A bamboo-based offshore floating unmanned marine ranching system

Abstract

This invention discloses a bamboo-based floating unmanned marine ranching system, relating to the field of marine engineering technology. It includes a facility module, a data integration and processing module, and an interaction module. The facility module comprises a self-healing bamboo-based floating platform, an intelligent sensor network, a camera monitoring system, an automatic feeder, an intelligent water purification device, and an automatic grading and sorting harvesting equipment. The data integration and processing module includes a cloud platform, artificial intelligence algorithms, and a big data analysis platform. The interaction module includes a VR aquaculture experience system, a remote monitoring and management system, and an early warning system. This invention uses bamboo as the base material for the floating platform. Compared to traditional plastic or metal materials, bamboo has higher renewability and lower carbon emissions. Furthermore, it employs a self-healing coating technology that can automatically repair micro-cracks on the bamboo surface, extending the lifespan of the floating platform and reducing maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, specifically to a bamboo-based floating unmanned marine ranch system. Background Technology

[0002] Marine ranching is a method of cultivating aquatic organisms such as fish, shellfish, and algae using marine resources, with the aim of achieving the sustainable use of marine resources. Marine ranching can be divided into two types: artificial aquaculture and natural aquaculture.

[0003] Artificial aquaculture: In a controlled marine environment, the growth and reproduction of aquatic organisms are promoted by feeding them and adjusting environmental parameters such as water quality, temperature, and dissolved oxygen. Artificial aquaculture typically requires the construction of aquaculture facilities, such as floating platforms, net cages, and culture ponds.

[0004] Natural aquaculture: Utilizing the natural marine environment, fish or shellfish fry are artificially introduced and allowed to grow under natural conditions. Natural aquaculture does not require the construction of aquaculture facilities and has less impact on the environment, but it is greatly affected by natural conditions such as typhoons and red tides.

[0005] The plastic or metal floating platform materials currently used have problems such as short lifespan, difficulty in degradation or environmental pollution, and require frequent maintenance and replacement. In addition, traditional aquaculture lacks data-based decision support. Therefore, we propose a bamboo-based floating unmanned marine ranch system to solve the problems mentioned above. Summary of the Invention

[0006] The purpose of this invention is to provide a bamboo-based floating unmanned marine ranching system to solve the problems currently found in the market as described in the background.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A bamboo-based floating unmanned marine ranch system includes a facility module, a data integration and processing module, and an interaction module;

[0009] The facility modules include self-healing bamboo-based floating platforms, intelligent sensor networks, camera monitoring systems, automatic feeders, intelligent water purification devices, and automatic grading and sorting fishing equipment.

[0010] The data integration and processing module includes a cloud platform, artificial intelligence algorithms, and a big data analytics platform;

[0011] The interactive modules include a VR aquaculture experience system, a remote monitoring and management system, and an early warning system.

[0012] As a further optimization of the present invention, the surface of the self-healing bamboo-based floating platform is coated with a self-healing coating, and a solar panel is installed on the top of the self-healing bamboo-based floating platform to collect solar energy and convert it into electrical energy for the use of the entire aquaculture system; a small wind turbine is installed on the self-healing bamboo-based floating platform to generate electricity using sea breeze, which, together with the solar panel, provides power to the system; the self-healing bamboo-based floating platform is equipped with an intelligent energy management system, which allocates the power supply of solar and wind power generation according to the energy consumption needs of the aquaculture system;

[0013] Solar panels and wind turbines continuously generate electricity during the day and in windy conditions, which is stored in batteries to power the daily operation of the aquaculture system. When the bamboo surface is damaged, the microcapsules in the self-healing coating rupture, releasing a repair agent to repair the damage.

[0014] As a further optimization of the present invention, the specific steps for the structure of bamboo and the self-healing coating are as follows:

[0015] Step 1: Cut the bamboo into long strips and weave and bind them to form a grid structure;

[0016] Step 2: Install multiple sealed air chambers inside the bamboo mesh structure to ensure the floating platform floats stably;

[0017] Step 3: Reinforce the intersections of the grid using nodes;

[0018] Step 4: Apply a self-healing coating evenly to the surface of the bamboo. When the bamboo is damaged, the microcapsules in the coating rupture, releasing the repair agent, which reacts with the catalyst in the bamboo to form new polymer chains, thereby repairing the cracks.

[0019] As a further optimization of the present invention, the intelligent sensor network includes a temperature sensor, a dissolved oxygen monitor, a water quality detector, and a biosensor;

[0020] Among them, the temperature sensor is used to monitor the temperature of the aquaculture water, with a range of ±0.1℃, and is distributed at different depths in the aquaculture area, transmitting the data to the cloud platform in real time;

[0021] Dissolved oxygen monitors are used to measure the dissolved oxygen content in water. They are distributed at different points in the aquaculture area, and the monitored data is transmitted in real time to adjust oxygenation equipment and change feeding strategies.

[0022] Water quality detectors are used to monitor pH, ammonia nitrogen, nitrite, nitrate, and phosphate levels. They are installed at the inlet and outlet of aquaculture water bodies, and the monitored data is transmitted in real time for use in water quality management and early warning systems.

[0023] The biosensor is an external, non-invasive biosensor used to monitor the physiological state of fish. It features high sensitivity and miniaturization, and transmits data to a cloud platform wirelessly.

[0024] As a further optimization of the present invention, the camera monitoring system includes a high-resolution high-definition camera with an IP66 protection rating and equipped with a low-light sensor. The high-definition camera is mounted on a floating platform using corrosion-resistant fasteners, covering the entire aquaculture area. The camera continuously collects images of the aquaculture area and transmits them to the cloud platform in real time via a wireless network. The collected images have a resolution of 1080p.

[0025] As a further optimization of the present invention, the automatic feeder is driven by a servo motor, has an internal feed storage space, and has multiple adjustable feeding ports on the outside. The automatic feeder has a control system inside, which integrates AI algorithms to analyze fish growth data, feeding behavior, and water quality. It also includes a microprocessor or single-chip microcomputer to receive AI analysis results and control the operation of the feeder.

[0026] As a further optimization of the present invention, the intelligent water purification device includes a biological filter, activated carbon nanoparticles, and an intelligent control system.

[0027] The biological filter consists of multiple biological filter units, each filled with biological media. The biological filter uses microorganisms to decompose ammonia nitrogen and nitrite produced during the breeding process, converting them into harmless or low-harm substances to achieve water purification.

[0028] Activated carbon nanoparticles are integrated into a biological filter to remove heavy metal ions, harmful chemicals, and pathogens from the water.

[0029] The intelligent control system includes water quality sensors that monitor water quality parameters in real time. Based on the sensor data, the intelligent control system automatically adjusts the water flow rate and oxygen supply, and feeds back the water quality data and purification effect to the cloud platform.

[0030] As a further optimization of the present invention, the automatic grading and sorting fishing equipment includes a fish collector, a fish sorter, and an automatic control system.

[0031] Among them, the fish collector is a channel type, used to guide and concentrate fish and bring them into the sorting system. It attracts fish by controlling water level changes or by using sound waves and light to attract them.

[0032] The fish sorter consists of multiple concentric circles, each with a different aperture. It automatically sorts and classifies fish according to their size and species. The fish are transported to a conveyor belt, where photoelectric sensors measure their size, and then they are automatically sorted into different collection boxes.

[0033] The automatic control system controls the automated operation of the entire grading and sorting process. The automatic control system uses a PLC combined with sensors and actuators to achieve automated control and control the actions of the actuators.

[0034] As a further optimization of the present invention, in the data integration and processing module, the cloud platform connects to sensors and cameras at the hardware facility layer via API. The cloud platform receives and processes various data formats, stores data information through a distributed database and data center, and performs rapid processing and analysis on real-time incoming data. For non-real-time data, batch processing is performed. The cloud platform has built-in data analysis tools to assist users in understanding the data. Users can view the environmental data, equipment status, and fish health of the aquaculture farm in real time through web pages or mobile applications. When abnormal data or equipment failure is detected, the system automatically issues an alarm to notify the management personnel.

[0035] As a further optimization of the present invention, in the VR aquaculture experience system, virtual reality technology is used to experience the observation of the aquaculture environment and the behavior of fish. Through the VR headset, the user interacts with elements in the virtual environment, adjusts aquaculture parameters, observes the growth of fish, enters the virtual aquaculture environment through the VR headset, and uses the VR software platform to create and run the virtual aquaculture experience.

[0036] In the remote monitoring and management system, managers can remotely view real-time data and video surveillance of the farm, and allow users and managers to remotely perform management operations through the system.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] This invention uses bamboo as the base material for the floating platform. Compared with traditional plastic or metal materials, bamboo has higher renewability and lower carbon emissions. It also adopts self-healing coating technology, which can automatically repair the tiny cracks on the surface of the bamboo, extend the service life of the floating platform, and reduce maintenance costs.

[0039] This invention provides clean energy by integrating solar panels and wind turbines, and monitors the aquaculture environment and fish health in real time by deploying a series of smart sensors. It also uses AI algorithms and big data analysis platforms to deeply mine aquaculture data, providing decision support and optimizing aquaculture strategies. Furthermore, it ensures the safety of aquaculture facilities and fish by providing extreme weather warnings and automatically adjusting the location of aquaculture facilities. Finally, it achieves automated grading, sorting, and harvesting of fish through automated grading and sorting equipment, improving harvesting efficiency and reducing manual operations.

[0040] This invention forms a closed-loop intelligent aquaculture management system, encompassing data collection, environmental control, aquaculture management, health monitoring, automated harvesting, and disaster prevention and mitigation, thereby improving aquaculture efficiency and fish survival rates.

[0041] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0042] Figure 1 This is a block diagram of a bamboo-based floating unmanned marine ranch system according to the present invention. Detailed Implementation

[0043] 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.

[0044] Example 1

[0045] Please see Figure 1 A bamboo-based floating unmanned marine ranch system includes a facility module, a data integration and processing module, and an interaction module.

[0046] The facility modules include self-healing bamboo-based floating platforms, intelligent sensor networks, camera monitoring systems, automatic feeders, intelligent water purification devices, and automatic grading and sorting fishing equipment.

[0047] The data integration and processing module includes a cloud platform, artificial intelligence algorithms, and a big data analytics platform;

[0048] The interactive modules include a VR aquaculture experience system, a remote monitoring and management system, and an early warning system.

[0049] The self-healing bamboo-based floating platform is coated with a self-healing coating. The top of the self-healing bamboo-based floating platform is equipped with solar panels to collect solar energy and convert it into electricity for the entire aquaculture system. A small wind turbine is installed on the self-healing bamboo-based floating platform to generate electricity using sea breezes, which, together with the solar panels, provides power to the system. The self-healing bamboo-based floating platform is equipped with an intelligent energy management system, which allocates the power supply from solar and wind power generation according to the energy consumption needs of the aquaculture system.

[0050] Specifically, the design steps for the structure and self-healing coating technology of bamboo are as follows:

[0051] Step 1: Cut bamboo into long strips, and weave and bind them to form a grid structure to improve the load-bearing capacity and deformation resistance of the floating platform.

[0052] Step 2: Install multiple sealed air chambers inside the bamboo mesh structure to ensure the floating platform floats stably.

[0053] Step 3: Reinforce the intersections of the grids using nodes to improve the overall stability of the structure.

[0054] Step 4: Apply a self-healing coating evenly to the surface of the bamboo. When the bamboo is damaged, the microcapsules in the coating rupture, releasing the repair agent, which reacts with the catalyst in the bamboo to form new polymer chains, thereby repairing the cracks.

[0055] Solar panels and wind turbines continuously generate electricity during the day and in windy conditions, which is stored in batteries to power the daily operation of the aquaculture system. When the bamboo surface is damaged, the microcapsules in the self-healing coating rupture, releasing a repair agent to repair the damage.

[0056] Smart sensor networks include temperature sensors, dissolved oxygen monitors, water quality detectors, and biosensors;

[0057] Among them, the temperature sensor is used to monitor the temperature of the aquaculture water, accurate to ±0.1℃, and is distributed at different depths in the aquaculture area, transmitting the data to the cloud platform in real time;

[0058] Dissolved oxygen monitors are used to measure the dissolved oxygen content in water. They are distributed at different points in the aquaculture area, and the monitored data is transmitted in real time to adjust oxygenation equipment and change feeding strategies.

[0059] Water quality detectors are used to monitor pH, ammonia nitrogen, nitrite, nitrate, and phosphate levels. They are installed at the inlet and outlet of aquaculture water bodies, and the monitored data is transmitted in real time for use in water quality management and early warning systems.

[0060] The biosensor is an external, non-invasive biosensor used to monitor the physiological state of fish. It features high sensitivity and miniaturization, and transmits data to a cloud platform wirelessly.

[0061] Furthermore, the workflow of intelligent sensor networks:

[0062] Each sensor collects data at a preset frequency and transmits the collected data wirelessly to the cloud platform. The cloud platform analyzes the data in real time, identifies anomalies, and automatically adjusts the aquaculture environment or aquaculture management strategies based on the analysis results. When a potential problem is detected, the system issues an early warning and sends a report to the management personnel. The sensor network continues to work to ensure the stability of the aquaculture environment and the health of the fish.

[0063] The camera monitoring system includes high-resolution HD cameras with an IP66 protection rating and equipped with low-light sensors. The HD cameras are mounted on the floating platform using corrosion-resistant fasteners, covering the entire aquaculture area. The cameras continuously collect images of the aquaculture area and transmit them to the cloud platform in real time via a wireless network. The collected images have a resolution of 1080p.

[0064] Specifically, the system analyzes fish activity patterns, behavioral characteristics, and health status through image analysis, regularly captures images of fish, measures fish length and weight using image processing technology, estimates growth rate, monitors for invasive alien species in the aquaculture area, stores image data on a cloud platform for easy historical retrieval and data analysis, and automatically identifies and analyzes the collected images using deep learning algorithms, combining camera data with data from other sensors.

[0065] The automatic feeder is driven by a servo motor and has an internal feed storage space and multiple adjustable feeding ports on the outside. The automatic feeder has an internal control system that integrates AI algorithms to analyze fish growth data, feeding behavior, and water quality. It also includes a microprocessor or single-chip microcomputer to receive the AI ​​analysis results and control the operation of the feeder.

[0066] Specifically, based on the analysis results of AI algorithms, the feeding time, frequency, and quantity are automatically adjusted. Different types of feed are automatically selected and mixed according to the fish's growth stage and nutritional needs. In the event of a malfunction, feeding is stopped and an alarm is issued.

[0067] The working process of an automatic feeder:

[0068] The automatic feeder collects fish growth and environmental data from a smart sensor network and camera monitoring system. AI algorithms process the collected data to predict the fish's current feed needs. Based on the AI ​​analysis results, it automatically selects the appropriate type of feed and prepares the appropriate amount of feed. The control unit starts the drive system and delivers the feed to the aquaculture area through the feeding port. After feeding, the system continues to collect data, and the AI ​​analyzes the feeding effect. If necessary, it adjusts the feeding strategy for the next feeding. The system continuously monitors the operating status of the feeder and performs maintenance and troubleshooting.

[0069] The intelligent water purification device includes a biological filter, activated carbon nanoparticles, and an intelligent control system;

[0070] The biological filter consists of multiple biological filter units, each filled with biological media. The biological filter uses microorganisms to decompose ammonia nitrogen and nitrite produced during the breeding process, converting them into harmless or low-harm substances to achieve water purification.

[0071] Activated carbon nanoparticles are integrated into a biological filter to remove heavy metal ions, harmful chemicals, and pathogens from the water.

[0072] The intelligent control system includes water quality sensors that monitor water quality parameters in real time. Based on the sensor data, the intelligent control system automatically adjusts the water flow rate and oxygen supply, and feeds back the water quality data and purification effect to the cloud platform.

[0073] Specifically, sensors in the device monitor various water quality indicators in the aquaculture water in real time, and the monitoring data is transmitted to the intelligent control system and synchronized with the cloud platform. The aquaculture water flows into the biological filter, where organic waste is removed by microorganisms, and then the water quality is further purified by activated carbon nanoparticles. The intelligent control system automatically adjusts the working status of the biological filter based on the water quality data.

[0074] Automatic grading and sorting fishing equipment includes a fish collector, a fish sorter, and an automatic control system;

[0075] The fish collector is a channel type, used to guide and concentrate fish into the sorting system. It attracts fish by controlling water level changes or using sound waves and light to lure them.

[0076] The fish sorter consists of multiple concentric circles, each with a different aperture. It automatically sorts and classifies fish according to their size and species. The fish are transported to a conveyor belt, where photoelectric sensors measure their size, and then they are automatically sorted into different collection boxes.

[0077] The automatic control system controls the automated operation of the entire grading and sorting process. The automatic control system uses a PLC combined with sensors and actuators to achieve automated control and control the actions of the actuators.

[0078] Specifically, fish in the aquaculture area are guided to the grading and sorting system by a fish collector. The fish are initially graded according to size by a grading wheel. The fish are then conveyed by a conveyor belt and passed through photoelectric sensors and weight sensors to measure their size or weight. Based on the measurement results, the automatic control system controls the sorting mechanism to sort the fish into different collection boxes. The data on the size, weight, and quantity of the fish are recorded during the sorting process and transmitted to the cloud platform.

[0079] In the data integration and processing module, the cloud platform connects to sensors and cameras at the hardware layer via APIs. The cloud platform receives and processes various data formats, stores data information through distributed databases and data centers, and rapidly processes and analyzes real-time data. For non-real-time data, it performs batch processing. The cloud platform has built-in data analysis tools to assist users in understanding the data. Users can view environmental data, equipment status, and fish health in the aquaculture farm in real time through web pages or mobile applications. When abnormal data or equipment malfunctions are detected, the system automatically issues an alarm to notify management personnel.

[0080] Specifically, the cloud platform uses relational or non-relational databases to store structured and unstructured data, and employs regular data backup and redundant storage strategies. It automatically cleans the data, removing invalid, erroneous, or duplicate data. AI and machine learning algorithms are used for trend analysis and pattern recognition. The monitoring interface includes an interactive map displaying the specific location of the farm and the distribution of equipment. Users can remotely operate the farm's equipment by sending control commands through the cloud platform.

[0081] Artificial intelligence algorithms use convolutional neural networks for automatic image recognition and analysis. They simulate fish growth curves to predict growth rate, weight, and body size, and combine machine learning and fish growth models to analyze aquaculture data and predict fish growth trends.

[0082] By simulating different marine environmental conditions using an environmental simulator, the effects of aquaculture under different environments can be predicted. Through advanced environmental simulation technology, combined with meteorological data and marine environmental parameters, the impact of environmental changes on aquaculture can be predicted. Aquaculture strategies can be learned and improved by supervising historical data.

[0083] Specifically, deep learning algorithms identify and classify images captured by cameras, analyze the growth status and behavior of fish, and machine learning models, combined with fish growth models, simulate fish growth curves to predict future growth. Environmental simulators, based on meteorological data and marine environmental parameters, predict the impact of environmental changes on aquaculture. Based on these analytical results, artificial intelligence algorithms optimize feed dosage, water quality management, and aquaculture environment control. According to the optimized aquaculture strategy, they automatically control and adjust feeders and aeration equipment.

[0084] The big data analytics platform integrates data from intelligent sensor networks, camera monitoring systems, and automatic feeders, and processes both structured and unstructured data. It uses a distributed data warehouse to store a large amount of aquaculture data, and combines the data warehouse with a data lake to store semi-structured and unstructured data.

[0085] Subsequently, invalid, erroneous, or duplicate data are removed, and valuable information is extracted from the large amount of data through machine learning. The analysis results are then used to generate reports, which are provided to aquaculture managers.

[0086] In the VR aquaculture experience system, virtual reality technology is used to observe the aquaculture environment and fish behavior. Users can interact with elements in the virtual environment through VR headsets, adjust aquaculture parameters, observe fish growth, enter the virtual aquaculture environment through VR headsets, and create and run virtual aquaculture experiences using VR software platforms.

[0087] In the remote monitoring and management system, managers can remotely view real-time data and video surveillance of the farm, and allow users and managers to remotely perform management operations through the system.

[0088] Specifically, real-time data is transmitted to a remote monitoring and management system via the network. The system has different access permissions to ensure that only authorized users and administrators can perform management operations. Through meteorological data and an early warning system, extreme weather events can be predicted in advance, and the system can automatically adjust the location of aquaculture facilities after an extreme weather warning is issued.

[0089] By integrating with meteorological data service providers, the system can acquire meteorological data in real time, set early warning thresholds, and issue warnings when extreme weather is predicted. It can also automatically control the movement of aquaculture facilities to move them to safe areas before storms arrive.

[0090] Intelligent sensor networks and camera monitoring systems collect real-time data on the aquaculture environment and fish growth. This data is uploaded to a cloud platform where artificial intelligence algorithms perform preliminary processing and analysis. Based on the AI ​​analysis results, temperature, dissolved oxygen, and water quality parameters are automatically adjusted. Intelligent water purification devices and automatic feeders adjust their operating status according to instructions. Machine learning algorithms continuously optimize aquaculture strategies, and a big data analysis platform provides decision support. Users manage and experience aquaculture through a VR aquaculture experience system and a remote monitoring and management system. Biosensors monitor the physiological state of fish, an early warning system prevents disease, analyzes fish health data, and adjusts aquaculture measures promptly. Automatic grading and sorting harvesting equipment grades and harvests fish according to size. The early warning system monitors extreme weather and issues warnings. Protective devices adjust the position of floating platforms according to instructions to protect aquaculture facilities and fish. Data on aquaculture performance is collected and fed back to AI algorithms for optimization. Users adjust their aquaculture plans and management strategies based on system feedback.

[0091] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0092] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0093] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0094] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0095] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0096] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A bamboo-based floating unmanned marine ranching system, characterized in that, It includes a facility module, a data integration and processing module, and an interaction module; The facility modules include a self-healing bamboo-based floating platform, an intelligent sensor network, a camera monitoring system, an automatic feeder, an intelligent water purification device, and an automatic grading and sorting fishing equipment. The data integration and processing module includes a cloud platform, artificial intelligence algorithms, and a big data analysis platform; The interactive module includes a VR aquaculture experience system, a remote monitoring and management system, and an early warning system; The self-healing bamboo-based floating platform has a grid-like structure with multiple sealed air bladders inside. The grid intersections are reinforced by nodes, and the surface is coated with a self-healing coating containing microcapsules. When the bamboo is damaged, the microcapsules rupture and release a repair agent that reacts with the catalyst in the bamboo to form a polymer chain to repair the cracks. The intelligent water purification device includes a biological filter, activated carbon nanoparticles, and an intelligent control system. The biological filter consists of multiple units filled with biological media. The activated carbon nanoparticles are integrated into the biological filter. The intelligent control system is linked with the intelligent sensor network to monitor water quality parameters in real time and automatically adjust the water flow rate and oxygen supply, and feed back water quality data and purification effect to the cloud platform. The automatic grading and sorting fishing equipment includes a channel-type fish collector, a concentric circle fish separator, and a PLC automatic control system. The fish collector attracts fish by sound waves or light, and the fish separator grades the fish by size and species through concentric circles with different apertures. When the fish are transported by the conveyor belt, the size of the fish is measured by photoelectric sensors, and the PLC automatic control system controls the sorting to different collection boxes. The VR aquaculture experience system supports entering a virtual aquaculture environment through a VR headset, interacting with elements in the virtual environment, adjusting aquaculture parameters, observing fish growth status, and creating and running virtual aquaculture experiences through a VR software platform. The self-healing bamboo-based floating platform is equipped with solar panels on its top for collecting solar energy and converting it into electricity. A small wind turbine is also installed on the platform, and the solar panels and wind turbine together provide power to the system. The self-healing bamboo-based floating platform is equipped with an intelligent energy management system, which allocates the power supply from solar and wind power generation according to the energy consumption needs of the aquaculture system. The solar panels and wind turbine continuously generate electricity during the day and in windy conditions, and the electricity is stored in batteries for the daily operation of the aquaculture system. The structure of the bamboo material and the preparation steps of the self-healing coating are as follows: Step 1: Cut the bamboo into long strips and weave and bind them to form a grid structure; Step 2: Install multiple sealed air chambers inside the bamboo mesh structure to ensure the floating platform floats stably; Step 3: Reinforce the intersections of the grid using nodes to improve the overall stability of the structure; Step 4: Apply a self-healing coating evenly to the surface of the bamboo. When the bamboo is damaged, the microcapsules in the coating rupture, releasing the repair agent, which reacts with the catalyst in the bamboo to form new polymer chains, thereby repairing the cracks.

2. The bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The intelligent sensor network includes temperature sensors, dissolved oxygen monitors, water quality detectors, and biosensors; Temperature sensors are used to monitor the temperature of aquaculture water with an accuracy of ±0.1℃. They are distributed at different depths in the aquaculture area and transmit data to the cloud platform in real time. Dissolved oxygen monitors are used to measure the dissolved oxygen content in water. They are distributed at different points in the aquaculture area, and the monitored data is transmitted in real time to adjust oxygenation equipment and change feeding strategies. Water quality detectors are used to monitor pH, ammonia nitrogen, nitrite, nitrate, and phosphate levels. They are installed at the inlet and outlet of aquaculture water bodies, and the monitored data is transmitted in real time for use in water quality management and early warning systems. The biosensor is an external, non-invasive device used to monitor the physiological state of fish. It features high sensitivity and miniaturization, and transmits data to a cloud platform wirelessly.

3. The bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The camera monitoring system includes a high-resolution HD camera with an IP66 protection rating, equipped with a low-light sensor, and is mounted on a floating platform using corrosion-resistant fasteners to cover the entire aquaculture area. The camera continuously collects images of the aquaculture area at a resolution of 1080p and transmits them in real time to the cloud platform via a wireless network. This data is used to analyze fish activity patterns, behavioral characteristics, health status, and growth rate, and to monitor for invasive alien species.

4. The bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The automatic feeder is driven by a servo motor, has an internal feed storage space, and multiple adjustable feeding ports on the outside. The control system of the automatic feeder integrates AI algorithms to analyze fish growth data, feeding behavior, and water quality. It also includes a microprocessor or single-chip microcomputer to receive AI analysis results and control the feeding time, frequency, quantity, and feed type of the feeder.

5. A bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The biofilter of the intelligent water purification device utilizes microorganisms to decompose ammonia nitrogen and nitrite produced during the breeding process, converting them into harmless or low-harm substances; activated carbon nanoparticles are used to remove heavy metal ions, harmful chemicals and pathogens from the water; the intelligent control system monitors water quality parameters in real time through water quality sensors, automatically adjusts water flow rate and oxygen supply, and feeds back water quality data and purification effect to the cloud platform.

6. The bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The fish collector of the automatic grading and sorting fishing equipment is a channel type, which attracts fish by controlling water level changes or by using sound waves and light. The fish sorter consists of multiple concentric circles, each with a different aperture, which automatically classifies the fish according to their size and species. The automatic control system uses a PLC combined with sensors and actuators to achieve automated control, controlling the actuators to complete the entire process of fish collection, grading, and sorting.

7. The bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The cloud platform of the data integration and processing module connects to sensors and cameras at the hardware layer via API, receives and processes various data formats, and stores data information through distributed databases and data centers. The system rapidly processes and analyzes real-time data and performs batch processing on non-real-time data. The cloud platform has built-in data analysis tools to help users understand the data. Users can view environmental data, equipment status, and fish health in the aquaculture farm in real time through web pages or mobile applications. When abnormal data or equipment failure is detected, the system automatically issues an alarm to notify the management personnel.

8. The bamboo-based floating unmanned marine ranching system according to claim 1, characterized in that: The VR aquaculture experience system uses virtual reality technology to observe the aquaculture environment and fish behavior. It supports interaction with elements in the virtual environment through VR headsets, allowing adjustment of aquaculture parameters and observation of fish growth. The remote monitoring and management system allows managers to remotely view real-time data and video monitoring of the aquaculture farm and remotely execute management operations.

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