Baijiu after-drinking slightly-smoked state evaluation method and device based on brain wave analysis and quantification

Abstract

The invention provides a method and a device for evaluating the slightly-swelled state of white spirit after drinking based on brain wave analysis and quantification. The method comprises the following steps: S101, collecting brain wave signals of a subject before and after drinking the white spirit; s103, carrying out preprocessing on the collected data; s105, analyzing brain wave feature change conditions; and S107, constructing a slight smoking state quantitative evaluation model based on brain wave characteristics, and outputting a quantified slight smoking state score or grade. According to the method, the brain wave analysis technology is applied to quantitative evaluation of the slightly-swelled state of the white spirit after drinking, a slightly-swelled state quantitative evaluation model based on brain wave characteristics is established, the dizziness degree of a consumer after drinking is evaluated through a quantifiable scientific method, and the method has the advantages of being non-invasive, real-time, convenient and the like, and is suitable for popularization and application. Scientific basis and technical support are provided for white spirit tasting, a new research direction and application field are opened up for cross fusion of neuroscience and the wine industry, and the method is suitable for large-scale population research and personalized service requirements.

Description

Technical Field

[0001] The present invention relates to the field of neuroscience and wine tasting technology, and in particular to a method and device for evaluating a tipsy state after drinking liquor based on quantification of brain wave analysis. Background Art

[0002] Feeling dizzy after drinking is a common bodily reaction to alcohol. This dizziness is primarily due to the effects of alcohol on multiple systems in the human body, particularly the nervous and circulatory systems. Once alcohol enters the body, it is rapidly distributed throughout the body, including the brain, via the bloodstream. Alcohol inhibits nerve cells in the brain, reducing their excitability and thus affecting the brain's normal function. This effect is not limited to the cerebral cortex but may also extend to areas of the brain responsible for balance and coordination, such as the cerebellum, leading to dizziness. Baijiu tasting is not only about taste and flavor, but also involves the subtle changes in physical and mental state after drinking. Traditional tasting methods rely heavily on sensory evaluation and subjective description, making it difficult to accurately quantify the state of intoxication.

[0003] A liquor designed to reduce alcohol dependence (CN113186067A) discloses the use of a mouse intoxication test and a human scale to evaluate the dizziness level after drinking the liquor. The results showed that the mice experienced significantly faster dizziness after drinking, while their sobering rate was largely unaffected. However, mice and humans metabolize alcohol differently, so the results from animal experiments may not necessarily apply to humans. The human dizziness assessment, conducted using questionnaires, is highly subjective.

[0004] A method for evaluating the hangover effect after drinking liquor (CN117643451A) uses the footprint symmetry and ataxia coefficient after gavage of alcohol-like samples to mice to evaluate the degree of dizziness after drinking. The behavior of humans after drinking is also very different from that of mice after drinking.

[0005] In recent years, with the rapid development of neuroscience and EEG technology, it has become possible to evaluate the physiological and psychological state of the human body through brain wave monitoring and analysis, providing a new perspective and method for liquor tasting. Summary of the Invention

[0006] The embodiment of the present application provides a method and device for evaluating the state of intoxication after drinking liquor based on quantification of brain wave analysis, which avoids the problems of unclear, inaccurate and highly subjective traditional subjective questionnaires. The method has the advantages of being non-invasive, real-time and convenient, and is suitable for large-scale population research and personalized service needs.

[0007] In a first aspect, an embodiment of the present application provides a method for evaluating the tipsy state after drinking liquor based on quantification of brain wave analysis, comprising: S101 collecting brain wave signals of a subject before and after drinking liquor; S103 preprocessing the collected data; S105 analyzing changes in brain wave characteristics; S107 constructing a quantitative evaluation model for the tipsy state based on brain wave characteristics, and outputting a quantified tipsy state score or grade.

[0008] Among them, in step S101, high-precision, portable brain wave acquisition equipment and sensors are used to collect brain wave signals and brain area electrodes before and after drinking white wine, and at the same time monitor heart rate, blood pressure, blood oxygen, etc.

[0009] The feature of the invention is that, in step S103, the pre-processing of the collected brain wave signal includes: power frequency noise suppression, frequency band optimization processing, artifact separation, signal slicing processing and feature extraction.

[0010] In step S105, the changes in the power spectrum density, frequency band energy distribution, and phase synchronization characteristic parameters of the brain waves before and after drinking are analyzed.

[0011] Among them, in step S107, based on machine learning or deep learning algorithms, a mapping relationship model between brain wave characteristics and the tipsy state is established. The tipsy state assessment model includes an input layer, a feature extraction layer, a time series modeling layer, a feature interaction layer and a classification decision layer; during the model training process, a large amount of sample data is used, including subjects of different genders, ages, physiques and drinking habits; the model outputs a quantitative tipsy state score or level, and the tipsy state is divided into mild tipsy, moderate tipsy and deep tipsy.

[0012] In the second aspect, an embodiment of the present application provides a device for evaluating the tipsy state after drinking liquor based on quantification of brain wave analysis, comprising: an acquisition unit for acquiring brain wave signals of a subject before and after drinking liquor; a preprocessing unit for preprocessing the acquired data; an analysis unit for analyzing changes in brain wave characteristics; and a construction unit for constructing a quantitative evaluation model of the tipsy state based on brain wave characteristics, and outputting a quantified tipsy state score or grade.

[0013] Among them, the collection unit uses high-precision, portable brain wave collection equipment and sensors to collect brain wave signals and brain area electrodes before and after drinking white wine, and at the same time monitors heart rate, blood pressure, blood oxygen, etc.

[0014] Among them, the preprocessing unit preprocesses the collected brain wave signals including: power frequency noise suppression, frequency band optimization processing, artifact separation, signal slicing processing and feature extraction.

[0015] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0016] In a fourth aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the program.

[0017] The method and device for evaluating the intoxication state after drinking liquor based on quantification of brain wave analysis in the embodiment of the present application have the following beneficial effects:

[0018] In this application, brain wave analysis technology is applied to the quantitative assessment of the state of tipsiness after drinking liquor, and a quantitative assessment model of the state of tipsiness based on brain wave characteristics is established. The degree of dizziness of consumers after drinking is evaluated through a quantifiable scientific method, avoiding the problems of unclear, inaccurate, and highly subjective traditional subjective questionnaires, and achieving an objective and accurate assessment of the state of tipsiness. This method has the advantages of being non-invasive, real-time, and convenient, and is suitable for large-scale population research and personalized service needs. It not only provides a scientific basis and technical support for liquor tasting, but also opens up new research directions and application fields for the cross-integration of neuroscience and the liquor industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a method for evaluating the intoxication state after drinking liquor based on quantification of brain wave analysis according to an embodiment of the present application;

[0020] Figure 2 This is the multimodal data fusion architecture of the embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the structure of the evaluation of the intoxicated state after drinking liquor based on quantification of brain wave analysis in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0023] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following description provides multiple embodiments of the present invention, and different embodiments can be replaced or combined, so this application can also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more of all other possible combinations of features A, B, C, and D, even though such embodiments may not be explicitly described in the following text.

[0024] Example 1

[0025] like Figure 1 As shown, the present application provides a method for evaluating the tipsy state after drinking liquor based on quantification of brain wave analysis, comprising: S101 collecting brain wave signals of the subject before and after drinking liquor; S103 preprocessing the collected data; S105 analyzing the changes in brain wave characteristics; S107 constructing a quantitative evaluation model for the tipsy state based on brain wave characteristics, and outputting a quantified tipsy state score or level.

[0026] This application uses a quantifiable scientific method to evaluate consumers' dizziness after drinking, avoiding the unclear, inaccurate, and highly subjective issues associated with traditional subjective questionnaires, thus addressing the shortcomings of traditional tasting methods. This application establishes a quantitative assessment model for the tipsy state based on brainwave characteristics, enabling objective and accurate assessment of the tipsy state. This method is non-invasive, real-time, and convenient, making it suitable for large-scale population studies and personalized service needs.

[0027] Example 2

[0028] This application provides a method for evaluating the state of tipsiness after drinking liquor based on quantification of brain wave analysis. This method is a method for quantitatively evaluating the state of tipsiness of an individual by real-time monitoring and analyzing changes in human brain waves after drinking liquor. The specific implementation is as follows:

[0029] Brainwave acquisition equipment: High-precision, portable brainwave acquisition equipment, such as smart EEG helmets, can be used to collect brainwave signals of subjects before and after drinking liquor in real time and non-invasively.

[0030] Data collection and preprocessing:

[0031] A standardized drinking process, including drinking volume, drinking speed, and time intervals before and after drinking, was established to ensure data consistency and comparability. The collected EEG signals were pre-processed using steps such as denoising, filtering, and feature extraction to improve data quality and analysis accuracy.

[0032] Brainwave characteristic analysis:

[0033] The team focused on brainwave types closely associated with a tipsy state, such as alpha waves (creative brainwaves), theta waves (relaxation waves), and beta waves (alertness waves). They analyzed changes in these brainwave characteristics, including power spectral density, frequency band energy distribution, and phase synchronization, before and after drinking.

[0034] Quantitative evaluation model of tipsy state:

[0035] Using machine learning or deep learning algorithms, a mapping model is established between EEG characteristics and intoxication. During model training, a large amount of sample data, including subjects of varying genders, ages, physical conditions, and drinking habits, is used to improve the model's generalization and accuracy. The model output is a quantified intoxication score or level, which facilitates intuitive understanding and comparison.

[0036] Application and Feedback:

[0037] This method is applied to areas such as liquor tasting, liquor improvement, and consumer behavior research, providing scientific basis and technical support for the liquor industry. Through user feedback and continuous optimization of the algorithm model, the accuracy and practicality of the evaluation method are continuously improved.

[0038] This method collects brain wave data of individuals before and after drinking liquor, and uses a specific algorithm to analyze changes in brain wave characteristics, thereby achieving an objective quantitative assessment of the state of intoxication. It avoids the problems of unclear, inaccurate, and highly subjective traditional subjective questionnaires, and provides a new perspective and method for liquor tasting.

[0039] Example 3

[0040] This application provides a method for evaluating the state of intoxication after drinking liquor based on quantification of brain wave analysis, the specific steps of which are as follows:

[0041] 1. Implementation Plan of EEG Data Acquisition System

[0042] 1. Selection and configuration of intelligent EEG equipment

[0043] Core equipment: NeurSky X12 portable EEG helmet (32 leads, sampling rate ≥ 1000Hz)

[0044] Sensor array: equipped with electrodes for key brain regions such as FP1 / FP2 / F3 / F4 / O1 / O2

[0045] Synchronous monitoring module: integrated PPG sensor (heart rate / blood oxygen monitoring); additional wrist blood pressure monitoring device (Bluetooth 5.0 synchronous transmission)

[0046] Calibration Process

[0047] (1) Complete baseline EEG acquisition 30 minutes before alcohol intake (resting eyes closed / eyes open dual mode)

[0048] (2) Calibrate the device impedance to below 5kΩ

[0049] 2. Standard drinking experiment design, as shown in the following table

[0050]

[0051] 2. EEG signal processing technology solution

[0052] 1. Preprocessing process

[0053] 1.1 Power frequency noise suppression

[0054] A digital notch filter is used to eliminate 50Hz power line interference, and the Q factor is set to 30 to maintain the integrity of the effective frequency band.

[0055] Implementation method: Construct an IIR Butterworth filter with a cutoff bandwidth of 49-51 Hz.

[0056] 1.2 Frequency Band Optimization Processing

[0057] Perform 0.5-45Hz bandpass filtering to retain characteristic wave bands such as α / θ / β

[0058] Low-pass filter order: 8th-order Chebyshev I type

[0059] High-pass filter cutoff slope: 12dB / octave

[0060] 1.3 Artifact Separation Technology

[0061] Application of Fast Independent Component Analysis (FastICA) to Eliminate Physiological Artifacts

[0062] Number of components set: 10 independent components

[0063] Artifact Identification Criteria

[0064] Eye movement artifacts: frontal distribution + low frequency dominance (<4Hz)

[0065] Myoelectric artifacts: global distribution + high-frequency components (>30Hz)

[0066] 1.4 Signal Slicing Processing

[0067] Adopting sliding window segmentation strategy

[0068] Window length: 2000 samples (2 seconds @ 1000 Hz)

[0069] Overlap rate: 50% (1000 sampling points overlap)

[0070] Segment annotation: Associated drinking timeline markers (pre / post drinking status)

[0071] 2. Feature Engineering Construction

[0072]

[0073] 3. Construction of a Quantification Model for the Tipsy State

[0074] 1. Such as Figure 2 As shown in the figure, in the multimodal data fusion architecture, the original EEG signal undergoes time-frequency analysis, the heart rate variability undergoes HRV feature extraction, and the blood pressure data undergoes dynamic trend modeling, and all are input into the feature fusion layer.

[0075] 2. Detailed Explanation of the Tipsy State Assessment Model Architecture

[0076] 2.1 Core Architecture Composition

[0077]

[0078] 2.2 Component parameter analysis

[0079] ① One-dimensional convolutional layer

[0080] Input Configuration

[0081] Number of channels: 32 (strictly corresponding to the number of EEG leads)

[0082] Time series length: L (determined by the sampling time, for example, 5 seconds of data corresponds to L = 500 @ 100Hz)

[0083] Convolution kernel characteristics

[0084] Width 5: covers a 50ms time window (assuming a sampling rate of 100Hz)

[0085] Step 1: Implementing dense feature scanning

[0086] Output channel 64: Generate 64-dimensional time domain feature code

[0087] Dimension Transformation

[0088] Lout=Lin-(kernel_size-1)=L-4 Example: Input 500 points → Output 496 points

[0089] ② Bidirectional LSTM layer

[0090] Hidden unit design

[0091] Feedforward layer: 128 hidden neurons

[0092] Backward layer: 128 hidden neurons

[0093] Output splicing: 256-dimensional time series feature vector

[0094] Timing modeling capabilities

[0095] Forward propagation: Simulating physiological responses during the alcohol absorption phase

[0096] Backpropagation: Unraveling the characteristic decay patterns of metabolic clearance processes

[0097] Output features

[0098]

[0099] Among them, h∈R256 is the compressed global feature, and the forward and backward outputs are concatenated into a 256-dimensional temporal feature vector (128+128)

[0100] ③Multi-head attention layer

[0101] Attention Computation

[0102]

[0103] Single-head attention: The head is obtained by calculating the dot product of the query Q and the key K, normalizing it with softmax, and finally weighted summing it with the value V.

[0104] Multi-head attention: The input is processed by multiple independent attention mechanisms (i.e. multiple heads), each with its own Q, K, V matrices and weights.

[0105] Splicing and transformation: The outputs of multiple heads are spliced ​​together and then transformed using a linear transformation matrix Wo to obtain the final output MultiHead. This mechanism can enhance the expressive power of the model.

[0106] Parameter configuration

[0107] Single head dimension: 32 (256 / 8)

[0108] Attention weights reflect the importance of different time points

[0109] Biological significance

[0110] Head 1-2: Focus on the sudden change in theta wave (4-8Hz) power

[0111] Head 5-6: Detection of gamma wave (30-45Hz) burst events

[0112] ④Fully connected classifier

[0113] Decision-making mechanism

[0114] P(y=c)=softmax(W T C h+b c )

[0115] symbol meaning Practical significance in the assessment of intoxication y=c Target category identifier For example: c = 0 (sober), c = 1 (slightly tipsy), c = 2 (drunk) h Input feature vector Mathematical representation of EEG characteristics (such as α / θ wave energy value, etc.) C Feature transformation matrix Dimensionality reduction / enhancement processing of EEG features <![CDATA[W T C ]]> Weight Matrix Learned EEG feature importance weights <![CDATA[b c ]]> Class bias Baseline thresholds for different tipsy states softmax() Normalized exponential function Convert the output into a probability distribution

[0116] Output Explanation

[0117] Node 0: Mildly tipsy (BAC ≈ 0.03%-0.06%)

[0118] Node 1: Moderately tipsy (BAC ≈ 0.06%-0.10%)

[0119] Node 2: Deep Tipsy (BAC>0.10%)

[0120] Note: BAC is the blood alcohol concentration

[0121] 3. Model training strategy

[0122] 3.1 Sample library construction

[0123] Collect data from 500 subjects (male to female ratio 1:1, age 20-55 years old)

[0124] Labeling system: Clinical observer scoring (60%) + self-report (40%) cross-validation 3.2 Training parameters

[0125] Learning rate: adaptive adjustment (initial value 0.001, decay coefficient 0.1 / 50epoch) Regularization: Dropout (0.5) + L2 regularization (λ = 0.01)

[0126] Loss function: weighted cross entropy (to cope with class imbalance)

[0127] 4. Verification and Optimization Plan

[0128] 4.1 Model Validation Metrics

[0129] index Target value Test Method Classification accuracy ≥89％ Five-fold cross validation AUC value ≥0.92 ROC curve analysis Response Delay <300ms Real-time stress testing

[0130] 4.2 Scenario Verification Solution

[0131] Horizontal comparison: correlation coefficient with the traditional BIS (brain state index) scale ≥ 0.75

[0132] Longitudinal follow-up: Intergroup consistency ICC ≥ 0.85 in repeated drinking experiments

[0133] Interference test: Performance degradation <5% in a simulated dining environment (65dB noise)

[0134] This application collects brain wave data of individuals before and after drinking white wine, combines specific algorithms to analyze changes in brain wave characteristics, and establishes a quantitative evaluation model for the tipsy state based on brain wave characteristics, thereby achieving objective quantitative evaluation of the tipsy state and avoiding the problems of unclear, inaccurate and highly subjective traditional subjective questionnaires. This method has the advantages of being non-invasive, real-time and convenient, and is suitable for large-scale population research and personalized service needs. It not only provides a scientific basis and technical support for white wine tasting, but also opens up new research directions and application fields for the cross-integration of neuroscience and the wine industry. It can be used to establish a tipsy characteristic spectrum library for white wines of different flavors, match suitable wines according to the user's tipsy curve, and set tipsy threshold reminders (such as safety checks before driving).

[0135] Example 4

[0136] like Figure 3 As shown, the present application provides a device for evaluating the tipsy state after drinking liquor based on quantification of brain wave analysis, comprising: an acquisition unit 201 for acquiring brain wave signals of a subject before and after drinking liquor; a preprocessing unit 202 for preprocessing the acquired data; an analysis unit 203 for analyzing changes in brain wave characteristics; and a construction unit 204 for constructing a quantitative evaluation model for the tipsy state based on brain wave characteristics, and outputting a quantified tipsy state score or grade.

[0137] In this application, the embodiment of the device for evaluating the state of slight intoxication after drinking liquor based on brain wave analysis and quantification is basically similar to the embodiment of the method for evaluating the state of slight intoxication after drinking liquor based on brain wave analysis and quantification. For relevant matters, please refer to the introduction of the embodiment of the method for evaluating the state of slight intoxication after drinking liquor based on brain wave analysis and quantification.

[0138] The embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for evaluating the state of intoxication after drinking liquor based on quantification of brain wave analysis. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0139] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the intoxication state after drinking liquor based on quantification of brain wave analysis, characterized in that: include: S101 collects brain wave signals of subjects before and after drinking liquor; S103 pre-processes the collected data; S105 analyzes changes in brain wave characteristics; S107 constructs a quantitative evaluation model of the tipsy state based on brain wave characteristics and outputs a quantitative tipsy state score or level.

2. The method for evaluating the intoxicated state after drinking liquor based on brain wave analysis and quantification according to claim 1, characterized in that: In step S101, high-precision, portable brain wave acquisition equipment and sensors are used to collect brain wave signals and brain area electrodes before and after drinking white wine, while monitoring heart rate, blood pressure and blood oxygen.

3. The method for evaluating the intoxication state after drinking liquor based on brain wave analysis and quantification according to claim 1 or 2, characterized in that: In step S103 , the collected brain wave signals are pre-processed, including power frequency noise suppression, frequency band optimization processing, artifact separation, signal slicing processing and feature extraction.

4. The method for evaluating the intoxication state after drinking liquor based on brain wave analysis and quantification according to claim 1 or 2, characterized in that: In step S105, changes in the power spectrum density, frequency band energy distribution, and phase synchronization characteristic parameters of the brain waves before and after drinking are analyzed.

5. The method for evaluating the intoxication state after drinking liquor based on brain wave analysis and quantification according to claim 1 or 2, characterized in that: In step S107, based on machine learning or deep learning algorithms, a mapping relationship model between brain wave features and the tipsy state is established. The tipsy state assessment model includes an input layer, a feature extraction layer, a time series modeling layer, a feature interaction layer, and a classification decision layer. During the model training process, a large amount of sample data is used, including subjects of different genders, ages, physical conditions, and drinking habits. The model outputs a quantitative tipsy state score or grade, and the tipsy state is divided into mild tipsy, moderate tipsy, and deep tipsy.

6. A device for evaluating the state of intoxication after drinking liquor based on quantification of brain wave analysis, characterized in that: include: An acquisition unit, used to collect brain wave signals of the subject before and after drinking liquor; A preprocessing unit, used for preprocessing the collected data; An analysis unit, used to analyze changes in brain wave characteristics; The construction unit is used to construct a quantitative evaluation model of the tipsy state based on brain wave characteristics and output a quantitative tipsy state score or level.

7. The device for evaluating the state of drunkenness after drinking liquor based on brain wave analysis and quantification according to claim 6, characterized in that: The acquisition unit uses high-precision, portable brain wave acquisition equipment and sensors to collect brain wave signals and brain area electrodes before and after drinking white wine, and simultaneously monitors heart rate, blood pressure and blood oxygen.

8. The device for evaluating the intoxication state after drinking liquor based on brain wave analysis and quantification according to claim 6, characterized in that: The preprocessing unit performs preprocessing on the collected EEG signals, including: power frequency noise suppression, frequency band optimization processing, artifact separation, signal slicing processing and feature extraction.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

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