66 results about "Aquaculture management" patented technology

The invention relates to the technical field of intelligent breeding monitoring, in particular to a breeding early warning method and system based on artificial intelligence image recognition. The method comprises the following steps of: 1, dynamically acquiring and adaptively preprocessing a multi-modal image; step 2, constructing a multi-branch convolutional neural network based on the preprocessed image data, respectively extracting space apparent characteristics and time behavior characteristics of a target object, and dynamically fusing characteristic weights by combining real-time environment parameters to generate an abnormal probability value; and step 3, performing multi-level dynamic early warning decision and feedback optimization, dynamically generating a multi-level early warning threshold according to environmental parameters and historical abnormal probability value distribution, triggering differential response operation, and updating parameters of the multi-branch convolutional neural network and the multi-level early warning threshold based on an artificial recheck result and equipment feedback data. The method can automatically adjust the early warning mechanism according to the change of the real-time environment, thereby effectively preventing the potential risk in the breeding process, improving the breeding management efficiency, and guaranteeing the sustainable development of the breeding industry.

The invention discloses an aquaculture environment intelligent regulation and control method and system based on an AI model, and relates to the technical field of aquaculture management and control. The AI model-based aquaculture environment intelligent regulation and control method comprises the steps of S1, collecting water quality state data, feeding behavior data and meteorological data in an aquaculture environment, preprocessing and storing the data, and then constructing an aquaculture regulation and control management database; s2, constructing a water body state time sequence causal model based on the multi-source time sequence data, and outputting a causal connection structure; s3, performing prediction and risk assessment on water quality evolution under different feeding schemes through multivariable time series and causal coupling; s4, analyzing a feeding scheme target based on the feeding decision amount, the water quality risk and the growth income; and S5, continuously correcting the causal relationship and the feeding strategy through feeding execution feedback. The problems that the bidirectional coupling relation between the feeding behavior and the water quality feedback is not clear, and the feeding decision and the water quality risk are difficult to cooperatively regulate and control are solved.

The invention relates to the technical field of the Internet of Things, and more specifically discloses an intelligent aquaculture management system and method based on the Internet of Things, which can provide a fine management scheme, optimize aquaculture conditions and improve yield and efficiency for farmers by monitoring aquaculture environment parameters in real time through a sensor and combining data analysis and artificial intelligence. The risk is reduced, and sustainable development of the aquaculture industry is promoted.

The invention discloses an underwater micropterus salmoides body length and mass measurement method, and relates to the technical field of aquaculture. Comprising the following steps: 1) target detection and key point detection: performing target detection and key point detection on underwater largemouth bass by using a YOLOv8 model to obtain key point coordinate information of a fish head and a fish tail; and 2) obtaining depth information: obtaining the depth information of the underwater image through a monocular depth estimation algorithm, and calculating the body length of the largemouth bass in combination with a key point detection result. And 3) mass estimation: based on the body length information, estimating the mass of the micropterus salmoides through a power function regression model. And 4) model deployment: deploying the model in a recirculating aquaculture monitoring system, and displaying and recording the body length and quality information of the micropterus salmoides in real time. According to the method, the body length and the body weight of the largemouth bass can be obtained in real time in a non-contact mode, the method is suitable for a factory-like circulating water culture mode, data support can be provided for culture management, and the practical value and the popularization prospect are high.

The invention discloses an aquaculture capacity prediction method and system based on multi-source data, and relates to the technical field of aquaculture management. An aquaculture capacity prediction system based on multi-source data comprises a data acquisition module, a capacity prediction module, a density adjustment analysis module, a typhoon correction module, an elastic capacity generation module and a space mapping module. According to the method, the typhoon influence correction model is constructed, and the basic capacity is dynamically corrected by using the capacity correction weight, so that automatic adjustment of the prediction capacity in the typhoon season is realized, and the robustness of a prediction result facing extreme meteorological conditions is enhanced; on the basis of recommended capacity, a historical disaster damage data fitting model and risk preference setting, three capacity strategy intervals of conservative, neutral and aggressive are generated, hierarchical selection is provided for breeding decisions of different risk bearing capacities, and decision elasticity and risk control capacity are improved.

The invention discloses an intelligent growth monitoring and analysis method and system for mariculture, and the method comprises the following steps: firstly, collecting breeding environment data in real time through a first sensor group; and acquiring growth data of the breeding object through the second sensor group. Then, the cultivation environment data and the growth data are transmitted to a cloud platform by using a data acquisition system, and the data are preprocessed by the cloud platform to generate a standardized data model; based on the standardized data, a deep learning algorithm is used for analyzing the growth trend of the breeding object and predicting the growth change in a period of time in the future. Through combining the breeding environment and the growth data, the cloud platform performs risk assessment, generates early warning information in real time, and identifies potential risk factors. According to the early warning information, the cloud platform automatically adjusts breeding environment parameters and management measures, and healthy growth of breeding objects is ensured. According to the method, intelligent decision support is provided for breeding management, and the breeding efficiency and the growth quality of bred objects are improved.

The invention provides an intelligent aquaculture management system based on big data and artificial intelligence, relates to the technical field of intelligent aquaculture management, and aims to generate an aquaculture monitoring system, perform regional aquaculture monitoring, further perform comprehensive aquaculture monitoring, train an aquaculture effect prediction model and calculate regional aquaculture coefficients of a preset region. Obtaining an area standard deviation coefficient of the preset area, and obtaining abnormal breeding area information; calculating a dimension breeding coefficient of the dimension of the preset area, further obtaining a dimension standard deviation coefficient of the corresponding dimension, and obtaining abnormal breeding dimension information; adjusting the abnormal breeding dimension of the abnormal breeding area according to the preset dimension adjusting parameters, predicting the effect through the breeding effect prediction model, and further judging whether the adjustment is continued or not. By integrating big data and an artificial intelligence technology, real-time monitoring of a breeding environment, accurate prediction of a breeding effect and automatic identification and adjustment of an abnormal breeding state are realized.

The invention relates to the technical field of aquaculture water quality prediction, in particular to a procambarus clarkii aquaculture water quality prediction method fusing LSTM and an Internet of Things sensing network. According to the technical scheme, the method comprises the steps that time sequence water quality parameters of a culture pond are obtained, dynamic sliding window preprocessing is conducted on data, an LSTM prediction model of a mixed gating mechanism is constructed, and a culture equipment control instruction is generated according to a prediction result. Through fusion of the Internet of Things sensing network and the improved LSTM model, the problems of insufficient utilization of spatio-temporal data, weak model adaptability and disjunction of prediction control in a traditional method are effectively solved, spatio-temporal correlation fusion and noise correction of multi-source water quality data are realized, water quality parameters are accurately predicted, the ammonia nitrogen mutation risk is synchronously detected, and the method is suitable for large-scale popularization and application. Therefore, artificial experience driving of breeding management is promoted to be converted into accurate data driving, a real-time dynamic environment regulation and control scheme is provided for healthy growth of shrimps, the breeding risk is remarkably reduced, and the management efficiency is improved.

The invention relates to the technical field of marine aquaculture, in particular to marine intelligent purse seine aquaculture management equipment which comprises an automatic bait casting system, a water quality detection module, an underwater inspection module, an intelligent sensing module, a photovoltaic power supply system, a light inducing device and an informatization center control module. Through real-time monitoring and intelligent early warning, the risk of sudden change of water quality is reduced, accurate feeding is realized through combination of an automatic bait casting system and fish shoal behaviors, the bait utilization rate is improved, resource waste is reduced, the fish shoal state and the purse seine safety can be dynamically monitored through an underwater patrol module, management timeliness and visibility are enhanced, and the system is suitable for popularization and application. A photovoltaic power supply system and low-power-consumption equipment are adopted, the long-time stable operation capacity is achieved, and the device adapts to the complex open sea environment.

The invention discloses an intelligent breeding supervision method and system based on artificial intelligence, and belongs to the technical field of breeding management. The system comprises a data acquisition module, a breeding analysis module, an intelligent management module and an automatic adjustment module. The data acquisition module is used for acquiring equipment information of a farm and a feeding database of bred objects; the breeding analysis module is used for analyzing the feed-meat relationship according to the breeding database and calculating the predicted weight increment of the breeding object; the intelligent management module is used for calculating an abnormal index of a breeding object by predicting weight increment so as to set an observation object and a reference object; and the automatic adjusting module is used for establishing an adjusting instruction according to the reference object and automatically adjusting equipment of the culture area where the observation object is located.

The invention relates to the technical field of intelligent aquaculture and facility agriculture automation control, in particular to an intelligent aquaculture management platform based on big data driving, and the platform comprises a multi-dimensional data sensing module which is used for obtaining target energy supply state data, water body environment physicochemical data and cultured organism behavior data; the energy supply and demand prediction module is used for generating an envelope line representing available energy in a future preset time window based on the energy supply state data and preset microweather forecast information; the biochemical response analysis module is combined with a preset water body biochemical inertia model to solve a stress accumulation value representing superposition; the multi-target game optimization module is used for solving a dynamic resource scheduling strategy by utilizing a game model based on an available energy envelope, a physiological tolerance function and the stress accumulation value; the execution control module responds to the dynamic resource scheduling strategy and generates a cooperative control instruction for feeding equipment and oxygenation equipment; the method effectively reduces the recessive death rate of cultured organisms.

The present invention discloses a full-process traceability system for aquatic products based on blockchain technology and its implementation method, which belongs to the field of quality control technology. The system includes a data acquisition layer composed of an environmental monitoring unit, a feeding monitoring unit and an RFID identification unit for collecting aquaculture environment data; an edge computing layer including an edge server and an industrial control computer, which pre-process the collected data; a blockchain layer constructed using the Hyperledger Fabric platform to achieve data tamper-proof storage and automatically verify data authenticity through smart contracts; an application layer including aquaculture management subsystem, a processing management subsystem, a logistics management subsystem and a sales management subsystem, and a multi-level authorization mechanism to manage data access rights. The present invention adopts the above-mentioned system and its implementation method to solve problems such as data falsification, high tracing costs and information opacity, and realize full-chain credible traceability of aquatic product quality and safety.

The invention discloses an aquatic product whole-course tracing system based on a block chain technology and an implementation method thereof, and belongs to the technical field of quality control, and the system comprises a data collection layer which is composed of an environment monitoring unit, a feeding monitoring unit and an RFID recognition unit and is used for collecting breeding environment data; the edge computing layer comprises an edge server and an industrial control computer, and the edge server and the industrial control computer preprocess the collected data; the block chain layer is constructed by adopting a Hyperledger Fabric platform, data tamper-proof storage is realized, and the data authenticity is automatically verified through an intelligent contract; the application layer comprises a breeding management subsystem, a processing management subsystem, a logistics management subsystem and a sales management subsystem, and adopts a multi-level authorization mechanism to manage data access authority. By adopting the system and the implementation method thereof, the problems of data counterfeiting, high tracing cost, opaque information and the like are solved, and full-chain credible tracing of aquatic product quality safety is realized.

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 present application relates to the technical field of aquaculture circulating water control, and discloses a sea horse aquaculture circulating water temperature and quality intelligent collaborative control system and method, which is based on the data of the change amount of the aquaculture water to perform independent prediction analysis on the source type of the sea horse aquaculture water replacement, realizes intelligent and reliable judgment of whether the sea horse aquaculture water replacement is from internal circulating water or external circulating water based on the theoretical change of the sea horse aquaculture water amount, and constructs sea horse aquaculture water regulation information based on the data processing of the sea horse aquaculture water replacement source, the water amount information of the replacement water, and the water temperature and water quality update information of the aquaculture water. Meanwhile, the water body update equipment is independently and reliably controlled by combining the aquatic product aquaculture management platform to perform the replacement of the aquaculture water body and the water temperature and water quality regulation operation, the precise regulation of the water body, the water temperature and the water quality in the sea horse aquaculture process is realized, and the efficiency and accuracy of the sea horse aquaculture management are improved.

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.

The invention discloses an industrial aquaculture management system for antibiotic-free bullfrogs, and belongs to the technical field of aquaculture. The system comprises circulating water antibiotic-free treatment, antibiotic-free seedling cultivation, antibiotic-free cultivation management and a novel drainage vertical pipe system. The circulating water non-antibiotic treatment adopts a closed-loop flow, and antibiotics and ammonia nitrogen are efficiently removed through the working procedures of sewage tank microfiltration, ozone oxidation disinfection, constructed wetland biological photodegradation, aeration tank ammonia nitrogen removal, clean water tank plant bacterium adding purification reflux and the like; a staged plant nutrition and probiotic feeding mode is adopted for antibiotic-free seedling cultivation; the antibiotic-free cultivation management realizes antibiotic-free cultivation through density control, water quality and water temperature regulation and control, scientific feeding and animal health care; the novel drainage vertical pipe adopts a circular hole and vertical seam structure to prevent blocking. According to the method, whole-course antibiotic-free breeding is achieved, the survival rate in the seedling stage reaches 80% or above, the adult frog breeding period is about 120 days, the product is free of antibiotic residues, the national environmental protection and food safety specifications are met, and an efficient solution is provided for factory-like antibiotic-free bullfrog breeding.

The invention discloses an unmanned boat-net cage locking device, and relates to the technical field of net cage culture, the unmanned boat-net cage locking device comprises a net cage, an unmanned boat device and a base station, the unmanned boat device comprises a double-body unmanned boat, the upper end of the double-body unmanned boat is provided with a rotating groove, the rotating groove is internally provided with a rotating table, the upper end of the rotating table is rotatably provided with a suspension arm, and the suspension arm is connected with the base station. The front end of the suspension arm is provided with an extendable moving seat, the moving seat is provided with a water quality detection mechanism, and the upper end of the rotary table is provided with a feed feeding mechanism connected with the suspension arm. Through integration of an autonomous sensor, a depth-adjustable water quality detection pod and a suspension arm type feeding mechanism, multi-point three-dimensional water quality detection and fixed-point feed feeding can be realized for a plurality of traditional net cages in an open water area by virtue of a laser radar point cloud precise positioning technology; the stability and high efficiency of operation inside and outside the net cage are guaranteed through the lifting arm depth control and pose calibration function, and the aquaculture management efficiency is greatly improved.

The present invention discloses an intelligent sensor for water quality index determination along with a method of determining the water quality index for an aquaculture pond. The present invention provides an intelligent sensor wherein the sampling is implemented by an automated sampling device. The present invention provides for timely alerting to enable corrective action for aquafarming management and wastewater treatment process management. The output value of the water quality index is notified to an end user by blockchain encryption for taking remediate actions.

The invention discloses an intelligent aquaculture management system and method integrating water quality monitoring, and relates to the field of aquaculture, and the method comprises the steps: building a three-dimensional monitoring network through the deployment of a multi-parameter sensor array, and completing the data cleaning and fusion through edge calculation; in combination with the breeding variety growth feature library and the dynamic threshold model, water quality state evaluation and 24-hour risk prediction are realized by adopting an LSTM-random forest fusion algorithm; an optimization regulation and control instruction is generated based on the three-level strategy library, and the execution equipment cluster is driven to cooperatively work and form closed-loop control; and finally, establishing a full-life-cycle data file, continuously optimizing the model through transfer learning and outputting a management report. The method has the advantages that through three-dimensional accurate monitoring and intelligent model analysis and pre-judgment, the precision and intelligence of breeding management are realized by combining personalized dynamic regulation and control with equipment collaborative linkage, and the effect can be continuously optimized through data iteration.

The invention relates to the field of breeding management, and discloses a recirculating aquaculture traceability management system and method based on a block chain, which is used for solving the problem that a risk early warning threshold value is inaccurate during breeding traceability management, and comprises the following steps: collecting breeding key parameters in real time, evaluating according to the breeding key parameters to obtain a risk index, and determining the risk index according to the risk index. The method comprises the following steps: acquiring an initial risk threshold, acquiring threshold influence data in real time, performing evaluation according to the threshold influence data to obtain an adjustment index, performing risk threshold adjustment according to the adjustment index to obtain an actual risk threshold, and judging whether to perform early warning prompt and risk node recording according to the risk index and the actual risk threshold. The key data is encrypted and then stored in the block chain, the key data comprises the breeding key parameters and the early warning data, and the accuracy of the tracing information in breeding tracing management is effectively improved.

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 invention discloses a fish density monitoring method based on big data and acoustic wave signals, comprising: constructing an acoustic wave reflection source type identification model to determine the acoustic wave reflection source type and location of the acoustic wave reflection signal; identifying and eliminating multipath reflection signals from the same acoustic wave reflection source based on the fish school acoustic wave reflection signal; constructing a fish school density prediction model to predict the fish school density and determine the fish school activity index; judging the fish school health status based on the baseline fish school activity index threshold and the fish school activity index; judging the fish school disease category based on the appearance image of fish with abnormal health status; formulating fish school health intervention measures based on the fish school disease category and judging the effectiveness of the fish school health intervention measures. The present invention improves the real-time and accuracy of fish school density and fish school health monitoring, reduces risks in aquaculture, and significantly improves the efficiency and scientific nature of aquaculture management through automated water quality control and drug administration equipment.

The invention relates to the technical field of aquaculture management, in particular to an aquaculture monitoring management method and system. The method comprises the following steps: firstly, dividing monitoring units and progressively feeding, and synchronously collecting visible light and infrared videos of fish schools for snatching food; secondly, key frames are extracted through an optical flow method, the total number of fish schools is accurately estimated through a density map integration technology, and the basic feeding amount is formulated according to the total number of fish schools; then, comparing a difference value between the current density and the proper density, and if the difference value exceeds a threshold value, automatically detecting and regulating and controlling water quality parameters to a proper range; then, the fish group length is analyzed based on the infrared image, and the water quality scheme and the future bait casting amount are further corrected according to the difference between the fish group length and the expected body length; and finally, the body length and the output in specific time in the future are predicted by integrating the water body adjustment scheme and the fish school growth cycle. According to the method, powerful data support is provided for the aquaculture production plan, and the scientificity and predictability of aquaculture management are greatly improved.

The present invention relates to a cultivation method for improving the survival rate of clownfish seedlings, including aquaculture water and equipment management, fertilized egg hatching treatment, larval stage cultivation management, and juvenile stage cultivation management. The specific operations include the following contents: (1) aquaculture water and equipment management, including the preparation of aquaculture water and the treatment of aquaculture equipment; (2) fertilized egg hatching treatment; (3) larval stage cultivation management, including water change management, bait feeding management, drug prevention, and aeration management; (4) juvenile stage cultivation management; and (5) juvenile stage cultivation management. The present invention performs differentiated aquaculture management on clownfish seedlings at different growth stages, and by adjusting the aquaculture environment, water quality, and feeding feed at each cultivation stage, effectively prevents and treats amyloidosis and dinoflagellate disease, and reduces damage to the fish body caused by other parasites and fungi; thereby ensuring a clean living environment for the clownfish and effectively improving the survival rate of the clownfish.

The disclosed open-water aquaculture method of the present invention proposes a new aquaculture mode with an open-net cage structure arranged outside the workboat. Through the design of a telescopic cabin structure, the characteristics of easy loading and transportation during the transfer and transportation processes are achieved. At the same time, the natural water environment is fully utilized for aquaculture to meet the design objectives of improving aquaculture efficiency, reducing the structural difficulty and energy consumption of the workboat. The full-cycle aquaculture management from fry to catch is implemented in a telescopic aquaculture cabin including at least two sets of telescopic frame components connected in sequence and nested. When the aquaculture workboat is sailing, the aquatic organisms cultured in the telescopic aquaculture cabin are transferred to the built-in cabin for temporary cultivation, and all the telescopic frame components are completely folded along the axial center. When the aquaculture workboat is stationary, all the telescopic frame components are completely extended along the axial center. Aquatic organisms are cultured in the telescopic aquaculture cabin on one or both sides outside the hull. During aquaculture, the telescopic aquaculture cabin is placed in the open-water environment outside the workboat.

The invention relates to the technical field of aquaculture management, in particular to a collaborative optimization method and system for energy consumption and operation and maintenance cost of a filtering device, which integrates three types of core data of equipment operation, water quality and cost, constructs an independent function of energy consumption and operation and maintenance cost, and also constructs an independent function of energy consumption and operation and maintenance cost. A weight coefficient, a nonlinear interaction item and a dynamic marginal effect item based on water quality data are introduced, a coupling optimization model is established, and the limitation of single optimization is broken through. The improved NSGA-III algorithm is used for solving, two-way pressure drop of energy consumption and operation and maintenance cost is achieved, frequent equipment failures and sharp increase of the operation and maintenance cost caused by low energy consumption are avoided, energy waste caused by excessive pursuit of convenient operation and maintenance is completely eradicated, the economical efficiency and sustainability of a breeding system are remarkably improved, and the economic benefit of the breeding system is improved. And a key technical support is provided for efficient, low-carbon and intelligent transformation in the aquaculture industry.

The invention discloses a biomarker combination based on pollutant exposure detection and application thereof, and relates to the technical field of biomarkers, the biomarker combination comprises an integrin subunit itg-alpha11, a nuclear transcription factor nf-kb, and an antioxidant stress factor nrf2; according to the biomarker combination disclosed by the invention, a complete association network from molecular response to phenotypic risk is constructed by accurately capturing an action mechanism of BDE-47 at molecular and cellular levels; the rapid diagnosis of pollutant exposure can be realized in the early stage of embryonic development, and the risk of development malformation and the like can be effectively early warned; and meanwhile, a quantitative basis is provided for evaluating the pollution remediation effect, and accurate formulation of an environment quality standard and optimization of an aquaculture management strategy are supported. The biomarker combination not only deepens the theoretical cognition of a pollutant toxicity mechanism, but also provides an innovative tool for environmental health risk assessment and ecological protection practice, and has important theoretical significance and wide application prospects.

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.

This invention discloses a silkworm breeding and management method and system based on multi-source data, belonging to the field of aquaculture management technology. This invention assesses the abnormal state of mulberry leaf diseases through mulberry leaf images, and adjusts environmental parameters in the larval stage based on the overall age of the silkworm and the abnormal state of mulberry leaf diseases. In the cocooning stage, environmental parameters are adjusted according to the abnormal state of the silkworm. The adjustment of environmental parameters is carried out according to different situations. The different growth cycles of the silkworm are used as the horizontal dimension and the abnormal state of mulberry leaf diseases is introduced as the vertical dimension. The adjustment of environmental parameters based on multiple dimensions improves the refinement and accuracy of silkworm breeding and management.