A wireless plant audio controller

Abstract

The present invention discloses a wireless plant audio controller, comprising: a sound recognition module, which is responsible for monitoring in real time the sound signals emitted by plants and analyzing the responses of plants to acoustic wave stimuli; a plant status monitoring module, which is responsible for monitoring in real time the growth status of plants and environmental conditions; a feedback learning module, which is responsible for analyzing the responses of plants to acoustic wave stimuli through a support vector machine algorithm and optimizing the regulation strategy; an adaptive regulation module, which is responsible for analyzing the feedback data in real time and automatically adjusting the acoustic wave stimulus parameters to optimize plant growth; in the present invention, the output of the feedback learning module provides key information for the adaptive regulation module, enabling it to automatically adjust parameters such as the frequency, intensity, and duration of acoustic wave stimuli based on the real-time responses of plants. This dynamic adjustment ensures an accurate match between the acoustic wave stimuli and plant requirements, improves the accuracy of regulation, and facilitates achieving the best growth promotion effect on plants.

Description

Technical Field

[0001] The present invention relates to the technical field of promoting plant growth by sound waves, and particularly to a wireless plant audio controller. Background Art

[0002] Plant audio control technology is a modern physical agriculture technology based on the theory of plant bioacoustics. This technology precisely measures the spontaneous sound and received sound frequencies of different plants, summarizes the quantitative relationship between the plant spontaneous sound frequency and the main environmental factors (such as temperature, humidity, and tissue water content), and then develops and manufactures a plant audio generator to perform audio processing on different plants. Its basic principle is to use the plant audio generator to apply sound waves of a specific frequency to the plant, resonate with the plant, thereby promoting the absorption, transmission, and transformation of various nutrient elements by the plant, enhancing the photosynthesis and absorption capacity of the plant, promoting growth and development, and achieving the goals of increasing production, increasing income, improving quality, and disease resistance.

[0003] After retrieval, a patent with the Chinese patent number CN203311195U discloses a wireless plant audio controller. The wireless plant audio controller includes: a Holtek MCU module for analyzing and processing plant physiological information and surrounding environment information; a sensor module for acquiring plant physiological characteristics and the surrounding environment; a wireless module for wirelessly controlling the audio device; an audio processing module for reading audio information, playing audio, and amplifying audio; a timing module for timing playback; a display module for displaying information; an input module for inputting information; a storage module for storing data; a power supply module for providing system operation; The advantages of this patent are: using a wireless control method, it is convenient to operate, and the installation is not restricted by conditions such as height and position. The controller has a timing function and can set the daily audio playback time period, so that audio playback can be automatically performed without the need for personnel to wait. In addition, the controller can automatically adjust the played track according to the plant surrounding environment, so that appropriate audio tracks can be automatically played.

[0004] However, in the actual use process of the above invention, there is a lack of a real-time feedback learning system, it is difficult to understand the response of plants to sound wave stimulation, it is difficult to accurately regulate parameters such as the frequency, intensity, and duration of sound wave stimulation in real time, and it is difficult to achieve the best growth and development promotion effect. Therefore, a wireless plant audio controller is proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art that there is a lack of a real-time feedback learning system, it is difficult to understand the response of plants to sound wave stimulation, it is difficult to accurately regulate parameters such as the frequency, intensity, and duration of sound wave stimulation in real time, and it is difficult to achieve the best growth and development promotion effect, and to propose a wireless plant audio controller.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A wireless plant audio controller, comprising:

[0008] A sound recognition module; responsible for real-time monitoring of the sound signals emitted by plants and analyzing the responses of plants to acoustic wave stimuli;

[0009] A plant status monitoring module: responsible for real-time monitoring of plant growth conditions and environmental conditions;

[0010] A feedback learning module: responsible for analyzing the responses of plants to acoustic wave stimuli through a support vector machine algorithm and optimizing the control strategy;

[0011] An adaptive regulation module: responsible for real-time analysis of feedback data and automatically adjusting the acoustic wave stimulus parameters to optimize plant growth;

[0012] An acoustic wave stimulus control module: responsible for real-time generating and outputting acoustic wave stimulus signals;

[0013] A user interface module: responsible for providing a user-friendly operation interface, including real-time monitoring of plant status, viewing feedback data, and adjusting acoustic wave settings;

[0014] The sound recognition module needs to receive the sounds emitted by plants and identify and analyze them. The sound recognition module outputs the responses of plants to acoustic wave stimuli obtained from the analysis to the feedback learning module. The plant status monitoring module outputs the detected plant growth status and environmental conditions to the feedback learning module. The feedback learning module combines the inputs of the sound recognition module and the plant status monitoring module and comprehensively analyzes the responses of plants to acoustic wave stimuli using a vector machine algorithm. The feedback learning module outputs the comprehensively analyzed responses of plants to acoustic wave stimuli to the adaptive module. The adaptive module uses a control algorithm to automatically adjust the frequency, intensity, and duration parameters of the acoustic wave stimuli according to the output of the feedback learning module. The adaptive module outputs the adjusted acoustic wave parameters to the acoustic wave stimulus control module, and the acoustic wave stimulus control module real-time generates and outputs acoustic wave stimulus signals.

[0015] The above technical solutions further include:

[0016] Preferably, the sound recognition module includes an audio acquisition unit, a signal processing unit, a feature extraction unit, a module recognition unit, and a result output unit. The audio acquisition unit is responsible for collecting the sound signals of plants through an audio sensor. The audio acquisition unit transmits the collected sound signals to the signal processing unit. The signal processing unit is responsible for preprocessing and analyzing the collected sound signals. The signal processing unit transmits the processed information to the feature extraction unit. The feature extraction unit is responsible for extracting important features from the processed sound signals. The feature extraction unit transmits the extracted features to the module recognition unit. The module recognition unit is responsible for classifying and identifying the extracted features to judge the reaction state of the plant. The module recognition unit transmits the judged reaction state of the plant to the result output unit, which is responsible for transmitting the judged reaction state of the plant to the feedback learning module.

[0017] Preferably, the plant status monitoring module includes a data acquisition and processing unit, a status evaluation unit, and a result output unit. The data acquisition and processing unit is responsible for using a single-chip microcomputer to collect various sensor data in real time and preprocess the collected data. The data acquisition and processing unit transmits the preprocessed data to the status evaluation and warning unit. The status evaluation unit is responsible for generating a plant health score based on the preprocessed data to help evaluate whether the plant growth is normal. The status evaluation unit is responsible for transmitting the evaluation data to the result output unit. The result output unit is responsible for transmitting the evaluation data to the feedback learning module and the user interface module.

[0018] Preferably, the various sensors include an environmental monitoring sensor, a plant growth status sensor, and a growth parameter sensor. The environmental monitoring sensor is responsible for monitoring the temperature of the plant growth environment, measuring the humidity of the air and soil, and detecting the light intensity. The plant growth status sensor is responsible for measuring the plant height, monitoring the root growth status and health, and monitoring the color, shape, and area of the leaves. The growth parameter sensor is responsible for monitoring the biomass of the plant, measuring the chlorophyll content, and the photosynthesis rate.

[0019] Preferably, the feedback learning module includes a data acquisition unit, a data processing and analysis unit, a model establishment and optimization unit, a result output unit, and an evaluation and decision-making unit. The data acquisition unit is responsible for collecting the data input by the voice recognition module and the plant status monitoring module. The data acquisition unit transmits the collected data to the data processing and analysis unit. The data processing and analysis unit is responsible for preprocessing the collected data and extracting key features. The data processing and analysis unit transmits the extracted key feature data to the model establishment and optimization unit. The model establishment and optimization unit is responsible for establishing a relationship model between plant growth and acoustic wave stimulation using the support vector machine algorithm, and training and adjusting the model using historical data and real-time data. The model establishment and optimization unit transmits the model output results and real-time data to the result output unit. The result output unit is responsible for transmitting the model output results and real-time data to the adaptive control module and the user interface module. The evaluation and decision-making unit is responsible for evaluating the impact of acoustic wave stimulation on plant growth, analyzing whether the expected effect is achieved, and providing adjustment suggestions based on the evaluation results.

[0020] Preferably, the specific steps for establishing a relationship model between plant growth and acoustic wave stimulation using the support vector machine algorithm are as follows:

[0021] Data preparation and preprocessing: Collect the data input from the voice recognition module and the plant status monitoring module, and clean the collected data to extract the features that affect plant growth from the original data;

[0022] Model training: Select an appropriate kernel function according to the characteristics of the problem, determine the parameters of the support vector machine model, and use the training data set to train the support vector machine model;

[0023] Model evaluation and optimization: Use the test set to test and evaluate the model performance, and adjust the parameters and kernel function parameters according to the evaluation results;

[0024] Model application: Apply the trained support vector machine model to the wireless plant audio controller.

[0025] Preferably, the adaptive control module uses the genetic algorithm to determine the optimal acoustic wave stimulation parameters according to the output results of the feedback learning module. The determination of the acoustic wave stimulation parameters includes frequency adjustment, intensity adjustment, and duration adjustment.

[0026] Preferably, the specific steps of the genetic algorithm are as follows:

[0027] Step 1: Set the range of parameters for the frequency f, intensity i, and duration t of the acoustic wave stimulation, and randomly generate N individuals, each of which is composed of a set of parameters;

[0028] Step 2: Define a fitness function F(x) according to the output result of the feedback learning model, where x = (f, i, t). Perform fitness evaluation on each individual x in the population P(t) to obtain its fitness value;

[0029] Step 3: Determine the individuals to be retained according to the fitness. Adopt the tournament strategy to select and retain several individuals with the highest fitness, and other individuals are replaced by new individuals;

[0030] Step 4: For the selected parent individuals, generate offspring individuals through crossover operations, and mutate the offspring individuals with a certain probability to increase the diversity of the population;

[0031] Step 5: Add the new offspring individuals generated after selection, crossover, and mutation operations to a new population to form the next generation population. Repeat fitness evaluation, selection operations, crossover operations, and mutation operations until the preset number of iterations is reached or other termination conditions are met;

[0032] Step 6: In the final population, select the individual with the highest fitness value as the optimal solution, and output the corresponding acoustic wave stimulation parameters of the optimal individual as the optimal regulation parameters of the wireless plant audio controller;

[0033] Preferably, the user interface module includes:

[0034] Main control panel: Responsible for setting acoustic wave parameters and controlling the start and stop of acoustic wave stimulation;

[0035] Real-time monitoring display: Responsible for displaying the current growth data of the plant and presenting the current acoustic wave stimulation parameters;

[0036] Feedback learning system interface: Responsible for the user to record and input the observed results of the plant's response and display past feedback and its corresponding system responses;

[0037] Digital visualization unit: Responsible for presenting the plant growth trend and the effect of acoustic wave stimulation through line charts or bar charts;

[0038] Wireless communication unit: Responsible for displaying the connection status with mobile devices and networks;

[0039] Notification and alarm unit: Responsible for issuing an alarm when the plant status is abnormal or the system requires the user's attention.

[0040] The present invention has the following beneficial effects:

[0041] 1. In the present invention, by introducing a feedback learning module, the controller can monitor and analyze the response of plants to acoustic stimuli in real time. Using the support vector machine algorithm, the feedback learning module can efficiently learn from the data collected by the sound recognition module and the plant status monitoring module, thereby achieving an in-depth understanding of plant responses.

[0042] 2. In the present invention, the output of the feedback learning module provides key information for the adaptive regulation module, enabling it to automatically adjust parameters such as the frequency, intensity, and duration of acoustic stimuli based on the real-time reactions of plants. This dynamic adjustment ensures an exact match between the acoustic stimuli and plant requirements, improving the precision of regulation and facilitating the achievement of the best growth and development effects for plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system architecture diagram of a wireless plant audio controller proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] As Figure 1 shown, a wireless plant audio controller proposed by the present invention includes:

[0046] A sound recognition module; responsible for real-time monitoring of the sound signals emitted by plants and analyzing the response of plants to acoustic stimuli;

[0047] A plant status monitoring module: responsible for real-time monitoring of plant growth conditions and environmental conditions;

[0048] A feedback learning module: responsible for analyzing the reaction of plants to acoustic stimuli through the support vector machine algorithm and optimizing the regulation strategy;

[0049] An adaptive regulation module: responsible for real-time analysis of feedback data and automatically adjusting acoustic stimulus parameters to optimize plant growth;

[0050] An acoustic stimulus control module: responsible for real-time generation and output of acoustic stimulus signals;

[0051] A user interface module: responsible for providing a user-friendly operation interface, including real-time monitoring of plant status, viewing feedback data, and adjusting acoustic settings;

[0052] The sound recognition module needs to receive the sounds emitted by plants and identify and analyze them. The sound recognition module outputs the response of the plant to the sound wave stimulation obtained from the analysis to the feedback learning module. The plant status monitoring module outputs the detected plant growth status and environmental conditions to the feedback learning module. The feedback learning module combines the inputs of the sound recognition module and the plant status monitoring module, and comprehensively analyzes the reaction of the plant to the sound wave stimulation using the support vector machine algorithm. The feedback learning module outputs the comprehensive analysis result of the plant's reaction to the sound wave stimulation to the adaptive module. The adaptive module uses a control algorithm to automatically adjust the frequency, intensity, and duration parameters of the sound wave stimulation according to the output of the feedback learning module. The adaptive module outputs the adjusted sound wave parameters to the sound wave stimulation control module, and the sound wave stimulation control module generates and outputs the sound wave stimulation signal in real time.

[0053] In the embodiment of the present invention, first, the sound recognition module receives the sound signals from plants, including the vibrations generated by plants due to sound wave stimulation, natural sounds during the growth process, etc. Then, the sound recognition module identifies and analyzes the received sound signals to judge the response of the plant to the sound wave stimulation. This includes extracting sound features, identifying sound patterns, and evaluating the intensity and frequency of the sound signals. The plant status monitoring module simultaneously monitors the growth status of the plant, such as leaf color, growth rate, flowering situation, etc., and environmental conditions, such as temperature, humidity, light intensity, etc. The feedback learning module receives the data transmitted from the sound recognition module and the plant status monitoring module, and uses the support vector machine algorithm to comprehensively analyze these data to understand the specific reaction of the plant to the sound wave stimulation. At the same time, based on the analysis results, the feedback learning module optimizes the regulation strategy of the sound wave stimulation. The adaptive regulation module receives the optimized regulation strategy output from the feedback learning module. The adaptive module uses an appropriate control algorithm to automatically adjust parameters such as the frequency, intensity, and duration of the sound wave stimulation according to the regulation strategy. Finally, the sound wave stimulation control module generates and outputs the sound wave stimulation signal in real time, and transmits these sound wave stimulation signals to the plant through wireless transmission to stimulate it.

[0054] In one embodiment, the voice recognition module includes an audio acquisition unit, a signal processing unit, a feature extraction unit, a module recognition unit, and a result output unit. The audio acquisition unit is responsible for collecting the sound signals of plants through an audio sensor. The audio acquisition unit transmits the collected sound signals to the signal processing unit. The signal processing unit is responsible for preprocessing and analyzing the collected sound signals. The signal processing unit transmits the processed information to the feature extraction unit. The feature extraction unit is responsible for extracting important features from the processed sound signals. The feature extraction unit transmits the extracted features to the module recognition unit. The module recognition unit is responsible for classifying and recognizing the extracted features to determine the response state of the plant. The module recognition unit transmits the determined response state of the plant to the result output unit, which is responsible for transmitting the determined response state of the plant to the feedback learning module.

[0055] In the embodiment of the present invention, extracting important features from the preprocessed sound signals includes the frequency, amplitude, duration, spectral distribution, etc. of the sound. These features can reflect the response state of the plant to the sound wave stimulation. The module recognition unit maps the features to specific plant response states, such as normal, excited, stressed, etc.

[0056] In one embodiment, the plant state monitoring module includes a data acquisition and processing unit, a state evaluation unit, and a result output unit. The data acquisition and processing unit is responsible for using a single-chip microcomputer to collect various sensor data in real time and preprocess the collected data. The data acquisition and processing unit transmits the preprocessed data to the state evaluation and warning unit. The state evaluation unit is responsible for generating a plant health score based on the preprocessed data to help evaluate whether the plant growth is normal. The state evaluation unit is responsible for transmitting the evaluation data to the result output unit. The result output unit is responsible for transmitting the evaluation data to the feedback learning module and the user interface module.

[0057] In the embodiment of the present invention, the state evaluation unit can also predict the future growth state of the plant according to the change trend of historical data and current data.

[0058] In one embodiment, the various sensors include environmental monitoring sensors, plant growth state sensors, and growth parameter sensors. The environmental monitoring sensors are responsible for monitoring the temperature of the plant growth environment, measuring the humidity of the air and soil, and detecting the light intensity. The plant growth state sensors are responsible for measuring the plant height, monitoring the root growth state and health condition, and monitoring the color, shape, and area of the leaves. The growth parameter sensors are responsible for monitoring the biomass of the plant, measuring the chlorophyll content, and the photosynthesis rate.

[0059] In one embodiment, the feedback learning module includes a data acquisition unit, a data processing and analysis unit, a model establishment and optimization unit, a result output unit, and an evaluation and decision-making unit. The data acquisition unit is responsible for collecting the data input by the sound recognition module and the plant status monitoring module. The data acquisition unit transfers the collected data to the data processing and analysis unit. The data processing and analysis unit is responsible for preprocessing the collected data and extracting key features. The data processing and analysis unit transfers the extracted key feature data to the model establishment and optimization unit. The model establishment and optimization unit is responsible for establishing a relationship model between plant growth and acoustic wave stimulation using the support vector machine algorithm, and training and adjusting the model using historical data and real-time data. The model establishment and optimization unit transfers the model output results and real-time data to the result output unit according to the model output results. The result output unit is responsible for transferring the model output results and real-time data to the adaptive regulation module and the user interface module. The evaluation and decision-making unit is responsible for evaluating the impact of acoustic wave stimulation on plant growth, analyzing whether the expected effect is achieved, and providing adjustment suggestions based on the evaluation results.

[0060] In the embodiment of the present invention, the formula for the evaluation and decision-making unit to evaluate the effect can be expressed by the following formula:

[0061]

[0062] Where E is the effect evaluation value, y i is the actual plant growth data, is the model prediction value.

[0063] The adjustment suggestion formula is:

[0064] ΔP = k·(E - ∈)

[0065] Where ΔP represents the suggested adjustment amount, k represents the adjustment coefficient, and ∈ represents the acceptable error range.

[0066] In one embodiment, the specific steps for establishing a relationship model between plant growth and acoustic wave stimulation using the support vector machine algorithm are as follows:

[0067] Data preparation and preprocessing: Collect the data input from the sound recognition module and the plant status monitoring module, and clean the collected data, and extract the features that affect plant growth from the original data;

[0068] Model training: Select a suitable kernel function according to the characteristics of the problem, determine the parameters of the support vector machine model, and use the training data set to train the support vector machine model;

[0069] Model evaluation and optimization: Use the test set to test and evaluate the model performance, and adjust the parameters and kernel function parameters according to the evaluation results;

[0070] Model application: Apply the trained support vector machine model to the wireless plant audio controller.

[0071] In one embodiment, the adaptive regulation module determines the optimal acoustic wave stimulation parameters using a genetic algorithm according to the output result of the feedback learning module. Determining the acoustic wave stimulation parameters includes frequency adjustment, intensity adjustment, and duration adjustment.

[0072] In one embodiment, the specific steps of the genetic algorithm are as follows:

[0073] Step 1: Set the range of parameters for the frequency f, intensity i, and duration t of the acoustic wave stimulation, and randomly generate N individuals, each individual consisting of a set of parameters;

[0074] Step 2: According to the output result of the feedback learning model, define a fitness function F(x), where x = (f, i, t), and perform fitness evaluation on each individual x in the population P(t) to obtain its fitness value;

[0075] Step 3: Determine the individuals to be retained according to the fitness, and use the tournament strategy to select and retain several individuals with the highest fitness, and other individuals are replaced by new individuals;

[0076] Step 4: For the selected parent individuals, generate offspring individuals through crossover operations, and mutate the offspring individuals with a certain probability to increase the diversity of the population;

[0077] Step 5: Add the new offspring individuals generated after selection, crossover, and mutation operations to a new population to form the next generation population, and repeat the fitness evaluation, selection operation, crossover operation, and mutation operation until the preset number of iterations is reached or other termination conditions are met;

[0078] Step 6: In the final population, select the individual with the highest fitness value as the optimal solution, and output the acoustic wave stimulation parameters corresponding to the optimal individual as the optimal regulation parameters of the wireless plant audio controller.

[0079] In one embodiment, the user interface module includes:

[0080] Main control panel: Responsible for setting the acoustic wave parameters and controlling the start and stop of the acoustic wave stimulation;

[0081] Real-time monitoring display: Responsible for displaying the current growth data of the plant and showing the current acoustic wave stimulation parameters;

[0082] Feedback learning system interface: Responsible for the user to record and input the observed results of the plant's response and display past feedback and its corresponding system responses;

[0083] Digital visualization unit: responsible for displaying the plant growth trend and the effect of acoustic wave stimulation through line charts or bar charts;

[0084] Wireless communication unit: responsible for displaying the connection status with mobile devices and networks;

[0085] Notification and alarm unit: responsible for issuing an alarm when the plant status is abnormal or the system requires the user's attention.

[0086] In the embodiments of the present invention, the average value of the data in the digital visualization unit can be calculated by the following formula:

[0087]

[0088] where, is the average value of the data, and y i is the value of each data point.

[0089] The standard deviation used to display volatility can be calculated by the following formula:

[0090]

[0091] The abnormal threshold setting in the notification and alarm unit can be expressed as:

[0092] A1 = μ + kσ

[0093] where, A1 represents the abnormal threshold, μ is the mean value of the normal value, σ is the standard deviation, and k is a constant, usually taking 2 or 3.

[0094] The alarm trigger condition can be expressed by the following formula:

[0095]

[0096] where, A represents the currently monitored plant status data.

[0097] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wireless plant audio controller, characterized in that: include: Voice recognition module; Responsible for real-time monitoring of the sound signals emitted by plants and analyzing the response of plants to sound wave stimulation; Plant status monitoring module: responsible for real-time monitoring of plant growth status and environmental conditions; Feedback learning module: responsible for analyzing the plant's response to sound wave stimulation through the support vector machine algorithm and optimizing the control strategy; Adaptive control module: responsible for real-time analysis of feedback data and automatic adjustment of acoustic stimulation parameters to optimize plant growth; Acoustic wave stimulation control module: responsible for real-time generation and output of acoustic wave stimulation signals; User interface module: responsible for providing a user-friendly operation interface, including real-time monitoring of plant status, viewing feedback data, and adjusting sonic settings; The sound recognition module needs to receive the sound emitted by the plant and perform recognition and analysis on it. The sound recognition module outputs the analyzed response of the plant to the sound wave stimulation to the feedback learning module. The plant state monitoring module outputs the detected plant growth state and environmental conditions to the feedback learning module. The feedback learning module combines the inputs of the sound recognition module and the plant state monitoring module, and uses a vector machine algorithm to comprehensively analyze the response of the plant to the sound wave stimulation. The feedback learning module outputs the comprehensive analysis of the response of the plant to the sound wave stimulation to the adaptive module. The adaptive module uses a control algorithm to automatically adjust the frequency, intensity and duration parameters of the sound wave stimulation according to the output of the feedback learning module. The adaptive module outputs the adjusted sound wave parameters to the sound wave stimulation control module, and the sound wave stimulation control module generates and outputs the sound wave stimulation signal in real time.

2. A wireless plant audio controller according to claim 1, characterized in that: The sound recognition module includes an audio acquisition unit, a signal processing unit, a feature extraction unit, a module recognition unit and a result output unit. The audio acquisition unit is responsible for collecting plant sound signals through an audio sensor. The audio acquisition unit transmits the collected sound signals to the signal processing unit. The signal processing unit is responsible for preprocessing and analyzing the collected sound signals. The signal processing unit transmits the processed information to the feature extraction unit. The feature extraction unit is responsible for extracting important features from the processed sound signals. The feature extraction unit transmits the extracted features to the module recognition unit. The module recognition unit is responsible for classifying and identifying the extracted features and judging the reaction state of the plants. The module recognition unit transmits the judged plant reaction state to the result output unit. The module is responsible for transmitting the judged plant reaction state to the feedback learning module.

3. A wireless plant audio controller according to claim 1, characterized in that: The plant status monitoring module includes a data acquisition and processing unit, a status evaluation unit, and a result output unit. The data acquisition and processing unit is responsible for using a single-chip microcomputer to collect various sensor data in real time and preprocess the collected data. The data acquisition and processing unit transmits the preprocessed data to the status evaluation and early warning unit. The status evaluation unit is responsible for generating a plant health score based on the preprocessed data to help evaluate whether the plant growth is normal. The status evaluation unit is responsible for transmitting the evaluation data to the result output unit, and the result output unit is responsible for transmitting the evaluation data to the feedback learning module and the user interface module.

4. A wireless plant audio controller according to claim 3, characterized in that: The various types of sensors include environmental monitoring sensors, plant growth status sensors and growth parameter sensors. The environmental monitoring sensors are responsible for monitoring the temperature of the plant growth environment, measuring the humidity of the air and soil and detecting the light intensity. The plant growth status sensors are responsible for measuring the plant height, monitoring the root growth status and health, and monitoring the color, shape and area of ​​the leaves. The growth parameter sensors are responsible for monitoring the plant biomass and measuring the chlorophyll content and photosynthesis rate.

5. A wireless plant audio controller according to claim 1, characterized in that: The feedback learning module includes a data acquisition unit, a data processing and analysis unit, a model building and optimization unit, a result output unit and an evaluation and decision unit. The data acquisition unit is responsible for collecting data input by the sound recognition module and the plant status monitoring module. The data acquisition unit transmits the collected data to the data processing and analysis unit. The data processing and analysis unit is responsible for preprocessing the collected data and extracting key features. The data processing and analysis unit transmits the extracted key feature data to the model building and optimization unit. The model building and optimization unit is responsible for using the support vector machine algorithm to establish a relationship model between plant growth and sound wave stimulation, and uses historical data and real-time data to train and adjust the model. The model building and optimization unit transmits the model output results and real-time data to the result output unit. The result output unit is responsible for transmitting the model output results and real-time data to the adaptive control module and the user interface module. The evaluation and decision unit is responsible for evaluating the impact of sound wave stimulation on plant growth, analyzing whether the expected effect is achieved, and providing adjustment suggestions based on the evaluation results.

6. A wireless plant audio controller according to claim 5, characterized in that: The specific steps of using the support vector machine algorithm to establish the relationship model between plant growth and sound wave stimulation are: Data preparation and preprocessing: Collect data input from the sound recognition module and the plant status monitoring module, clean the collected data, and extract features that affect plant growth from the raw data; Model training: Select a suitable kernel function based on the characteristics of the problem, determine the parameters of the support vector machine model, and use the training data set to train the support vector machine model; Model evaluation and optimization: Use the test set to test the model to evaluate model performance, and adjust parameters and kernel function parameters based on the evaluation results; Model application: Apply the trained support vector machine model to the wireless plant audio controller.

7. A wireless plant audio controller according to claim 1, characterized in that: The adaptive control module uses a genetic algorithm to determine the optimal sound wave stimulation parameters according to the output result of the feedback learning module. The determined sound wave stimulation parameters include frequency adjustment, intensity adjustment and duration adjustment.

8. A wireless plant audio controller according to claim 7, characterized in that: The specific steps of the genetic algorithm are: Step 1: Set the range of the frequency f, intensity i and duration t parameters of the acoustic stimulus, and randomly generate N individuals, each of which consists of a set of parameters; Step 2: Based on the output of the feedback learning model, define a fitness function F(x), where x = (f, i, t), and perform fitness evaluation on each individual x in the population P(t) to obtain its fitness value; Step 3: Determine the individuals to be retained based on their fitness, and use the tournament strategy to select the individuals with the highest fitness, and the other individuals are replaced by new individuals; Step 4: For the selected parent individuals, generate offspring individuals through crossover operation, and mutate the offspring individuals with a certain probability to increase the diversity of the population; Step 5: Add the new offspring individuals generated after selection, crossover and mutation operations to the new population to form the next generation population, and repeat the fitness evaluation, selection operation, crossover operation and mutation operation until the preset number of iterations is reached or other termination conditions are met; Step 6: In the final population, select the individual with the highest fitness value as the optimal solution, and output the sound wave stimulation parameters corresponding to the optimal individual as the optimal control parameters of the wireless plant audio controller.

9. A wireless plant audio controller according to claim 1, characterized in that: The user interface module comprises: Main control panel: responsible for setting the sound wave parameters and controlling the start and stop of sound wave stimulation; Real-time monitoring display: responsible for displaying the current plant growth data and current acoustic stimulation parameters; Feedback learning system interface: responsible for users to record and input observations of plant responses and display past feedback and its corresponding system response; Digital visualization unit: responsible for displaying plant growth trends and sound wave stimulation effects through line graphs or bar graphs; Wireless communication unit: responsible for displaying the connection status with mobile devices and networks; Notification and alarm unit: responsible for issuing an alarm when the plant status is abnormal or the system requires user attention.

Images