Ultrasonic algae removal system and algae removal control method and device thereof

Abstract

The invention discloses an ultrasonic algae removal system and an algae removal control method and device thereof, the ultrasonic algae removal system is used for removing algae in a water body, and the ultrasonic algae removal system comprises a mounting and supporting structure, a solar power supply module, an ultrasonic generation module, a water quality sensor module, a control module and a monitoring module. The solar power supply module is used for supplying power; the water quality sensor module is used for detecting the water quality of a water body; the control module is used for processing the one or more water quality parameters based on the artificial intelligence algorithm module to obtain an ultrasonic control instruction, and the ultrasonic control instruction at least comprises a power control parameter and a frequency control parameter; the ultrasonic generation module is connected with the control module and used for transmitting ultrasonic waves to the water body based on the ultrasonic control instruction; and the monitoring module is used for collecting a plurality of operation parameters of the ultrasonic algae removal system and feeding back the plurality of operation parameters to the upper computer. The ultrasonic waves emitted by the system are not fixed in frequency, but are different along with different water qualities, so that the algae removal requirements of different water qualities are met, and the algae removal effect is improved.

Description

Technical Field

[0001] The present application relates to the technical field of environmental protection equipment, and more specifically, to an ultrasonic algae removal system, an algae removal control method and device thereof. Background Art

[0002] For large water bodies such as lakes, ponds, reservoirs, and water plants, in order to ensure that the water quality meets the standards, it is crucial to remove algae in the water body and inhibit the excessive reproduction of algae. Currently, generally, algae removal equipment using ultrasonic technology is used to remove algae in the water body. The ultrasonic algae removal system uses the cavitation effect, mechanical effect, and thermal effect of ultrasonic waves to exert an impact on algae to achieve the effect of algae removal. The cavitation effect refers to the generation of a large number of tiny bubbles in the water body by ultrasonic waves. The bubbles rapidly expand and close under the alternating action of high pressure and low pressure, generating a powerful impact force, destroying the cell structure of algae, and making it lose its activity. The mechanical effect can cause algae cells to be subjected to shear force and friction, resulting in cell rupture. The thermal effect is that under the action of ultrasonic waves, the local temperature of the water body rises, inhibiting the growth of algae or even causing its death.

[0003] However, the inventors of the present application have found in actual work that since the ultrasonic components of the current ultrasonic algae removal system can only emit ultrasonic waves of a fixed frequency, and different types of algae have different sensitivities to ultrasonic waves of different frequencies, the algae removal effect is poor. Summary of the Invention

[0004] In view of this, the present application provides an ultrasonic algae removal system, an algae removal control method, device, electronic device, and storage medium thereof, for improving the algae removal effect on the water body.

[0005] In order to achieve the above object, the following solutions are proposed:

[0006] An ultrasonic algae removal system for removing algae in a water body, the ultrasonic algae removal system includes an installation and support structure, and a solar power supply module, an ultrasonic generation module, a water quality sensor module, a control module, and a monitoring module deployed on the installation and support structure, wherein:

[0007] The solar power supply module is used to supply power to each power-consuming module of the ultrasonic algae removal system;

[0008] The water quality sensor module is used to detect the water quality of the water body, obtain one or more water quality parameters, and output the water quality parameters to the control module;

[0009] The control module is connected to the water quality sensor module and is configured to process the one or more water quality parameters based on the artificial intelligence algorithm module to obtain an ultrasonic control instruction, where the ultrasonic control instruction includes at least a power control parameter and a frequency control parameter;

[0010] The ultrasonic generation module is connected to the control module and is configured to emit ultrasonic waves to the water body based on the ultrasonic control instruction;

[0011] The monitoring module is configured to collect a plurality of operating parameters of the ultrasonic algae removal system and feedback the plurality of operating parameters to the host computer.

[0012] Optionally, the solar power supply module includes a solar panel, a charge controller, and a storage battery, where:

[0013] The solar panel is configured to directly convert sunlight into direct current electricity and supply power to the ultrasonic generation module, the control module, and the monitoring module;

[0014] The charge controller is respectively connected to the solar panel and the storage battery and is configured to manage the energy flow from the solar panel to the storage battery to prevent overcharging or over-discharging of the battery;

[0015] The storage battery is configured to store the excess electric energy generated by the solar panel.

[0016] Optionally, the solar generation module includes an ultrasonic generator and an ultrasonic transducer;

[0017] The ultrasonic generator is configured to convert the input electric energy into a high-frequency alternating current signal;

[0018] The ultrasonic transducer is configured to convert the high-frequency alternating current signal into mechanical vibration.

[0019] Optionally, the control module includes a microcontroller, a power management circuit, a sensor interface circuit, an ultrasonic drive circuit, a communication interface circuit, and a human-machine interaction device, where:

[0020] The power management circuit is configured to convert and regulate the direct current electricity generated by the solar power supply module;

[0021] The sensor interface circuit is configured to collect the one or more water quality parameters;

[0022] The microcontroller is configured to process the one or more water quality parameters and output a control signal;

[0023] The ultrasonic drive circuit is configured to amplify the power of the control signal to obtain and output the ultrasonic control instruction to the ultrasonic generation module;

[0024] The communication interface circuit is used to implement a communication connection with a remote monitoring center or a mobile terminal;

[0025] The human - machine interaction device is used to visually display the operating status, current parameter settings, and / or fault alarm information of the ultrasonic algae removal system to the user through a display interface.

[0026] Optionally, the monitoring module includes a data acquisition unit, a data transmission unit, a remote monitoring platform, and a local monitoring terminal.

[0027] An algae removal control method is applied to the ultrasonic algae removal system as described above. The algae removal control method includes the steps:

[0028] Adjust the system operating parameters through a reinforcement learning algorithm;

[0029] Use a long - short - term memory network model to predict the water quality change trend of the water body.

[0030] Optionally, the step of adjusting the system operating parameters through a reinforcement learning algorithm includes the steps:

[0031] Identify and quantify various factors affecting the algae removal effect, and determine adjustable system parameters;

[0032] Design a reward function, clarify the desired goal, and set a reward mechanism according to the goal;

[0033] In the reinforcement learning training process, at the beginning, set the system parameters to the initial values. Then, at each time step, according to the current state, select an action through an exploration - exploitation strategy and execute the action. After executing the action, calculate a reward value according to the new state and the reward function;

[0034] Continuous learning and optimization: As the environment and equipment change, it is necessary to regularly collect new data to update the reinforcement learning model. At the same time, establish a user feedback channel, and users can feedback on the adjustment results of the algorithm according to the actual algae removal effect and equipment operation conditions.

[0035] Optionally, the system parameters include ultrasonic frequency, power, emission angle, and duration, as well as the charging / discharging strategy of the solar power supply module.

[0036] Optionally, the step of using a long - short - term memory network model to predict the water quality change trend of the water body includes the steps:

[0037] Collect historical water quality data;

[0038] Construct a long - short - term memory network structure and train the long - short - term memory network model based on the historical water quality data;

[0039] Evaluate the long short - term memory network model, calculate the error index between the prediction result and the real water quality data, and optimize and adjust the model according to the evaluation result;

[0040] Input the real - time collected water quality data and equipment operation parameters into the long short - term memory network model to predict the water quality change trend in the future for a period of time.

[0041] An algae removal control device is applied to the ultrasonic algae removal system as described above. The algae removal control device includes the following steps:

[0042] A parameter adjustment module is configured to adjust the system operation parameters through a reinforcement learning algorithm;

[0043] A water quality prediction module is configured to use a long short - term memory network model to predict the water quality change trend of the water body.

[0044] As can be seen from the above technical solutions, the present application discloses an ultrasonic algae removal system, its algae removal control method and device for removing algae in water bodies, including an installation and support structure, a solar power supply module, an ultrasonic generation module, a water quality sensor module, a control module and a monitoring module. The solar power supply module is used for power supply; the water quality sensor module is used for detecting the water quality of the water body; the control module is used to process one or more water quality parameters based on an artificial intelligence algorithm module to obtain an ultrasonic control instruction, and the ultrasonic control instruction at least includes a power control parameter and a frequency control parameter; the ultrasonic generation module is connected to the control module and is used to emit ultrasonic waves to the water body based on the ultrasonic control instruction; the monitoring module is used to collect multiple operation parameters of the ultrasonic algae removal system and feedback the multiple operation parameters to the host computer. Since the ultrasonic waves emitted by this system are not of a fixed frequency but vary with different water qualities to meet the algae removal requirements of different water qualities, the algae removal effect is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic diagram of an ultrasonic algae removal system according to an embodiment of the present application;

[0047] Figure 2 It is a flowchart of an algae removal control method according to an embodiment of the present application;

[0048] Figure 3 Flow chart for adjusting operation parameters of the reinforcement learning algorithm in the embodiment of the present application;

[0049] Figure 4 Flow chart for predicting water quality change trend using the long short-term memory network model in the embodiment of the present application;

[0050] Figure 5 Block diagram of an algae removal device in the embodiment of the present application. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0052] Figure 1 Schematic diagram of an ultrasonic algae removal system in the embodiment of the present application.

[0053] As Figure 1 shown, the ultrasonic algae removal system provided in this embodiment is used to remove algae in water based on a solar power source. The system includes an installation and support structure, which is used to carry each working module of the ultrasonic algae removal system. In addition to this structure, it also includes a solar power supply module 10 partially or wholly installed on the installation and support structure, and also includes an ultrasonic generation module 20, a water quality sensor module 30, a control module 40, and a monitoring module 50 that are electrically connected to the solar power supply module.

[0054] The solar power supply module is used to convert solar energy into electrical energy to supply power to the above-mentioned ultrasonic generation module, water quality sensor module, control module, and health module. Using this solar power supply module can enable the system to operate independently of the power grid infrastructure, which is of particular significance for the monitoring and algae removal of water bodies in remote areas and can save additional power consumption.

[0055] The installation and support structure is used to provide a stable support platform; it should be noted that the installation and support structure includes a solar panel installation structure, an ultrasonic generation module installation structure, and an overall equipment support frame. The solar panel installation structure is used to provide stable support for the solar photovoltaic panel, the ultrasonic generation module installation structure is used to provide stable support for the ultrasonic generator and the ultrasonic transducer, and the overall equipment support frame is used to provide stable support for the overall equipment;

[0056] The installation structure of the solar panel includes a fixed bracket and a tracking system. The fixed bracket is the basic part for installing the solar panel, usually made of metal materials (such as aluminum alloy or steel). Its shape and size are determined according to the size of the solar panel and the requirements of the installation site. The inclined bracket can be adjusted according to the local latitude and solar altitude angle to obtain the best lighting effect. For some solar ultrasonic algae removal systems with higher requirements for power generation efficiency, a solar tracking system may be equipped;

[0057] The installation structure of the ultrasonic generation module includes an above-water installation platform and an underwater installation bracket;

[0058] Above-water installation platform: When the device is used for algae removal on the water surface or in shallow water areas, the ultrasonic generation module is usually installed on an above-water platform. This platform can be a floating type, such as a floating platform composed of floating barrels made of plastic or fiberglass. The floating barrels are fixed through connectors to form a stable working plane, and the ultrasonic generator and transducer are installed on this platform;

[0059] Underwater installation bracket: When removing algae in deep water areas, the ultrasonic generation module needs to be installed on an underwater bracket. The underwater bracket is generally made of corrosion-resistant metal materials, and its shape can be a frame type or a rod type. During installation, the bracket is fixed to the bottom of the water or the shore, and then the ultrasonic generator and transducer are installed on the bracket and connected to the control system and power supply above the water through a waterproof cable;

[0060] It should be noted that the solar power supply module includes a solar panel, a charge controller, a storage battery, and an inverter. The solar panel is used to directly convert sunlight into direct current. The charge controller is used to manage the energy flow from the solar panel to the battery, prevent overcharging or over-discharging of the battery, and optimize the charging efficiency. The storage battery is used to store the excess electric energy generated by the solar panel for use at night or on cloudy days. The inverter is used to convert direct current into alternating current;

[0061] Among them, for the solar panel to ensure the normal operation of the system, the power requirement of the solar panel is set to 10.5 - 21 kW, and in order to provide sufficient energy for the solar panel, the calculation formula for the area of the solar panel is as follows:

[0062]

[0063] In the formula, A is the area of the required solar panel, P is the power requirement, η is the conversion efficiency of the photovoltaic panel, and Is is the solar irradiance;

[0064] For the storage battery, in order to ensure the stable operation of the system, it is necessary to calculate the required battery pack capacity, and the calculation formula for the storage battery pack capacity is as follows:

[0065]

[0066] Wherein, C is the capacity of the battery pack of the storage battery, P is the power demand, T is the operating time, and V is the battery voltage;

[0067] Moreover, in consideration of the battery pack's lifespan and the long-term operation reliability of the system, high-performance lithium-ion batteries are selected for the storage battery, and an intelligent management system is equipped to monitor the battery status in real time and extend its service life. Meanwhile, to cope with the impact of extreme weather on the power generation efficiency of the solar panels, a backup power interface is added to ensure the stable operation of the device even in continuous rainy weather.

[0068] The water quality sensor module is used to monitor the water quality changes of the corresponding water body in real time, thereby obtaining one or more water quality parameters based on the monitoring of the water body's water quality and outputting the water quality parameters to the control module.

[0069] It should be noted that the data sampling frequency is set to once every 10 seconds to ensure the real-time monitoring of water quality changes, and the main monitoring parameters include chlorophyll concentration, dissolved oxygen, turbidity, and water temperature, which are the key indicators for evaluating the water quality status.

[0070] This application uses a LoRa communication module to achieve low-power remote transmission, ensuring the real-time and stable transmission of data to the monitoring center; the LoRa communication module is a wireless communication technology designed specifically for low-power wide-area networks (LPWANs), which is particularly suitable for long-distance data transmission in Internet of Things applications;

[0071] The control module is used to set the working parameters of the device; and uses advanced algorithms to deeply analyze the water quality data, identify the types and quantities of algae in the water body, and output the optimal algae removal parameters based on this information. Then, it starts the transducer array. According to the optimized parameters provided by the artificial intelligence module, the control module instructs the transducer array to start and begins ultrasonic algae removal work;

[0072] It should be noted that the control module includes a microcontroller, a power management circuit, a sensor interface circuit, an ultrasonic drive circuit, a communication interface circuit, and a human-machine interface circuit. The microcontroller is used to process data transmitted from various sensors. The power management circuit is used to convert and regulate the direct current generated by the solar panels. The sensor interface circuit is used to collect analog or digital signals detected by the sensors. The ultrasonic drive circuit is used to amplify the control signals output by the microcontroller and provide sufficient drive power for the ultrasonic transducers. The communication interface circuit is used to achieve the communication connection between the device and the remote monitoring center or mobile terminal. The human-machine interface circuit is used to intuitively display the operating status, current parameter settings, and fault alarm information of the device to the user;

[0073] An ultrasonic generation module for generating ultrasonic waves with a specific frequency and intensity, which can penetrate water and affect the algal cell structure;

[0074] It should be noted that the ultrasonic generation module includes an ultrasonic generator and an ultrasonic transducer. The ultrasonic generator is used to convert the input electrical energy into a high-frequency alternating current signal, and the ultrasonic transducer is used to convert the electrical signal into mechanical vibration;

[0075] The ultrasonic generator is a device that converts mains power (alternating current) or direct current into a high-frequency alternating current signal. It mainly generates high-frequency signals based on an electronic oscillation circuit;

[0076] The ultrasonic transducer, and the ultrasonic transducers will be arranged in an array;

[0077] The radius of the array layout is set to 20 meters, and the interval between each layer is 1 meter to ensure that the transducer array covers a sufficient area. The calculation formula for the spacing of the transducers is as follows:

[0078]

[0079] In the formula, λ is the acoustic wavelength, and the operation formula for λ is as follows:

[0080]

[0081] In the formula, c is the speed of sound and f is the frequency;

[0082] The transducer array is composed of multiple independent ultrasonic transducer units. Each unit includes key components such as piezoelectric ceramics, resonance cavities, and protective covers. These units are arranged according to a specific geometric layout to ensure the uniform distribution and efficient propagation of ultrasonic waves in water;

[0083] The propagation of ultrasonic waves in water follows the laws of acoustic propagation. The acoustic intensity will decay according to the inverse square of the distance as the distance increases. The specific formula is as follows:

[0084]

[0085] In the formula, I is the acoustic intensity, r is the propagation distance, and P is the transmission power of the transducer. When designing the ultrasonic transducer array, we first need to select the type. We choose piezoelectric ceramic materials because they have high strength and good durability;

[0086] As can be seen from the above technical solution, this embodiment provides an ultrasonic algae removal system for removing algae in water bodies, including an installation and support structure, a solar power supply module, an ultrasonic generation module, a water quality sensor module, a control module, and a monitoring module. The solar power supply module is used for power supply; the water quality sensor module is used for detecting the water quality of the water body; the control module is used for processing one or more water quality parameters based on an artificial intelligence algorithm module to obtain an ultrasonic control instruction, and the ultrasonic control instruction at least includes a power control parameter and a frequency control parameter; the ultrasonic generation module is connected to the control module and is used for emitting ultrasonic waves to the water body based on the ultrasonic control instruction; the monitoring module is used for collecting multiple operating parameters of the ultrasonic algae removal system and feeding back the multiple operating parameters to the host computer. Since the ultrasonic waves emitted by this system are not of a fixed frequency but vary with different water qualities to meet the algae removal requirements of different water qualities, the algae removal effect is improved.

[0087] The monitoring module is used for real-time monitoring of various parameters of the water body and the energy generation and consumption of the solar power supply module;

[0088] It should be noted that the monitoring module includes a data acquisition unit, a data transmission unit, a remote monitoring platform, and a local monitoring terminal. The data acquisition unit is responsible for collecting data from each sensor, performing analog-to-digital conversion on it, and converting the analog signal into a digital signal for subsequent processing and transmission;

[0089] The data transmission unit is used for transmitting the collected data to a remote monitoring center or user terminal device. Wired communication can also be used and follow specific data transmission protocols, such as Modbus and MQTT;

[0090] Modbus is a serial communication protocol used for exchanging information between industrial electronic devices, and it has become one of the most widely used network protocols in the field of industrial automation;

[0091] MQTT is a lightweight message transmission protocol designed for low-bandwidth, high-latency, or unreliable network environments, especially suitable for communication between Internet of Things devices. It is based on the publish / subscribe model, enabling devices to efficiently exchange information without the need for direct connection;

[0092] The remote monitoring platform consists of a communication device and a cloud platform. The communication device supports 4G / 5G networks to ensure high-bandwidth data upload, enabling users to obtain the device status and data in real time;

[0093] For the cloud platform, users can monitor the device status in real time through a dedicated APP and adjust the operating parameters as needed to achieve remote control and data management;

[0094] The control module in this application is configured with an artificial intelligence algorithm module. This artificial intelligence algorithm module is used to calculate the ecological value in real time, adjust the system operation parameters through the reinforcement learning algorithm, design a reward function according to the ecological value calculation result and the system operation target, build a long short-term memory network model, determine the structure and parameters of the model, and then use the long short-term memory network model to predict the water quality change trend. Among them, the role of long-term memory provides a global background framework for short-term water quality changes, integrating information such as the overall water quality condition and change trend over a long period of time in the past. Short-term memory mainly focuses on recent water quality data changes and can quickly capture the immediate changes in water quality within a short period of time.

[0095] The working process of the solar ultrasonic algae removal system of the present invention is as follows:

[0096] 1. The solar panel, as the main energy source of the device, absorbs solar energy and converts it into electrical energy to provide stable power support for the entire system. To ensure the normal operation of the device even in the absence of sunlight or rainy weather, the energy storage system stores the electrical energy generated by the solar panel and, through the intelligent management system, stably outputs power according to the actual power consumption requirements of the device.

[0097] 2. The water quality sensor continuously monitors various indicators of the water body, such as pH value, turbidity, temperature, etc., and transmits these data to the control module in real time. The collected data is quickly transmitted to the control module by wireless or wired means. The control module performs preliminary processing and analysis on the data to provide a basis for subsequent decision-making.

[0098] 3. The artificial intelligence module analyzes the data and outputs optimization parameters: The artificial intelligence module uses advanced algorithms to deeply analyze the water quality data, identifies the types and quantities of algae in the water body, and outputs the optimal algae removal parameters based on this information. Then, it activates the transducer array. According to the optimization parameters provided by the artificial intelligence module, the control module instructs the transducer array to start and begin ultrasonic algae removal work.

[0099] 4. The transducer array dynamically adjusts the ultrasonic frequency and power: The transducer array dynamically adjusts the ultrasonic frequency and power according to the instructions of the control module to adapt to different types and quantities of algae and achieve efficient algae removal. By precisely controlling the ultrasonic frequency and power, the transducer array can effectively destroy the cell walls of algae and achieve the purpose of efficient algae removal.

[0100] 5. The data and prediction results are uploaded to the cloud platform: All the collected data and the prediction results of the artificial intelligence module are uploaded to the cloud platform for users to perform remote monitoring and analysis. Users can log in to the cloud platform through mobile devices or computers, view the device operation status and water quality conditions in real time, and remotely adjust the working parameters of the device as needed to achieve intelligent management.

[0101] Figure 2 This is a flowchart of an algae removal control method according to an embodiment of the present application.

[0102] As Figure 2 shown, the algae removal control method provided in this embodiment is applied to the control module in the above embodiment. This algae removal method is applied to the solar algae removal system in the previous embodiment. The specific steps of this algae removal control method are as follows:

[0103] S1. Adjust the system operation parameters through a reinforcement learning algorithm;

[0104] Specifically, adjust the system operation parameters through a reinforcement learning algorithm, design a reward function according to the ecological value calculation result and the system operation target, and at the same time build a long short-term memory network model to determine the structure and parameters of the model.

[0105] The specific steps of adjusting the system operation parameters by the reinforcement learning algorithm include the following steps, as Figure 3 shown:

[0106] S101. Environmental modeling, identify and quantify various factors affecting the algae removal effect, and determine adjustable system parameters;

[0107] It should be noted that the various factors affecting the algae removal effect include water temperature, pH value, dissolved oxygen concentration, algae density, and weather conditions;

[0108] The adjustable system parameters include ultrasonic frequency, power, emission angle, duration, and the charging / discharging strategy of the solar power supply system;

[0109] S102. Design a reward function, clarify the desired goals, and set a reward mechanism according to the goals;

[0110] It should be noted that the desired goals include algae removal effect goals, energy utilization goals, equipment performance goals, and economic and social benefit goals;

[0111] S103. Reinforcement learning training process. At the beginning of the algorithm, set the system parameters to the initial values. Then, at each time step, according to the current state, select an action through an exploration-exploitation strategy and execute the selected action. After executing the selected action, the state of the system will change and a new state will be generated. Then, according to the new state and the predefined reward function, calculate a reward value;

[0112] S104. Continuous learning and optimization. As the environment and equipment change, it is necessary to regularly collect new data to update the reinforcement learning model. At the same time, establish a user feedback channel, and users can feedback on the adjustment results of the algorithm according to the actual algae removal effect and equipment operation conditions;

[0113] S2. Use a long short-term memory network model to predict the water quality change trend of the water body.

[0114] Among them, the role of long-term memory provides a global background framework for short-term water quality changes, integrating information such as the overall water quality status and change trends over a relatively long period in the past. Short-term memory mainly focuses on recent changes in water quality data and can quickly capture the immediate changes in water quality within a short period.

[0115] Among them, using a long short-term memory network model to predict the water quality change trend specifically includes the following steps; as Figure 4 shown:

[0116] S201. Data collection and preprocessing. Collect historical water quality data from the detection module set in the solar ultrasonic algae removal system, and at the same time record the operating parameters of the equipment and the algae removal history record. After the collection is completed, remove the outliers and incorrect data in the data, and perform standardization processing on the range and water quality data of different magnitudes;

[0117] S202. Model construction and training. Construct a long short-term memory network structure, determine the number of layers of the long short-term memory network, the number of neurons in each layer, and the input and output dimension hyperparameters. Then divide the preprocessed water quality data into a training set, a validation set, and a test set, and use the training set to train the long short-term memory network model. Continuously adjust the weights and biases of the model through the backpropagation algorithm to minimize the prediction error;

[0118] S203. Model evaluation and optimization. Use the test set to evaluate the trained long short-term memory network model, calculate the error index between the prediction result and the real water quality data, and optimize and adjust the model according to the evaluation result;

[0119] S204. Prediction of water quality change trend. Input the real-time collected water quality data and equipment operating parameters into the trained long short-term memory network model. The model predicts the water quality change trend in the future period according to the input real-time data and historical memory, and outputs the prediction result in an intuitive way;

[0120] At the same time, the artificial intelligence algorithm module uses NVIDIA Jetson Xavier NX as the core computing platform. Its computing power requirement is 10 TOPS, supports INT8 optimized models, and can efficiently process complex AI algorithms;

[0121] NVIDIA Jetson Xavier NX is a high-performance, low-power module designed for edge computing, especially suitable for application scenarios that require strong computing power for artificial intelligence, deep learning, and computer vision tasks;

[0122] INT8 (8-bit integer) refers to a data type that uses 8-bit binary digits to represent integer values. The range of this data type is typically from -128 to 127 (signed integer) or from 0 to 255 (unsigned integer);

[0123] Figure 5 This is a block diagram of an algae removal control device according to an embodiment of the present application.

[0124] As Figure 5 shown, the algae removal control device provided in this embodiment is applied to the control module in the above embodiment, and this algae removal method is applied to the solar algae removal system in the previous embodiment. This algae removal control specifically includes a parameter adjustment module 401 and a water quality prediction module 402

[0125] The parameter adjustment module is used to adjust the system operation parameters through a reinforcement learning algorithm;

[0126] Specifically, it adjusts the system operation parameters through a reinforcement learning algorithm, designs a reward function according to the ecological value calculation result and the system operation target, and at the same time builds a long short-term memory network model to determine the structure and parameters of the model.

[0127] The water quality prediction module is used to predict the water quality change trend of the water body by using the long short-term memory network model.

[0128] Among them, the role of long-term memory provides a global background framework for short-term water quality changes, integrates information such as the overall water quality condition and change trend over a relatively long period in the past, and short-term memory mainly focuses on recent water quality data changes and can quickly capture the immediate changes in water quality in a short period of time.

[0129] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0130] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0131] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or terminal device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising said element.

[0132] The technical solutions provided by the present invention have been introduced in detail above. Specific examples are used herein to illustrate the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An ultrasonic algae removal system for removing algae from water, characterized in that: The ultrasonic algae removal system includes a mounting and supporting structure, a solar power supply module deployed on the mounting and supporting structure, an ultrasonic generating module electrically connected to the solar power supply module, a water quality sensor module, a control module and a monitoring module, wherein: The solar power supply module is used to supply power to each power-consuming module of the ultrasonic algae removal system; The water quality sensor module is used to detect the water quality of the water body, obtain one or more water quality parameters, and output the water quality parameters to the control module; The control module is connected to the water quality sensor module and is used to process the one or more water quality parameters based on the artificial intelligence algorithm module to obtain an ultrasonic control instruction, wherein the ultrasonic control instruction at least includes a power control parameter and a frequency control parameter; The ultrasonic wave generating module is connected to the control module and is used to emit ultrasonic waves to the water body based on the ultrasonic control instruction; The monitoring module is used to collect multiple operating parameters of the ultrasonic algae removal system and feed back the multiple operating parameters to a host computer.

2. The ultrasonic algae removal system according to claim 1, characterized in that: The solar power supply module includes a solar panel, a charge controller and a battery, wherein: The solar panel is used to directly convert sunlight into direct current and to supply power to the ultrasonic generating module, the control module and the monitoring module; The charging controller is connected to the solar panel and the storage battery respectively, and is used to manage the energy flow from the solar panel to the storage battery to prevent the battery from being overcharged or over-discharged; The storage battery is used to store the excess electric energy generated by the solar panel.

3. The ultrasonic algae removal system according to claim 1, characterized in that: The solar energy generation module includes an ultrasonic generator and an ultrasonic transducer; The ultrasonic generator is used to convert input electrical energy into a high-frequency alternating current signal; The ultrasonic transducer is used to convert the high-frequency alternating current signal into mechanical vibration.

4. The ultrasonic algae removal system according to claim 1, characterized in that: The control module includes a microcontroller, a power management circuit, a sensor interface circuit, an ultrasonic drive circuit, a communication interface circuit and a human-computer interaction device, wherein: The power management circuit is used to convert and stabilize the direct current generated by the solar power supply module; The sensor interface circuit is used to collect the one or more water quality parameters; The microcontroller is used to process the one or more water quality parameters and output a control signal; The ultrasonic driving circuit is used to amplify the power of the control signal to obtain and output the ultrasonic control instruction to the ultrasonic generating module; The communication interface circuit is used to realize the communication connection with the remote monitoring center or the mobile terminal; The human-computer interaction device is used to intuitively display the operating status, current parameter settings and / or fault alarm information of the ultrasonic algae removal system to the user through a display interface.

5. The ultrasonic algae removal system according to claim 1, characterized in that: The monitoring module includes a data acquisition unit, a data transmission unit, a remote monitoring platform and a local monitoring terminal.

6. An algae removal control method, applied to the ultrasonic algae removal system according to any one of claims 1 to 5, characterized in that: The algae removal control method comprises the steps of: Adjust system operating parameters through reinforcement learning algorithms; The long short-term memory network model is used to predict the water quality change trend of the water body.

7. The algae control method according to claim 6, characterized in that: The method of adjusting the system operating parameters by using a reinforcement learning algorithm comprises the following steps: Identify and quantify the various factors that affect algae removal effectiveness and determine adjustable system parameters; Design a reward function, clearly define the goals you want to achieve, and set the reward mechanism based on the goals; The reinforcement learning training process sets the system parameters to initial values ​​at the beginning, and then selects an action at each time step according to the current state through the exploration-exploitation strategy and executes the action. After the action is executed, a reward value is calculated according to the new state and the reward function. Continuous learning and optimization: As the environment and equipment change, new data needs to be collected regularly to update the reinforcement learning model. At the same time, a user feedback channel should be established so that users can provide feedback on the algorithm adjustment results based on the actual algae removal effect and equipment operation status.

8. The algae control method according to claim 7, characterized in that: The system parameters include ultrasonic frequency, power, emission angle and duration, and the charging / discharging strategy of the solar power supply module.

9. The algae control method according to claim 6, characterized in that: The method of using the long short-term memory network model to predict the water quality change trend of the water body comprises the following steps: Collect historical water quality data; Constructing a long short-term memory network structure, and training the long short-term memory network model based on the historical water quality data; Evaluate the long short-term memory network model, calculate the error index between the prediction result and the real water quality data, and optimize and adjust the model according to the evaluation result; The water quality data and equipment operating parameters collected in real time are input into the long short-term memory network model to predict the water quality change trend in the future.

10. An algae removal control device, applied to the ultrasonic algae removal system according to any one of claims 1 to 5, characterized in that: The algae removal control device comprises the steps of: a parameter adjustment module, configured to adjust system operation parameters through a reinforcement learning algorithm; The water quality prediction module is configured to use a long short-term memory network model to predict the water quality change trend of the water body.

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