15 results about "Aquaculture management" patented technology

This invention discloses an intelligent control method and system for aquaculture environments based on an AI model, belonging to the field of aquaculture management technology. The AI-based intelligent control method and system for aquaculture environments includes: S1, collecting water quality status data, feeding behavior data, and meteorological data from the aquaculture environment, preprocessing and storing them to construct an aquaculture control and management database; S2, constructing a time-series causal model of water body status based on multi-source time-series data and outputting a causal connection structure; S3, predicting and assessing the water quality evolution under different feeding schemes through multivariate time-series and causal coupling; S4, analyzing the feeding scheme objectives based on feeding decision quantities, water quality risks, and growth benefits; and S5, continuously correcting the causal relationship and feeding strategy through feeding execution feedback. This solves the problems of unclear bidirectional coupling between feeding behavior and water quality feedback, and the difficulty in coordinating the control of feeding decisions and water quality risks.

The application discloses an intelligent fish body surface lesion detection system for an underwater aquaculture scene, and relates to the technical field of intelligent aquaculture monitoring.The system comprises an underwater image acquisition module, which is used for acquiring underwater images of fish in real time in an aquaculture water body, ensuring illumination through a light supplement lamp, and transmitting image data to an edge computing processing module; the edge computing processing module is based on an edge inference chip and is used for quality enhancement, target positioning, feature extraction and enhancement, target detection and multi-task decoding of the underwater images, and real-time detection and analysis of fish body lesions; an aquaculture management cloud platform module is used for receiving and storing detection results, statistically analyzing lesion data, and providing disease early warning, and for pushing alarm information to aquaculture personnel when a lesion is found, and for realizing account and permission management.The system is used to realize high-precision, real-time and robust detection and classification of various lesions on the surface of fish, and to provide remote monitoring and disease early warning services.

This invention provides a method and system for intelligent aquaculture management and feeding optimization on offshore fish rafts, relating to the field of aquaculture management technology. The method includes the following steps: acquiring energy status information of energy equipment and biological characteristic information of the aquaculture species; acquiring multiple preset candidate feeding adjustment schemes, and calculating the estimated energy savings corresponding to each candidate feeding adjustment scheme based on the power consumption characteristics of the feeding equipment; calculating the aquaculture loss assessment value corresponding to each candidate feeding adjustment scheme based on the biological characteristic information, determining the target feeding scheme from the multiple candidate feeding adjustment schemes, and controlling the feeding equipment to execute the feeding operation. The method of this invention aims to solve the problem that existing intelligent aquaculture management systems for offshore fish rafts, when facing energy challenges, adjust feeding schemes solely based on power levels and equipment power consumption information, ignoring the physiological needs of the aquaculture species, leading to a decline in aquaculture efficiency. It achieves a smart balance between energy utilization efficiency and aquaculture benefits.

The present disclosure discloses a whole-process traceability system for aquatic products based on blockchain technology, wherein: The data acquisition layer is composed of environmental monitoring unit, feeding monitoring unit and RFID identification unit, which is used to collect aquaculture environmental data; the edge computing layer includes an edge server and an industrial control computer. The edge server and the industrial control computer preprocess the collected data; the blockchain layer is built on the Hyperledger Fabric platform to realize data tamper-proof storage, and the data authenticity is automatically verified through smart contracts. The application layer includes the aquaculture management subsystem, processing management subsystem, logistics management subsystem, and sales management subsystem, and adopts a multi-level authorization mechanism to manage data access rights. This method solves problems of data fraud, high traceability cost, and opaque information, and realizes the whole chain credible traceability of aquatic product quality and safety.

The application provides a biomass estimation method and device for aquaculture water and electronic equipment. The method comprises the following steps: acquiring a continuous dynamic sonar image sequence collected by an imaging sonar; performing signal enhancement preprocessing on the sequence; inputting the enhanced sequence into a deep learning detection model to identify and output fish body targets and their positions in each frame; based on the detection results of multiple continuous frames, performing cross-frame association and tracking on the fish body targets, and using a time-weighted fusion strategy for deduplication to obtain time-series fish school data; based on the time-series data, performing regional fish school quantity statistics on the aquaculture water, and fusing and estimating the global fish school quantity; finally, based on the fish school quantity and the fish body individual mass estimation result, the total biomass of the aquaculture water is calculated. The application realizes non-contact and automatic global estimation of the fish school biomass of the aquaculture water, which is beneficial to improving the refinement and decision-making efficiency of aquaculture management.

This invention relates to a method and system for hatching shrimp larvae based on multimodal data fusion, belonging to the field of tiger prawn hatching technology. The invention constructs a reinforcement learning framework based on digital twins and twin model mechanisms, ultimately acquiring image data information of the current target aquaculture area within a preset time period. The reinforcement learning framework is then used to analyze this image data information to find the action sequence that maximizes cumulative rewards. This invention systematically solves the technical challenges of extensive perception, lagging regulation, and experience-dependent decision-making during shrimp larvae hatching through the deep integration of multimodal perception, digital twin modeling, and reinforcement learning decision-making. It significantly improves the survival rate and growth uniformity of larvae during the hatching stage, while reducing the risk of water quality deterioration and energy and material consumption, providing reliable technical support for intelligent aquaculture management.

This invention discloses primers, probes, and a detection method for detecting the yellow croaker (Croton tigrinus). The primers include an upstream primer and a downstream primer. The nucleotide sequence of the upstream primer is SEQ ID NO:1, and the nucleotide sequence of the downstream primer is SEQ ID NO:2. The probe is labeled with a 6-carboxyfluorescein (FAM) reporter group at its 5' end and a Black Hole Quencher 1 (BHQ1) quencher group at its 3' end. The primer-probe combination can be used for species identification of yellow croaker tissue sample DNA and for the detection and quantitative / semi-quantitative monitoring of yellow croaker environmental DNA (eDNA) in water samples. Compared with existing technologies, this invention has advantages such as high species specificity, low cross-reactivity with closely related Sciaenidae species, high detection sensitivity, low background signal, and applicability to various water substrates, making it suitable for yellow croaker resource monitoring and aquaculture management.

This invention relates to the field of intelligent management and control technology for recirculating aquaculture systems (RAS), and discloses a cloud-based RAS management and control method and cloud platform. During the operation of the RAS system, behavioral characteristic data of the cultured organisms are collected to construct a behavioral characteristic time series. Based on the behavioral characteristic time series, structural features representing behavioral change patterns are extracted. The behavioral characteristic time series and its corresponding structural features are input into a deep learning model, which outputs the risk evolution rate representing the rate of change of the RAS risk state over time. The risk state is updated recursively over time based on the current risk state and the risk evolution rate. When the updated risk state does not reach a preset alarm threshold and the risk evolution rate shows a continuous upward trend, the system is determined to have entered a potential stress state, triggering early control operations. Through this method, the proactive identification and control of the risk state of the RAS system is achieved.

This invention discloses a method, system, equipment, and medium for forecasting ultra-local aquaculture operation windows, belonging to the field of smart aquaculture management technology. It collects and fuses meteorological and oceanographic data from offshore wind farms with regional numerical weather prediction data to obtain fused data. Based on the fused data, the regional numerical weather prediction data is processed through downscaling or correction models to generate ultra-local environmental parameter forecasts for target aquaculture sites. According to preset environmental parameter suitability rules corresponding to specific aquaculture operations, the ultra-local environmental parameter forecasts are judged, and suitable time windows for specific aquaculture operations are identified and output. This effectively solves the pain points of insufficient targeting and low accuracy in traditional aquaculture environmental forecasts.

This invention provides an ecological aquaculture method for catfish based on a land-based circular pond and a pipeline tidal circulation system, belonging to the field of catfish aquaculture technology. The method includes the following steps: System construction: Constructing a circular aquaculture pond on land, with a central sewage outlet at the bottom, an inlet pipe above, and tidal drainage pipes on the side walls or bottom connecting to the pipeline tidal circulation system; Pre-stocking preparation: Cleaning and disinfecting the pond before stocking, adjusting the water quality, and then stocking catfish seedlings; Tidal circulation management: Controlling the periodic rise and fall of the water level through a tidal control valve: During drainage, sewage from the bottom of the pond is discharged into a sedimentation tank, exposing the bottom of the pond during low water levels; replenishing water to restore a high water level; Water treatment and reuse: Sewage undergoes sedimentation and solid-liquid separation, and the supernatant is purified by a biological filter and then recycled; Daily aquaculture management: Daily feeding, water replenishment, water quality monitoring, and disease control. The clean environment and periodic water flow within the circular aquaculture pond of this invention stimulate the catfish's feeding and movement, improving growth rate and survival rate.

The present application belongs to the technical field of aquaculture, and discloses a shellfish behavior continuous monitoring and stress early warning method and system based on computer vision. The method uses a fixed camera to continuously record videos of cultured shellfish, extracts image frames at minute-level intervals through FFmpeg, and uses a YOLOv8n target detection model to automatically classify the shell valve state (open / close) of each individual, and calculate behavior indicators such as opening rate and state switching rate. The present application proposes to use the sign inversion (from positive to negative) of the Pearson correlation coefficient between the opening rate and the state switching rate as the discrimination mark of the stress state at the group level, and realizes early warning 3-5 days before death based on the multi-dimensional behavior trajectory (opening rate decrease + switching rate anomaly + circadian rhythm disorder) of the individual. The method is completely non-contact and does not require physical sensors, and is suitable for aquaculture management and marine environment monitoring.

This invention discloses a method for counting Microcystis colonies based on controlled ultrasonic depolymerization, relating to the field of aquatic ecological monitoring technology. The method involves obtaining a pretreated water sample after impurity removal from the source water sample. The supernatant of the pretreated water sample is then collected and filtered to remove small and large particle sizes of impurities. After a second filtration to enrich the algae, a Microcystis cell suspension is prepared. The Microcystis cell suspension is then ultrasonically dispersed to obtain a dispersed water sample. A hemocytometer is used to accurately count the number of Microcystis cells. This method overcomes the difficulties of incomplete dispersion or cell damage in Microcystis colonies, enabling accurate counting of Microcystis cells in aquatic ecosystems. The experimental steps are simple, the time required is short, and the number of Microcystis cells can be accurately counted. It can be applied to environmental monitoring, water pollution assessment, and aquaculture management.

This invention discloses a method and apparatus for assessing fish feeding desire by coupling adaptive forgetting learning and deep learning. The method includes: collecting videos of fish during rearing and feeding periods to train a recurrent neural network (RNN) model for assessing fish feeding desire; optimizing the model using adaptive forgetting learning technology, which mainly includes three innovative technologies: an adaptive label flipping strategy, a dual-gradient correction mechanism, and multi-round forgetting cumulative error analysis and control. This allows the model to forget previously collected but outdated training data without needing to retrain from scratch, effectively improving the accuracy and self-updating adaptive capability of the fish feeding desire assessment model. This invention is precise, simple, and efficient, with a simple device structure. It effectively solves the problem of decreased accuracy in current aquaculture feeding desire assessment models due to outdated data, ensuring the model's self-adaptive and self-optimizing performance over long-term operation, thus facilitating intelligent aquaculture management.