Aquaculture control system based on big data analysis
By introducing an edge computing module and a lightweight LSTM model into the aquaculture control system, local data analysis and cloud-based collaborative control are achieved, solving the response latency problem caused by cloud dependency and improving the system's stability and control effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-22
- Publication Date
- 2026-04-14
AI Technical Summary
In existing technologies, the reliance of aquaculture control systems on the cloud leads to response delays in areas with poor network coverage, affecting the effectiveness of aquaculture environment control.
The aquaculture control system based on big data analysis includes a data acquisition module, an edge computing module, and a control execution module. It uses an FPGA main controller and a lightweight LSTM model for local data analysis and coordinates control with the cloud through a communication module.
It reduces reliance on the cloud, improves the stability and response speed of the aquaculture control system, and ensures effective regulation of the aquaculture environment even in areas with poor network coverage.
Smart Images
Figure CN224122907U_ABST
Abstract
Description
Technical Field
[0001] This utility model relates to the field of aquaculture technology, and in particular to an aquaculture control system based on big data analysis. Background Technology
[0002] In existing technologies, as the scale of aquaculture expands, the demand for water quality management in high-density aquaculture environments is also constantly increasing. Therefore, to meet the needs of water quality management, data models are generally used to analyze various data collected in the aquaculture environment to determine aquaculture control schemes such as feeding and aeration. However, due to the computational requirements of data models, they are currently generally configured on cloud devices. By transmitting various data collected from the local aquaculture environment to the cloud devices for data analysis, this approach relies too heavily on the cloud and can easily cause response delays of several minutes or more in areas with poor network coverage, affecting the actual control effect of the aquaculture environment.
[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Utility Model Content
[0004] The purpose of this invention is to provide an aquaculture control system based on big data analysis to solve one or more technical problems existing in the prior art, or at least provide a beneficial option or create conditions.
[0005] The solution to the technical problem of this utility model is:
[0006] A big data analysis-based aquaculture control system is provided. The big data analysis-based aquaculture control system includes: a data acquisition module, an edge computing module, a control execution module, and a communication module. The edge computing module is connected to the data acquisition module and the control execution module respectively. The data acquisition module and the control execution module are also connected to the communication module. The communication module is used to transmit the environmental data collected by the data acquisition module to the cloud and transmit the first control command issued by the cloud to the control execution module.
[0007] The data acquisition module includes a sensor device;
[0008] The edge computing module includes an FPGA main controller and a data processing device. The data processing device is an embedded device and is equipped with a lightweight LSTM model. The data processing device is used to determine the analysis results based on the environmental data. The FPGA main controller is used to determine a second control command based on the analysis results.
[0009] The control execution module includes a feeding control device and an oxygenation control device, which are used to perform feeding operations or oxygenation operations according to the first control command and / or the second control command, respectively.
[0010] In some embodiments, the sensor device is configured as an integrated sensor group comprising a dissolved oxygen sensor, a pH sensor, an ammonia nitrogen sensor, a nitrite sensor, a temperature sensor, a turbidity sensor, a redox potential sensor, a conductivity sensor, a chlorophyll sensor, a blue-green algae sensor, a total dissolved solids sensor, and a carbon dioxide sensor.
[0011] In some embodiments, the aquaculture control system based on big data analysis further includes multiple transmission channels corresponding to each type of sensor. The transmission channels are used to transmit the environmental data to the FPGA main controller, and different transmission channels are magnetically isolated from each other.
[0012] In some embodiments, the sensor device includes an anti-biofouling structure comprising a protective shell, a rotating ceramic scraper, an ultrasonic transducer layer, a nano-hydrophobic coating, and a sensor probe core layer.
[0013] In some embodiments, the data acquisition module further includes a meteorological device, which is used to acquire meteorological information within a preset surrounding range, and the environmental data includes the meteorological information.
[0014] In some embodiments, the data acquisition module further includes an underwater imaging device for acquiring image information of the underwater aquaculture environment, and the environmental data includes the image information.
[0015] In some embodiments, the aquaculture control system based on big data analysis further includes an array of buoys arranged on the water surface, and the sensor device is disposed on the buoys.
[0016] In some embodiments, the control execution module further includes an audible and visual alarm device, which is used to perform an alarm prompt operation according to the first control instruction and / or the second control instruction.
[0017] In some embodiments, the aquaculture control system based on big data analysis further includes an energy supply module, which includes a solar power supply device and a wave power supply device, and is used to provide energy for the data acquisition module, the edge computing module and the control execution module.
[0018] In some embodiments, the edge computing module further includes a storage device for storing the environmental data and the analysis results.
[0019] The beneficial effects of this invention are as follows: By setting up an aquaculture control system based on big data analysis, including a data acquisition module, an edge computing module, a control execution module, and a communication module, environmental data is collected using the sensor devices of the data acquisition module. The environmental data is analyzed using the FPGA main controller and a data processing device configured with a lightweight LSTM model in the edge computing module to achieve edge computing and determine the second control command. On the other hand, the environmental data is transmitted to the cloud using the communication module, and the cloud analyzes the environmental data in collaboration to determine the first control command. Under the coordinated command of the first control command and / or the second control command, the feeding control device and the aeration control device of the control execution module respectively control the feeding operation and the aeration operation. Compared with relying entirely on cloud devices and data models configured on the cloud for analysis, this application, by configuring the edge computing module locally, sets some data analysis and processing work locally, and controls the system collaboratively with the cloud. In some scenarios, the second control command generated locally can be used for control independently, thereby reducing the dependence of aquaculture control on the cloud and improving the stability of the aquaculture control system. Attached Figure Description
[0020] To more clearly illustrate the technical solutions in the embodiments of this utility model, the accompanying drawings used in the description of the embodiments will be briefly explained below. Obviously, the described drawings are only a part of the embodiments of this utility model, and not all of them. Those skilled in the art can obtain other design schemes and drawings based on these drawings without creative effort.
[0021] Figure 1 This is a schematic diagram of the structure of an aquaculture control system based on big data analysis according to this utility model;
[0022] Figure 2 This is a schematic diagram illustrating one application of the transmission channel of this utility model;
[0023] Figure 3 This is a schematic diagram of the power supply module of this utility model;
[0024] Figure 4 This is a schematic diagram illustrating one application of the power management unit of this utility model.
[0025] In the diagram: Data acquisition module-100, sensor device-110, meteorological device-120, underwater imaging device-130, edge computing module-200, FPGA main controller-210, data processing device-220, control execution module-300, feeding control device-310, oxygenation control device-320, communication module-400, solar power supply device-510, wave power supply device-520, power management unit-530. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0027] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0028] In related technologies, as the scale of aquaculture expands, the demand for water quality management in high-density aquaculture environments is also constantly increasing. Therefore, to meet the water quality management needs of aquaculture, data models are generally used to analyze various data collected in the aquaculture environment to determine aquaculture control schemes such as feeding and aeration. However, due to the computational requirements of data models, they are generally configured on cloud devices. By transmitting various data collected from the local aquaculture environment to the cloud devices and using the cloud's computing resources for data analysis, subsequent aquaculture control schemes can be determined. However, this approach relies too heavily on the cloud. In areas with poor network coverage, communication between the cloud and the local area can easily experience response delays of several minutes or more, or even communication failures, affecting the actual aquaculture environment control effect.
[0029] Figure 1 This is a schematic diagram of a big data analysis-based aquaculture control system provided in an embodiment of this application. Figure 1 The aquaculture control system based on big data analysis shown may include, but is not limited to: a data acquisition module 100, an edge computing module 200, a control execution module 300, and a communication module 400. The edge computing module 200 is connected to the data acquisition module 100 and the control execution module 300, respectively. The data acquisition module 100 and the control execution module 300 are also connected to the communication module 400. The communication module 400 is used to transmit the environmental data collected by the data acquisition module 100 to the cloud and transmit the first control command issued by the cloud to the control execution module 300.
[0030] The data acquisition module 100 includes a sensor device 110;
[0031] The edge computing module 200 includes an FPGA main controller 210 and a data processing device 220. The data processing device 220 is an embedded device and is equipped with a lightweight LSTM model. The data processing device 220 is used to determine the analysis results based on environmental data, and the FPGA main controller 210 is used to determine the second control command based on the analysis results.
[0032] The control execution module 300 includes a feeding control device 310 and an oxygenation control device 320, which are used to perform feeding operations or oxygenation operations respectively according to a first control command and / or a second control command.
[0033] The aquaculture control system based on big data analysis described in this embodiment includes a data acquisition module 100, an edge computing module 200, a control execution module 300, and a communication module 400. The edge computing module 200 is connected to both the data acquisition module 100 and the control execution module 300, forming the local control part of the aquaculture control system. Furthermore, the data acquisition module 100 and the control execution module 300 are also connected to the communication module 400, which connects to an external cloud network. The communication module 400 communicates with the cloud using 4G and / or LoRa, thereby communicating with external servers and other devices, forming the external control part of the aquaculture control system. In other embodiments, the edge computing module 200 may also be connected to the communication module 400, depending on the system's communication requirements.
[0034] Specifically, the data acquisition module 100 is responsible for collecting environmental data of the aquaculture environment. This environmental data includes data related to the surrounding area of the aquaculture environment and the actual underwater environment. Therefore, the data acquisition module 100 includes a sensor device 110, which is a device for monitoring the underwater environment.
[0035] The edge computing module 200 is responsible for implementing edge computing locally. Edge computing refers to providing edge intelligence services at the network edge, which is close to the source of objects or data, to meet the key needs of industry digitalization in agile connectivity and data optimization, and to make the data processing process closer to the data source. The edge computing module 200 includes an FPGA main controller 210 and a data processing device 220. The FPGA main controller 210 is a programmable logic chip that can perform general-purpose functions, meaning it can be programmed to implement certain logic processing functions. Compared with devices such as programmable array logic (PAL), general-purpose array logic (GAL), and erasable programmable logic devices (EPLD), it has higher integration, stronger logic functions, and greater flexibility. In this embodiment, the FPGA main controller 210 controls the data processing flow of the control system. The FPGA main controller 210 can be a product such as the Xilinx Zynq-7020. The data processing device 220 is physically set as an embedded device. In this embodiment, the type of embedded device is not limited. The embedded device is equipped with a lightweight LSTM model, i.e., a long short-term memory network model, deployed on the PS end of the FPGA main controller 210. It utilizes the characteristic of the LSTM model to process sequential data to analyze environmental data. The lightweight processing includes sparsification, pruning, simplified architecture, low-rank approximation, quantization, or knowledge distillation, etc., so that an LSTM big data analysis model with computing power that meets the requirements can be configured locally. It should be noted that the use of LSTM models for aquaculture analysis and the related technologies of lightweight models are existing technologies, and this embodiment does not include any improvements to the methods.
[0036] The control execution module 300 is the module that actually performs aquaculture-related operations. In this embodiment, it includes a feeding control device 310 and an oxygenation control device 320. The feeding control device 310 is used to control the feeding operation, i.e., to feed the aquatic organisms, and the oxygenation control device 320 is used to control the oxygenation operation, i.e., to oxygenate the aquaculture water. In addition, in this embodiment, the local control part and the remote control part cooperate to control the control execution module 300. The control command issued by the local control part, i.e., the FPGA main controller 210, is defined as the second control command, and the control command issued by the remote control part, i.e., the cloud, is defined as the first control command. In other special scenarios, the control execution module 300 can also be controlled by only the first control command or the second control command. That is, the aquaculture control system of this embodiment can also be controlled independently by the local or cloud and can realize the corresponding aquaculture control functions.
[0037] Compared to relying entirely on cloud devices and data models configured on the cloud for analysis, this application reduces the dependence of aquaculture control on the cloud and improves the stability of the aquaculture control system by configuring an edge computing module 200 locally, setting some data analysis and processing work locally, and coordinating control between the local and cloud. In some scenarios, control can be performed independently by a second control command generated locally.
[0038] In some embodiments, the sensor device 110 is configured as an integrated sensor group comprising a dissolved oxygen sensor, a pH sensor, an ammonia nitrogen sensor, a nitrite sensor, a temperature sensor, a turbidity sensor, a redox potential sensor, a conductivity sensor, a chlorophyll sensor, a blue-green algae sensor, a total dissolved solids sensor, and a carbon dioxide sensor.
[0039] For the underwater environment being monitored, this embodiment proposes to monitor at least 12 types of data and configure corresponding sensors for each type. Specifically, these include a dissolved oxygen sensor for detecting dissolved oxygen (DO), a pH sensor for detecting pH, an ammonia nitrogen sensor for detecting ammonia nitrogen (NH3-N), a nitrite sensor for detecting nitrite (NO2-), a temperature sensor for detecting temperature, a turbidity sensor for detecting turbidity (NTU), an oxidation-reduction potential sensor for detecting oxidation-reduction potential (ORP), a conductivity sensor for detecting conductivity (EC), a chlorophyll a sensor for detecting chlorophyll, a cyanobacteria sensor for detecting cyanobacteria, a total dissolved solids sensor for detecting total dissolved solids (TDS), and a carbon dioxide sensor for detecting carbon dioxide (CO2).
[0040] The 12 sensors configured above collect environmental data from at least 12 different underwater environments that affect aquaculture, thereby supporting subsequent data models to conduct multi-faceted analysis based on various environmental data and improving the control effect of aquaculture.
[0041] In some embodiments, the aquaculture control system based on big data analysis also includes multiple transmission channels corresponding to each type of sensor. The transmission channels are used to transmit environmental data to the FPGA main controller 210, and different transmission channels are set to magnetically coupled isolation.
[0042] Furthermore, a transmission channel is provided between the sensor device 110 and the FPGA main controller 210 for transmitting environmental data collected by the sensor. For each type of sensor described in the above embodiments, a corresponding transmission channel is provided. This transmission channel includes an instrumentation amplifier and an analog-to-digital converter, thereby converting the environmental data format into a type recognizable by the FPGA main controller 210. The instrumentation amplifier can be a model such as AD8226, and the analog-to-digital converter can be a model such as ADS1256. (See reference...) Figure 2 , Figure 2This is a schematic diagram of one application of the transmission channel, illustrating the transmission channel corresponding to the dissolved oxygen sensor.
[0043] To avoid signal interference, each transmission channel is designed with magnetic coupling isolation to prevent signal interference. This can be achieved by using a digital isolator, such as the ADuM3151. In other embodiments, the PCB board of the sensor device 110 can also adopt a 4-layer board design to separate the sensor ground line from the digital ground line, thereby further improving the anti-interference performance.
[0044] In addition, a calibration circuit can be set in the transmission channel, including setting a voltage reference chip as an integrated voltage reference source, such as the REF5025, and setting a calibration terminal for the analog-to-digital conversion section that connects to the analog signal of the standard liquid via a jumper cap, thereby realizing signal calibration in the transmission channel.
[0045] In some embodiments, the sensor device 110 includes an anti-biofouling structure, which comprises a protective shell, a rotating ceramic scraper, an ultrasonic transducer layer, a nano-hydrophobic coating, and a sensor probe core layer.
[0046] For the sensors used in this application that are installed underwater to collect underwater environmental data, there is still a problem in the existing technology: the sensors are easily attached to by aquatic organisms, and the amount of attached organisms will increase over time, affecting the detection accuracy and performance of the sensors. Therefore, how to design for preventing biofouling is also a difficult problem related to equipment maintenance in existing aquaculture technology.
[0047] In this embodiment, to improve the biofouling resistance of the sensor device 110, the sensor device 110 is provided with an anti-biofouling structure, which consists of five layers, from the outside to the inside: a protective shell, a rotating ceramic scraper, an ultrasonic transducer layer, a nano-hydrophobic coating, and a sensor probe core layer; wherein, the protective shell is a 316L stainless steel mesh grille, and its pore size is set as shown in the figure. The rotary ceramic scraper is made of zirconia ceramic with a Mohs hardness of 8.5. It can be driven by a stepper motor, with a rotation speed set to one revolution every 120 minutes. Furthermore, the gap between the scraper and the sensor probe is at least 0.3mm to avoid damaging other structural coatings. The ultrasonic transducer layer consists of four transducers spaced apart. Ring array setup at 40kHz 5% frequency and It operates based on sound pressure level, and is waterproofed by epoxy resin potting with an IP68 rating; the nano-hydrophobic coating uses a PTFE-202 composite coating containing silica nanoparticles, with a thickness of 50 mm. 5 Contact angle greater than Surface energy is less than The material of the core layer of the sensor probe needs to be set according to the sensor type. For example, glass electrodes are used for pH sensors and ammonia nitrogen sensors, while optical windows are used for turbidity sensors and chlorophyll sensors. The surface of these electrodes is polished to a certain degree. This reduces dirt adhesion. It should be noted that the parameters mentioned above for the anti-biofouling structure are set based on the developers' experience and are intended as examples. Depending on the specific application scenario, if better parameter selections exist, the above parameters are not required.
[0048] By setting up a five-layer anti-biofouling structure, the anti-biofouling capability of the sensor device 110 is improved, ensuring the detection accuracy and long-term operational stability of the sensor.
[0049] In some embodiments, the data acquisition module 100 further includes a meteorological device 120, which is used to acquire meteorological information within a preset range, and the environmental data includes meteorological information.
[0050] Optionally, in addition to collecting underwater environmental data as reference for the aquaculture environment, it is understood that weather changes also affect the aquaculture environment. Therefore, the data acquisition module 100 in this embodiment also includes a meteorological device 120. The meteorological device 120 can be an interface for obtaining meteorological information from an external meteorological station, or it can be a small meteorological detection device configured locally. In this embodiment, no limitation is made. Through the meteorological device 120, meteorological information within a preset range is obtained, and the meteorological information is transmitted to the data processing device 220 and / or the cloud as one type of information in the environmental data for analysis and processing. In this way, combined with the meteorological changes, the weather is predicted, and an aquaculture control plan is determined to improve the control effect of aquaculture.
[0051] In some embodiments, the data acquisition module 100 further includes an underwater imaging device 130, which is used to acquire image information of the underwater aquaculture environment, and the environmental data includes the image information.
[0052] Optionally, in addition to various data, actual video footage can also serve as a reference for staff to judge the underwater aquaculture environment. Therefore, in this embodiment, the data acquisition module 100 also includes an underwater imaging device 130, which can capture image information of the underwater environment. In other embodiments, it can also be used to capture video information. This image information, as part of the environmental data, can be processed and analyzed by the edge computing module 200 and / or the cloud, or it can be stored and recorded separately as log data, thereby providing staff with a reference of actual video images of the underwater aquaculture environment and improving the control effect of aquaculture.
[0053] In some embodiments, the aquaculture control system based on big data analysis further includes an array of buoys arranged on the water surface, with sensor devices 110 disposed on the buoys.
[0054] Optionally, in this embodiment, multiple buoys are set on the water surface of the aquaculture environment. These buoys are used to mark various locations in the aquatic space of the aquaculture environment. By setting the sensor device 110 on the buoy, the location for collecting environmental data can be determined. The shell of the buoy is made of an IP68 protection level structure and is set in an array on the water surface to ensure that the collected environmental data can cover various locations in the aquaculture environment, thereby improving the effectiveness of the environmental data.
[0055] In some embodiments, the control execution module 300 further includes an audible and visual alarm device, which is used to perform an alarm prompt operation according to a first control command and / or a second control command.
[0056] In addition to executing the aquaculture control plan through the control execution module 300, the control execution module 300 also includes an audible and visual alarm device to deal with various abnormal problems that may occur during the aquaculture process. The audible and visual alarm device includes a buzzer for sound alarm and an LED light for visual alarm. The LED light can be a yellow light with a warning effect. When the audible and visual alarm device executes an alarm prompting operation according to the first control command and / or the second control command, it controls the LED light to flash and / or controls the buzzer to sound, thereby serving as an alarm prompting staff.
[0057] In addition, the feeding control device 310 and the oxygenation control device 320 can also be used to deal with abnormal problems. When the audible and visual alarm device performs an alarm prompt operation, according to the designed abnormal response plan, the feeding can be cut off by the feeding control device 310, or the oxygenation control device 320 can automatically increase oxygenation in an emergency, thereby improving the ability to deal with abnormal problems in the aquaculture process.
[0058] In some embodiments, the aquaculture control system based on big data analysis further includes an energy supply module, which includes a solar power supply device 510 and a wave power supply device 520. The energy supply module is used to provide energy for the data acquisition module 100, the edge computing module 200 and the control execution module 300.
[0059] In this embodiment, the aquaculture control system based on big data analysis also includes an energy supply module, which is configured for dual-mode energy supply, as referenced. Figure 3 The system includes a solar power supply device 510 powered by solar energy and a wave power supply device 520 powered by wave energy. Furthermore, the power supply module also includes a power management unit 530, which internally houses a battery charging controller, a lithium battery pack, and a step-down regulator. The electrical energy output from both the solar power supply device 510 and the wave power supply device 520 is transmitted to the power management unit 530. While charging the lithium battery pack through the battery charging controller, the power management unit also provides energy to the system load of the aquaculture control system (including the data acquisition module 100, edge computing module 200, and control execution module 300, etc.) through the step-down regulator. Figure 3 (Illustrated with system load in the diagram), this lithium battery pack can serve as a backup power source when the main power supply fails. Furthermore, a battery charging controller such as the BQ24650 can be selected, which provides maximum power point tracking technology to improve the accuracy of the output power to the load when charging the lithium battery pack. A buck regulator such as the TPS54360 can be selected. (See reference...) Figure 4 , Figure 4 This is a schematic diagram of one application of the power management unit 530, in which the energy input is shown as one end connected to the solar power supply device 510 and the wave power supply device 520.
[0060] By setting up a power supply module that includes a dual-mode power supply device 510 and a wave power supply device 520, the aquaculture control system is ensured to have a sufficient and stable energy supply, thereby improving the stability of system operation and aquaculture work.
[0061] In some embodiments, the edge computing module 200 further includes a storage device for storing recorded environmental data and analysis results.
[0062] The environmental data collected by the aforementioned sensor device 110, meteorological device 120, and underwater imaging device 130, after being transmitted to the edge computing module 200, can be analyzed by the data processing device 220 and recorded by the storage device as part of the log data. On the other hand, the analysis results of the local LSTM model and / or the analysis results in the cloud can also be recorded by the storage device and associated with the corresponding environmental data to ensure data traceability, thereby facilitating retrieval and review by staff.
[0063] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.
Claims
1. An aquaculture control system based on big data analysis, characterized in that, The aquaculture control system based on big data analysis includes: a data acquisition module, an edge computing module, a control execution module, and a communication module. The edge computing module is connected to the data acquisition module and the control execution module, respectively. The data acquisition module and the control execution module are also connected to the communication module. The communication module is used to transmit the environmental data collected by the data acquisition module to the cloud and transmit the first control command issued by the cloud to the control execution module. The data acquisition module includes a sensor device; The edge computing module includes an FPGA main controller and a data processing device. The data processing device is an embedded device and is equipped with a lightweight LSTM model. The data processing device is used to determine the analysis results based on the environmental data. The FPGA main controller is used to determine a second control command based on the analysis results. The control execution module includes a feeding control device and an oxygenation control device, which are used to perform feeding operations or oxygenation operations according to the first control command and / or the second control command, respectively.
2. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The sensor device is configured as an integrated sensor group comprising a dissolved oxygen sensor, a pH sensor, an ammonia nitrogen sensor, a nitrite sensor, a temperature sensor, a turbidity sensor, a redox potential sensor, a conductivity sensor, a chlorophyll sensor, a blue-green algae sensor, a total dissolved solids sensor, and a carbon dioxide sensor.
3. The aquaculture control system based on big data analysis according to claim 2, characterized in that, The aquaculture control system based on big data analysis also includes multiple transmission channels corresponding to each type of sensor. These transmission channels are used to transmit environmental data to the FPGA main controller, and different transmission channels are magnetically isolated from each other.
4. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The sensor device includes an anti-biofouling structure, which consists of a protective shell, a rotating ceramic scraper, an ultrasonic transducer layer, a nano-hydrophobic coating, and a sensor probe core layer.
5. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The data acquisition module also includes a meteorological device, which is used to acquire meteorological information within a preset surrounding area, and the environmental data includes the meteorological information.
6. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The data acquisition module also includes an underwater imaging device, which is used to acquire image information of the underwater aquaculture environment, and the environmental data includes the image information.
7. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The aquaculture control system based on big data analysis also includes an array of buoys set on the water surface, and the sensor device is set on the buoys.
8. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The control execution module further includes an audible and visual alarm device, which is used to perform an alarm prompt operation according to the first control command and / or the second control command.
9. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The aquaculture control system based on big data analysis also includes an energy supply module, which includes a solar power supply device and a wave power supply device. The energy supply module is used to provide energy for the data acquisition module, the edge computing module, and the control execution module.
10. The aquaculture control system based on big data analysis according to claim 1, characterized in that, The edge computing module also includes a storage device for storing and recording the environmental data and the analysis results.