sea cucumber growth monitoring process, including:

[0006] Aquaculture ponds, the aquaculture ponds comprising original ponds currently culturing sea cucumbers and new ponds for replacement;

[0007] A conveyor belt, which is arranged between the original pond and the new pond and is used to transport sea cucumbers;

[0008] An image acquisition module, which is arranged on the conveyor belt and is used to acquire images containing sea cucumbers;

[0009] An image recognition module, wherein the image recognition module obtains an image containing a sea cucumber, identifies the sea cucumber using a pre-built visual recognition model, draws an identification frame using the longest and widest parts of the sea cucumber edge as critical points, records the size of the identification frame, and sequentially sorts each identification frame in the identification area, and counts the total number of sea cucumber individuals;

[0010] A data processing module, wherein the data processing module obtains the size of the recognition frame and calculates the actual size of the recognition frame;

[0011] A prediction module, wherein the prediction module obtains the actual size of the identification frame, uses a pre-built prediction model, takes the actual size of the sea cucumber identification frame as a prediction parameter, selects multiple fitting models with the highest confidence and a confidence greater than a threshold, and calculates the average value of the fitting model to obtain the monitoring data of the sea cucumber size and the prediction data of the wet weight and dry weight.

[0012] Specifically, the conveyor belt is a U-shaped conveyor belt, which is arranged on the outside of the breeding pond. The original pond and the new pond are spaced apart. A fishing mechanism is arranged in the breeding pond. The fishing mechanism includes a transverse track, a movable frame and a fishing shovel. The transverse track is arranged on the breeding pond. A movable frame that can move along the transverse track is arranged on the transverse track. A fishing shovel that can be connected to the movable frame through a lifting rope is arranged in the breeding pond.

[0013] Specifically, the mobile frame includes a longitudinal track arranged on the transverse track and a frame body installed on the longitudinal track, a rack is arranged on one side of the longitudinal track, a driving motor fixedly installed in the frame body is arranged above the rack, a gear is arranged on the output shaft of the driving motor, and the gear is meshed in the rack, a first rope winding shaft and a second rope winding shaft are arranged in the frame body, two rope winding drums are arranged in each of the first rope winding shaft and the second rope winding shaft, a lifting rope that can be connected to a fishing shovel is wound in the rope winding drum, and a first lifting motor and a second lifting motor are arranged at the ends of the first rope winding shaft and the second rope winding shaft, respectively.

[0014] Specifically, the fishing shovel includes a bottom plate, a rear plate and a side plate, an opening is arranged on the front side of the fishing shovel, the width of the fishing shovel matches the width of the breeding pond, a plurality of drainage holes are arranged in the bottom plate of the fishing shovel, a plurality of filter holes are arranged in the rear plate of the fishing shovel, and a hanging ring is arranged on the top of the fishing shovel.

[0015] The second object of the present invention is to provide a method for integrating intelligent pond inversion, growth monitoring and prediction of sea cucumbers, comprising:

[0016] The sea cucumbers are caught and placed on a conveyor belt, and a calibrated image acquisition module captures images containing the sea cucumbers;

[0017] The pre-built visual recognition model recognizes the sea cucumber image, draws a recognition frame with the longest and widest parts of the sea cucumber edge as critical points, and records the size of the recognition frame;

[0018] Obtain the actual length and width of a single pixel of the image acquisition module, calculate the actual size of the identification frame, and sort each identification frame in turn to count the total number of sea cucumber individuals;

[0019] The actual size of the identification frame is loaded as a prediction parameter into a pre-constructed prediction model. The prediction model monitors and predicts the sea cucumber growth trait data based on the relationship between the actual size of the identification frame and the growth traits of the sea cucumber, selects multiple fitting models with the highest confidence and confidence greater than a threshold, and calculates the average value of the fitting model to obtain the monitoring data of the sea cucumber size and the prediction data of the wet weight and dry weight.

[0020] Specifically, the image recognition module adopts an improved YOLOv8-seacucumber visual recognition model to identify sea cucumbers, identifies targets with confidence greater than a threshold as sea cucumbers and draws an identification box. The YOLOv8-seacucumber visual recognition model includes an input end, a backbone network and a detection head. On the basis of the original YOLOv8 visual recognition model, a C2f-F-SA module is used to replace the C2f module in the backbone network and the detection head. The C2f-F-SA module includes a convolutional layer, a segmentation layer, a plurality of FasterBlock structures, a connection layer and a convolutional layer arranged in sequence. The bottleneck layer in the original C2f module is replaced by a FasterBlock structure, and a Shuffle Attention mechanism is added to the FasterNet Block module; some convolutional layers of the detection head are replaced by Adown modules.

[0021] Specifically, the Shuffle Attention mechanism includes an input end, a segmentation layer, an upper branch, a lower branch, a connection layer and an output end;

[0022] Input port input sea cucumber characteristics Figure X ∈R (C，H，W) , the sea cucumber feature map is divided into G groups according to the channel dimension, expressed as:

[0023] X=[X 1 , …, X G ], X k ∈R (C / G×H×W) ,

[0024] Where C is the number of channels of the sea cucumber feature map, H is the spatial height of the sea cucumber feature map, W is the spatial width of the sea cucumber feature map, R represents the set of all images in the dataset, and X k is the input feature;

[0025] The segmentation layer takes as input the feature X k Along the channel dimension, it is divided into upper branch X K1 With the lower branch X K2 , X K1 , X K2 ∈R (C / 2G×H×W) , the upper branch uses channel attention and transforms the parameter w 1 , b 1 Scaling and displacement generate channel information S First, global average pooling (GAP) is performed to embed global information to generate channel information S∈R (C / 2G×1×1) , the channel information is obtained by k1 It is calculated by shrinking in the spatial dimension H×W,

[0026]

[0027] Use the fully connected layer to transform the channel information S and apply the σ activation function (sigmoid) to generate the weight matrix and multiply it by X k1 , and get the weighted features Figure X ' x1 :

[0028] X′ k1 =σ(F C (S))·X k1 =σ(w 1 S+b 1 )·X k1 ,

[0029] In the formula, w 1 ∈R (C / 2G×1×1) , b 1 ∈R (C / 2G×1×1) ;

[0030] The lower branch uses spatial attention to capture the spatial dependencies between features and transforms the parameter w 2 , b 2 Scaling and shifting the amount of spatial information, using the group norm (GN) to generate statistics of spatial attention:

[0031] X′ k2 =σ(w 2 GN(X k2 )+b 2 )·X k2 ,

[0032] In the formula, w 2 ∈R (C / 2G×1×1) , b 2 ∈R (C / 2G×1×1) ;

[0033] The connection layer processes the X′ obtained by the upper and lower branchesK1 and X′ K2 Combined, the output feature map has the same size as the input.

[0034] X′ k =[X′ k1 , X′ k2 ]R (C / G×H×W) .

[0035] Specifically, the ADown module includes an input end, an average pooling layer, a segmentation layer, a first path, a second path, a connection layer, and an output end.

[0036] Input characteristics Figure X ∈R (C，H，W) , the average pooling layer takes the input feature Figure X The average pooling feature is obtained by performing an average pooling operation through an average pooling layer with a pooling kernel k of size 2, a step size s of 1, and no padding (p = 0). Figure X avg ,

[0037] X avg =Avgpool(X, k=2, s=1, p=0),

[0038] The segmentation layer will average the pooled features Figure X avg Split into two sub-features along the channel dimension Figure X 1 and X 2 , sub-feature Figure X 1 and X 2 Downsampling operations are performed simultaneously through the first path and the second path to obtain the feature map Y 1 and Y 2 ,

[0039] The first path is,sub-feature Figure X 1 Directly downsample through a traditional convolutional layer to obtain the feature map Y 1 ,

[0040] Y 1 =Conv 1 (X 1 , k=3, s=2, p=1);

[0041] The second path is,sub-feature Figure X 2 First, a maximum pooling layer with a pooling kernel size k of 3, a step size s of 2, and a padding p of 1 is used to extract the maximum value X in the local window. 2, highlighting the significant features. Subsequently, the feature map is further extracted and refined through a convolution layer with a convolution kernel k of size 1, a step size s of 1, and a padding p of 0, to obtain the feature map Y 2 ,

[0042] X 2，max =MaxPool(X 2 , k=3, s=2, p=1),

[0043] Y 2 =Conv 2 (X 2，max , k=1, s=1, p=0),

[0044] Two learnable weight factors α and β are introduced in the connection layer. Through the training process optimization, the downsampling results are weighted and fused. The weight factors α and β control Y 1 and Y 2 In the fusion process, the contribution of the weight factors α and β are both 1×1×1. The feature map Y after weighted fusion is fusion for:

[0045] Y fusion =α·Y 1 +β·Y 2 ,

[0046] In the formula, · represents element-by-element multiplication,

[0047] Normalize the weight factors α and β to ensure that their sum is 1.

[0048]

[0049] The weighted fusion feature map is recalculated using the normalized weight factor:

[0050] Y fusion =α norm ·Y 1 +β norm ·Y 2 ,

[0051] From the output end, the weighted fused feature map Y fusion As output, its dimension is Where C' is the number of output channels.

[0052] Specifically, the YOLOv8-seacucumber visual recognition model construction method is as follows: grouping sea cucumbers according to their regions and species, and performing sampling training, obtaining photos of each sea cucumber under natural conditions with different shooting angles, different lighting and different backgrounds, and dividing the pictures into a training set and a test set in proportion, manually annotating the sea cucumbers in the training set with a bounding box, performing model training on all labeled sea cucumber image information through a convolutional neural network, testing the model with a test set, manually reviewing the training results, and obtaining a visual recognition model.

[0053] Specifically, the prediction model construction method is: based on the marked sea cucumber images and sea cucumber growth trait data, the actual size of the identification box of the same variety of sea cucumbers in the same area is fitted with its growth trait data, and multiple regression models and machine learning are used to perform model training to obtain the prediction model.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The present invention organically combines the process of sea cucumber pond inversion with sea cucumber growth monitoring, monitors the growth status of sea cucumbers during the process of sea cucumber pond inversion, and provides intelligent sea cucumber pond inversion equipment, which can automatically catch sea cucumbers and put them on a conveyor belt, use the improved YOLOv8-seacucumber visual recognition model to identify sea cucumbers, draw an identification frame with the longest and widest parts of the edge of the sea cucumber as critical points, and calculate the real size of the identification frame based on the size of the identification frame. The real size of the identification frame is used as a prediction parameter, and a pre-built prediction model is used to select multiple fitting models to obtain the growth monitoring data of the sea cucumber size and the prediction data of the wet weight and dry weight, thereby realizing the intelligent monitoring of the growth status of sea cucumbers and effectively reducing the workload of sea cucumber farming.

[0056] 2. The present invention adopts an improved YOLOv8-seacucumber visual recognition model to identify sea cucumbers. On the basis of the original YOLOv8 visual recognition model, the C2f-F-SA module is used to replace the C2f module in the backbone network and the detection head. The C2f-F-SA module includes a convolutional layer, a segmentation layer, a FasterBlock structure, a connection layer and a convolutional layer arranged in sequence. The bottleneck layer in the original C2f module is replaced by the FasterBlock structure to improve the feature extraction efficiency of the network and reduce the computational complexity in the backbone network, thereby more effectively utilizing the computing resources of the device and further improving the ability of the backbone network in spatial feature extraction. The key features of the sea cucumber in the image can be effectively extracted, and the model's perception ability of the input sea cucumber spatial features can be enhanced. At the same time, the amount of calculation can be reduced and the detection speed can be increased, thereby improving the overall performance of the model in the sea cucumber image feature extraction task. The Shuffle Attention mechanism is added to the FasterNet Block module, and the convolutional layer of the detection head is replaced with the Adown module to prevent potential information loss during the downsampling process and improve the model's sensitivity to detail features. At the same time, a weight factor learning mechanism is introduced to achieve more refined downsampling result fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the overall structure of the first embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the structure of the fishing mechanism of the first embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the structure of the frame and the fishing shovel according to the first embodiment of the present invention;

[0060] Figure 4 It is a schematic diagram of the overall structure of the second embodiment of the present invention;

[0061] Figure 5 Determine the chessboard pattern for the present invention;

[0062] Figure 6 It is a structural block diagram of the YOLOv8-seacucumber visual recognition model of the present invention;

[0063] Figure 7 It is a structural block diagram of the C2f-F-SA module of the present invention;

[0064] Figure 8 It is a structural block diagram of the FasterNet Block structure of the present invention;

[0065] Fig. 9This is a structural diagram of the Shuffle Attention mechanism of the present invention;

[0066] Fig.10 It is a structural block diagram of the ADown module of the present invention;

[0067] Fig.11 This is the recognition effect diagram of the YOLOv8-seacucumber visual recognition model of the present invention;

[0068] Fig.12 It is the fitting model of the sea cucumber mass and the length and width of the identification frame of the present invention.

[0069] Figure numerals: 1. breeding pond; 11. original pond; 12. new pond; 2. conveyor belt; 21. mounting frame; 22. camera; 23. partition plate; 3. fishing mechanism; 31. horizontal track; 32. fishing shovel; 321. bottom plate; 322. side plate; 323. back plate; 324. drain hole; 325. filter hole; 326. lifting ring; 33. mobile frame; 331. longitudinal track; 332. frame; 333. rack; 334. gear; 335. driving motor; 336. first rope winding shaft; 337. second rope winding shaft; 338. first lifting motor; 339. second lifting motor; 3310. rope winding drum; 3311. lifting rope; 4. guide ramp; 5. guide rail. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] A device integrating intelligent pond emptying, growth monitoring and prediction of sea cucumbers comprises a breeding pond 1, a conveyor belt 2, an image acquisition module and a host arranged on the conveyor belt 2. The image acquisition module takes photos of sea cucumbers transported by the conveyor belt 2 during the pond emptying process, obtains the size of the sea cucumbers to monitor their growth status, and uses a constructed prediction model to predict the dry weight and wet weight of the sea cucumbers.

[0072] As a first embodiment of the present invention, Figure 1-Figure 3As shown, the integrated device for intelligent inverting and growth monitoring and prediction of sea cucumbers includes a breeding pond 1, which includes an original pond 11 and a new pond 12. Due to long-term use, the water quality in the original pond 11 has deteriorated, and pollutants such as sea cucumber feces and residual bait may be precipitated and adsorbed on the bottom and wall of the pond. The new pond 12 is a breeding pond 1 for receiving sea cucumbers caught from the original pond 11, and has a clean cultivation environment in the new pond 12. A fishing mechanism 3 is provided in the breeding pond 1. A U-shaped conveyor belt 2 connecting the original pond 11 and the new pond 12 is provided on the outside of the breeding pond 1, and an image acquisition module is provided on the conveyor belt 2. A host for processing data and information is also provided.

[0073] The fishing mechanism 3 includes a transverse track 31, a fishing shovel 32 and a mobile frame 33. The transverse track 31 is arranged on the wall of the culture pond 1. The mobile frame 33 can move along the transverse track 31. The transverse track 31 is used to carry the mobile frame 33. The mobile frame 33 moves along the transverse track 31 to the original pond 11 to be emptied. The mobile frame 33 is arranged on the transverse track 31, and the fishing shovel 32 is arranged in the culture pond 1.

[0074] The fishing shovel 32 includes a bottom plate 321, a rear plate 323 and a side plate 322, and an opening is provided on the front side thereof, and an inclined surface is provided in the bottom plate 321 and the side plate 322. The width of the fishing shovel 32 matches the width of the culture pond 1. When the fishing shovel 32 moves along the culture pond 1, the sea cucumbers adsorbed on the bottom and wall of the pond can be shoveled and collected in the fishing shovel 32. A plurality of drainage holes 324 are provided in the bottom plate 321 of the fishing shovel 32, and a drainage groove is provided at the angle between the bottom plate 321 and the rear plate 323, which is used to drain the water in the fishing shovel 32 when the fishing shovel 32 is lifted. A plurality of water filtering holes 325 are provided in the rear plate 323 to balance the front and rear water pressures of the fishing shovel 32 when the fishing shovel 32 moves in the culture pond 1. Hanging rings 326 are provided at the four corners of the top of the fishing shovel 32, and the fishing shovel 32 is connected to the mobile frame 33 provided on the culture pond 1 through a hanging rope 3311. In the normal breeding state of sea cucumbers, the catching shovel 32 is placed at one end of the breeding pond 1, and when the pond is turned over, the hanging ring 326 is connected to the mobile frame 33 to catch the sea cucumbers cultured in the breeding pond 1.

[0075] The moving frame 33 includes a longitudinal track 331, which is arranged on the transverse track 31. A frame 332 is installed on the longitudinal track 331, and the moving frame 33 can be pushed to move the moving frame 33 to the top of the pool to be emptied. A rack 333 is arranged on one side of the longitudinal track 331, and a driving motor 335 fixedly installed in the frame 332 is arranged above the rack 333. A gear 334 is fixedly arranged on the output shaft of the driving motor 335, and the gear 334 is meshed in the rack 333. The driving motor 335 makes the frame 332 move along the longitudinal track 331. In the frame 332, a first rope reel 336 and a second rope reel 337 are disposed above the lifting ring 326. A rope reel 3310 is disposed at a position corresponding to the lifting ring 326 in the first rope reel 336 and the second rope reel 337. A rope 3311 is wound in the rope reel 3310. One end of the rope 3311 is disposed in the rope reel, and the other end is connected to the fishing shovel 32. A first lifting motor 338 and a second lifting motor 339 are disposed at the ends of the first rope reel 336 and the second rope reel 337, respectively. The first lifting motor 338 and the second lifting motor 339 control the rotation of the first rope reel 336 and the second rope reel 337, respectively. The first lifting motor 338 and the second lifting motor 339 control the lifting and flipping of the fishing shovel 32.

[0076] When in use, the driving motor 335 drives the frame 332 to move along the breeding pond 1, and the frame 332 pulls the fishing shovel 32 from one end of the breeding pond 1 to the other end. The sea cucumbers in the bottom and wall of the pond are all caught in the fishing shovel 32 in the breeding pond 1, and the fishing shovel 32 is lifted by the first lifting motor 338 and the second lifting motor 339. First, the first lifting motor 338 controls the first winding shaft 336 to rotate, lifts the front side of the fishing shovel 32 to prevent the sea cucumbers in the fishing shovel 32 from falling, and then, the first lifting motor 338 and the second lifting motor 339 move together to lift the fishing shovel 32 from the breeding pond 1, and lift it to a height that matches the height of the fishing shovel 32 with the height of the conveyor belt 2. Finally, the second lifting motor 339 controls the rear side of the movable frame 33 to lift, so that the sea cucumbers collected in the fishing shovel 32 are slowly dumped onto the conveyor belt 2, thereby completing the fishing of the sea cucumbers.

[0077] The conveyor belt 2 is a U-shaped conveyor belt 2, which can rotate forward and reverse, and the direction of the conveyor belt 2 is adjusted according to the positions of the original pool 11 and the new pool 12. The conveyor belt 2 is set on the ground outside the culture pool 1, and the original pool 11 and the new pool 12 are set at intervals. The input end of the conveyor belt 2 is set close to the original pool 11, and the output end of the conveyor belt 2 is set close to the new pool 12. Bottom casters are installed at the bottom of the conveyor belt 2 to facilitate the movement of the conveyor belt 2. A mounting frame 21 is set in the middle of the conveyor belt 2, and an image acquisition module and a fill light are installed in the mounting frame 21. The image acquisition module is a camera 22, and the distance between the camera 22 and the conveyor belt 2 is a known height. The camera 22 takes pictures of the sea cucumbers transported by the conveyor belt 2. Partition columns are provided on both sides of the conveyor belt 2. According to the movement direction of the conveyor belt, a partition plate 23 is installed in the partition column on the output side of the conveyor belt to prevent the sea cucumbers from being stacked when the catching shovel 32 dumps the sea cucumbers onto the conveyor belt 2, which affects the image recognition of the sea cucumbers. A plurality of installation grooves are provided in the partition column along the vertical direction, and the partition plate 23 is engaged in the installation groove. The height of the partition plate can be adjusted according to the size of the sea cucumbers cultured in the breeding pond 1.

[0078] A guide slope 4 is provided at the output end of the transmission belt, and the guide slope 4 can be engaged in the track or installed at the output end of the conveyor belt 2. One end of the guide slope is connected to the output end of the conveyor belt 2, and the other end extends to the bottom of the new pool 12. The sea cucumbers transported by the conveyor belt 2 slide into the new pool 12 through the guide slope 4 to prevent the sea cucumbers from falling.

[0079] As a second embodiment of the present invention, Figure 4 As shown, the original pool 11 and the new pool 12 are arranged at intervals, and the conveyor belt 2 is set above the culture pool 1 between the original pool 11 and the new pool 12. A guide rail 5 is provided on the culture pool 1 to facilitate the movement of the conveyor belt 2. The conveyor belt 2 is installed in the guide rail 5, and the conveyor belt 2 can be pushed to move along the guide rail 5. A mounting frame 21 is provided in the middle of the conveyor belt 2, and an image acquisition module and a fill light are installed in the mounting frame 21. The image acquisition module is a camera 22, and the distance between the camera 22 and the conveyor belt 2 is a preset height. A guide slope 4 is provided at the end of the conveyor belt 2. The staff uses a net bag to scoop the sea cucumber onto the conveyor belt 2, and the camera 22 takes photos of the sea cucumber transported by the conveyor belt 2.

[0080] Before shooting, the camera is calibrated using Zhang's Calibration Method to obtain the actual external parameters and actual internal parameters of the camera. The actual internal parameters of the camera include the camera sensor length, camera sensor width, image length pixel number, image width pixel number, focal length, and distortion coefficient. The actual external parameters of the camera include: horizontal viewing angle and vertical viewing angle. The specific steps of camera calibration are:

[0081] 1. Prepare the calibration plate:

[0082] A high-precision checkerboard pattern of known size is used. Figure 5 As shown, the checkerboard pattern is a real-size square block with each square alternating between black and white.

[0083] 2. Collect calibration images:

[0084] Fix the camera in one position and make sure it does not move during the entire calibration process. Change the position and angle of the calibration plate and take multiple images from different viewing angles. Take at least 10 images to get more accurate results.

[0085] During the shooting process, ensure that different parts of the chessboard are in the camera's field of view; ensure that the calibration plate is shot at different angles and distances to increase diversity and obtain rich projection information; ensure that there is no obvious blur or distortion in the image, keep it clear, and the corners are clearly visible.

[0086] 3. Extract chessboard corner points:

[0087] In the captured image, the checkerboard corner point extraction algorithm in OpenCV is used to extract the inner corner points of the checkerboard in each image (that is, the four corner points of each small square). The position of the corner point of each checkerboard is used as the input data for calibration. These corner points will be used as the geometric relationship data between the camera and the calibration plate. The extraction steps are as follows:

[0088] (1) Corner detection: Use the corner detection algorithm, i.e. cv::findChessboardCorners in OpenCV, to automatically identify the corners of the chessboard from the image;

[0089] (2) Sub-pixel corner positioning: To improve accuracy, the cv::cornerSubPix function in OpenCV is used to accurately locate corners at the sub-pixel level;

[0090] (3) Storing corner point coordinates: The corner points of each image need to be saved and converted into two-dimensional coordinates in the image coordinate system.

[0091] 4. Construct the calibration matrix:

[0092] Convert the corner point coordinates detected in each image into coordinates in the world coordinate system, and construct the camera's intrinsic parameter matrix (including parameters such as focal length and principal point) and extrinsic parameter matrix (including rotation and translation vectors);

[0093] The mathematical model of Zhang's calibration is:

[0094] Assume that point P in the world coordinate system w =(X, Y, Z) T , point P in the camera coordinate system c =(X′, Y′, Z′)T , whose coordinates projected onto the image plane are p = (x, y) T Transformed by the camera projection matrix P:

[0095]

[0096] Where K is the camera's intrinsic matrix, [R|t] is the camera's extrinsic matrix, where R is the rotation matrix, t is the translation vector, and (x, y) is the image coordinate;

[0097] The relationship between image coordinates (x, y) and normalized coordinates (x', y') is:

[0098]

[0099] Where (c x , c y ) is the principal point coordinate, f x and f y is the focal length in the x and y directions;

[0100] The distortion model is:

[0101] x distorted =x′(1+k 1 r 2 +k 2 r 4 +k 3 r 6 )+2p 1 x′y′+p 2 (r 2 +2x′ 2 ),

[0102] y distorted =y′(1+k 1 r 2 +k 2 r 4 +k 3 r 6 )+p 1 (r 2 +2y′ 2 )+2p 2 x′y′,

[0103] where r 2 = x′ 2 +y′ 2 , k 1 , k 2 , k 3 is the radial distortion coefficient, p 1 , p 2 is the tangential distortion coefficient;

[0104] The reprojection error is:

[0105]

[0106] where p i is the observation point in the image, P i is a point in the world coordinate system, K is the intrinsic parameter matrix, R i and t i is the external parameter matrix of the i-th image;

[0107] During the calibration process, the Levenberg-Marquardt algorithm is used to minimize the error between the actual corner points and the reprojected points on each image, and the internal and external parameters of the camera are estimated. Then, the internal and external parameters are iteratively optimized and continuously adjusted to minimize the overall reprojection error.

[0108] 5. Calculate camera parameters:

[0109] After the calibration is completed, the following important parameters are obtained:

[0110] Camera Intrinsic Parameters:

[0111]

[0112] c x and c y are the principal point coordinates, usually close to the center of the image;

[0113] Distortion Coefficients: including radial distortion coefficient (k 1 , k 2 , k 3 ) and tangential distortion coefficient (P 1 , P 2 );

[0114] Camera extrinsic parameters: includes the rotation matrix R and translation vector t of each image, which are used to transform points in the world coordinate system to the camera coordinate system;

[0115] Reprojection Error: It refers to the error between the theoretical projection and the actual corner point position in the image.

[0116] 6. Reprojection error analysis and result verification:

[0117] Using the obtained camera parameters, the corner point coordinates in the world coordinate system are projected back to the image plane, and the error between the reprojected points and the actually detected corner points is calculated to evaluate the accuracy of the calibration. Finally, the calibration result is verified by placing the calibration plate in a known position and using the parameters obtained by calibration to reconstruct its position in three-dimensional space.

[0118] Since the calibrated camera is installed at a fixed position on the conveyor belt, the camera parameters and the distance between the camera and the conveyor belt are fixed, and the internal and external parameters of the camera are determined through calibration, the actual length and width of a single pixel in the photo taken by the calibrated camera can be obtained.

[0119] The host is connected to the camera for communication, and the photos taken by the camera are transmitted to the host. The host includes an image recognition module, a data processing module and a prediction module.

[0120] The image recognition module obtains images containing sea cucumbers and uses a pre-built visual recognition model to identify the sea cucumbers. Sea cucumbers with the same feature points in consecutive images are recorded only once.

[0121] like Figure 6 As shown, the image recognition module uses an improved YOLOv8-seacucumber visual recognition model to identify sea cucumbers and draw an identification frame. The improved YOLOv8-seacucumber visual recognition model includes an input terminal (Input), a backbone network (Backbone) and a detection head (Head). The present invention uses the FasterNet Block module to replace the Bottleneck module in C2f to obtain a C2f-F (C2f-Faster) structure, and adds a ShuffleAttention (SA) attention mechanism in the FasterNet Block module, designs a new C2f-F-SA (C2f-Faster-ShuffleAttention) structure, and replaces the C2f structure in the traditional YOLOv8 visual recognition model with this structure; and replaces all the Conv modules of the Head part in the YOLOv8 visual recognition model with Adown modules. Provides strong technical support for the rapid and accurate identification of sea cucumbers.

[0122] like Figure 7As shown in the figure, the C2f-F-SA module includes a convolutional layer (Conv), a segmentation layer (Split), multiple FasterBlock structures, a connection layer (Concat) and a convolutional layer (Conv) arranged in sequence. The convolutional layer extracts the features of the input data, and the features are divided into multiple branches through the segmentation layer. Each branch further extracts features and fuses information through a FasterBlock structure composed of multiple sub-FasterBlock structures, which are spliced ​​together through the connection layer, and finally the final feature fusion and output are performed through the convolutional layer.

[0123] The traditional C2f structure consists of a series of bottleneck layers, which are responsible for extracting and enhancing local features. In this technical solution, the bottleneck layer structure in the YOLOv8 model is replaced with the FasterBlock structure to improve the feature extraction efficiency of the network. This replacement strategy aims to reduce the computational complexity in the backbone network, thereby more effectively utilizing device computing resources and further improving the backbone network's ability to extract spatial features. Through this replacement, the FasterBlock structure replaces the bottleneck layer structure in C2f, providing strong technical support for the rapid and accurate identification of sea cucumbers.

[0124] The specific steps for replacing the bottleneck layer structure are:

[0125] Step 1: Identification and positioning

[0126] First, we conduct an in-depth analysis of the C2f module of the YOLOv8 model to accurately locate the existing bottleneck layer and prepare for the subsequent replacement process;

[0127] Step 2: Replacement and Integration

[0128] (1) Removal operations, gradually removing the existing bottleneck layers in the C2f module to free up space for the integration of the new FasterBlock;

[0129] (2) Insertion operation: insert the FasterBlock structure into the C2f module according to the position order of the original bottleneck layer to ensure the coherence of the network structure;

[0130] (3) Dimension matching: To ensure the compatibility of FasterBlock with the original network architecture, the input and output dimensions of FasterBlock are adjusted. This process includes adding or removing 1x1 convolutional layers (linear layers) when necessary to achieve dimensional consistency.

[0131] S3: Model reconstruction

[0132] After completing the integration of the FasterBlock structure, recompile the entire model to ensure that all network layers are connected correctly and that information flows smoothly in the model;

[0133] Step 4: Training and Optimization

[0134] The updated model structure is used for training, and during this process, the hyperparameters are carefully adjusted to optimize the performance indicators of the model. Through this series of training and optimization steps, it is ensured that the improved YOLOv8 model provided by the present invention shows excellent performance in the sea cucumber identification task.

[0135] When identifying sea cucumbers, some sea cucumbers account for a small proportion, resulting in an excessively high proportion of the image background. In order to more accurately and quickly identify small and medium-sized individual sea cucumbers, such as Figure 8 As shown in the figure, the SA attention mechanism (ShuffleAttention) is added to the C2f-F structure. When identifying individual sea cucumbers, the SA attention mechanism can effectively extract the key features of sea cucumbers in the image. When identifying normal sea cucumbers and diseased sea cucumbers, it enhances the model's perception of the input sea cucumber spatial features, while reducing the amount of calculation and increasing the detection speed, thereby improving the overall performance of the model in the task of extracting sea cucumber image features.

[0136] like Fig. 9 As shown in the figure, the SA attention mechanism includes an input end (Input), a segmentation layer (Split), an upper branch, a lower branch, a connection layer (Concat) and an output end (Output).

[0137] Input port input sea cucumber characteristics Figure X ∈R (C，H，W) , the sea cucumber feature map is divided into G groups according to the channel dimension, expressed as X = [X 1 , ..., X G ], X k ∈R (C / G×H×W) , where C is the number of channels of the sea cucumber feature map, H is the spatial height of the sea cucumber feature map, W is the spatial width of the sea cucumber feature map, R represents the set of all images in the dataset, and X k is the input feature.

[0138] The segmentation layer takes as input the feature X k Along the channel dimension, it is divided into upper branch X K1 With the lower branch X K2 , X K1 , X K2 ∈R (C / 2G×H×W) , the upper branch uses channel attention and transforms the parameter w 1 , b 1Scaling and displacement generate channel information S, first perform global average pooling (GAP) to embed global information, and generate channel information S∈R (C / 2G×1×1) , the channel information is obtained by k1 It is calculated by shrinking in the spatial dimension H×W,

[0139]

[0140] Use the fully connected layer to transform the channel information S and apply the σ activation function (sigmoid) to generate the weight matrix and multiply it by X k1 , and get the weighted features Figure X ' k1 :

[0141] X′ k1 =σ(F C (S))·X k1 =σ(w 1 S+b 1 )·X k1 ,

[0142] In the formula, w 1 ∈R (C / 2G×1×1) , b 1 ∈R (C / 2G×1×1) ,

[0143] The mathematical expression of the σ activation function is:

[0144] Where z is the input of the function, which can be a scalar, vector, matrix, or higher-dimensional tensor;

[0145] The lower branch uses spatial attention to capture the spatial dependencies between features and transforms the parameter w 2 , b 2 Scaling and shifting the amount of spatial information, using the group norm (GN) to generate statistics of spatial attention:

[0146] X′ k2 =σ(w 2 GN(X k2 )+b 2 )·X k2 ,

[0147] In the formula, w 2 ∈R (C / 2G×1×1) , b 2 ∈R (C / 2G×1×1) .

[0148] The connection layer processes the X′ obtained by the upper and lower branches K1 and X′ K2 Combined, the output feature map has the same size as the input.

[0149] X′ k =[X′ k1 , X′ k2 ]R (C / G×H×W) .

[0150] To address the challenges faced by the backbone network in performing the sea cucumber feature extraction task, an innovative ADown module is used to optimize the downsampling process. Traditional standard convolution downsampling reduces the spatial size of the feature map by increasing the step size. However, this method often leads to a significant reduction in the size of the feature map, so that the sea cucumber area that accounts for a small proportion of the original image may be mistakenly merged into the larger background area, resulting in the loss of key information. This information loss phenomenon makes the features of small sea cucumbers become blurred or diluted during the downsampling process, seriously affecting the model's ability to recognize these targets in subsequent layers.

[0151] like Fig. 9 As shown in the figure, the ADown downsampling method combines the advantages of average pooling (AvgPool2d) and maximum pooling (MaxPool2d), where average pooling is responsible for retaining global information, while maximum pooling emphasizes significant features. By performing convolution operations on the outputs of these two pooling methods respectively, the ADown module can effectively retain multi-level information, thereby preventing potential information loss during the downsampling process and improving the model's sensitivity to detail features. At the same time, a weight factor learning mechanism is introduced to achieve a more refined fusion of downsampling results.

[0152] By replacing the traditional downsampling Conv module with the downsampling Adown module, the model's ability to perceive the target is significantly improved, thereby improving the accuracy and reliability of sea cucumber detection. This innovative design provides a new solution for the field of sea cucumber feature extraction and detection, and has broad application prospects and practical value.

[0153] The ADown module includes an input terminal (Input), an average pooling layer (AvgPool2d), a segmentation layer (Split), a first path (cv1), a second path (cv2), a connection layer (Concat) and an output terminal.

[0154] Input characteristics Figure X ∈R (C，H，W) , the average pooling layer takes the input feature Figure X The average pooling feature is obtained by performing an average pooling operation through an average pooling layer with a pooling kernel k of size 2, a step size s of 1, and no padding (p = 0). Figure X avg ,

[0155] X avg =Avgpool(X, k=2, s=1, p=0).

[0156] The segmentation layer will average the pooled features Figure X avg Split into two sub-features along the channel dimension Figure X 1 and X 2 , sub-feature Figure X 1 and X 2 Downsampling operations are performed simultaneously through the first path and the second path to obtain the feature map Y 1 and Y 2 .

[0157] The first path is: sub-feature Figure X 1 Directly downsample through a traditional convolutional layer to obtain the feature map Y 1 ,

[0158] Y 1 =Conv 1 (X 1 , k=3, s=2, p=1);

[0159] The second path is: sub-feature Figure X 2 First, a maximum pooling layer with a pooling kernel size k of 3, a step size s of 2, and a padding p of 1 is used to extract the maximum value X in the local window. 2 , highlighting the significant features. Subsequently, the feature map is further extracted and refined through a convolution layer with a convolution kernel k of size 1, a step size s of 1, and a padding p of 0, to obtain the feature map Y 2 ,

[0160] X 2，max =MaxPool(X 2 , k=3, s=2, p=1),

[0161] Y 2 =Conv 2 (X 2，max , k=1, s=1, p=0),

[0162] Two learnable weight factors α and β are introduced in the connection layer. Through the training process optimization, the downsampling results are weighted and fused. The weight factors α and β control Y 1 and Y 2 The contribution of the weight factors α and β in the fusion process. The dimensions of the weight factors α and β are both 1×1×1. The feature map Y after weighted fusion fusion for:

[0163] Y fusion =α·Y 1 +β·Y 2 ,

[0164] where · represents element-wise multiplication.

[0165] Normalize the weight factors α and β to ensure that their sum is 1:

[0166]

[0167] The weighted fusion feature map is recalculated using the normalized weight factor:

[0168] Y fusion =α norm ·Y 1 +β norm ·Y 2 .

[0169] From the output end, the weighted fused feature map Y fusion As output, its dimension is Where C' is the number of output channels.

[0170] The construction method of the visual recognition model is as follows: grouping sea cucumbers according to their regions and species, and making the sizes of sea cucumbers in each group in a similar range, and the number of sea cucumbers in each group is not less than 100; obtaining photos of each sea cucumber under natural conditions with different shooting angles, different lighting and different backgrounds, the number of photos is not less than 1000, obtaining the growth trait data of each sea cucumber, the growth trait data including body length, body width, wet weight and dry weight, matching the sea cucumber images with their growth trait data, and constructing a data set; dividing the photographed pictures into training sets and test sets in proportion, manually using bounding boxes to annotate the sea cucumbers in the training set, training the model for all labeled sea cucumber image information through the YOLOv8-seacucumber visual recognition model, testing the model using the test set until the detection accuracy and detection speed of the model meet the requirements, and manually reviewing the training results; performing random tests on the trained model, and if the average accuracy (Average Accuracy) of the test results is not satisfactory, the training results will be compared with the training set. If the average precision (AP) of the confidence level is greater than the threshold, the visual recognition model is successfully trained, and the confidence level AP (IoU = 0.75) threshold is preferably 0.8. This visual recognition model can be used to identify sea cucumbers of the same species in the region. If the region where the sea cucumber is located or the species of the sea cucumber is changed, it is necessary to resample and repeat the above steps to re-establish the model. The YOLOv8-seacucumber visual recognition model identifies the sea cucumbers that may exist in the image and calculates its confidence level. The targets with confidence levels greater than the threshold are identified as sea cucumbers and marked in the figure. The confidence level threshold is preferably 0.9. The longest and widest points of the edge of the sea cucumber are used as critical points to draw the recognition frame, and the recognition frame size is recorded. The size includes the number of pixels in the length and width of the recognition frame. Each recognition frame is sorted in turn, and the final sort value of the recognition frame is recorded as the total number of sea cucumber individuals identified in this round.

[0171] Furthermore, the image recognition module is also provided with a sea cucumber disease recognition model, which monitors the health status of sea cucumbers based on sea cucumber images, and monitors the health status of sea cucumbers during the tank emptying process, so as to timely discover the disease risk of sea cucumbers. The sea cucumber disease library model is trained using photos of diseased sea cucumbers, and the pictures are divided into training sets and test sets in proportion. The disease conditions of sea cucumbers in the training set are manually annotated using bounding boxes. The model is trained on all annotated sea cucumber image information using the YOLOv8-seacucumber visual recognition model, and the model is tested using the test set until the detection accuracy and detection speed of the model meet the requirements, and the training results are manually reviewed. While identifying sea cucumbers, the sea cucumber disease recognition model is also used to identify the health status of sea cucumbers, and diseased or abnormal sea cucumbers are additionally marked and labeled with warning labels for easy individual distinction. Staff can take corresponding treatment measures based on the identification information of the health status of sea cucumbers to ensure the healthy growth of sea cucumbers.

[0172] The data processing module obtains the size of the recognition frame and calculates the actual size of the recognition frame based on the actual length and width of a single pixel of the calibrated camera. The actual length and width of the recognition frame are obtained by multiplying the number of pixels of the length and width of the recognition frame with the actual length and width of a single pixel.

[0173] The prediction model is used to monitor and predict the growth status of sea cucumbers based on the actual size of the identification box. The prediction model is based on the labeled sea cucumber image and the sea cucumber growth trait data. The actual size of the identification box of the same species of sea cucumbers in the same area is fitted with its growth trait data, and the prediction model is obtained by model training using multiple regression models and machine learning. The method for constructing the prediction model is to use the image of the sea cucumber and its growth trait data as a data set, and use multiple regression models to train the data set based on the actual size of the identification box in the sea cucumber image. The training models include multiple linear regression (Multiple Linear Regression), polynomial regression (Polynomial Regression), generalized linear models (Generalized Linear Models, GLM), decision tree regression (Decision Tree Regression), ridge regression (Ridge Regression), random forests (Random Forests) and support vector regression (Support Vector Regression, SVR), etc. The trained model is tested and the determination coefficient R is used. 2 Evaluate model performance, coefficient of determination R 2 It indicates the degree of explanation of the target variable by the variables explained by the regression model. The larger the determination coefficient is, the smaller the errors are, which means that the corresponding model is better. If the evaluation result R 2 If it is greater than 0.9, the model is considered qualified.

[0174] The actual size of each sea cucumber identification frame is loaded into the prediction model as a prediction parameter, and the growth state of the sea cucumber is fitted and predicted. The prediction model selects multiple fitting models with the highest confidence and a confidence greater than a threshold, and the growth trait data of multiple fitting models that meet the requirements are averaged to obtain the growth monitoring data of the length and width of a single sea cucumber and the prediction data of the current wet weight and dry weight, and the predicted value is stored in the database to record the growth trait data of each sea cucumber. Specifically, the actual size of a target sea cucumber identification frame is obtained and loaded into the prediction model. The prediction model selects three fitting models with the highest confidence and greater than 0.9 according to the actual size of the sea cucumber identification frame, which are the fitting models closest to the sea cucumber, and the average value of these three fitting models is calculated to obtain the growth trait data of the target sea cucumber.

[0175] Sea cucumbers often present a twisted shape under the real growth state, and it is difficult to measure the real length, width and other information of sea cucumbers under the living state of sea cucumbers, and the dry weight of sea cucumbers cannot be measured. The length and width of sea cucumbers calculated based on the twisted arc of sea cucumbers are also easily caused by the shrinkage of sea cucumbers themselves, which results in a certain error in the calculation result and the real length of sea cucumbers. Therefore, the present invention takes into account the twisted state of sea cucumbers, even if sea cucumbers are in a twisted state, the length-width ratio of its identification frame changes, and the high confidence regression model established according to the growth property data of sea cucumbers fitted by the identification frame actual size can carry out growth monitoring and prediction of the growth property data of sea cucumbers, and obtain the growth monitoring data of sea cucumber size and the prediction data of wet weight and dry weight. In addition, compared with calculating the real length of sea cucumbers, using the identification frame actual size to monitor and predict the growth property data of sea cucumbers can significantly reduce the computing intensity and improve the detection efficiency.

[0176] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A device for integrating intelligent pond inversion, growth monitoring and prediction of sea cucumbers, characterized in that: include: Aquaculture ponds, the aquaculture ponds comprising original ponds currently culturing sea cucumbers and new ponds for replacement; A conveyor belt, which is arranged between the original pond and the new pond and is used to transport sea cucumbers; An image acquisition module, wherein the image acquisition module is disposed on the conveyor belt and is used to acquire images containing sea cucumbers; An image recognition module, wherein the image recognition module obtains an image containing a sea cucumber, identifies the sea cucumber using a pre-built visual recognition model, draws an identification frame using the longest and widest parts of the sea cucumber edge as critical points, records the size of the identification frame, and sequentially sorts each identification frame in the identification area, and counts the total number of sea cucumber individuals; A data processing module, wherein the data processing module obtains the size of the recognition frame and calculates the actual size of the recognition frame; A prediction module, wherein the prediction module obtains the actual size of the identification frame, uses a pre-built prediction model, takes the actual size of the sea cucumber identification frame as a prediction parameter, selects multiple fitting models with the highest confidence and a confidence greater than a threshold, and calculates the average value of the fitting model to obtain the monitoring data of the sea cucumber size and the prediction data of the wet weight and dry weight.

2. The integrated device for intelligent inverting pond, growth monitoring and prediction of sea cucumbers according to claim 1, characterized in that: The conveyor belt is a U-shaped conveyor belt, which is arranged on the outside of the breeding pond. The original pond and the new pond are arranged at intervals. A fishing mechanism is arranged in the breeding pond, and the fishing mechanism includes a transverse track, a movable frame and a fishing shovel. The transverse track is arranged on the breeding pond, and a movable frame that can move along the transverse track is arranged on the transverse track. A fishing shovel that can be connected to the movable frame through a lifting rope is arranged in the breeding pond.

3. The sea cucumber intelligent pond inversion and growth monitoring and prediction integrated device according to claim 2 is characterized in that: The mobile frame includes a longitudinal track arranged on the transverse track and a frame body installed on the longitudinal track, a rack is arranged on one side of the longitudinal track, a driving motor fixedly installed in the frame body is arranged above the rack, a gear is arranged on the output shaft of the driving motor, and the gear is meshed in the rack, a first rope winding shaft and a second rope winding shaft are arranged in the frame body, two rope winding drums are arranged in each of the first rope winding shaft and the second rope winding shaft, a lifting rope that can be connected to a fishing shovel is wound in the rope winding drum, and a first lifting motor and a second lifting motor are arranged at the ends of the first rope winding shaft and the second rope winding shaft, respectively.

4. The integrated device for intelligent inverting pond and growth monitoring and prediction of sea cucumbers according to claim 2 is characterized in that: The fishing shovel includes a bottom plate, a rear plate and side plates, an opening is arranged on the front side of the fishing shovel, the width of the fishing shovel matches the width of the breeding pond, a plurality of drainage holes are arranged in the bottom plate of the fishing shovel, a plurality of water filtering holes are arranged in the rear plate of the fishing shovel, and a hanging ring is arranged on the top of the fishing shovel.

5. A method for integrating intelligent pond inversion, growth monitoring and prediction of sea cucumbers, characterized in that: include: The sea cucumbers are caught and placed on a conveyor belt, and a calibrated image acquisition module captures images containing the sea cucumbers; The pre-built visual recognition model recognizes the sea cucumber image, draws a recognition frame with the longest and widest parts of the sea cucumber edge as critical points, and records the size of the recognition frame; Obtain the actual length and width of a single pixel of the image acquisition module, calculate the actual size of the identification frame, and sort each identification frame in turn to count the total number of sea cucumber individuals; The actual size of the identification frame is loaded as a prediction parameter into a pre-constructed prediction model. The prediction model monitors and predicts the sea cucumber growth trait data based on the relationship between the actual size of the identification frame and the growth traits of the sea cucumber, selects multiple fitting models with the highest confidence and confidence greater than a threshold, and calculates the average value of the fitting model to obtain the monitoring data of the sea cucumber size and the prediction data of the wet weight and dry weight.

6. The integrated method for intelligent inverting pond and growth monitoring and prediction of sea cucumbers according to claim 5 is characterized in that: The image recognition module adopts an improved YOLOv8-seacucumber visual recognition model to identify sea cucumbers, identifies targets with confidence greater than a threshold as sea cucumbers and draws an identification frame. The YOLOv8-seacucumber visual recognition model includes an input end, a backbone network and a detection head. On the basis of the original YOLOv8 visual recognition model, a C2f-F-SA module is used to replace the C2f module in the backbone network and the detection head. The C2f-F-SA module includes a convolutional layer, a segmentation layer, a plurality of FasterBlock structures, a connection layer and a convolutional layer arranged in sequence. The bottleneck layer in the original C2f module is replaced by a FasterBlock structure, and a Shuffle Attention mechanism is added to the FasterNet Block module; some convolutional layers of the detection head are replaced by Adown modules.

7. The integrated method for intelligent inversion of sea cucumber ponds and growth monitoring and prediction according to claim 6 is characterized in that: The Shuffle Attention mechanism includes an input end, a segmentation layer, an upper branch, a lower branch, a connection layer and an output end; Input the sea cucumber feature map X∈R (C,H,W) , the sea cucumber feature map is divided into G groups according to the channel dimension, expressed as: X=[X1,…,X G ],X k ∈R (C / G×H×W) , Where C is the number of channels of the sea cucumber feature map, H is the spatial height of the sea cucumber feature map, W is the spatial width of the sea cucumber feature map, R represents the set of all images in the dataset, and X k is the input feature; The segmentation layer takes as input the feature X k Along the channel dimension, it is divided into upper branch X K1 With the lower branch X K2 , X K1 ,X K2 ∈R (C / 2G×H×W) , the upper branch uses channel attention, and generates channel information S by scaling and shifting the transformation parameters w1, b1. First, global average pooling (GAP) is performed to embed global information to generate channel information S∈R (C / 2G×1×1) , the channel information is obtained by k1 It is calculated by shrinking in the spatial dimension H×W, Use the fully connected layer to transform the channel information S and apply the σ activation function (sigmoid) to generate the weight matrix and multiply it by X k1 , get the weighted feature map X' k1 : X′ k1 =σ(F C (S))·X k1 =σ(w1S+b1)·X k1 , Where w1∈R (C / 2G×1×1) , b1∈R (C / 2G×1×1) ; The lower branch uses spatial attention to capture the spatial dependencies between features, scales and shifts the amount of spatial information by transforming parameters w2, b2, and uses the group norm (GN) to generate statistics of spatial attention: X′ k2 =σ(w2·GN(X k2 )+b2)·X k2 , Where w2∈R (C / 2G×1×1) , b2∈R (C / 2G×1×1) ; The connection layer processes the X' obtained by the upper and lower branches K1 and X' K2 Combined, the output feature map has the same size as the input. X' k =[X' k1 ,X' k2 ]R (C / G×H×W) 。 8. The integrated method for intelligent inversion of sea cucumber ponds and growth monitoring and prediction according to claim 6 is characterized in that: The ADown module includes an input terminal, an average pooling layer, a segmentation layer, a first path, a second path, a connection layer, and an output terminal. Input feature map X∈R (C,H,W) , the average pooling layer performs an average pooling operation on the input feature map X through an average pooling layer with a pooling kernel k of size 2, a step size s of 1, and no padding (p = 0), and obtains the average pooling feature map X avg , X avg =Avgpool(X,k=2,s=1,p=0), The segmentation layer averages the pooled feature map X avg The sub-feature maps X1 and X2 are divided into two sub-feature maps X1 and X2 along the channel dimension. The sub-feature maps X1 and X2 are downsampled simultaneously through the first path and the second path respectively to obtain feature maps Y1 and Y2. The first path is that the sub-feature map X1 is directly downsampled through a traditional convolutional layer to obtain the feature map Y1. Y1=Conv1(X1,k=3,s=2,p=1); The second path is that the sub-feature map X2 first passes through a maximum pooling layer with a pooling kernel size k of 3, a step size s of 2, and a padding p of 1 to extract the maximum value X2 in the local window and highlight the significant features. Subsequently, the feature map passes through a convolution layer with a convolution kernel size k of 1, a step size s of 1, and a padding p of 0 to further extract and refine the features and obtain the feature map Y2. X 2,max =MaxPool(X2,k=3,s=2,p=1), Y2=Conv2(X 2,max ,k=1,s=1,p=0), Two learnable weight factors α and β are introduced in the connection layer. Through the training process optimization, the downsampling results are weighted fused. The weight factors α and β control the contribution of Y1 and Y2 in the fusion process respectively. The dimensions of the weight factors α and β are both 1×1×1. The feature map Y after weighted fusion is fusion for: Y fusion =α·Y1+β·Y2, In the formula, · represents element-by-element multiplication, Normalize the weight factors α and β to ensure that their sum is 1. The weighted fusion feature map is recalculated using the normalized weight factor: Y fusion =a norm ·Y1+β norm ·Y2, From the output end, the weighted fused feature map Y fusion As output, its dimension is Where C' is the number of output channels.

9. The fully automatic sea cucumber high-throughput growth trait measurement and prediction method according to claim 6, characterized in that: The YOLOv8-seacucumber visual recognition model construction method is as follows: sea cucumbers are grouped according to their regions and species, and sampling training is performed, photos of each sea cucumber under natural conditions with different shooting angles, different lighting and different backgrounds are obtained, and the pictures are proportionally divided into a training set and a test set, the sea cucumbers in the training set are manually labeled using a bounding box, model training is performed on all labeled sea cucumber image information through a convolutional neural network, the model is tested using a test set, the training results are manually reviewed, and a visual recognition model is obtained.

10. The fully automatic high-throughput growth trait measurement and prediction method of sea cucumber according to claim 5, characterized in that: The prediction model construction method is as follows: based on the marked sea cucumber images and sea cucumber growth trait data, the actual size of the identification frame of the same variety of sea cucumbers in the same area is fitted with its growth trait data, and a prediction model is obtained by model training using multiple regression models and machine learning.

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