5985 results about "Aquaculture" patented technology

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention belongs to the technical field of aquaculture automation control, and particularly discloses an aquaculture environment intelligent disease early warning system and a storage medium, the system comprises a data acquisition layer, a data transmission layer, a processing platform layer and an application service layer; the data acquisition layer comprises an environment monitoring unit, a water quality sensor group, portable detection equipment and a data acquisition module; the data transmission layer comprises a wireless transmission module and an edge computing node; the processing platform layer comprises a machine learning prediction model and an environment factor correlation analysis model; the application service layer comprises an early warning information push module and a prevention and control scheme generation module. According to the intelligent disease early warning system for the aquaculture environment and the storage medium, a correlation model of environmental factors and disease occurrence is innovatively established, the early warning accuracy is 85% or above by adopting a machine learning technology, comprehensive monitoring and real-time early warning in the aquaculture process can be achieved, and accurate and efficient disease prevention and control are supported.

The invention discloses a multi-objective optimization decision control method for shrimp and silkworm polyculture environment parameters, and relates to the technical field of polyculture control. The method comprises the following steps: deploying a water quality sensor network, and combining prawn and nereis growth monitoring equipment; extracting criticality of environmental parameters and biological growth indexes by using historical breeding data and experimental data; constructing a dynamic coupling model, and establishing a dynamic model of the polyculture system based on a differential equation; developing a hybrid optimization algorithm to balance the competition and symbiotic relationship between the prawns and the nereis; and generating a control instruction set based on an optimization result, and adjusting the control equipment through a fuzzy PID controller. According to the invention, intelligent regulation and control of shrimp and silkworm polyculture environment parameters are realized, and economic benefits of shrimp and silkworm polyculture are improved.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention discloses an aquaculture intelligent feeding management system based on the Internet of Things, and relates to the technical field of aquaculture, a camera and a dissolved oxygen detector are utilized to construct an Internet of Things monitoring module, fish school behaviors and water quality environment data are collected in real time, and the movement track, group density and feeding state of a fish school are extracted; secondly, light-weight algorithms such as TinyYOLO and the like are adopted, rapid analysis of feeding intensity and feeding requirements is achieved in a local embedded computing module, a feeding model is generated, and dependence on high-performance hardware and cloud computing is reduced; meanwhile, the feeding control logic device dynamically calculates the optimal feeding amount and the optimal feeding opportunity based on the feeding state and the environmental characteristics; by monitoring the change of the ingestion intensity, the system catches the optimal ingestion window of the fish school, and maximization of the feed utilization rate is achieved; meanwhile, in combination with feedback analysis and a closed-loop optimization mechanism, model parameters are dynamically adjusted according to the feeding effect of the last round, and the environmental adaptability of the feeding plan is improved.

The invention relates to the technical field of automatic control, in particular to an intelligent aquaculture automatic control system which comprises a feeding feedback control module, a feeding rhythm allocation module, a water body disturbance partition module, a control unit recognition module and a control path construction module. According to the method, the bait residue density is quantified through image recognition, a feeding state dynamic judgment mechanism is constructed, the accuracy and timeliness of feeding response are improved, the feeding speed is calculated through linkage of time difference and density change, a rhythm allocation strategy based on behavior feedback is formulated, the matching degree of the feeding rhythm to the actual feeding ability is enhanced, and the feeding speed is increased. A main flow path continuous block is constructed by combining flow velocity and flow direction, disturbance levels are marked in a grading manner, a key equipment action area is judged according to cross of fish school active density and dissolved oxygen trend, and an equipment linkage path is constructed by connectivity of a physical channel and an inhabitation area. And an intelligent control scheme of cooperative linkage of ingestion behavior driving, disturbance partition recognition and space layout optimization is realized.

The invention provides a river crab feed feeding mixed monitoring method, system and device and a medium, and belongs to the field of aquaculture intelligent monitoring. Data is collected and normalized, and a chain type data collection system is constructed; the edge computing and block chain technology is adopted to realize data distributed storage and transmission; a fuzzy neural network hybrid model is constructed, fuzzification processing, feature mining and defuzzification output are integrated, and a feeding amount prediction value is generated; associating the feed proportioning library with the knowledge graph, and iteratively training the model and generating a multi-target feeding decision parameter; feeding equipment is controlled to execute actions according to the decision parameters, and the real-time monitoring model outputs and triggers an early warning signal; and updating the knowledge graph and the feed matching library based on the feedback data to form closed-loop self-adaptive regulation and control. According to the method, various factors can be comprehensively considered, scientific and intelligent feed feeding decision is realized, feed waste and insufficient feeding are effectively avoided, and the growth quality of the river crabs is improved.

The invention provides an ecological circulation barrel-in-barrel culture data acquisition method and system, and relates to the technical field of intelligent control, and the method comprises the steps: carrying out the feature extraction of a historical data flow through a multivariable coupling analysis model, carrying out the mode matching through a preset environment threshold interval and a culture environment state matrix, and obtaining a culture environment state matrix; generating a water quality health degree evaluation index and an ecological imbalance early warning signal; and analyzing the water quality health degree evaluation index and the ecological imbalance early warning signal based on a fuzzy control algorithm, dynamically calculating a final feeding amount curve and a water change intensity function, and generating a regulation and control instruction set containing the feeding frequency, the bait particle size and the water pump rotating speed. According to the invention, real-time evaluation and accurate intervention of the water quality health state can be realized.

The invention provides a fish disease identification method based on deep learning. Firstly, a new multi-branch adaptive reparametric module CKDB is provided, a plurality of parallel branches are used in a training stage to capture abundant fish disease features, and the complex branches are combined into an efficient convolutional layer through adaptive reparameterization in a reasoning stage, so that the feature expression ability of the model is effectively improved. Secondly, providing a novel multi-scale adaptive feature pyramid network N-MAFPN: the network adopts an adaptive feature fusion strategy, and through effective fusion and multi-branch feature extraction of features of different scales, the detection precision of the model on fish diseases is improved; finally, a multi-scale attention enhancement module SM-Detect is introduced to the head of the network; and disease area features concerned by the model are dynamically adjusted by using a self-adaptive attention mechanism on feature maps of different scales, so that the fish disease detection capability of the model is improved. The effectiveness of the proposed algorithm structure is proved through an ablation experiment, the problem of insufficient identifiability of fishes caused by diversity of intensive aquaculture environments and rapidity of movement is solved, and the method is a key progress for promoting intelligent health monitoring of fish diseases.

The invention provides a multi-mode fish school ingestion intensity detection method, system and device, a medium and a product, and relates to the technical field of aquaculture, the method comprises the following steps: obtaining fish school ingestion target video data and fish school ingestion target audio data in a time period corresponding to the fish school ingestion target video data; inputting the fish school ingestion target video data and the fish school ingestion target audio data into a multi-mode ingestion intensity detection model to obtain fish school ingestion intensity information output by the multi-mode ingestion intensity detection model; wherein the multi-modal ingestion intensity detection model is obtained by training a convolutional neural network according to fish shoal ingestion sample video data and fish shoal ingestion sample audio data marked with a fish shoal ingestion behavior intensity label; the convolutional neural network comprises a Res2Net network, a RepViT network, a feature fusion residual connection module and a full connection layer. According to the invention, more accurate fish school ingestion intensity information can be obtained.

The invention discloses an integrated water quality monitoring and processing system for aquaculture, and the system comprises a data collection module which is used for obtaining dissolved oxygen, ammonia nitrogen, pH value, temperature and flow data from a plurality of spatial distributed sensing nodes; the data preprocessing module is used for completing noise removal, correction and time sequence synchronization; the water quality state estimation module is used for outputting real-time multi-point water quality states based on a distributed unscented Kalman filtering algorithm; the space-time fluid modeling module is used for constructing a Lagrange model to extract a flow velocity field and a diffusion coefficient; the scheduling optimization module is used for generating an aeration and dosing scheme by adopting an improved multi-target Pareto optimization algorithm based on a space-time sensitive boundary; the execution module is used for controlling an air blower, an aeration pipe and a dosing pump to complete oxygenation and dosing operations; and the self-adaptive updating module is used for collecting feedback data to adjust filtering gain and optimizing parameters so as to realize closed-loop control of the system.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention belongs to the technical field of aquatic pathogen diagnosis, and particularly relates to a Taqman multiple fluorescent quantitative PCR (Polymerase Chain Reaction) detection system and kit for common vibrio pathogens in aquaculture as well as application and a detection method. The system comprises primers and probes for detecting four common vibrio pathogens in aquaculture, the 5'end of each probe is modified with different fluorophores, the 3 'end of each probe is modified with corresponding quenching groups, and the common vibrio pathogens comprise vibrio anguillarum Va, vibrio alginolyticus Val, vibrio harveyi Vh and vibrio scophthalmi Vsc.

The invention discloses a mixed fishery medicine Raman spectrum detection method combining a data enhancement method and an RA-CTNet model. Data is expanded by combining a stoichiometric method and an experimental random error simulation method, so that the database building cost is reduced, and the model robustness is improved. In the aspect of model building, the advantages of CNN on local feature extraction and the capability of Transform on global feature modeling are fully utilized, training strategies of pre-training and transfer learning are adopted, rich feature information of a single fishery medicine is effectively utilized, the accuracy of mixed fishery medicine prediction is improved, and the prediction accuracy of the mixed fishery medicine is improved. And quantitative detection of a nanomole-level concentration mixture under a complex mixed sample is realized. Compared with the prior art, the technical scheme provided by the invention aims to detect forbidden fishery drugs in aquaculture water and realize nanomole-level concentration forbidden drug quantitative detection under a low data sample, and aims to improve the accuracy and robustness of Raman spectrum detection and quickly qualitatively and quantitatively analyze mixed fishery drugs.

The invention discloses a photodynamic inactivation parameter adjusting method based on hybrid fuzzy-PID control, relates to the technical field of parameter fuzzy control, and aims at the problem that photodynamic inactivation efficiency and aquatic organism safety in aquaculture are difficult to consider at the same time through multi-sensor fusion and environment prediction. Then, a fuzzy-PID self-adaptive putting technology is adopted to be coupled with an illumination strategy, and accurate matching of the photosensitizer dosage and the light intensity is achieved; water body fluctuation and pathogen diffusion are dealt with through real-time closed-loop and multi-scene collaborative scheduling; multi-dimensional index evaluation is utilized, reinforcement learning is introduced to carry out self-learning optimization, and putting and illumination parameters are continuously improved; and finally, the method is expanded to large-scale distributed control and cloud big data training, and efficient inactivation and flexible deployment can be kept in different breeding scenes. Pathogens can be effectively prevented from being rapidly diffused in an adverse environment, damage to aquatic animals caused by excessive accumulation of reactive oxygen species (ROS) is reduced, and meanwhile energy consumption and resource waste are reduced.

The invention belongs to the field of aquaculture, and particularly relates to an underwater fish multi-target identification, tracking and motion analysis method, the method is based on a multi-target attitude tracking framework based on direction perception, and the framework comprises an improved YOLOv11 model (YOT model) and a directional bounding box attitude tracking module (OPBT module). The YOT model integrates directional bounding box detection and attitude estimation, adopts a task-aligned dynamic detection head structure, and combines a multi-task loss function to realize efficient direction perception detection and key point positioning. The OPBT module is based on an unscented Kalman filter, integrates rotation IoU, angle similarity and body length similarity, and improves identity continuity and occlusion recovery capability in multi-target tracking. The framework is suitable for complex scenes such as high-density fish schools, the real-time processing performance can be kept, meanwhile, the detection and tracking precision can be remarkably improved, and stable and reliable technical support is provided for individual behavior analysis.

The invention discloses an aquaculture organism health state monitoring system based on multi-dimensional feature association, and belongs to the technical field of aquaculture, and the system comprises a visual feature collection layer which is composed of an image collection module, an image preprocessing module, a target analysis module and a feature extraction module; the molecular biology verification layer is composed of an immune gene monitoring module and a metabolic gene monitoring module; the data association prediction layer is composed of a standardization module, a feature screening module, a deep learning module and an evaluation output module; a self-adaptive optimization mechanism, a fault-tolerant mechanism and an environment adaptability adjustment mechanism. According to the aquaculture organism health state monitoring system based on multi-dimensional feature association, intelligent monitoring of the health state of aquaculture organisms is achieved by integrating a computer vision technology and a molecular biology detection method, and a scientific basis is provided for aquaculture production management.

The invention discloses an aquaculture comprehensive guarantee method and system based on multi-modal data acquisition, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting aquaculture multi-modal data and an aquaculture image of an aquaculture region, carrying out the aquaculture feature capture of the aquaculture image, and outputting a visual feature group; inputting the visual feature group into a MobileNet lightweight model, and outputting a health state label and a confidence score; based on the health state label and the confidence score, outputting disease type data through a residual network structure optimized by transfer learning; constructing a graph neural network according to the disease type data, and generating a dynamic regulation and control scheme; and the control center executes the dynamic regulation and control scheme, carries out regulation and control effect detection and target comparison on the aquaculture area of the dynamic regulation and control scheme, judges whether the regulation and control effect is effective or not, and generates a feedback regulation instruction. According to the invention, through multi-modal data fusion and intelligent decision closed loop, dynamic and accurate guarantee of aquatic product health management is realized.

The invention discloses a fishery pond water temperature control optimization method based on a swarm intelligence algorithm, and belongs to the technical field of control optimization, and the method comprises the following steps: S1, constructing a fishery pond water temperature PID control system introducing an improved single-corner whale optimization algorithm module; s2, introducing an improved single-corner whale optimization algorithm, and constructing an improved single-corner whale optimization algorithm module; s3, setting and optimizing the pond water temperature PID control parameters by using an improved single-corner whale optimization algorithm to obtain optimal control parameters; and S4, setting an optimal control parameter obtained through optimization by using the improved single-corner whale optimization algorithm as a parameter of a pool water temperature PID controller, and optimizing a pool water temperature regulation control effect. According to the method, a swarm intelligent optimization algorithm, namely an improved unicorn whale optimization algorithm, is introduced, high-performance control over the pond water temperature is achieved, and reliable control technical support is provided for efficient, stable and safe operation of aquaculture.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention discloses a monitoring system for aquaculture tail water treatment, and belongs to the technical field of aquaculture tail water treatment, the monitoring system comprises a data center and a control center, the control center comprises an acquisition module, a real-time database, a visualization module and a feedback control module, the data center comprises a data preprocessing module, a data fusion module, a data analysis module and a judgment module, the system can monitor the treatment conditions of a plurality of aquaculture tail water treatment ponds, the situation that one aquaculture tail water treatment pond is matched with one monitoring system is avoided, the cost is reduced, meanwhile, treatment equipment can be synchronously and rapidly adjusted, and the treatment efficiency is improved. And abnormal points can be monitored for multiple times, errors during data acquisition are avoided, error data can be corrected, meaningless adjustment is avoided, cost consumption is reduced, and the quality of tail water can be fully guaranteed.

The invention provides an industrial aquaculture-oriented fish identification method, which comprises the following steps of: acquiring fish images in different time periods, different illumination conditions, different water environments and different fish school densities, preprocessing the fish images, constructing a fish image data set, and identifying the fish images according to the fish image data set. Dividing the images into a fish image training set, a fish image verification set and a fish image test set according to the proportion of 7: 2: 1; the method comprises the following steps: constructing a Faster YOLOv9-Slim model through backbone network selection, detection head pruning, Faster RepNCSPELAN4 module optimization and neck network design, inputting a fish image data set into the Faster YOLOv9-Slim model, training the Faster YOLOv9-Slim model by using the fish image data set, inputting a to-be-detected fish image into the trained Faster YOLOv9-Slim model, and outputting fish detection information. According to the fish identification method for industrial aquaculture, the problems of high hardware requirements, insufficient environmental adaptability, difficulty in balancing light weight and precision and the like in fish identification due to too complex water environment in existing industrial aquaculture are solved.

The invention relates to a fence, in particular to an aquaculture fence. In order to solve the technical problems, the aquaculture fence which can save time and is beneficial for screening is provided. The aquaculture fence comprises an installation plate, supporting columns, a guide plate, first connecting plates, a first baffle, a second baffle and the like, wherein the supporting columns are installed on the left side and the right side of the top of the installation plate respectively, the tops of the two supporting columns are connected with the guide plate, a first strip-shaped opening with a guiding effect is formed in the top of the guide plate, a second strip-shaped opening is formed in the rear side of the guide plate and communicated with the first strip-shaped opening, and thetwo first connecting plates are connected between the two supporting columns. Through the effect of the second baffle and the second baffle, fish fry small in size can be screened and does not need tobe fished up by people or screened by people with naked eyes, and therefore screening can be convenient and time can be saved.

The invention relates to an intelligent breeding method for crayfish offspring seeds in a winter fallow field, and belongs to the technical field of aquaculture. In order to solve the problems of dissolved oxygen management lag, high ammonia nitrogen accumulation risk and overlarge energy consumption caused by low utilization rate of winter fallow fields and dependence of water quality regulation and control on artificial experience in the prior art, the method comprises the following steps: deploying a water quality dynamic monitoring system based on the Internet of Things to collect data of water temperature, dissolved oxygen, pH value, ammonia nitrogen and nitrite in real time; the power and the water level of the oxygenation equipment are dynamically adjusted after analysis of the edge calculation module; graded regulation and control are achieved by combining a three-level response mechanism, when the dissolved oxygen content is lower than 6 mg / L or the pH value is abnormal, the power of the nanometer oxygenation device is started to 50%, and when the dissolved oxygen content continuously deteriorates, the irrigation system is linked to inject the water body with the temperature being adjusted in advance. According to the method, through intelligent multi-parameter cooperative control and ecological bait design, the resource utilization rate of the winter fallow field is increased, the survival rate of the young crayfish is increased to 85% or above, and the method is suitable for large-scale and green efficient breeding of the young crayfish in a rice-shrimp rotation mode.

The invention relates to the technical field of intelligent aquaculture and automatic processing, in particular to an appearance characteristic-based hippocampus intelligent grading and pond-dividing culture method and system, and the method comprises the following steps: S1, collecting sparse visual data and multi-source physiological data of hippocampus; s2, generating a three-dimensional digital twinborn model based on the sparse visual data; calculating a growth state grading index based on the three-dimensional digital twinborn model; s3, calculating a physiological stress index based on the multi-source physiological data; s4, comparing the physiological stress index calculated in real time with a preset safety stress threshold value and a critical stress threshold value, and generating a hierarchical adaptive correction strategy; s5, according to a grading self-adaptive correction strategy, controlling a flexible mechanical arm to execute fishing and pond separation operation; according to the method, the appearance and physiological data of the hippocampus are synchronously collected in a non-contact mode, the technical problems that the real condition of the hippocampus cannot be evaluated and damage is possibly caused due to target stress response caused by traditional physical intervention are solved, and the authenticity and effectiveness of original data are guaranteed.

The invention belongs to the technical field of aquaculture, and provides a feed feeding efficiency assessment method and system, and the method specifically comprises the steps: firstly, arranging a monitoring node camera in a farm, reading real-time monitoring data at a monitoring node to calculate a feeding fluctuation value, then calculating a feeding abnormal factor according to the feeding fluctuation value, and recognizing a feeding risk state; and finally, evaluating the feed feeding efficiency through the feeding risk state. The feeding abnormity is that the feeding fluctuation value of each sampling point of each monitoring node in a monitoring period is calculated, the fish feeding enthusiasm microscopic decline performance observed by each monitoring node in a long-period feeding strategy is captured, and the identified feeding risk state can more sensitively prevent the excessive feeding amount of feed; the risks of feed waste and water quality deterioration are avoided, and the feed utilization efficiency and the breeding quality under the long-period feeding strategy are improved.

The invention relates to the technical field of marine ecological breeding, and discloses a combined net cage for marine ecological breeding, the inner side of a top breeding net cage is adjustably connected with a middle breeding net cage, the inner side of the middle breeding net cage is adjustably connected with a bottom breeding net cage, and the bottom end of the bottom breeding net cage is fixedly connected with a bottom net frame; the middle aquaculture net cage is connected to the inner side of the top aquaculture net cage in a sleeved connection mode, and the bottom aquaculture net cage is connected to the inner side of the middle aquaculture net cage in a sleeved connection mode, so that the net cage can be stretched and adjusted in the vertical direction, the overall height of the net cage structure is changed, and the net cage structure is suitable for aquaculture requirements of different water area depths; meanwhile, the capacity of the breeding space in the net cage is changed by adjusting the height of the net cage structure, the breeding space in the net cage can be adaptively adjusted according to changes of the size of the aquatic products, the enough growth space of the aquatic products is guaranteed, and the yield of the aquaculture products is improved to the maximum extent.

A pneumatically controlled aquaculture apparatus is provided. The apparatus has a lift vessel that lifts containers holding shellfish out of the water when the lift vessel is floating in order to allow air desiccation to prevent bio-fouling of the equipment and shellfish. The apparatus has a floatation control tank positioned above the containers holding shellfish in a centered position of the apparatus. The apparatus also has a suspension buoy for suspending the apparatus in the water column. The apparatus may be deployed in two different suspended positions with either the floatation control tank or the suspension buoy floating on the water surface and the containers holding shellfish suspended below the surface. The apparatus may also be deployed in a submerged position in which the apparatus is resting on the water bottom.

The invention discloses an aquaculture disease prediction method based on multi-modal data fusion, and relates to the technical field of aquaculture, and the method mainly comprises the steps: collecting aquaculture data containing structured data and unstructured text data; inputting the structured data into a TabTransform model, carrying out column embedding and modeling of a context relationship between features through a multi-layer Transform encoder, and outputting a structured feature vector; inputting the unstructured text data into a pre-trained BERT encoder, and extracting an output vector marked by the CLS as a text semantic feature; and splicing the structured feature vector and the text semantic feature into a fusion feature, inputting the fusion feature into a full connection layer, and predicting the incidence probability of each target disease through a Sigmoid function. The prediction accuracy is improved, and the problems of single prediction dimension and weak generalization ability in the prior art are effectively solved.

An intelligent large-water-surface ecological fishery management system based on multi-source data fusion relates to the technical field of intelligent fishery, and comprises the following steps: dividing a large-water-surface aquaculture area into a plurality of sub-areas, setting an environment monitoring point and a biological monitoring point in each sub-area, collecting multi-source data, and constructing a multi-source data integrated visual graph; outputting real-time threshold intervals corresponding to the water quality monitoring indexes, the meteorological monitoring indexes and the biological characteristic indexes in the current collection period; performing real-time monitoring on each sub-region in the multi-source data integrated visual graph, and generating a sudden abnormal alarm or performing hidden trend analysis according to a real-time monitoring result; performing intelligent decision support operation on the sub-regions according to the hidden trend analysis result or marking the sub-regions as ecological risk regions; based on the multiple groups of time data of the sub-regions, the optimal fishing opportunity is obtained through a dual-drive fishing algorithm, and comprehensive monitoring, accurate sensing and risk prevention and control of ecological fishery management are achieved.