Wetland intelligent control station based on artificial intelligence and control method thereof

Abstract

The invention discloses a wetland intelligent control station based on artificial intelligence and a control method thereof, and relates to the technical field of wetland intelligence. The control station is connected with and manages an electric valve, a water pump and a liquid level sensor on a wetland site through the equipment integration module; the data processing and analyzing module acquires various types of data including water quality, liquid level and equipment state, and uploads the data to the remote AI platform through the wireless communication module; the intelligent control module drives the field equipment after central control enabling judgment according to prediction parameters and control instructions issued by the remote AI platform; the man-machine interaction module displays the system operation state and the water quality data in real time; and the outdoor adaptive module ensures stable operation of the equipment in an outdoor complex environment. According to the invention, intelligent prediction control, remote upgrading and fault diagnosis of wetland operation are realized, and the automation and intelligence level of wetland management is obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to an intelligent control system for wetlands, and in particular to an intelligent control station for wetlands based on artificial intelligence and a control method thereof. BACKGROUND

[0002] Traditional wetland control systems rely on manual operation or partial automation, and have problems such as response lag, low control accuracy, lack of intelligent decision-making and remote management capabilities. The existing technology cannot realize real-time prediction and adaptive regulation of wetland water quality and equipment state, and cannot meet the comprehensive needs of modern wetlands for efficient operation, energy saving and environmental protection, and public display. However, if the original PLC control system is completely eliminated and replaced with a new AI intelligent control system, not only the investment cost is huge, but also it is very wasteful. SUMMARY

[0003] In view of the above problems, the present application provides an intelligent control station for wetlands based on artificial intelligence and a control method thereof, which adds an intelligent control module based on the original PLC control system, applies the model prediction results of the remote AI platform to the control of the field equipment, and is compatible with the original PLC control system, thereby reducing the investment cost.

[0004] The technical scheme of the present application is as follows: An intelligent control station for wetlands based on artificial intelligence, comprising a device integration module, an intelligent control module, a data processing and analysis module, a wireless communication module, and a remote AI platform. The device integration module collects electric valves, water pumps and liquid level sensors in the field of the wetland through 4~20mA analog quantity and RS-485 communication, and transmits the collected data to the data processing and analysis module. The data processing and analysis module performs ADC conversion, data cleaning and standardization processing on the data from the device integration module, and transmits the processed data to the wireless communication module. The wireless communication module transmits the data from the data processing and analysis module to the remote AI platform, and transmits the data from the AI platform to the intelligent control module. The remote AI platform uses a machine learning algorithm to establish a wetland water quality prediction model for adjusting water flow distribution in advance. The remote AI platform inputs the data from the wireless communication module into the wetland water quality prediction model to obtain AI control parameters and AI control instructions, and transmits the control parameters and control instructions to the wireless communication module. The intelligent control module has a built-in remotely upgradeable MCU. It receives control parameters and AI control commands from a remote AI platform through a wireless communication module, and generates drive signals for field equipment based on the AI ​​control parameters and AI control commands. The equipment integration module drives the electric valves and water pumps in the wetland field according to the drive signals from the intelligent control module.

[0005] Furthermore, the wetland intelligent control station includes a human-machine interaction module; the human-machine interaction module adopts a touch screen display to display wetland operation data in real time, including the amount of water processed, the quality of the effluent, and the operating status of the equipment, and supports remote access.

[0006] Furthermore, the wetland intelligent control station includes a PLC control system; the PLC control system collects data from electric valves, water pumps, and level sensors through an equipment integration module, and drives the electric valves and water pumps.

[0007] Furthermore, when the AI ​​control parameters are valid, the AI ​​control instructions are executed first; otherwise, the system switches to the PLC control system.

[0008] Furthermore, the data transmission frame structure of the wireless communication module includes a frame header, device ID, data content, and frame trailer, and supports an ACK confirmation mechanism to initiate a retransmission strategy when communication fails.

[0009] Furthermore, the wetland intelligent control station includes an outdoor adapter module; the equipment integration module, intelligent control module, data processing and analysis module, wireless communication module, human-machine interaction module, and PLC control system are all located inside the outdoor adapter module; the outdoor adapter module provides waterproof, dustproof, lightning protection, and temperature control protection to ensure the long-term stable operation of the control station in the outdoor environment.

[0010] Furthermore, this includes the following steps: S1. The equipment integration module collects data from sensors and actuators at the wetland site and transmits the collected data to the data processing and analysis module. S2, the data processing and analysis module preprocesses the collected data and transmits the processed data to the wireless communication module; S3. The wireless communication module uploads data from the data processing and analysis module to the remote AI platform; S4. The remote AI platform analyzes and predicts based on historical and real-time data, generates AI control parameters and AI control commands, and sends them to the intelligent control module and PLC control system through the wireless communication module. The AI ​​control commands include the central control enable status. If the central control enable status is true, the field equipment is driven through the PLC control system; otherwise, the field equipment is driven through the intelligent control module. S5. If the AI ​​control parameters are valid and the central control enable status is false, the intelligent control module generates a drive signal, and the equipment integration module drives the field equipment according to the drive signal; otherwise, the PLC control system drives the field equipment through the equipment integration module. S6, the human-computer interaction module updates and displays the system's operating status in real time.

[0011] The beneficial technical effects of this invention are as follows: By introducing AI predictive control, wireless remote management, and industrial-grade outdoor protection, intelligent regulation, remote operation and maintenance, and stable operation of the wetland system have been achieved. The system has good scalability, supports iterative upgrades of algorithms and hardware, and is suitable for various scenarios such as modern wetland management, ecological protection, and public education; It is compatible with existing PLC control systems and achieves AI intelligent control effects with minimal investment costs, making it highly valuable for widespread adoption. Attached Figure Description

[0012] Figure 1 This is a system hardware architecture diagram of an embodiment; Figure 2 This is a schematic diagram of the communication protocol frame structure of an embodiment; Figure 3 This is a schematic diagram of the human-computer interaction interface of an embodiment. Detailed Implementation

[0013] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0014] As attached Figure 1 As shown, the embodiment mainly consists of an equipment integration module, an intelligent control module, a data processing and analysis module, a wireless communication module, a remote AI platform, a human-machine interaction module, a PLC control system, and an outdoor adaptation module. The design concept addresses the shortcomings of existing systems in terms of intelligence, reliability, and scalability by introducing AI prediction models, wireless remote communication, multi-source data fusion, and outdoor industrial-grade protection.

[0015] The equipment integration module collects data such as water quality, liquid level, and equipment status at the wetland site through 4~20mA analog signals and RS-485 communication, and transmits the collected data to the data processing and analysis module.

[0016] The data processing and analysis module performs ADC conversion, data cleaning and standardization on the data from the device integration module, and transmits the processed data to the wireless communication module.

[0017] The wireless communication module uses a GPRS / 4G network to transmit data from the data processing and analysis module to the remote AI platform, and then transmits data from the AI ​​platform to the intelligent control module. (See attached image) Figure 2 As shown, the data transmission frame structure of the wireless communication module includes a frame header, device ID, data content, and frame trailer, and supports an ACK confirmation mechanism to initiate a retransmission strategy when communication fails.

[0018] The remote AI platform uses machine learning algorithms to build a wetland water quality prediction model, which is used to adjust water flow distribution and equipment operating parameters in advance. The remote AI platform inputs data from the wireless communication module into the wetland water quality prediction model to obtain AI control parameters and AI control commands, and then transmits the AI ​​control parameters and AI control commands to the wireless communication module.

[0019] The intelligent control module has a built-in remotely upgradeable MCU. It receives AI control parameters and AI control commands from the remote AI platform through the wireless communication module, and generates drive signals for the field equipment based on the AI ​​control parameters and AI control commands. The equipment integration module drives the electric valves and water pumps in the wetland field according to the drive signals of the intelligent control module.

[0020] The human-machine interface module uses a touch screen to display real-time wetland operation data, including treated water volume, effluent quality, and equipment operating status, and supports remote access. (See attached image) Figure 3 As shown, the interactive interface centrally displays the cumulative treated water volume, real-time water quality indicators (COD, ammonia nitrogen, DO), and the operational status of each wetland unit (water distribution / outlet valve status). The interface supports touch operation and remote access, making it suitable for on-site management and public display.

[0021] The PLC control system collects data from electric valves, water pumps, and level sensors through an integrated equipment module, and then drives these valves and pumps. When AI control parameters are valid, AI control commands are executed first; otherwise, the system switches to the PLC control system. This design allows the implementation to be upgraded to an AI intelligent control system while remaining compatible with the existing PLC control system. The remote AI platform's wetland water quality prediction model can make more accurate predictions about wetlands, allowing for proactive adjustments to water flow distribution, resulting in control effects far exceeding those of traditional PLC control systems. Simultaneously, the implementation retains the original PLC control system, ensuring continued operation even in the event of remote communication failures.

[0022] All of the above modules are housed inside the outdoor adapter module. The outdoor adapter module has an IP65 protection rating and a built-in temperature control system and lightning protection device, making it adaptable to complex outdoor environments such as high and low temperatures, humidity, and lightning strikes, ensuring long-term stable operation of the control station in outdoor environments.

[0023] Workflow of the example: S1. The equipment integration module collects data from sensors and actuators at the wetland site and transmits the collected data to the data processing and analysis module. S2, the data processing and analysis module preprocesses the collected data and transmits the processed data to the wireless communication module; S3. The wireless communication module uploads data from the data processing and analysis module to the remote AI platform; S4. The remote AI platform analyzes and predicts based on historical and real-time data, generates AI control parameters and AI control commands, and sends them to the intelligent control module and PLC control system through the wireless communication module. The AI ​​control commands include the central control enable status. If the central control enable status is true, the field equipment is driven through the PLC control system; otherwise, the field equipment is driven through the intelligent control module. S5. If the AI ​​control parameters are valid and the central control enable status is false, the intelligent control module generates a drive signal, and the equipment integration module drives the field equipment according to the drive signal; otherwise, the PLC control system drives the field equipment through the equipment integration module. S6, the human-computer interaction module updates and displays the system's operating status in real time.

[0024] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, and for those of ordinary skill in the art, various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. Therefore, the present invention is not limited to the specific details without departing from the general concept defined by the claims and their equivalents.

Claims

1. An intelligent wetland control station based on artificial intelligence, characterized in that: It includes an equipment integration module, an intelligent control module, a data processing and analysis module, a wireless communication module, and a remote AI platform; The equipment integration module collects data on electric valves, water pumps, and liquid level sensors at the wetland site via 4~20mA analog signal and RS-485 communication, and transmits the collected data to the data processing and analysis module. The data processing and analysis module performs ADC conversion, data cleaning and standardization on the data from the device integration module, and transmits the processed data to the wireless communication module. The wireless communication module transmits data from the data processing and analysis module to the remote AI platform, and transmits data from the AI ​​platform to the intelligent control module. The remote AI platform uses machine learning algorithms to establish a wetland water quality prediction model for advance water flow distribution. The remote AI platform inputs data from the wireless communication module into the wetland water quality prediction model to obtain AI control parameters and AI control commands, and then transmits the control parameters and control commands to the wireless communication module. The intelligent control module has a built-in remotely upgradeable MCU. It receives control parameters and AI control commands from a remote AI platform through a wireless communication module, and generates drive signals for field equipment based on the AI ​​control parameters and AI control commands. The equipment integration module drives the electric valves and water pumps in the wetland field according to the drive signals from the intelligent control module.

2. The wetland intelligent control station based on artificial intelligence according to claim 1, characterized in that: The wetland intelligent control station includes a human-machine interaction module; the human-machine interaction module uses a touch screen to display wetland operation data in real time, including the amount of water processed, the quality of the effluent, and the operating status of the equipment, and supports remote access.

3. The wetland intelligent control station based on artificial intelligence according to claim 1, characterized in that: The wetland intelligent control station includes a PLC control system; the PLC control system collects data from electric valves, water pumps and liquid level sensors through the equipment integration module, and drives the electric valves and water pumps.

4. The wetland intelligent control station based on artificial intelligence according to claim 1, characterized in that: When the AI ​​control parameters are valid, the AI ​​control instructions are executed first; otherwise, the PLC control system is switched to the PLC control system.

5. The wetland intelligent control station based on artificial intelligence according to claim 1, characterized in that: The data transmission frame structure of the wireless communication module includes a frame header, device ID, data content, and frame trailer, and supports an ACK confirmation mechanism to initiate a retransmission strategy when communication fails.

6. The wetland intelligent control station based on artificial intelligence according to claim 1, characterized in that: The wetland intelligent control station includes an outdoor adapter module; the equipment integration module, intelligent control module, data processing and analysis module, wireless communication module, human-machine interaction module, and PLC control system are all located inside the outdoor adapter module; the outdoor adapter module provides waterproof, dustproof, lightning protection, and temperature control protection to ensure the long-term stable operation of the control station in the outdoor environment.

7. A control method based on the device of claim 1, characterized in that, Includes the following steps: S1. The equipment integration module collects data from sensors and actuators at the wetland site and transmits the collected data to the data processing and analysis module. S2, the data processing and analysis module preprocesses the collected data and transmits the processed data to the wireless communication module; S3. The wireless communication module uploads data from the data processing and analysis module to the remote AI platform; S4. The remote AI platform analyzes and predicts based on historical and real-time data, generates AI control parameters and AI control commands, and sends them to the intelligent control module and PLC control system through the wireless communication module. The AI ​​control commands include the central control enable status. If the central control enable status is true, the field equipment is driven through the PLC control system; otherwise, the field equipment is driven through the intelligent control module. S5. If the AI ​​control parameters are valid and the central control enable status is false, the intelligent control module generates a drive signal, and the equipment integration module drives the field equipment according to the drive signal; otherwise, the PLC control system drives the field equipment through the equipment integration module. S6, the human-computer interaction module updates and displays the system's operating status in real time.

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