Plant emotion visualization method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field related to bio-electricity signals, in particular to a plant emotion visualization method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring bio-electricity signals of different parts of a plant in different scenes by adopting a signal amplification circuit; extracting feature vectors of the corresponding bio-electricity signals under different scenes; based on the feature vector, a reinforcement learning algorithm is adopted for classification so as to identify the emotion type of the plant electric signal, and the corresponding relation between the bio-electricity signal and the emotion type is constructed; wherein different emotion types correspond to different scenes; acquiring real-time bio-electricity signals of different parts of the plant; and determining the emotion type of the plant based on the real-time bio-electricity signal and the corresponding relationship, and further determining the actual scene of the plant.

Description

Technical Field

[0001] The present application relates to the technical field related to bioelectric signals, and specifically to a method, device, electronic device and storage medium for visualizing plant emotions. Background Art

[0002] Plant maintenance is not an easy task. Their growth and health status are often affected by many factors, including environmental conditions such as light, water, temperature, fertilizer, and the physiological state of the plant itself.

[0003] Currently, most plant care relies on the grower's experience and intuition. Growers often use changes in the plant's appearance, such as leaf color and shape, to determine whether it needs watering or fertilizing. While simple, this method lacks scientific accuracy and often fails to accurately reflect the plant's actual needs. This can lead to poor growth or even death due to improper care.

[0004] Furthermore, research has shown that plants are influenced by their "emotions" during their growth process—that is, their responses to environmental changes. These responses are often manifested through physiological changes in the plant, such as growth rate and leaf transpiration. However, these subtle physiological changes are difficult to observe with the naked eye. Therefore, accurately capturing and interpreting a plant's "emotions" has become key to improving plant care quality. Summary of the Invention

[0005] In view of this, the embodiments of the present application are dedicated to providing a method, device, electronic device and storage medium for visualizing plant emotions to better maintain plants.

[0006] This application provides a method for visualizing plant emotions, including:

[0007] Using signal amplification circuits, bioelectrical signals from different parts of plants in different scenarios are collected;

[0008] Extract the feature vectors of bioelectric signals corresponding to different scenarios;

[0009] Based on the feature vectors, a reinforcement learning algorithm is used for classification to identify the emotion type of plant electrical signals and to establish the corresponding relationship between bioelectrical signals and emotion types.

[0010] Different emotion types correspond to different situations;

[0011] Obtain real-time bioelectric signals from different parts of the plant;

[0012] Based on the real-time bioelectric signal and the corresponding relationship, the emotion type of the plant is determined, and then the actual situation of the plant is determined.

[0013] In some embodiments, it further includes:

[0014] Displays the type of emotion or actual situation of the plant in question.

[0015] In some embodiments, it further includes:

[0016] Play out the type of emotion or actual situation of said plant;

[0017] In some embodiments, the method further includes: judging the needs or maintenance measures of the plant based on the emotional type or actual situation of the plant.

[0018] In some embodiments, it further includes:

[0019] Indicates the plant's needs or maintenance measures.

[0020] In some embodiments, it further includes:

[0021] Broadcast the needs or care measures of the plant in question.

[0022] The present application also provides a device for visualizing plant emotions, comprising:

[0023] An acquisition module is used to collect bioelectrical signals from different parts of plants in different scenarios using a signal amplification circuit;

[0024] Extraction module, used to extract the feature vectors of bioelectric signals corresponding to different scenarios;

[0025] Building a module for classification based on feature vectors using a reinforcement learning algorithm to identify the emotion type of plant electrical signals and establish a correspondence between bioelectrical signals and emotion types;

[0026] Among them, different emotion types correspond to different situations;

[0027] Acquisition module, used to obtain real-time bioelectric signals from different parts of the plant;

[0028] The determination module is used to determine the emotion type of the plant based on the real-time bioelectric signal and the corresponding relationship, and then determine the actual situation of the plant.

[0029] The present application also provides an electronic device, comprising:

[0030] A processor, and a memory for storing a program executable by the processor;

[0031] The processor is used to implement the above-mentioned method for visualizing plant emotions by running the program in the memory.

[0032] The application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program causes a processor to execute the plant emotion visualization method when the computer program is run by the processor.

[0033] The plant emotion visualization method provided in the application first acquires bioelectric signals of different parts of a plant in different scenarios by using a signal amplification circuit; extracts feature vectors of the bioelectric signals corresponding to different scenarios; classifies the feature vectors by using a reinforcement learning algorithm to identify emotion types of the plant electric signals and construct a corresponding relationship between the bioelectric signals and the emotion types; wherein different emotion types correspond to different scenarios; acquires real-time bioelectric signals of different parts of the plant; determines emotion types of the plant based on the real-time bioelectric signals and the corresponding relationship, and further determines actual scenarios of the plant. In the scheme provided in the application, the bioelectric signals of the plant in different scenarios are acquired by using the signal amplification circuit, which can more accurately capture physiological responses of the plant, thereby identifying emotion states of the plant. The feature vectors of the bioelectric signals can effectively extract information related to plant emotions from complex signals, providing key data for subsequent emotion recognition. The reinforcement learning algorithm is used to classify the feature vectors, which can be continuously optimized over time to improve the accuracy and efficiency of emotion recognition. The corresponding relationship between the bioelectric signals and the emotion types enables the system to determine specific scenarios of the plant according to the signal types, providing a scientific basis for plant maintenance. Real-time bioelectric signals of different parts of the plant are acquired, and the emotion types and actual scenarios of the plant are determined in real time based on these signals and the established corresponding relationship, thereby providing timely maintenance measures for the plant. By accurately identifying emotions and needs of the plant, more reasonable maintenance measures such as timely watering, fertilization or adjustment of light can be taken to promote healthy growth of the plant. BRIEF DESCRIPTION OF DRAWINGS

[0034] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:

[0035] Figure 1 is a flowchart of the plant emotion visualization method provided in an embodiment of the present application.

[0036] Figure 2 is a partial flowchart of the plant emotion visualization method provided in an embodiment of the present application.

[0037] Figure 3 This is a schematic diagram of the structure of a device for visualizing plant emotions provided in one embodiment of the present application. Figure 4 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Application Overview

[0040] Research has found that plants also have rich emotions and, like humans, are affected by emotions during their growth. However, plants cannot make sounds or move. When growing plants indoors, they often need to be watered. Users generally water them every few days, and the frequency and amount of water are implemented based on their own experience. Plants can survive healthily with water.

[0041] If we can quantify and count the emotions of plants, we can provide timely and reasonable maintenance measures to promote plant growth.

[0042] In order to solve the above problems, the present application provides a solution that uses a signal amplification circuit to collect the bioelectric signals of plants and form bioelectric signals of different parts of the plants. The characteristics of these data curves are analyzed, and an algorithm for proposing the characteristics of plant electrical signals is developed. The characteristics of plant electrical signals in different scenarios are extracted and combined to form a feature vector of the classification algorithm. Based on the feature vector, a reinforcement learning algorithm is used for classification to identify the type of plant electrical signals. Emotion classification includes: encountering damage (scratches on the leaves, insect bites, high temperature burns on the leaves, etc.), environmental sounds (elegant music, noisy music), lack of water, etc.; the final equipment results are: electrical signal acquisition, single-chip microcomputer (integrated plant electrical signal processing and classification algorithm model), display screen, with a speaker (can make sounds). When plants face various problems, they generate electrical signals. After identification, they are displayed on the display screen and the identification results are played out through the speaker. In this way, the signals emitted by plants are measured by electrodes, and the electrical signals are mapped into plant emotional information. According to the plant emotional information, reasonable maintenance measures are given in time to promote plant growth.

[0043] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0044] Exemplary Methods

[0045] Figure 1 This is a flow chart of a method for visualizing plant emotions provided by an embodiment of the present application. Figure 1 As shown, the method includes the following contents.

[0046] Step S110, using a signal amplification circuit to collect bioelectrical signals from different parts of the plant in different scenarios;

[0047] Signal amplification circuits are electronic devices used to amplify weak electrical signals, making them more noticeable and easier to measure. In plant emotion analysis, plant bioelectrical signals are often very weak, so they need to be amplified by signal amplification circuits. Bioelectrical signals refer to the weak electrical currents generated by cellular activity during plant growth. These currents can reflect a plant's physiological state and response to environmental changes. Different scenarios refer to the various environmental conditions a plant may encounter, such as light, temperature, humidity, and soil conditions. These conditions affect a plant's physiological activities and, consequently, its bioelectrical signals.

[0048] Specifically, select appropriate sensors: Use specialized bioelectric sensors, such as electrochemical sensors or capacitive sensors, to detect the bioelectric activity of plants. Connect the signal amplification circuit: Connect the sensor to a signal amplification circuit that can amplify weak bioelectric signals to make them strong enough for further processing and analysis. Deploy sensors: Deploy sensors in different parts of the plant (such as leaves, stems, and roots) to collect bioelectric signals in different scenarios. Record data: Use a data acquisition system to record the amplified bioelectric signals for subsequent analysis.

[0049] Step S120: extracting the characteristic vectors of the bioelectric signals corresponding to different scenarios;

[0050] Extraction refers to using signal processing techniques to identify and extract eigenvectors representing plant emotions from raw bioelectric signals. In data analysis, eigenvectors are numerical values ​​that represent data characteristics. In this system, eigenvectors are key information extracted from bioelectric signals and used to distinguish different emotional states.

[0051] The specific steps include: Signal preprocessing: Filtering, denoising, and other preprocessing operations are performed on the collected bioelectric signals to improve signal quality. Feature extraction: Using signal processing techniques (such as Fourier transform and wavelet transform) to extract key features such as frequency, amplitude, and waveform from the preprocessed signals. Feature vector construction: The extracted features are combined into a feature vector that can represent the bioelectric signal characteristics of the plant in a specific scenario.

[0052] Step S130 , based on the feature vector, a reinforcement learning algorithm is used to perform classification to identify the emotion type of the plant electrical signal and to establish a correspondence between the bioelectrical signal and the emotion type;

[0053] Different emotion types correspond to different scenarios. Reinforcement learning is a machine learning technique that learns to make optimal decisions through interaction with the environment. In this system, a reinforcement learning algorithm is used to identify the plant's emotion type based on feature vectors. Classification involves assigning feature vectors to different categories, each representing a specific emotion type. The mapping relationship between bioelectric signals and emotion types is established, allowing the system to identify the plant's emotional state based on the signals.

[0054] Specifically, select a reinforcement learning algorithm: Choose an appropriate reinforcement learning algorithm, such as Q-learning or Deep Q-Network (DQN), for the classification task. Train the model: Use labeled training data (bioelectric signal feature vectors and corresponding emotion types) to train the reinforcement learning model. Evaluate the model using a validation set to ensure that the model can accurately identify different emotion types. Establish correspondences: Use the trained model to establish correspondences between bioelectric signal feature vectors and emotion types.

[0055] Step S140, acquiring real-time bioelectric signals from different parts of the plant;

[0056] Specifically, real-time bioelectric signals refer to the system's ability to instantly measure and record plant bioelectric signals, which is crucial for quickly responding to plant needs. The specific process includes: Real-time monitoring: Continuously monitoring plant bioelectric signals using a real-time data acquisition system. Signal processing: Performing necessary preprocessing on the real-time signal, such as amplification and filtering. Feature extraction: Extracting feature vectors from the real-time signal using the same method as step S120.

[0057] Step S150 , based on the real-time bioelectric signal and the corresponding relationship, determining the emotion type of the plant, and then determining the actual situation of the plant.

[0058] Determining emotion type involves using real-time bioelectrical signals and established relationships to determine the plant's current emotional state. This includes inferring the plant's specific environmental conditions, such as water shortages and light exposure, based on the plant's emotion type.

[0059] Specifically, real-time classification: Using the model trained in step S130, the real-time feature vectors are classified to identify the plant's emotion type. Scenario inference: Based on the identified emotion type, the plant's actual situation (e.g., water shortage, insufficient light, etc.) is inferred. Feedback: The analysis results are fed back to the plant maintenance system so that appropriate maintenance measures can be taken.

[0060] Through these detailed implementation steps, the plant emotion visualization system can effectively monitor and analyze the emotional state of plants, providing more scientific and personalized maintenance suggestions for plants.

[0061] In some embodiments, the method further includes: displaying the emotion type or actual situation of the plant.

[0062] In an embodiment of the plant emotion visualization system, displaying a plant's emotion type or actual situation is one of its core functions. Specifically, it includes: Graphical display: Using charts, color coding, or icons to visually display a plant's emotional state on screen. Text description: Providing detailed textual descriptions explaining the plant's current emotion type and actual situation. This plant emotion visualization system presents the plant's emotion type and actual situation to users in an intuitive and easy-to-understand manner, helping them better understand and meet the needs of plants, thereby improving the quality and efficiency of plant care.

[0063] In some embodiments, it also includes: playing the emotion type or actual situation of the plant; conveying this information to the user through audio playback. This function can provide convenience for users with visual impairments or users who want to receive information in different ways, specifically including: Audio description: Through speech synthesis technology, the emotional state of the plant is converted into voice output for the user to listen. The plant emotion visualization system can not only display information visually, but also enhance the user experience through audio playback, making the information transmission more comprehensive and convenient. This multimodal information expression method helps to improve the user's understanding of the plant status, thereby more effectively maintaining the plant.

[0064] In some embodiments, the system further includes: judging the plant's needs or maintenance measures based on the plant's mood type or actual situation. Specifically, data analysis: analyzing the plant's mood type and actual situation to infer the plant's possible needs, such as water, light, fertilizer, etc. Intelligent recommendation: based on the plant's needs, the system automatically recommends appropriate maintenance measures, such as increasing watering frequency, adjusting light exposure time, etc. The specific steps are as follows:

[0065] 1. Emotional type and situation analysis

[0066] Emotion recognition: First, the system identifies the plant's emotion type (such as stress, comfort, water shortage, etc.) by analyzing the plant's bioelectric signals.

[0067] Scenario inference: Based on the emotion type, the system infers the actual situation the plant may be in. For example, if the recognized emotion is "lack of water", the actual situation may be that the soil is too dry.

[0068] 2. Needs Identification

[0069] Demand database: Create a database that contains the correspondence between different emotion types and plant needs. For example, the emotion type "stress" may correspond to the need for "reduced light."

[0070] Intelligent matching: The system retrieves and matches the corresponding plant needs from the database based on the identified emotion type.

[0071] 3. Recommended maintenance measures

[0072] Practice Database: Create a database of various maintenance practices, each associated with a specific plant need.

[0073] Intelligent recommendation: Based on the identified plant needs, the system selects the most appropriate maintenance measures from the database and recommends them to the user.

[0074] 4. Decision Support System

[0075] Decision logic: Develop a decision logic to process the relationship between emotion recognition results and plant needs and generate specific maintenance recommendations.

[0076] User Interface: Present these recommended actions on the user interface so that users can easily understand and implement them.

[0077] 5. Implement maintenance measures

[0078] Operation Instructions: Provides detailed operation steps and instructions to help users correctly implement recommended maintenance measures.

[0079] Automated control: Where possible, the system can be integrated with automated equipment (such as automatic watering systems and lighting control systems) to directly implement recommended maintenance measures.

[0080] 6. Feedback and Optimization

[0081] User feedback: Allow users to provide feedback on the effects of maintenance measures to evaluate the effectiveness of recommended measures.

[0082] System optimization: Based on user feedback and subsequent plant performance, we continuously optimize decision-making logic and recommendation algorithms to improve the accuracy and practicality of the system.

[0083] Technical Implementation

[0084] Machine Learning: Use machine learning algorithms to continuously learn and optimize the matching relationship between mood types and plant needs.

[0085] Database management: Use database technology (such as MySQL, MongoDB) to store data on plant needs and maintenance measures.

[0086] User Interface: Use modern front-end technologies (such as React, Vue.js) to develop intuitive and easy-to-use user interfaces.

[0087] Through these steps, the plant emotion visualization system can intelligently identify the needs of plants and provide corresponding maintenance measures to help users care for plants more scientifically and effectively, thereby improving the growth quality and health status of plants.

[0088] Through these steps, the plant emotion visualization system can intelligently identify the needs of plants and provide corresponding maintenance measures to help users care for plants more scientifically and effectively, thereby improving the growth quality and health status of plants.

[0089] In some embodiments, the method further includes: displaying the needs or maintenance measures of the plant. Playing the needs or maintenance measures of the plant. Specifically, displaying the needs or maintenance measures of the plant includes:

[0090] 1. User Interface Design

[0091] Clear presentation: Design an intuitive user interface that clearly displays the plant's needs and recommended care measures.

[0092] Information classification: Display information by categories, such as "immediate action" and "daily care", to help users distinguish maintenance tasks of different urgency.

[0093] 2. Information presentation

[0094] Text Description: Provide a detailed text description explaining the plant's needs and recommended care measures.

[0095] Graphical aids: Use icons, charts, or other visual aids to help users understand maintenance measures more intuitively.

[0096] 3. Real-time updates

[0097] Dynamic information: Ensure that the user interface can update plant needs and care recommendations in real time to reflect the latest status of the plant.

[0098] 4. Interactive Features

[0099] User interaction: Allow users to interact with the interface, such as marking tasks completed, requesting more information, etc.

[0100] The needs or care measures for broadcast plants include:

[0101] 1. Speech Synthesis Technology

[0102] Text-to-Speech (TTS): Use text-to-speech technology to convert plant needs and care measures into spoken output, making it audible.

[0103] 2. Audio content production

[0104] Recording voice: You can pre-record a series of voice descriptions related to plant care, or generate them in real time using TTS technology.

[0105] Emotional expression: Choose the appropriate tone and speed according to the needs of the plants to better convey the emotional color of the information.

[0106] 3. Audio playback system integration

[0107] Player Control: Integrate an audio player into the system and allow users to control play, pause, and stop.

[0108] Multi-language support: Provides voice options in multiple languages ​​to meet the needs of different users.

[0109] 4.Synchronous display and playback

[0110] Synchronize visuals and sounds: Ensure that when the plant's needs and care measures are displayed on the screen, the relevant audio descriptions are also played simultaneously.

[0111] 5. User experience optimization

[0112] Volume Control: Allows users to adjust playback volume to suit different environments and personal preferences.

[0113] Repeat playback option: Provides an option for users to choose whether to repeat the plant needs and maintenance measures to deepen their understanding.

[0114] Technical Implementation

[0115] Front-end technologies: Use front-end technologies such as HTML5, CSS3, and JavaScript to build the user interface and control audio playback.

[0116] Backend logic: Use Python, Java and other backend technologies to process plant demand data and generate voice output.

[0117] Database management: Use database technology (such as MySQL, MongoDB) to store data on plant needs and maintenance measures.

[0118] Through these embodiments, the plant emotion visualization system can provide users with plant needs and maintenance measures in a variety of ways, which not only enhances the user experience but also improves the efficiency and quality of plant maintenance.

[0119] Based on the above preferred embodiments, Figure 2 , describe the solution provided in this application:

[0120] Plant bioelectricity acquisition: Using signal amplification circuits and specialized bioelectric sensors (such as electrochemical sensors and capacitive sensors), bioelectric signals are collected from various parts of plants, such as leaves, stems, and roots. The weak currents generated by cellular activity during plant growth are bioelectric signals, which can reflect the plant's physiological state and response to environmental changes. Because the signals are weak, they must be amplified by signal amplification circuits before being recorded by a data acquisition system to provide raw data for subsequent analysis.

[0121] Data normalization: The range of values ​​collected for bioelectrical signals varies widely, hindering subsequent analysis. Data normalization maps signal data to a specific interval (e.g., [0, 1]), unifying the data scale and eliminating differences in dimension and value range between different features. This improves model training efficiency and accuracy, allowing different signal features to be compared and analyzed under the same standard.

[0122] Low-pass filtering: Collected bioelectric signals often contain high-frequency noise, which can interfere with signal feature extraction. A low-pass filter allows low-frequency signals to pass through, effectively filtering out high-frequency noise while retaining the low-frequency, effective signal components related to plant emotions. This improves signal quality and lays the foundation for subsequent accurate signal feature extraction.

[0123] Extracting time-domain features: Time-domain features such as amplitude, mean, variance, and peak value are extracted from filtered bioelectric signals. These features reflect the signal's temporal variation. Amplitude reflects signal strength, mean and variance describe the overall signal level and fluctuations, and peak value reveals instantaneous signal changes, providing key information for identifying plant emotions.

[0124] Power spectrum calculation: The power spectrum analyzes the distribution of signal power over frequency, converting bioelectric signals from the time domain to the frequency domain to display the energy distribution of different frequency components. By calculating the power spectrum, we can understand the energy contribution of each frequency component in plant bioelectric signals, discover signal characteristics and patterns hidden in the frequency domain, and provide data support for subsequent frequency domain feature extraction.

[0125] Extracting frequency domain features: Based on the power spectrum calculation results, frequency domain features such as dominant frequency, frequency band energy ratio, and frequency center of gravity are extracted. The dominant frequency represents the frequency where the signal's energy is primarily concentrated, the frequency band energy ratio reflects the relative energy levels of different frequency intervals, and the frequency center of gravity indicates the central position of the signal's frequency distribution. These frequency domain features can further characterize the characteristics of bioelectric signals from a frequency perspective and assist in plant emotion recognition.

[0126] CNN-RNN classifier: Convolutional neural networks (CNNs) excel at extracting spatial features from data, while recurrent neural networks (RNNs) are advantageous for processing time series data. The combined CNN-RNN classifier can fully exploit the spatiotemporal characteristics of bioelectrical signals. The extracted time and frequency domain features are input into the classifier, which learns through training the mapping between different features and plant emotion types. It then identifies the emotion type of the plant's electrical signals, establishes a correspondence between bioelectrical signals and emotion types, and enables classification and recognition of plant emotions.

[0127] Through the above process, the solution can effectively extract features from plant bioelectric signals, use the CNN-RNN classifier to accurately identify plant emotions, determine the actual situation of the plant, provide a scientific basis for plant maintenance, and help users take reasonable maintenance measures in a timely manner to promote healthy plant growth.

[0128] Exemplary devices

[0129] The device embodiments of this application can be used to execute the method embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.

[0130] Figure 3 The figure shows a block diagram of a plant emotion visualization device provided by one embodiment of the present application. Figure 3 As shown, the device includes:

[0131] The acquisition module 31 is used to collect bioelectrical signals from different parts of the plant in different scenarios using a signal amplification circuit;

[0132] An extraction module 32 is used to extract the feature vectors of the bioelectric signals corresponding to different scenarios;

[0133] Building a module for classification based on feature vectors using a reinforcement learning algorithm to identify the emotion type of plant electrical signals and establish a correspondence between bioelectrical signals and emotion types;

[0134] Among them, different emotion types correspond to different situations;

[0135] An acquisition module 33 is used to acquire real-time bioelectric signals from different parts of the plant;

[0136] The determination module 34 is configured to determine the emotion type of the plant based on the real-time bioelectric signal and the corresponding relationship, and further determine the actual situation of the plant.

[0137] Exemplary electronic devices

[0138] Below, reference Figure 4 To describe the electronic device according to the embodiment of the present application. Figure 4 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0139] like Figure 4 As shown, electronic device 400 includes one or more processors 410 and memory 420 .

[0140] The processor 410 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0141] The memory 420 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 410 may execute the program instructions to implement the plant emotion visualization method of each embodiment of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0142] In one example, the electronic device 400 may further include an input device 430 and an output device 440 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0143] In addition, the input device 430 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 440 may output various information to the outside, including analysis results, etc. The output device 440 may include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0144] Of course, to simplify, Figure 4 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0145] Exemplary computer program products and computer-readable storage media

[0146] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for visualizing plant emotions according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0147] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0148] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method for visualizing plant emotions according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0149] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0150] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A systematic method for visualizing plant emotions, characterized in that: include: Using signal amplification circuits, bioelectrical signals from different parts of plants in different scenarios are collected; Extract the feature vectors of bioelectric signals corresponding to different scenarios; Based on the feature vectors, a reinforcement learning algorithm is used for classification to identify the emotion type of plant electrical signals and to establish the corresponding relationship between bioelectrical signals and emotion types. Different emotion types correspond to different situations; Obtain real-time bioelectric signals from different parts of the plant; Based on the real-time bioelectric signal and the corresponding relationship, the emotion type of the plant is determined, and then the actual situation of the plant is determined.

2. The method for visualizing plant emotions according to claim 1, characterized in that: Also includes: Displays the type of emotion or actual situation of the plant in question.

3. The method for visualizing plant emotions according to claim 1, characterized in that: Also includes: Play out the type of emotion or actual situation the plant is experiencing.

4. The method for visualizing plant emotions according to claim 1, characterized in that: Also includes: Based on the mood type or actual situation of the plant, the needs or maintenance measures of the plant are determined.

5. The method for visualizing plant emotions according to claim 4, characterized in that: Also includes: Indicates the plant's needs or maintenance measures.

6. The method for visualizing plant emotions according to claim 4, characterized in that: Also includes: Broadcast the needs or care measures of the plant in question.

7. A device for visualizing plant emotions, characterized in that: include: An acquisition module is used to collect bioelectrical signals from different parts of plants in different scenarios using a signal amplification circuit; Extraction module, used to extract the feature vectors of bioelectric signals corresponding to different scenarios; Building a module for classification based on feature vectors using a reinforcement learning algorithm to identify the emotion type of plant electrical signals and establish a correspondence between bioelectrical signals and emotion types; Among them, different emotion types correspond to different situations; Acquisition module, used to obtain real-time bioelectric signals from different parts of the plant; The determination module is used to determine the emotion type of the plant based on the real-time bioelectric signal and the corresponding relationship, and then determine the actual situation of the plant.

8. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is configured to implement the method for visualizing plant emotions according to any one of claims 1 to 6 by running the program in the memory.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to perform the method for visualizing plant emotions according to any one of claims 1 to 6.

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