A factory breeding system for small crayfish seedlings

Abstract

The present application relates to the technical field of aquaculture, and discloses a factory breeding system for small lobster fry, which comprises a modular circulating water breeding unit, a water quality intelligent regulation and control unit, a light and temperature cycle control unit, an online biological monitoring unit, a precise feeding management system, a stage-type grading cultivation unit and a central control platform. Through the environment collaborative regulation and control module, when factory breeding is carried out, the linkage mechanism of water quality and light and temperature parameters is established, the environmental adaptability of different development stages is ensured, the water quality and light and temperature parameters are monitored and automatically adjusted in real time, the problems of abnormal fluctuation of water quality and mismatch of light and temperature can be eliminated, the stability of the hatching process is ensured, the stress response of the larvae is reduced, through the intelligent grading monitoring module, the breeding density is ensured to be in the optimal range, and the risk of competitive injury and cross-infection of diseases is reduced.

Description

Technical Field

[0001] This invention relates to the field of aquaculture technology, specifically to a crayfish seedling industrialized breeding system. Background Technology

[0002] The crayfish, scientifically known as *Procambarus clarkii*, originated in North America and is an important farmed freshwater shrimp species in the United States. It was introduced to Japan in 1918 and then to my country during World War II. Initially, it multiplied in Nanjing and its suburbs in Jiangsu Province. After more than half a century of expansion, it is now found throughout the rivers, lakes, canals, and ponds of the middle and lower reaches of my country, becoming an important freshwater shrimp species. Crayfish are characterized by low susceptibility to disease, omnivorous diet, rapid growth, and high reproductive capacity. They are also not particularly demanding in terms of water width or depth, and are currently farmed in many countries around the world.

[0003] Currently, due to the multi-stage environmental control involved in the artificial breeding of crayfish, the circulating water system and environmental control equipment used in factory-scale seedling production cannot coordinate and regulate water quality and light and temperature parameters in real time. When the concentrations of dissolved oxygen and ammonia nitrogen in the water fluctuate abnormally, or when the light and temperature cycle is mismatched, it will cause the stress response of the larvae to intensify, resulting in a decrease in hatching rate and uneven growth. At the same time, when the larvae are raised in stages, it is impossible to monitor the accuracy of larval size grading in real time. Sorting errors will cause imbalances in stocking density, which will induce competitive damage and cross-infection of diseases. Moreover, there is a lack of automated correction mechanisms when abnormal density occurs. In the process of larval disease control, due to the dynamic changes of pathogenic microorganisms, existing technologies cannot achieve early warning based on biological behavior, resulting in delayed control when disease outbreaks occur, reducing larval survival rate and seedling uniformity.

[0004] Therefore, a crayfish seedling industrialized breeding system is proposed to solve the above problems. Summary of the Invention

[0005] (0) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a crayfish seedling industrial breeding system, which solves the problems mentioned in the background section.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a crayfish seedling industrial breeding system, the system comprising a central control platform and the following units connected to it for data transmission: The modular recirculating aquaculture unit consists of multiple standardized breeding ponds, each of which integrates a recirculating aquaculture subsystem to create a stable aquatic environment. The intelligent water quality control unit includes a multi-parameter sensor array and an automatic adjustment actuator, which monitors and adjusts water quality parameters in real time. The light-temperature cycle control unit consists of an LED light source group and a temperature control device, which simulates natural light-temperature changes; The online biological monitoring unit uses a camera network and an AI processor to track the behavior and growth status of larvae in real time. The precision feeding management system includes a feed storage bin, a metering feeder, and a biomass feedback controller, which automatically feeds the animals based on growth stage data from the online biological monitoring unit. The staged and graded breeding unit is equipped with an automatic sorting machine and a graded breeding area, and is managed in a graded manner according to the body length identification results of the online biological monitoring unit; The central control platform is an IoT gateway that enables real-time collaboration among various units through data aggregation and algorithm execution, specifically including: Based on the data on the larval development stage fed back by the online biological monitoring unit, water quality parameters and light and temperature parameters are adjusted synchronously. Based on the density monitoring results of the staged and graded breeding units, the feeding amount of the precision feeding management system is dynamically triggered for correction. When the online biological monitoring unit identifies disease risks, it activates the modular circulating water breeding unit to enhance disinfection.

[0007] Preferably, the real-time collaboration is specifically manifested as follows: When the larvae are in the incubation period, the intelligent water quality control unit maintains pH=7.2±0.1, while the light and temperature cycle control unit sets the water temperature to 26℃ and the light intensity to 100 lux. When the density of first-level larvae detected by the staged grading breeding unit is greater than 1200 larvae / cubic meter, the precision feeding management system automatically increases the feeding frequency to once every 2 hours; When the online biological monitoring unit detects a 30% decrease in swimming speed for more than 30 minutes, it triggers the ultraviolet disinfection frequency of the modular circulating water breeding unit to double.

[0008] Preferably, the intelligent water quality control unit and the light and temperature cycle control unit exchange data through an environmental linkage protocol. When the water temperature is greater than 28℃, the aeration equipment of the water quality unit is automatically activated, and when the ammonia nitrogen concentration is greater than 0.2mg / L, the water temperature is forcibly lowered to below 24℃. The water temperature adjustment amount is calculated using the following formula: in This is an adjustment coefficient, ranging from 0.5 to 1.0. This represents the real-time ammonia nitrogen concentration. This refers to the degree of water temperature reduction.

[0009] Preferably, the AI ​​processor of the online biological monitoring unit is deployed with a dual-model collaborative architecture: The YOLOv5 model analyzes the swimming trajectory of larvae in real time and outputs an abnormal behavior index. The ResNet model simultaneously detects surface lesion features; When the sum of the risk scores from the two models exceeds the threshold, a coordinated disinfection command is triggered on the central control platform. The abnormal behavior index is calculated using the following formula: in As a baseline swimming speed, To monitor swimming speed in real time, This is a behavioral abnormality index.

[0010] Preferably, the circulating water treatment subsystem of the modular circulating water breeding unit includes a graded disinfection module: The ultraviolet disinfection unit is in normal operation. The ozone generator is activated when the online biological monitoring unit identifies disease risk, and the concentration is controlled at 0.05 ppm.

[0011] Preferably, the biomass feedback controller of the precision feeding management system has a built-in dynamic compensation algorithm: When the density monitoring value of the staged graded cultivation unit exceeds the preset threshold of 10%, the feeding amount correction coefficient K will automatically increase by 0.2, and the feeding frequency will be increased by one level.

[0012] Preferably, the automatic sorting machine is equipped with a pressure-adaptive suction nozzle assembly: Level 1: Less than 5mm, using a 0.8mm orifice nozzle, negative pressure value less than 5kPa; Level 3 larvae: larger than 10mm, using a 1.5mm orifice nozzle, negative pressure value less than 8kPa; The nozzle aperture dynamically switches according to the body length data identified by the online biological monitoring unit.

[0013] Preferably, the central control platform executes fuzzy control rules: Input parameters include water quality fluctuation rate, larval activity level, and density deviation. The output command is a coordinated adjustment of water circulation frequency, LED light intensity, and feeding coefficient, with a response delay of less than 10 seconds.

[0014] Preferably, the multi-parameter sensor array includes a spectral analysis sensor to detect the algae concentration in the water in real time and is linked with the light-temperature cycle control unit. When algae concentration is greater than When the cell / mL level is reached, the light cycle is automatically shortened to 10 hours / day.

[0015] Preferably, the central control platform's coordinated disinfection mechanism adopts a three-level response strategy: Level 1 alert: Ultraviolet disinfection power increased by 20%; Level 2 alert: Activate the ozone generator and simultaneously reduce the stocking density by 10%; Level 3 alert: Isolate the infected patient and inject 5 ppm oxytetracycline, then shut off the water circulation for 24 hours.

[0016] (III) Beneficial Effects Compared with the prior art, the present invention provides a crayfish seedling industrialized breeding system, which has the following beneficial effects: 1. In this invention, by setting up an environmental collaborative control module, during factory seedling cultivation, a linkage mechanism between water quality and light and temperature parameters is established to ensure environmental adaptability at different developmental stages; at the same time, real-time monitoring and automatic adjustment of water quality and light and temperature parameters can eliminate abnormal fluctuations in water quality and light and temperature mismatch, ensure the stability of the hatching process, and reduce stress response in larvae.

[0017] 2. In this invention, by setting up an intelligent grading and monitoring module, during the staged rearing of larvae, the system automatically identifies the size of the larvae and calculates the density deviation, and adjusts the sorting parameters in real time. This enables the system to reduce density imbalance caused by grading errors and automatically trigger a correction mechanism when the density is abnormal, ensuring that the rearing density is within the optimal range and reducing the risk of competitive damage and cross-infection of diseases.

[0018] 3. In this invention, by setting up a behavior early warning module, the risk of disease is dynamically assessed by analyzing the behavioral characteristics of larvae in real time during the larval rearing process; a graded prevention and control strategy is initiated according to different risk levels, so that the system can achieve early warning and proactive intervention, reduce the incidence of disease, and ensure the survival rate of larvae and the uniformity of seedlings. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of a crayfish seedling industrial breeding system according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 This is a crayfish seedling industrial breeding system, which includes a central control platform and the following units connected to it for data transmission: The modular recirculating aquaculture unit consists of multiple standardized breeding ponds, each of which integrates a recirculating aquaculture subsystem to create a stable aquatic environment. The intelligent water quality control unit includes a multi-parameter sensor array and an automatic adjustment actuator, which monitors and adjusts water quality parameters in real time. The light-temperature cycle control unit consists of an LED light source group and a temperature control device, which simulates natural light-temperature changes; The online biological monitoring unit uses a camera network and an AI processor to track the behavior and growth status of larvae in real time. The precision feeding management system includes a feed storage bin, a metering feeder, and a biomass feedback controller, which automatically feeds the animal based on growth stage data from an online biological monitoring unit. The staged and graded breeding unit is equipped with an automatic sorting machine and a graded breeding area, and is managed in a graded manner based on the body length identification results of the online biological monitoring unit; The central control platform is an IoT gateway that enables real-time collaboration among various units through data aggregation and algorithm execution, specifically including: Based on the data on the larval development stage fed back by the online biological monitoring unit, water quality parameters and light and temperature parameters are adjusted synchronously. Based on the density monitoring results of the staged and graded breeding units, the feeding amount of the precision feeding management system is dynamically triggered to adjust. When the online biological monitoring unit identifies disease risks, it will activate the modular circulating water breeding unit to enhance disinfection.

[0022] Real-time collaboration is specifically manifested in: When the larvae are in the incubation period, the water quality intelligent control unit maintains pH=7.2+0.1, while the light and temperature cycle control unit sets the water temperature to 26℃ and the light intensity to 100 lux. When the density of first-level larvae in the staged grading breeding unit exceeds 1200 larvae / cubic meter, the precision feeding management system automatically increases the feeding frequency to once every 2 hours. When the online biological monitoring unit detects a 30% drop in swimming speed that lasts for more than 30 minutes, it triggers a doubling of the ultraviolet disinfection frequency in the modular recirculating aquaculture unit.

[0023] The intelligent water quality control unit and the light and temperature cycle control unit exchange data through an environmental linkage protocol. When the water temperature exceeds 28℃, the aeration equipment of the water quality unit is automatically activated, and when the ammonia nitrogen concentration exceeds 0.2 mg / L, the water temperature is forcibly lowered to below 24℃. The water temperature adjustment is calculated using the following formula: in The adjustment coefficient is set to 0.8. This represents the real-time ammonia nitrogen concentration. This refers to the degree of water temperature reduction.

[0024] The AI ​​processor of the online biological monitoring unit is deployed with a dual-model collaborative architecture: The YOLOv5 model analyzes the swimming trajectory of larvae in real time and outputs an abnormal behavior index. The ResNet model simultaneously detects surface lesion features; When the sum of the risk scores from the two models exceeds the threshold, a coordinated disinfection command is triggered on the central control platform. The abnormal behavior index is calculated using the following formula: in As a baseline swimming speed, To monitor swimming speed in real time, This is a behavioral abnormality index.

[0025] The circulating water treatment subsystem of the modular circulating water breeding unit includes a staged disinfection module: The ultraviolet disinfection unit is in normal operation. The ozone generator is activated when the online biological monitoring unit identifies disease risks, and the concentration is controlled at 0.05 ppm.

[0026] The biomass feedback controller of the precision feeding management system has a built-in dynamic compensation algorithm: When the density monitoring value of the staged graded cultivation unit exceeds the preset threshold of 10%, the feeding amount correction coefficient K will automatically increase by 0.2, and the feeding frequency will be increased by one level.

[0027] The automatic sorting machine is equipped with a pressure-adaptive suction nozzle assembly: Level 1: Less than 5mm, using a 0.8mm orifice nozzle, negative pressure value less than 5kPa; Level 3 larvae: larger than 10mm, using a 1.5mm orifice nozzle, negative pressure value less than 8kPa; The nozzle aperture dynamically switches according to the body length data identified by the online biological monitoring unit.

[0028] The central control platform executes fuzzy control rules: Input parameters include water quality fluctuation rate, larval activity level, and density deviation. The output command is a coordinated adjustment of water circulation frequency, LED light intensity, and feeding coefficient, with a response delay of less than 10 seconds.

[0029] The multi-parameter sensor array includes a spectral analysis sensor to detect algae concentration in the water in real time and is linked with a light-temperature cycle control unit. When algae concentration is greater than When the cell / mL level is reached, the light cycle is automatically shortened to 10 hours / day.

[0030] The central control platform's coordinated disinfection mechanism employs a three-tiered response strategy: Level 1 alert: Ultraviolet disinfection power increased by 20%; Level 2 alert: Activate the ozone generator and simultaneously reduce the stocking density by 10%; Level 3 alert: Isolate the infected patient and inject 5 ppm oxytetracycline, then shut off the water circulation for 24 hours.

[0031] The system includes the following steps: S1. Design and layout modular recirculating aquaculture units to build a stable and controllable water environment foundation. The modular recirculating aquaculture units include multiple independent and adjustable aquaculture modules, each equipped with a recirculating water treatment system to maintain the stability of water quality parameters. S2. Implement a precision feeding management system to automatically calculate and deliver customized feed based on larval growth stage and biomass data to optimize larval nutrition. S3. Apply a staged and graded breeding process to divide crayfish larvae into multiple breeding stages according to their size and manage them independently in different modules in order to optimize growth space and reduce competitive damage. S4. Integrated intelligent water quality control technology, which monitors water quality parameters in real time through a sensor network and automatically adjusts pH, dissolved oxygen, ammonia nitrogen and nitrite concentrations based on preset thresholds; S5. Control the light and temperature cycle, using LED light sources and temperature control equipment to simulate natural light and temperature changes, including light intensity, light cycle and temperature gradient, to promote physiological synchronization of larvae. S6. Deploy online biological monitoring technology to track the behavior, growth rate and disease signs of larvae in real time through cameras and AI algorithms, and generate biological monitoring data. S7. Based on the integrated data from S1 to S6, precise management of environmental factors is achieved, including dynamic adjustment of water circulation frequency, feed amount and light and temperature parameters, in order to improve the hatching rate of fertilized eggs and the survival rate of larvae, reduce diseases, ensure the uniformity of seedling size, and ultimately achieve continuous, stable and efficient large-scale breeding and production throughout the year.

[0032] S1 includes the following steps: S11. The modular recirculating aquaculture unit is designed with standardized-sized breeding ponds, each with a volume of 3 cubic meters, and is equipped with independent inlet pipes, outlet pipes and biological filters. S12. The circulating water treatment system includes a physical filtration layer, a biofilm reactor, and an ultraviolet disinfection unit to remove suspended solids, degrade organic matter, and kill pathogenic microorganisms. S13. The layout adopts a matrix arrangement, with modules connected by a pipeline network and equipped with a central control system to uniformly regulate water flow and pressure, and control the fluctuation range of water environment parameters within 5%.

[0033] S2 includes the following steps: S21. The precision feeding management system includes a feed storage bin, an automatic feeder, and an IoT controller. The feed is a customized formula containing micro-particles with a protein content of 50% and a fat content of 15%. S22. Based on the data of the larval growth stages, the larvae are divided into the incubation period, the seedling period and the rearing period, and different feeding strategies are adopted accordingly: feed once every 2 hours during the incubation period, feed once every 4 hours during the seedling period, and feed once every 6 hours during the rearing period. S23. Formula for automatically calculating feeding amount: in For feeding amount, This is the total weight of the juveniles. This is the feeding coefficient. The correction factor is dynamically adjusted based on the larval activity level in the online biological monitoring data.

[0034] S3 includes the following steps: S31. The staged grading and cultivation process divides the larvae into three levels according to their body length: Level 1: less than 5mm in body length, Level 2: 8mm in body length, and Level 3: greater than 10mm in body length. Each level is assigned to an independent module. S32. Graded transfer is achieved through an automated sorting device, which uses a convolutional neural network (CNN) algorithm to identify body length and classify juveniles based on images captured by a camera. S33. Optimizing growth space includes adjusting stocking density: Level 1 density is 1200 fish / cubic meter, Level 2 density is 600 fish / cubic meter, and Level 3 density is 300 fish / cubic meter, in order to reduce space competition and promote uniform growth.

[0035] S4 includes the following steps: S41. The intelligent water quality control technology monitors water quality through a multi-parameter sensor network, including pH, dissolved oxygen, ammonia nitrogen, and nitrite sensors, with a data sampling frequency of once per minute. S42. The automatic adjustment mechanism includes: activating the aeration equipment when dissolved oxygen is below 5 mg / L; injecting buffer when pH deviates from 7.2; and triggering the biological filter enhancement mode when ammonia nitrogen concentration exceeds 0.2 mg / L and nitrite concentration exceeds 0.1 mg / L. S43. The control algorithm is based on a fuzzy logic controller. The input parameters include real-time monitoring data and historical trends, and the output is a control command to maintain water quality parameters stable within the preset safety threshold.

[0036] S5 includes the following steps: S51, the light and temperature cycle control device includes an adjustable LED array and a PID temperature control system. The LED spectrum is full-spectrum simulation and the light intensity is 300 lux. S52. Simulation of natural light and temperature changes includes: daily light cycle set to 12 hours of light and 12 hours of darkness, temperature gradient of 26℃ during the day and 23℃ at night, and rate of change of 1℃ / hour. S53. Light and temperature parameters are adjusted according to the developmental stage of the larvae: 100 lux light intensity and 26℃ during the incubation period, 200 lux light intensity and 24℃ during the seedling period, and 300 lux light intensity and 22℃ during the growth period, in order to synchronize with the physiological rhythm.

[0037] S6 includes the following steps: S61, Online biological monitoring technology deploys high-definition cameras and motion sensors. The cameras have a resolution higher than 1080p and are installed on the top and sides of the module to capture images of larvae from multiple angles. S62. The AI ​​algorithm includes a behavior recognition model and a disease detection model: the behavior recognition model tracks swimming speed and feeding behavior using the YOLOv5 architecture, and the disease detection model analyzes body surface spots, gill abnormalities, and signs of activity inhibition based on the ResNet architecture, with a sensitivity of 95%. S63. Generate biological monitoring data, including growth curve calculation, using the following formula: in For growth rate, Let t be the average body length of the larvae. Initial body length, For time intervals.

[0038] S7 includes the following steps: S71. Precise management of environmental factors is based on a data fusion platform, which integrates the output data from S1 to S6, including water quality parameters, light and temperature status, biological monitoring and feeding records. S72. Dynamic adjustment strategies include: increasing the frequency of ultraviolet disinfection when online biological monitoring shows an increased risk of disease; and optimizing feed formulation and adjusting light and temperature cycles when the growth rate is lower than the preset value. S73. Performance indicators include: fertilized egg hatching rate increased to over 85%, larval survival rate increased to over 90%, disease incidence rate reduced to below 5%, seedling size uniformity controlled within 10%, continuous production throughout the year, and production capacity fluctuation less than 5%.

[0039] Example 1: Implementation Process of the Environmental Collaborative Regulation Module This embodiment details the complete implementation process of a modular circulating water synergistic control system. Within a standardized seedling workshop, a 12-meter-long and 8-meter-wide space is meticulously planned, with six fiberglass aquaculture ponds arranged in a ring layout, each with a precisely controlled volume of 3 cubic meters. Independent water circulation pathways are constructed using 50mm diameter inlet pipes and 75mm diameter outlet pipes, maximizing hydrodynamic efficiency. The physical filtration layer utilizes an 80-mesh stainless steel filter screen, combined with a high-surface-area polyethylene packing biofilm carrier, forming a double filtration barrier. An ultraviolet disinfection device operates continuously, its 300-watt power effectively killing pathogenic microorganisms in the water. Dissolved oxygen sensors and ammonia nitrogen detection probes are deployed at key monitoring points to collect water quality data in real time. When dissolved oxygen falls below 5 mg / L, the bottom aeration device is automatically activated; when ammonia nitrogen concentration exceeds 0.2 mg / L, the biological filtration enhancement program is immediately triggered. The full-spectrum LED array lighting system operates according to a preset program: the lighting period is from 06:00 to 18:00 daily, with the light intensity gradually increasing from 150 lux during the incubation period to 450 lux during the rearing period; at night, the temperature steadily decreases from 26 degrees Celsius to 23 degrees Celsius, with the cooling rate strictly controlled at 1 degree Celsius per hour. This environmental coordination mechanism achieves intelligent matching of water quality parameters and light and temperature conditions through a central controller, successfully eliminating the stress response of larvae caused by the disconnect of environmental parameters in traditional models.

[0040] Example 2: Application Process of Intelligent Hierarchical Monitoring Module This embodiment comprehensively demonstrates the entire operation process of the intelligent grading and rearing system for juveniles. Inside the grading workshop, two 20-megapixel industrial cameras are precisely mounted 1.2 meters above the sorting tank, with dedicated 520nm wavelength supplementary lighting effectively eliminating water refraction interference. As the juveniles flow through the sorting tank, the cameras capture high-definition images at 30 frames per second. The image preprocessing system automatically performs Gaussian filtering for noise reduction and HSV color space conversion. The contour extraction stage employs the Canny edge detection algorithm combined with morphological closing operations, and body length calculation is achieved through pixel distance conversion using a reference ruler calibration. Strict grading standards are set: primary juveniles less than 5 mm in length are marked in blue, intermediate juveniles 5-10 mm in length are marked in yellow, and advanced juveniles exceeding 10 mm in length are marked in red. A real-time density monitoring unit continuously tracks data from each rearing area. When the density in the primary rearing area exceeds the threshold of 1500 juveniles / cubic meter, the pneumatic sorting arm immediately initiates the transfer procedure; when the density in the intermediate rearing area reaches the critical value of 800 juveniles / cubic meter, the siphon pipeline system automatically performs precise diversion. The entire process is managed in a closed loop through an IoT controller. A three-axis robotic arm completes the sorting of larvae with a positioning accuracy of 0.1 mm, and the diameter of the negative pressure suction port is intelligently adjusted within a range of 3 mm to accommodate larvae of different sizes. This fully automated grading mechanism solves the density imbalance problem caused by manual sorting errors.

[0041] Example 3: Verification of the Behavior Early Warning Module This embodiment fully documents the operational verification process of the behavioral early warning system in a real production environment. 4K fisheye cameras deployed at the four corners of the aquaculture unit, positioned 50 cm above the water surface, utilize an 850 nm infrared mode for all-weather monitoring. The behavioral analysis system calculates average speed based on swimming trajectories; a sustained decrease in swimming speed exceeding 30 minutes automatically triggers a Level 1 warning. The feeding behavior monitoring module accurately tracks mouthpart opening and closing frequency; abnormal data below 3 times per minute activates a Level 2 warning response. The system establishes a three-tiered prevention and control mechanism: during a Level 1 warning, dissolved oxygen concentration is automatically increased to saturation, and a 5% vitamin C fortification packet is added to the feed; during a Level 2 warning, enhanced ultraviolet disinfection is initiated, extending the water residence time to 40 seconds; during a Level 3 warning, when the clustering density exceeds 80%, the group is automatically isolated, and a 5 ppm antibiotic solution is injected. This system successfully intervened at the initial stage of clustering in juveniles, achieving a 93.2% warning accuracy rate, an average response time of 8 minutes and 37 seconds, and a 100% disease outbreak suppression rate. This proactive prevention and control model based on biological behavioral characteristics transforms traditional passive handling into precise predictive management.

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A crayfish seedling industrialized breeding system, characterized in that: The system includes a central control platform and the following units connected to it via data: The modular recirculating aquaculture unit consists of multiple standardized breeding ponds, each of which integrates a recirculating aquaculture subsystem to create a stable aquatic environment. The intelligent water quality control unit includes a multi-parameter sensor array and an automatic adjustment actuator, which monitors and adjusts water quality parameters in real time. The light-temperature cycle control unit consists of an LED light source group and a temperature control device, which simulates natural light-temperature changes; The intelligent water quality control unit and the light and temperature cycle control unit exchange data through an environmental linkage protocol. When the water temperature is greater than 28℃, the aeration equipment of the intelligent water quality control unit is automatically activated, and when the ammonia nitrogen concentration is greater than 0.2mg / L, the water temperature is forcibly lowered to below 24℃. The water temperature adjustment is calculated using the following formula: in This is an adjustment coefficient, ranging from 0.5 to 1.

0. This represents the real-time ammonia nitrogen concentration. This refers to the degree of water temperature reduction; The online biological monitoring unit uses a camera network and an AI processor to track the behavior and growth status of larvae in real time. The AI ​​processor of the online biological monitoring unit deploys a dual-model collaborative architecture: The YOLOv5 model analyzes the swimming trajectory of larvae in real time and outputs an abnormal behavior index. The ResNet model simultaneously detects surface lesion features; When the sum of the risk scores from the two models exceeds the threshold, a coordinated disinfection command is triggered on the central control platform. The abnormal behavior index is calculated using the following formula: in As a baseline swimming speed, To monitor swimming speed in real time, This is a behavioral abnormality index; The precision feeding management system includes a feed storage bin, a metering feeder, and a biomass feedback controller, which automatically feeds the animals based on growth stage data from the online biological monitoring unit. The biomass feedback controller of the precision feeding management system has a built-in dynamic compensation algorithm: When the density monitoring value of the staged and graded cultivation unit exceeds the preset threshold by 10%, the feeding amount correction coefficient K automatically increases by 0.2, and the feeding frequency is increased by one level. The feeding amount is automatically calculated using the following formula: in For feeding amount, This is the total weight of the juveniles. This is the feeding coefficient. The correction factor is dynamically adjusted based on the larval activity level in the online biological monitoring data. The staged, graded rearing unit is equipped with an automatic sorting machine and a graded rearing area. It manages the rearing based on the body length identification results from the online biological monitoring unit. The automatic sorting machine is equipped with a pressure-adaptive suction nozzle assembly. First-grade larvae: less than 5mm, using a 0.8mm orifice nozzle, negative pressure value less than 5kPa; Level 3 larvae: larger than 10mm, using a 1.5mm orifice nozzle, negative pressure value less than 8kPa; The nozzle aperture dynamically switches based on the body length data identified by the online biological monitoring unit; The central control platform is an IoT gateway that enables real-time collaboration among various units through data aggregation and algorithm execution, specifically including: Based on the data on the larval development stage fed back by the online biological monitoring unit, water quality parameters and light and temperature parameters are adjusted synchronously. Based on the density monitoring results of the staged and graded breeding units, the feeding amount of the precision feeding management system is dynamically triggered for correction. When the online biological monitoring unit identifies disease risks, it activates the modular circulating water breeding unit to enhance disinfection.

2. The crayfish seedling industrialized breeding system according to claim 1, characterized in that: The real-time collaboration is specifically manifested in the following ways: When the larvae are in the incubation period, the intelligent water quality control unit maintains pH=7.2±0.1, while the light and temperature cycle control unit sets the water temperature to 26℃ and the light intensity to 100 lux. When the density of first-level larvae detected by the staged grading breeding unit is greater than 1200 larvae / cubic meter, the precision feeding management system automatically increases the feeding frequency to once every 2 hours; When the online biological monitoring unit detects a 30% decrease in swimming speed for more than 30 minutes, it triggers the ultraviolet disinfection frequency of the modular circulating water breeding unit to double.

3. The crayfish seedling industrialized breeding system according to claim 1, characterized in that: The circulating water treatment subsystem of the modular circulating water breeding unit includes a graded disinfection module: The ultraviolet disinfection unit is in normal operation. The ozone generator is activated when the online biological monitoring unit identifies disease risk, and the concentration is controlled at 0.05 ppm.

4. The crayfish seedling industrialized breeding system according to claim 1, characterized in that: The central control platform executes fuzzy control rules: Input parameters include water quality fluctuation rate, larval activity level, and density deviation. The output command is a coordinated adjustment of water circulation frequency, LED light intensity, and feeding coefficient, with a response delay of less than 10 seconds.

5. The crayfish seedling industrialized breeding system according to claim 1, characterized in that: The multi-parameter sensor array includes a spectral analysis sensor to detect the concentration of algae in the water in real time and is linked with the light-temperature cycle control unit. When algae concentration is greater than When the number of cells / mL is reached, the light cycle is automatically shortened to 10 hours / day.

6. The crayfish seedling industrialized breeding system according to claim 1, characterized in that: The central control platform's coordinated disinfection mechanism employs a three-tiered response strategy: Level 1 alert: Ultraviolet disinfection power increased by 20%; Level 2 alert: Activate the ozone generator and simultaneously reduce the stocking density by 10%; Level 3 alert: Isolate the infected patient and inject 5 ppm oxytetracycline, then shut off the water circulation for 24 hours.

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