Fragrance machine control method, system and device based on brain wave analysis

Abstract

The invention provides a fragrance machine control method, system and device based on brain wave analysis, and the method comprises the steps: generating a feature scene based on the environment information around a user and the behavior information of the user, and determining the first emotion condition of the user based on the first brain wave information of the user; controlling the fragrance machine to release fragrance; determining a second emotional condition of the user based on the second brain wave information after the user smells the fragrance; determining an emotion conversion result based on the first emotion condition and the second emotion condition; and if the emotion conversion result meets the forward conversion, establishing the identity information of the user, the first emotion condition, the fragrance released by the fragrance machine and the current feature scene, and when the user enters the feature scene the same as the current feature scene in the first emotion condition next time, controlling the fragrance machine to release the corresponding fragrance. In the scheme, when the user enters the scene with the same emotion next time, the system automatically triggers the release of the corresponding fragrance, so that the personalized adaptation of the user is improved, and the emotion of the user is improved.

Description

Technical Field

[0001] This application relates to the field of electroencephalogram (EEG) analysis technology, and in particular to a method, system and device for controlling an aromatherapy device based on EEG analysis. Background Technology

[0002] Electroencephalography (EEG) is a technique that records the weak electrical signals generated by the activity of neurons in the brain by placing electrodes on the scalp of the test subject. These electrical signals reflect patterns of brain activity in different states, such as sleep, wakefulness, focus, relaxation, and mood swings. A fragrance diffuser is a device that disperses essential oils or fragrance molecules into the air to improve the smell of a space, create an atmosphere, and promote physical and mental well-being.

[0003] Currently, EEG analysis is mostly used in fields such as neurofeedback, brain-computer interfaces and sleep monitoring, while aromatherapy devices mainly release fragrance based on timed control or sensors. Although EEG analysis technology and aromatherapy systems have each made some progress, a complete solution has not yet been formed for the integration of the two at the level of coordination mechanism and interaction.

[0004] Therefore, how to provide a control method for aromatherapy devices based on electroencephalogram (EEG) analysis is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a method, system, and device for controlling a fragrance diffuser based on electroencephalogram (EEG) analysis. Based on EEG monitoring and emotion transformation analysis, it establishes a correlation between user identity, emotion, fragrance, and scene, as well as a closed-loop feedback mechanism, thereby improving personalized adaptation for users and enhancing their mood.

[0006] The technical solution of this application is as follows: In the first aspect, a method for controlling an aroma diffuser based on electroencephalogram (EEG) analysis is provided, including: The system identifies the user's identity information, obtains the user's surrounding environment information, user behavior information and user's first brainwave information, generates a feature scene based on the environmental information and the behavior information, and determines the user's first emotional state based on the first brainwave information. Control the release of fragrance from the fragrance diffuser; Acquire the user's second brainwave information after smelling the fragrance, and determine the user's second emotional state based on the second brainwave information; The emotional transformation result is determined based on the first emotional state and the second emotional state; If the emotion transformation result satisfies positive transformation, then a connection is established between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current characteristic scene. When the user enters the same characteristic scene with the first emotional state again, the fragrance diffuser is controlled to release the corresponding fragrance.

[0007] Preferably, the method further includes: When the current user is the previous user, and the current time and the time when the previous fragrance release ended meet a preset time interval, the fragrance machine is controlled to release fragrance.

[0008] Preferably, the method further includes: If the emotional transformation result does not meet the positive transformation requirement, the release parameters of the fragrance machine are adjusted based on the second emotional state, wherein the release parameters include fragrance type, release concentration, release flow rate and release frequency; Acquire the user's third brainwave information after smelling the adjusted fragrance, and determine the user's third emotional state based on the third brainwave information; The emotion transformation result is updated based on the second and third emotional states.

[0009] Preferably, the method further includes: Input the first EEG information and / or the second EEG information into a preset emotion classification model, and output the corresponding emotion status.

[0010] Preferably, determining the user's first emotional state based on the first EEG information includes: The first EEG information is preprocessed; Extract from the preprocessed first EEG information Wave, Wave, Waves and The power spectral density of the wave; Based on the above Wave, Wave, Waves and The power spectral density of the wave determines the relative power of each frequency band, the power ratio of different frequency bands, and the asymmetric characteristics of EEG. A multidimensional emotion feature vector is generated based on the relative power, the power ratio, and the EEG asymmetry features. The multidimensional emotion feature vector is input into a preset emotion classification model to output the user's first emotional state.

[0011] Preferably, the generation of feature scenarios based on the environmental information and the behavioral information includes: The system acquires spatial location, light intensity, sound intensity, and temperature and humidity from the environmental information, as well as user body posture and action type from the behavioral information. A scene feature vector is generated based on the spatial location, light intensity, sound intensity, temperature and humidity, user body posture, and action type. The scene feature vector is input into a preset scene classification model to output the corresponding feature scene.

[0012] Preferably, the steps of identifying the user's identity information and obtaining the user's surrounding environment information, user behavior information, and user's first brainwave information include: The system controls the biosensors, environmental sensors, motion sensors, and EEG sensors integrated into the headband worn on the user's head, and simultaneously collects the identity information, environmental information, behavioral information, and the first EEG information.

[0013] Preferably, the control of the fragrance diffuser to release fragrance includes: The first EEG information is collected by the headband, and a control signal is generated based on the first EEG information and sent to the fragrance machine. The fragrance diffuser starts releasing fragrance based on the control signal.

[0014] Preferably, the headband and the fragrance diffuser achieve wireless data transmission via Bluetooth, WIFI, or Zigbee communication protocols.

[0015] Preferably, the method further includes: Based on a preset time difference, the brainwave information of the user before and after smelling the fragrance is continuously acquired, and a brainwave change trend curve is generated. Based on the preset time difference, continuously acquire the user's emotional state before and after smelling the fragrance, and generate an emotional change trend curve; Based on the EEG change trend curve, the peak time point when the EEG reaches its peak value within each time window is obtained by the sliding window averaging method. Based on the emotional change trend curve, the user's emotional state and the type of fragrance the user smelled at the peak time point are obtained; Based on the EEG trend curve, the emotion trend curve, the peak time point when the EEG reaches its peak within each time window, the user's emotional state at the peak time point, and the type of fragrance the user smells, a correlation dataset of fragrance, emotion, and EEG information is established and stored to generate a user insight report.

[0016] Preferably, determining the emotion transformation result based on the first emotional state and the second emotional state includes: Determine the first emotional state identifier corresponding to the first emotional state; Determine the second emotional state identifier corresponding to the second emotional state; Based on the first emotional state identifier and the second emotional state identifier, an emotional transformation pair representing the change in emotional state is generated as the result of the emotional transformation.

[0017] Preferably, establishing the connection between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current characteristic scene includes: Determine the feature scene identifier corresponding to the feature scene; Obtain the type of fragrance currently being released by the fragrance diffuser; Based on the user's identity information, the first emotional state, the characteristic scene identifier, and the type of fragrance, a record of association between user identity, emotion, scene, and fragrance is generated; Store the associated records in the preset mapping database.

[0018] In the second aspect, a fragrance machine control system based on electroencephalogram (EEG) analysis is provided, including: The user identification module is used to identify the user's identity information; The first acquisition module is used to acquire environmental information around the user, behavioral information of the user and the user's first brainwave information, and generate a feature scene based on the environmental information and behavioral information, and determine the user's first emotional state based on the first brainwave information. Fragrance control module, used to control the release of fragrance from the fragrance diffuser; The second acquisition module is used to acquire the second brainwave information of the user after smelling the fragrance, and to determine the user's second emotional state based on the second brainwave information. The result determination module is used to determine the emotion transformation result based on the first emotional state and the second emotional state. The association module is used to establish a connection between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser and the current characteristic scene if the emotion transformation result satisfies the positive transformation. When the user enters the same characteristic scene with the first emotional state again, the module controls the fragrance diffuser to release the corresponding fragrance.

[0019] In the third aspect, a fragrance diffuser control device based on electroencephalogram (EEG) analysis is provided, comprising: Fragrance diffuser, headband and controller; The headband integrates a biosensor, an environmental sensor, a motion sensor, and an EEG sensor. The biosensor is used to identify the user's identity information, the environmental sensor is used to acquire information about the user's surrounding environment, the motion sensor is used to acquire information about the user's behavior, and the EEG sensor is used to acquire the user's first brainwave information. The controller is used to generate a feature scene based on the environmental information and the behavioral information, and to determine the user's first emotional state based on the first EEG information. The controller is also used to control the fragrance diffuser to release fragrance, acquire the second EEG information collected by the EEG sensor after the user smells the fragrance, and determine the user's second emotional state based on the second EEG information. Based on the first emotional state and the second emotional state, an emotion transformation result is determined. If the emotion transformation result satisfies a positive transformation, a connection is established between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current characteristic scene. When the user enters the same characteristic scene with the first emotional state again, the controller controls the fragrance diffuser to release the corresponding fragrance.

[0020] Preferably, the headband is worn on the user's head, the biosensors are fitted to the user's temples on both sides, the environmental sensors are located on the outside of the user's forehead or behind the ears, the motion sensors are located on the top of the user's head, and the EEG sensors are fitted to the back of the user's head and forehead.

[0021] Preferably, the headband and the fragrance diffuser achieve wireless data transmission via Bluetooth, WIFI, or Zigbee communication protocols; And / or, The fragrance diffuser has a spray nozzle equipped with a directional airflow component, which is configured to adjust the release direction of the fragrance according to the user's breathing rhythm.

[0022] In a fourth aspect, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described in the first aspect.

[0023] In a fifth aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0024] According to the specific embodiments provided in this application, the following technical effects are disclosed: The technical solution of this application provides a method, system, and device for controlling a fragrance diffuser based on electroencephalogram (EEG) analysis. The method includes: identifying the user's identity information; acquiring environmental information surrounding the user, the user's behavioral information, and the user's first EEG information; generating a feature scene based on the environmental and behavioral information; determining the user's first emotional state based on the first EEG information; controlling the fragrance diffuser to release fragrance; acquiring the user's second EEG information after smelling the fragrance; determining the user's second emotional state based on the second EEG information; determining an emotion transformation result based on the first and second emotional states; if the emotion transformation result satisfies a positive transformation, establishing a connection between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current feature scene; and controlling the fragrance diffuser to release fragrance corresponding to the feature scene when the user enters the same feature scene with the same first emotional state again. The solution monitors users' brainwaves and analyzes changes in their emotional state before and after fragrance release. If the user's emotions improve, it indicates that the current fragrance matches the user's preferences and emotional needs. It also establishes a connection between user identity, emotions, fragrance, and scene. When the user enters the scene with the same emotion again, the system can automatically trigger the release of the corresponding fragrance, forming a closed-loop feedback mechanism. This improves personalized adaptation for users and has a good effect on improving emotions. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the overall process of the aroma diffuser control method based on electroencephalogram analysis provided in the embodiments of this application; Figure 2 This is a schematic diagram of the method flow when the forward conversion is not satisfied, provided in the embodiments of this application; Figure 3 This is a schematic flowchart of a method for determining a user's first emotional state provided in an embodiment of this application; Figure 4 This is a schematic diagram of the method for generating feature scenes provided in the embodiments of this application; Figure 5 This is a schematic flowchart of a method for obtaining environmental information, behavioral information, and first EEG information provided in an embodiment of this application; Figure 6 This is a schematic flowchart of a method for controlling the release of fragrance from a fragrance diffuser, provided in an embodiment of this application. Figure 7This is a schematic flowchart of the method for determining the result of emotion transformation provided in the embodiments of this application; Figure 8 This is a schematic flowchart of a method for establishing a connection between fragrance and a characteristic scene, provided in an embodiment of this application. Figure 9 This is a schematic diagram of the method flow for generating user insight reports provided in an embodiment of this application; Figure 10 This is a schematic diagram of the modules of the aroma diffuser control system based on electroencephalogram analysis provided in the embodiments of this application; Figure 11 This is a schematic diagram of the computer device provided in the embodiments of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] As described in the background section, EEG analysis is currently used in fields such as neurofeedback, brain-computer interfaces, and sleep monitoring. Fragrance diffusers mainly release fragrance based on timed control or sensors. Although EEG analysis technology and fragrance systems have each made some progress, a complete solution has not yet been formed for the integration of the two at the level of coordination mechanism and interaction.

[0029] Based on this, this application provides a method, system and device for controlling a fragrance diffuser based on electroencephalogram (EEG) analysis, aiming to solve the problem of synergy between EEG analysis and fragrance diffuser control in the prior art.

[0030] The embodiments of this application will be analyzed in detail below with reference to the accompanying drawings.

[0031] Example 1 This embodiment provides a method for controlling an aromatherapy device based on electroencephalogram (EEG) analysis, such as... Figure 1 As shown, the method includes: S1: Identify the user's identity information, obtain the user's surrounding environment information, user behavior information and user's first brainwave information, and generate feature scenes based on the environmental information and behavior information, and determine the user's first emotional state based on the first brainwave information. S2: Controls the release of fragrance from the fragrance diffuser; S3: Obtain the user's second brainwave information after smelling the fragrance, and determine the user's second emotional state based on the second brainwave information; S4: Determine the outcome of emotion transformation based on the first and second emotional states; S5: If the emotional transformation result satisfies the positive transformation, then establish the connection between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser and the current characteristic scene. When the user enters the same characteristic scene with the first emotional state again, control the fragrance diffuser to release the corresponding fragrance.

[0032] In step S1, identity information includes, but is not limited to, the user's digital identity, such as ID account, and biometrics, such as fingerprints / face / iris scans. Environmental information includes, but is not limited to, spatial location, such as living room / bedroom; time, such as morning / night; air quality, such as oxygen concentration / carbon dioxide concentration; light intensity, such as strong light / weak light; sound intensity, such as loud sound / low sound; temperature and humidity, etc. Environmental information can provide basic data for scene classification; for example, a bedroom at night with low light and high humidity may correspond to a sleep scene. Behavioral information includes, but is not limited to, the user's action type, such as sitting down / standing up; body posture, such as bending over / lying down; movement trajectory, such as moving from the bedroom to the living room; and device usage behavior, such as using electronic products. Combining behavioral information with environmental information can generate a more accurate scene description. Featured scenes are high-dimensional feature labels generated by fusing environmental and behavioral information, used to describe the user's current specific situation; for example, a dimly lit bedroom + a user lying down without moving may correspond to a nighttime sleep scene. The first EEG information is the raw EEG information collected by the user through an EEG device such as a headband before the fragrance is released; it is the raw data for determining the user's emotional state. The primary emotional state is derived from the analysis of primary brainwave information, including but not limited to anxiety, focus, and calmness.

[0033] In step S2, the fragrance diffuser can connect to the electroencephalogram (EEG) device via Bluetooth, Wi-Fi, or Zigbee communication protocols to achieve signal transmission and interaction. The fragrance diffuser can then perform fragrance diffusion operations based on the current scene and the user's emotional needs.

[0034] In step S3, the second EEG information is the EEG information collected from the user within a specific time period, such as three minutes, after the fragrance is released, and is used to evaluate the intervention effect of the fragrance. The second emotional state is derived from the analysis of the second EEG information and is used to determine whether the fragrance effectively improves the user's mood. The second emotional state is the same as the first emotional state and includes, but is not limited to, anxiety, focus, and calmness.

[0035] In step S4, the emotion transformation result is the result of quantifying the impact of fragrance intervention on the user's emotions. There are several judgment criteria: first, positive transformation, that is, the emotional state improves in the direction of the goal, such as from anxiety to calmness; second, negative transformation, that is, the emotional state deteriorates in the opposite direction of the goal, such as from calmness to anxiety; third, no significant change, that is, the emotional state does not fluctuate significantly, such as from calmness to calmness.

[0036] In step S5, if the emotion transformation result satisfies positive transformation, it indicates that the current fragrance and the characteristic scene have a high degree of matching, which can be used as a personalized strategy for the user. Therefore, the user's identity, emotion, and specific fragrance such as lavender are bound to the characteristic scene such as low light and high humidity in the bedroom, forming a four-way mapping of the user's identity, emotion, fragrance, and scene. When the user is in the same scene composed of the same environmental and behavioral information again, and the current emotional state is consistent with the first emotional state, the preset fragrance release strategy is automatically executed according to the four-way mapping to improve the user's mood.

[0037] It should be noted that emotions include, but are not limited to, anger, sadness, fear, happiness, love, surprise, disgust, shame, anxiety, focus, fatigue, loneliness, excitement, and depression; fragrances include, but are not limited to, lavender, chamomile, mint, rosemary, rose, vanilla, caramel, lemon, eucalyptus, bergamot, grapefruit, and sweet orange; emotions and fragrances are not specifically defined here, and the emotions a user experiences after smelling a fragrance vary from person to person, so no specific definition is made here.

[0038] As a preferred implementation, the method further includes: controlling the fragrance machine to release fragrance when the current user is the previous user, the current characteristic scene is the same as the previous one, and the current time and the time when the previous fragrance release ended meet a preset time interval.

[0039] As an example, to ensure the accuracy of the correlation between EEG information and emotions, this embodiment first randomly selects one fragrance from a variety of candidate fragrances and presents it to the user. Then, it records the user's EEG information for varying durations during the fragrance inhalation process. Furthermore, a preset time interval is set between two adjacent fragrance tests; this preset time interval is the washout period, used to reduce the residual influence of the previous fragrance scent, avoid cross-interference, and ensure the purity of the corresponding EEG information for each fragrance stimulus. The preset time interval can be set to 1 minute, 1.5 minutes, 2 minutes, 2.5 minutes, or 3 minutes. Before conducting the next test, it must be confirmed that the user is the same person and the environment is consistent.

[0040] As an example of a daytime driving scenario, a user (account ID: User_001) drives onto a highway. The system obtains the user's specific environment as moderate light (600 lux), temperature (28°C), and carbon dioxide concentration (600 ppm). The system detects the user's behavior as frequently changing lanes and detects signal frequency bands in the first EEG information collected from the headband, such as... Wave power information, The power of the wave decreased, indicating the user's current emotional state was anxiety. One and a half minutes later, the system controlled the aromatherapy device to release lavender fragrance at a concentration of 30% at a frequency of once per minute. The system continuously monitored the user's second EEG information over the next 10 minutes, and analysis revealed that the user's second EEG information occurred at the 3-minute mark. Once the wave power returns to the baseline value and the user's emotional state becomes calm, the best fragrance for this scenario is determined to be lavender, with the optimal release parameters being a concentration of 30% and a release frequency of 1 time per minute. A mapping relationship is then established between "user digital identity + anxiety + lavender fragrance + daytime driving scenario".

[0041] As an example of a nighttime driving scenario, a user (fingerprint ID: Finger_002) drives into a tunnel at midnight. The system obtains the user's specific environment as low light intensity (50 lux), temperature (22°C), and humidity (70%). It detects the user's behavior as driving continuously for more than 2 hours with reduced steering wheel operation frequency. The system also detects signal frequency bands in the first EEG information collected from the headband, such as... Wave power information, The increased power of the brainwave indicates the user's current emotion is fatigue. One minute later, the system controls the aromatherapy device to release a 50% concentration of mint fragrance at a frequency of twice per minute. The system continuously monitors the user's second EEG information over the next 10 minutes, and analysis reveals that the user's second EEG information at the 5-minute mark... Once the wave power returns to the baseline value and the user's emotional state returns to alertness, the optimal fragrance for this scenario is determined to be mint, with the optimal release parameters being a concentration of 50% and a release frequency of 2 times per minute. A mapping relationship is then established between "user biological information + fatigue + mint fragrance + nighttime driving scenario".

[0042] As an example in an office work scenario, a user (Face ID: Face_003) enters the office area at 9:00 AM. The system obtains the user's specific environment as follows: high light intensity 800 lux, temperature 24℃, and background noise 65dB. The system detects the user's behavior as handling multiple tasks and detects signal frequency bands in the first EEG information collected based on the headband, such as... Wave power information, The power of the wave decreased, indicating the user's current emotion was irritability. One minute later, the system controlled the aromatherapy device to release rosemary fragrance at a concentration of 20% at a frequency of 0.5 times per minute. The system continuously monitored the user's second EEG information over the next 10 minutes, and analysis revealed that the user's second EEG information was... Once the wave returns to the baseline value and the user's emotional state shifts to focus, the best fragrance for this scenario is determined to be rosemary, with the optimal release parameters being a concentration of 20% and a release frequency of 0.5 times per minute. A mapping relationship is then established between "user bio-information + irritability + rosemary fragrance + office work scenario".

[0043] In summary, the solution monitors users' brainwaves and analyzes changes in their emotional state before and after fragrance release. If the user's emotional state changes well, it indicates that the current fragrance matches the user's preferences and emotional needs. At the same time, it establishes a connection between user identity, emotions, fragrance, and scene. When the user enters the scene with the same emotion again, the system can automatically trigger the release of the corresponding fragrance, forming a closed-loop feedback mechanism. This improves the personalized adaptation for users and has a good effect on improving emotions.

[0044] As a preferred implementation, such as Figure 2 As shown, the method also includes: S6: If the emotional transformation result does not meet the positive transformation requirement, the release parameters of the fragrance machine are adjusted based on the second emotional state. The release parameters include fragrance type, release concentration, release flow rate and release frequency. S7: Obtain the user's third brainwave information after smelling the adjusted fragrance, and determine the user's third emotional state based on the third brainwave information; S8: Update the emotion transformation results based on the second and third emotional states.

[0045] In step S6, if the user's emotional state does not meet the positive transformation requirements after fragrance intervention, the system is triggered to enter optimization mode. This mode adjusts the release parameters of the fragrance machine, such as changing or mixing different fragrances to match the user's emotional needs, adjusting the intensity of fragrance diffusion to increase or decrease the fragrance concentration, adjusting the fragrance release flow rate per unit time to increase or decrease the spatial coverage of the fragrance, and adjusting the periodic frequency of fragrance release to adapt to the user's physiological rhythm.

[0046] In step S7, the third EEG information is the EEG information collected again by the user under the adjusted fragrance. It is important to note that the third EEG information should be collected again after a stabilization period, such as five minutes, following the release of the adjusted fragrance to eliminate transient interference. The third emotional state is derived from the analysis of the third EEG information and is used to verify whether the adjusted fragrance can effectively improve the user's mood.

[0047] In step S8, the emotion transformation result is updated by reassessing the adjusted intervention effect as positive transformation, negative transformation, or no significant change.

[0048] As an example, if the updated mood transformation result is a positive transformation, it indicates that the adjusted fragrance has a high degree of matching with the characteristic scene, and can be used as a personalized strategy for the user. Therefore, the user's identity, mood, fragrance, and characteristic scene are bound together to form a four-way mapping of the user's identity, mood, fragrance, and scene. When the user is in a scene composed of the same environmental and behavioral information again, and the current emotional state is consistent with the second emotional state, the preset fragrance release strategy is automatically executed according to the four-way mapping to improve the user's mood.

[0049] As an example, if the updated emotion conversion result is a negative conversion, the release parameters of the fragrance machine are adjusted based on the third emotion state, the fourth brainwave information of the user after smelling the adjusted fragrance is obtained, and the user's fourth emotion state is determined based on the fourth brainwave information. The emotion conversion result is then updated again based on the fourth emotion state and the third emotion state.

[0050] It should be noted that if the automatic adjustment fails multiple times, such as after three adjustments, and still cannot match a fragrance that can positively transform the user's emotions, the user can actively intervene and select the fragrance they want.

[0051] As an example, if the updated emotional transformation result shows no significant change, such as the user's emotions tending to calm down, the fragrance diffuser can be controlled to adjust the fragrance release concentration. For example, reducing the concentration can reduce excessive stimulation to the user, avoid olfactory fatigue, and maintain the comfort of the environment. Increasing the concentration can enhance the perception depth of the fragrance and stimulate more subtle emotional regulation effects.

[0052] In summary, by iteratively optimizing and finding effective fragrance solutions, we can ensure that effective mood regulation support is continuously provided in various scenarios to improve users' moods.

[0053] As a preferred embodiment, the method further includes: inputting first EEG information and / or second EEG information into a preset emotion classification model, and outputting the corresponding emotion status.

[0054] As an example, the preset emotion classification model is a support vector machine classification model, which is used to analyze brainwave signals to classify user emotions. The emotions classified by this model can be further subdivided into pleasure, excitement, anxiety relief, flow, alertness, mood change, sleep aid, and tranquility.

[0055] As a preferred implementation, such as Figure 3 As shown, determining a user's first emotional state based on first EEG information includes: S10A: Preprocessing the first EEG information; S11A: Extracting from preprocessed first EEG information Wave, Wave, Waves and The power spectral density of the wave; S12A: Based on Wave, Wave, Waves and The power spectral density of the wave determines the relative power of each frequency band, the power ratio of different frequency bands, and the asymmetric characteristics of EEG. S13A: Generating multidimensional emotion feature vectors based on relative power, power ratio, and EEG asymmetry features; S14A: Input the multidimensional emotion feature vector into the preset emotion classification model to output the user's first emotional state.

[0056] In step S10A, preprocessing includes, but is not limited to, signal amplification and filtering, noise and artifact removal, data segmentation and correction, feature extraction and data optimization.

[0057] In step S11A, The wave frequency band is 8 to 13 Hz, which is mostly related to the user's relaxed state and closed eyes. The frequency band of the wave is 13 to 30 Hz, which is mostly related to the user's active thinking and anxiety. The wave frequency band is 4 to 7 Hz, which is mostly associated with drowsiness and meditation in users; The frequency band of the wave is 30 to 100 Hz, and it is mostly related to the user's cognitive processing and attention concentration. Power spectral density is used to describe the energy distribution of a signal in different frequency bands and is a key indicator in frequency analysis. As an example, Increased wave power can reflect relaxation. Excessively high blood pressure can reflect anxiety.

[0058] In step S12A, relative power refers to the proportion of power in a single frequency band to the total power, and power ratio refers to the ratio of power between frequency bands, such as... The ratio, or EEG asymmetry, refers to the difference in power across corresponding frequency bands between the left and right hemispheres, such as the difference in power between the left and right hemispheres. A higher wave power than the right hemisphere can reflect a user's positive emotions.

[0059] In step S13A, the multidimensional emotion feature vector is used to characterize the user's multidimensional emotional state. By integrating the scattered frequency domain indicators into a unified feature vector, it is easier for the system's analysis model to process.

[0060] In step S14A, the emotion classification model is trained on a dataset of EEG information labeled with emotional states using a machine learning or deep learning algorithm.

[0061] As a preferred implementation, determining the user's second emotional state based on second EEG information includes: Preprocessing of the second EEG information; Extract from preprocessed second EEG information Wave, Wave, Waves and The power spectral density of the wave; based on Wave, Wave, Waves and The power spectral density of the wave determines the relative power of each frequency band, the power ratio of different frequency bands, and the asymmetric characteristics of EEG. A multidimensional emotion feature vector is generated based on relative power, power ratio, and EEG asymmetry features. Input the multidimensional emotion feature vector into the preset emotion classification model to output the user's second emotion state.

[0062] The method for determining a user's second emotional state is the same as the method for determining a user's first emotional state, and will not be repeated here.

[0063] As a preferred implementation, such as Figure 4 As shown, the scenarios for generating features based on environmental and behavioral information include: S10B: Acquires spatial location, light intensity, sound intensity, temperature and humidity from environmental information, as well as user body posture and action type from behavioral information; S11B: Generates scene feature vectors based on spatial location, light intensity, sound intensity, temperature and humidity, user body posture and action type; S12B: Input the scene feature vector into the preset scene classification model to output the corresponding feature scene.

[0064] In step S10B, spatial location reflects the user's coordinates or area in physical space; light intensity reflects the light intensity value in the environment; sound intensity reflects the noise level in decibels in the environment; temperature and humidity reflect the temperature and relative humidity of the user's environment. The user's body posture refers to the state of limb movement, such as standing up, sitting, or bending over, and the action type refers to the specific action performed by the user, such as walking, running, or waving.

[0065] In step S11B, the scene feature vector is used to characterize the comprehensive features of the scene, which is achieved by encoding environmental information and behavioral information into a high-dimensional vector.

[0066] In step S12B, the preset scene classification model is a pre-trained machine learning or deep learning model, which is used to map feature vectors to specific scene categories.

[0067] In summary, this process achieves the recognition of complex scenes through a chain of data collection of environment and behavior, generation of feature vectors, and classification of scenes.

[0068] As a preferred implementation, such as Figure 5 As shown, identifying the user's identity information and obtaining information about the user's surrounding environment, user behavior, and the user's initial brainwave information includes: S10C: Controls the biosensors, environmental sensors, motion sensors, and EEG sensors integrated into the headband worn on the user's head, simultaneously collecting identity information, environmental information, behavioral information, and first EEG information.

[0069] In step S10C, the headband is a wearable device integrating multiple sensors, worn on the user's head, and collects the user's identity information, environmental information, behavioral information, and brainwave information through sensing technology. Biosensors are used to collect the user's digital identity and biometrics. Digital identity includes an ID account, which the user can register via mobile phone number or email address and bind to the headband device. Biometrics include fingerprint recognition and facial recognition; the biosensors can acquire the user's fingerprint through a built-in fingerprint sensor and the user's facial contours through a built-in infrared camera. Environmental sensors are used to collect environmental information such as spatial location, light intensity, sound intensity, temperature, and humidity. Specific environmental sensors include a spatial location sensor with a Bluetooth beacon, a light sensor with a photosensitive element, a sound intensity sensor with a microphone array, and a capacitive temperature and humidity sensor. Motion sensors are used to collect the user's body posture and movement type, specifically including an inertial measurement sensor integrating a three-axis accelerometer, gyroscope, and magnetometer, and a flexible pressure sensor embedded inside the headband. The brainwave sensor is used to collect the user's brainwave information in real time, can connect to the user's mobile app, and supports data upload and analysis report generation.

[0070] As a preferred implementation, such as Figure 6 As shown, controlling the release of fragrance from the fragrance diffuser includes: S20: Collects the first EEG information through the headband, and generates a control signal based on the first EEG information and sends it to the fragrance machine; S21: The fragrance diffuser starts releasing fragrance based on a control signal.

[0071] As an example, the headband detects when the user feels stressed. The system generates a control signal and sends it to the fragrance diffuser, which then randomly releases essential oils. The fragrance is diffused through atomization technology to help relieve user anxiety.

[0072] As a preferred implementation, the headband and the fragrance diffuser achieve wireless data transmission via Bluetooth, Wi-Fi, or Zigbee communication protocols.

[0073] Bluetooth offers low data transmission latency, making it suitable for real-time interaction between headbands and fragrance diffusers. Wi-Fi boasts high transmission speeds but slightly higher latency than Bluetooth, making it suitable for scenarios with less stringent real-time requirements, such as the current interaction between headbands and fragrance diffusers. Zigbee, based on the IEEE 802.15.4 standard, is suitable for communication in smart home scenarios.

[0074] As a preferred implementation, such as Figure 7 As shown, determining the emotion transformation outcome based on the first and second emotional states includes: S40: Determine the first emotional state identifier corresponding to the first emotional state; S41: Determine the second emotional state identifier corresponding to the second emotional state; S42: Generate emotion transformation pairs representing changes in emotional state based on the first emotion state identifier and the second emotion state identifier, as the result of emotion transformation.

[0075] In steps S40 and S41, the emotional state identifier maps the emotional state into an operable label or code, which facilitates subsequent algorithm processing.

[0076] In step S42, the emotion transformation pair is an ordered pair consisting of two emotion labels, used to characterize the dynamic change process of emotional state, so as to facilitate the system to guide subsequent aromatherapy intervention strategies.

[0077] As a preferred implementation, such as Figure 8 As shown, establishing the connection between the user's identity information, initial emotional state, the fragrance released by the aroma diffuser, and the current characteristic scene includes: S50: Determine the feature scene identifier corresponding to the feature scene; S51: Obtain the type of fragrance currently being released by the fragrance diffuser; S52: Generate a record of the association between user identity, emotion, scene and fragrance based on user identity information, primary emotional state, characteristic scene identifier and fragrance type; S53: Store the associated records in the preset mapping database.

[0078] In step S50, the feature scene identifier is a standardized label formed by scene mapping, which facilitates the system's recognition and processing.

[0079] In steps S51 and S52, key-value pairs composed of feature scene identifiers and fragrance types are used to represent the recommended fragrance scheme in a specific scene. Then, the user's identity and emotions are judged to realize the association of the four.

[0080] In step S53, the preset mapping database contains fields used to store association management and evaluation indicators. Through the storage of the database, the system can retain user preferences for a long time, and it is also convenient to quickly retrieve the association information of user identity, mood, fragrance and scene in the future.

[0081] As a preferred implementation, such as Figure 9 As shown, the method also includes: S9: Based on a preset time difference, continuously acquire the user's brainwave information before and after smelling the fragrance, and generate a brainwave change trend curve. S10: Based on a preset time difference, continuously acquire the user's emotional state before and after smelling the fragrance, and generate an emotional change trend curve; S11: Based on the EEG change trend curve, the peak time point when the EEG reaches its peak value within each time window is obtained by the sliding window averaging method. S12: Based on the emotional change trend curve, obtain the user's emotional state and the type of fragrance the user smelled at the peak time point; S13: Based on the EEG change trend curve, the emotion change trend curve, the peak time point when the EEG reaches its peak within each time window, the user's emotional state at the peak time point, and the type of fragrance the user smells, establish and store the associated dataset of fragrance, emotion, and EEG information, and generate a user insight report.

[0082] In steps S9 and S10, the EEG trend curve can intuitively reflect the real-time impact of fragrance on brain activity, and the mood trend curve can intuitively reflect the dynamic process of mood improvement.

[0083] In step S11, the sliding window averaging method reduces noise interference, enabling accurate identification of EEG peaks. EEG peaks reflect significant changes in the user's brain activity, specifically turning points in emotional states, such as... Peak values ​​may correspond to a user entering a relaxed state. Peak wave values ​​reflect whether a user is entering a state of anxiety or focus; the peak time of brainwave peak reflects the golden window for the effect of aromatherapy intervention, such as lavender aromatherapy at the 5th minute. When the wave reaches its peak, it indicates that the user's emotional state has changed from anxiety to calm.

[0084] In step S12, the peak value of the brainwave is associated with the specific emotional state and the type of fragrance to help establish a connection between the fragrance effect and the user's response.

[0085] In step S13, the content of the user insight report, such as the EEG trend curve, mood trend curve, EEG peak values ​​and corresponding peak times, and fragrance type, can be presented in the form of visual charts via a mobile app. The EEG trend curve reflects the dynamic changes in EEG information before and after fragrance release, such as... Wave, Wave power can be displayed in the form of a line graph; the emotional change trend curve reflects the evolution of the user's emotional state over time, such as from anxiety to calm, and can be displayed in the form of a bar chart; peak time points can be highlighted on the graph; the association between fragrance and emotion can be displayed in a card list, showing the "time-fragrance-emotion" triple.

[0086] As an example, when a user opens the app and views the user insight report, they see a trend curve of brainwave changes. Wave activity significantly increased before the fragrance was released, and the mood change trend curve showed the user transitioning from fatigue to focus, clicking at the 3-minute mark. At the peak of the wave, the app pop-up window displays "Rosemary fragrance is released at this time, concentration 20%". "Waves rise by 30%, mood improves to focus."

[0087] Example 2 This second embodiment provides a fragrance machine control system based on electroencephalogram (EEG) analysis, such as... Figure 10 As shown, the system includes: The user identification module is used to identify the user's identity information; The first acquisition module is used to acquire environmental information around the user, behavioral information of the user and the user's first brainwave information, and generate a feature scene based on the environmental information and behavioral information, and determine the user's first emotional state based on the first brainwave information. Fragrance control module, used to control the release of fragrance from the fragrance diffuser; The second acquisition module is used to acquire the second brainwave information of the user after smelling the fragrance, and to determine the user's second emotional state based on the second brainwave information. The result determination module is used to determine the emotion transformation result based on the first emotional state and the second emotional state. The association module is used to establish a connection between the user's identity information, first emotional state, fragrance released by the fragrance diffuser and the current characteristic scene if the emotion transformation result meets the positive transformation. When the user enters the same characteristic scene with the first emotional state again, the fragrance diffuser will be controlled to release the corresponding fragrance.

[0088] By monitoring users' brainwaves, the system analyzes changes in users' emotional state before and after fragrance release. If the user's emotional transformation is positive, it indicates that the current fragrance matches the user's preferences and emotional needs. At the same time, it establishes a connection between user identity, emotions, fragrance, and scene. When the user enters the scene with the same emotion again, the system can automatically trigger the release of the corresponding fragrance, forming a closed-loop feedback mechanism. This improves the personalized adaptation for users and has a good effect on improving emotions.

[0089] Example 3 This embodiment provides a fragrance diffuser control device based on electroencephalogram (EEG) analysis, comprising: a fragrance diffuser, a headband, and a controller; the headband integrates a biosensor, an environmental sensor, a motion sensor, and an EEG sensor. The biosensor is used to identify the user's identity information, the environmental sensor is used to acquire environmental information around the user, the motion sensor is used to acquire the user's behavioral information, and the EEG sensor is used to acquire the user's first EEG information; the controller is used to generate a feature scene based on the environmental and behavioral information, and to determine the user's first emotional state based on the first EEG information; the controller is also used to control the fragrance diffuser to release fragrance, acquire the second EEG information collected by the EEG sensor after the user smells the fragrance, and determine the user's second emotional state based on the second EEG information, and determine the emotion transformation result based on the first and second emotional states. If the emotion transformation result satisfies positive transformation, a connection is established between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current feature scene. When the user enters the same feature scene with the first emotional state again, the fragrance diffuser is controlled to release the corresponding fragrance.

[0090] As an example, a biosensor integrates a fingerprint sensor and an infrared camera sensor. After wearing the headband, the user can touch the fingerprint sensor with their finger to collect their fingerprint, while the infrared camera sensor can capture the user's facial features after the headband is fully worn. Environmental sensors can be composite environmental sensors to measure the user's environment, such as temperature, humidity, atmospheric pressure, and light intensity. Motion sensors can be six-axis sensors to detect the user's movements, such as posture and movement trajectory. An electroencephalogram (EEG) sensor is used to collect the user's... Wave, Brainwave information, such as EEG waves. The specific parameters or information collected by each sensor have been described in detail in the aforementioned Example 1, and will not be repeated here.

[0091] In a preferred embodiment, the headband is worn on the user's head, the biosensors are positioned on the user's temples, the environmental sensors are located on the outer side of the user's forehead or the outer side of the ears, the motion sensors are located on the top of the user's head, and the EEG sensors adopt a single-electrode design with the electrodes positioned on the back of the user's head and forehead.

[0092] The biosensor integrates a fingerprint sensor and an infrared camera sensor. The fingerprint sensor is placed on the temple with the fingerprint detection side facing outwards for easy touch by the user. The infrared camera sensor is located above the fingerprint sensor and close to the temple near the face. It also uses a wide-angle lens to expand the field of view so that it can capture the user's facial and iris information at the same time.

[0093] In a preferred embodiment, the diffuser's spray nozzle is equipped with a directional airflow assembly configured to adjust the direction of fragrance release according to the user's breathing rhythm.

[0094] Among them, the directional airflow component can be set to match the user's breathing cycle, preventing the fragrance from spreading to unwanted areas, reducing waste, and avoiding irritation to the user's eyes and skin.

[0095] As a preferred implementation, the headband and the fragrance diffuser achieve wireless data transmission via Bluetooth, Wi-Fi, or Zigbee communication protocols.

[0096] As an example, after working in the office for an extended period, a user (fingerprint ID: Finger_007) wears a headband and completes authentication via a fingerprint sensor integrated with a biometric sensor; an environmental sensor detects environmental information, determining the office temperature to be 26°C, humidity to be 50%, and air pressure to be 1013 hPa; a motion sensor detects that the user has been sitting still for one hour without significant limb movement, marking it as prolonged sitting; and an EEG sensor detects... Wave power increases, The controller determined that the user was experiencing work-related anxiety upon hearing the decrease in wave power. The controller then activated the aromatherapy diffuser to release lavender fragrance at a concentration of 30% at a frequency of once per minute. After 3 minutes, the EEG sensor detected the user's... Waves and Once the wave returns to the baseline value and the emotion transforms into calm, the controller records and establishes a mapping relationship between the user's identity (fingerprint), initial emotion (anxiety), fragrance (lavender), and scene (sitting in a warm office for a long time). When the user enters the same scene again and is in the same anxious state, the fragrance machine automatically releases lavender fragrance at a concentration of 30% and a release frequency of once per minute, without requiring manual intervention from the user.

[0097] Example 4 Embodiment 4 of this application provides a computer device, including a memory and a processor; the memory stores a computer program that can run on the processor, and when the computer program is executed by the processor, it executes the fragrance machine control method based on electroencephalogram analysis provided in Embodiment 1 above.

[0098] Among them, such as Figure 11As shown, an exemplary computer device of this embodiment is illustrated, which may specifically include a processor 1510, a video display adapter 1511, a disk drive 1512, an input / output interface 1513, a network interface 1514, and a memory 1520. The processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, and memory 1520 can be communicatively connected via a communication bus 1530.

[0099] The processor 1510 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solution provided in this application.

[0100] The memory 1520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1520 can store the operating system 1521 for controlling the operation of the computer device, and the basic input / output system 1522 for controlling the low-level operations of the computer device. Additionally, it can store a web browser 1523, a data storage management system 1524, and a device identification information processing system 1525, etc. The aforementioned device identification information processing system 1525 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 1520 and is called and executed by the processor 1510.

[0101] Input / output interface 1513 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0102] Network interface 1514 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0103] The communication bus 1530 includes a pathway for transmitting information between various components of the device, such as processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, and memory 1520.

[0104] In addition, the device can also obtain information on specific claim conditions from the virtual resource object claim condition information database for condition judgment, and so on.

[0105] It should be noted that although the above-described device only shows the processor 1510, video display adapter 1511, disk drive 1512, input / output interface 1513, network interface 1514, memory 1520, communication bus 1530, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0106] Example 5 Embodiment 5 of this application provides a computer-readable storage medium storing a computer program. When the computer program is executed, it implements the aroma diffuser control method based on electroencephalogram analysis provided in Embodiment 1 above.

[0107] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of the embodiments of this application.

[0108] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for controlling an aroma diffuser based on electroencephalogram (EEG) analysis, characterized in that, The method includes: The system identifies the user's identity information, obtains the user's surrounding environment information, user behavior information and user's first brainwave information, generates a feature scene based on the environmental information and the behavior information, and determines the user's first emotional state based on the first brainwave information. Control the release of fragrance from the fragrance diffuser; Acquire the user's second brainwave information after smelling the fragrance, and determine the user's second emotional state based on the second brainwave information; The emotional transformation result is determined based on the first emotional state and the second emotional state; If the emotion transformation result satisfies positive transformation, then a connection is established between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current characteristic scene. When the user enters the same characteristic scene with the first emotional state again, the fragrance diffuser is controlled to release the corresponding fragrance.

2. The aroma diffuser control method based on electroencephalogram (EEG) analysis according to claim 1, characterized in that, The method further includes: When the current user is the same as the previous user, the current characteristic scene is the same as the previous one, and the current time and the time when the previous fragrance release ended meet the preset time interval, the fragrance machine is controlled to release fragrance.

3. The aroma diffuser control method based on electroencephalogram (EEG) analysis according to claim 1, characterized in that, The method further includes: If the emotional transformation result does not meet the positive transformation requirement, the release parameters of the fragrance machine are adjusted based on the second emotional state, wherein the release parameters include fragrance type, release concentration, release flow rate and release frequency; Acquire the user's third brainwave information after smelling the adjusted fragrance, and determine the user's third emotional state based on the third brainwave information; The emotion transformation result is updated based on the second and third emotional states.

4. The aroma diffuser control method based on electroencephalogram (EEG) analysis according to claim 1, characterized in that, The method further includes: Input the first EEG information and / or the second EEG information into a preset emotion classification model, and output the corresponding emotion status.

5. The aroma diffuser control method based on electroencephalogram (EEG) analysis according to claim 1, characterized in that, Determining the user's first emotional state based on the first EEG information includes: The first EEG information is preprocessed; Extract from the preprocessed first EEG information Wave, Wave, Waves and The power spectral density of the wave; Based on the above Wave, Wave, Waves and The power spectral density of the wave determines the relative power of each frequency band, the power ratio of different frequency bands, and the asymmetric characteristics of EEG. A multidimensional emotion feature vector is generated based on the relative power, the power ratio, and the EEG asymmetry features. The multidimensional emotion feature vector is input into a preset emotion classification model to output the user's first emotional state.

6. The aroma diffuser control method based on electroencephalogram (EEG) analysis according to claim 1, characterized in that, The feature-generating scenarios based on the environmental information and the behavioral information include: The system acquires spatial location, light intensity, sound intensity, and temperature and humidity from the environmental information, as well as user body posture and action type from the behavioral information. A scene feature vector is generated based on the spatial location, light intensity, sound intensity, temperature and humidity, user body posture, and action type. The scene feature vector is input into a preset scene classification model to output the corresponding feature scene.

7. The aroma diffuser control method based on electroencephalogram (EEG) analysis according to claim 1, characterized in that, The method further includes: Based on a preset time difference, the brainwave information of the user before and after smelling the fragrance is continuously acquired, and a brainwave change trend curve is generated. Based on the preset time difference, continuously acquire the user's emotional state before and after smelling the fragrance, and generate an emotional change trend curve; Based on the EEG change trend curve, the peak time point when the EEG reaches its peak value within each time window is obtained by the sliding window averaging method. Based on the emotional change trend curve, the user's emotional state and the type of fragrance the user smelled at the peak time point are obtained; Based on the EEG trend curve, the emotion trend curve, the peak time point when the EEG reaches its peak within each time window, the user's emotional state at the peak time point, and the type of fragrance the user smells, a correlation dataset of fragrance, emotion, and EEG information is established and stored.

8. A fragrance diffuser control system based on electroencephalogram (EEG) analysis, characterized in that, The system includes: The user identification module is used to identify the user's identity information; The first acquisition module is used to acquire environmental information around the user, behavioral information of the user and the user's first brainwave information, and generate a feature scene based on the environmental information and behavioral information, and determine the user's first emotional state based on the first brainwave information. Fragrance control module, used to control the release of fragrance from the fragrance diffuser; The second acquisition module is used to acquire the second brainwave information of the user after smelling the fragrance, and to determine the user's second emotional state based on the second brainwave information. The result determination module is used to determine the emotion transformation result based on the first emotional state and the second emotional state. The association module is used to establish a connection between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser and the current characteristic scene if the emotion transformation result satisfies the positive transformation. When the user enters the same characteristic scene with the first emotional state again, the module controls the fragrance diffuser to release the corresponding fragrance.

9. A fragrance diffuser control device based on electroencephalogram (EEG) analysis, characterized in that, The device includes: Fragrance diffuser, headband and controller; The headband integrates an EEG sensor for acquiring the user's first EEG information. The controller is used to generate a feature scene based on the user's surrounding environmental information and the user's behavioral information, and to determine the user's first emotional state based on the first EEG information. The controller is also used to control the fragrance diffuser to release fragrance, acquire the second EEG information collected by the EEG sensor after the user smells the fragrance, and determine the user's second emotional state based on the second EEG information. Based on the first emotional state and the second emotional state, an emotion transformation result is determined. If the emotion transformation result satisfies a positive transformation, a connection is established between the user's identity information, the first emotional state, the fragrance released by the fragrance diffuser, and the current characteristic scene. When the user enters the same characteristic scene with the first emotional state again, the controller controls the fragrance diffuser to release the corresponding fragrance.

10. The aroma diffuser control device based on electroencephalogram (EEG) analysis according to claim 9, characterized in that, The headband and the fragrance diffuser achieve wireless data transmission via Bluetooth, WIFI, or Zigbee communication protocols. And / or, The fragrance diffuser has a spray nozzle equipped with a directional airflow component, which is configured to adjust the release direction of the fragrance according to the user's breathing rhythm.

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