A method for adjusting a human living environment based on comfort perception

Abstract

The application discloses a kind of based on comfort perception's human living environment regulation method, comprising the following steps: collection environment parameter;User physiological data are collected;The user physiological data collected are handled, and attention concentration degree is obtained according to brain wave data, muscle tension is obtained according to myoelectric data, and cognitive load degree is obtained according to eye movement data;According to attention concentration degree, muscle tension and cognitive load degree, the comfort index of user under current environment is evaluated;If comfort index is less than threshold value, then with user interaction, obtain user feedback;According to user feedback and comfort index, adjust environment parameter.The application according to attention concentration degree, muscle tension and cognitive load degree, the comfort index of user under current environment is evaluated, and comprehensively and accurately reflects user experience;And increase user feedback, satisfy the individualized demand of user, provide more comfortable use environment for user.

Description

Technical Field

[0001] This invention belongs to the field of computer control system technology, specifically relating to a method for adjusting the human living environment based on comfort perception. Background Technology

[0002] As people's living standards continue to improve, their requirements for the user environment are no longer limited to the size of the space and the provision of basic facilities, but rather they are paying more attention to the comfort of the environment. A comfortable user environment can not only improve people's quality of life, but also help with learning and relieve fatigue. For example, the following two environmental parameters are suitable for learning and relieving fatigue, respectively. First: Temperature 20 to 24℃, humidity 40% to 60%; sufficient and suitable natural light or artificial lighting at 4000 to 5000K can reduce visual fatigue; noise levels below 40 to 50 decibels can avoid interference; fresh air can ensure brain activity and improve learning efficiency. Second: Temperature 22 to 25℃, humidity 45% to 55%, reduced light intensity, warm-colored lighting, soothing soft music or natural sounds below 30 to 40 decibels; good air quality can make people feel refreshed.

[0003] Therefore, human-centered environmental regulation technology based on comfort perception has emerged. Its aim is to create a more comfortable and healthy living space for residents through real-time monitoring and intelligent adjustment of environmental parameters, achieving personalized environmental regulation to meet the individual needs of different residents. This technology primarily utilizes a combination of physiological signal sensing and environmental parameter sensing technologies to automatically adjust indoor environmental parameters such as temperature, light, and humidity based on residents' real-time comfort requirements.

[0004] However, influenced by factors such as age, gender, and lifestyle habits, individuals have vastly different perceptions and needs regarding environmental comfort. Most current adjustment algorithms are developed based on general models, making it difficult to fully consider each individual's unique preferences and physiological characteristics, thus failing to achieve truly personalized and precise adjustments. How to consider each individual's unique preferences and accurately reflect the user's comfort needs is a key issue that needs to be addressed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a method for adjusting the living environment based on comfort perception. This method has a simple structure and reasonable design. It assesses the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load, comprehensively and accurately reflecting the user's overall feelings and state in the environment. It also increases user feedback, meets the user's personalized needs, and provides the user with a more comfortable and healthy user environment.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for adjusting the human living environment based on comfort perception, characterized by including the following steps:

[0007] S1: Collect environmental parameters through the first data acquisition module and output the real-time environmental parameters;

[0008] S2: Collect user physiological data through the second data acquisition module; user physiological data includes at least electroencephalogram (EEG) data, electromyography (EMG) data, and eye movement data.

[0009] S3: Determine if the EEG data shows high stress. If yes, proceed to step S4; otherwise, proceed to step S5.

[0010] S4: Adjust the light and temperature to a soothing mode;

[0011] S5: Process the collected user physiological data, obtain attention concentration based on EEG data, obtain muscle tension based on EMG data, and obtain cognitive load based on eye movement data;

[0012] S6: Assess the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load;

[0013] S7: If the comfort index is less than the comfort index threshold, then interact with the user to obtain user feedback;

[0014] S8: Adjust environmental parameters based on user feedback and comfort index.

[0015] The above-mentioned method for adjusting the living environment based on comfort perception is characterized in that: after adjusting the environmental parameters in step S8, the process returns to step S2, re-collects the user's physiological data, and iteratively calculates the user's comfort index in the current environment until the comfort index reaches the comfort index threshold or the user feedback indicates comfort.

[0016] The aforementioned method for adjusting the human living environment based on comfort perception is characterized by further comprising:

[0017] Integrate user feedback to form a user preference dataset;

[0018] The user preference dataset is correlated with the environmental parameter dataset and the user physiological dataset;

[0019] A user preference dataset retrieval mechanism is set to trigger based on specific conditions, which is used to adjust environment parameters.

[0020] The aforementioned method for adjusting a person's living environment based on comfort perception is characterized in that: the specific method for "obtaining attention concentration based on EEG data" in step S5 includes:

[0021] S51, Electroencephalogram (EEG) Data Processing:

[0022] S511, According to formula S EEG =Butterworth(S raw f low f high ) Filter the EEG data; where S EEG S represents the filtered EEG data. raw This represents raw electroencephalogram (EEG) data, f low f represents the lower cutoff frequency of the bandpass filter. high This indicates the upper cutoff frequency of the bandpass filter;

[0023] S512, Extract the filtered EEG data S EEG The frequency band energy;

[0024] S513, Calculate attention concentration C EEG C EEG =C α ×ω α +C β ×ω β , where C α ω represents the energy proportion of the alpha wave. α C represents the weight of the alpha wave. β ω represents the energy proportion of the beta wave. β This represents the weight of the beta wave.

[0025] The aforementioned method for adjusting a human living environment based on comfort perception is characterized in that: the specific method for "obtaining muscle tension based on electromyographic data" in step S5 includes:

[0026] S52, Electromyography Data Processing:

[0027] S521, According to formula S EMG =abs(S raw )*LPF(f cutoff Processing raw electromyographic signals S raw Obtain the envelope signal S EMG ;

[0028] S522. Calculate the root mean square value and the integral electromyographic value respectively: Where RMS represents the root mean square value of the electromyography (EMG) data, N represents the number of EMG data sampling points, and S... EMGi Let i represent the electromyography (EMG) data at the i-th sampling point, iEMG represent the integrated EMG value, t1 and t2 represent the integration interval, and S represent the integral EMG value. EMG (t) represents the expression for the change of electromyographic data over time;

[0029] S523, Calculating muscle tension T EMG T EMG =ω RMS ×RMS+ω iEMG ×iEMG, where ω RMS ω represents the weight of the root mean square value. iEMG This represents the weight of the integrated electromyographic value.

[0030] The aforementioned method for adjusting a person's living environment based on comfort perception is characterized in that: the specific method for "obtaining cognitive load based on eye movement data" in step S5 includes:

[0031] S53, Eye-tracking data processing:

[0032] S531, Eye-tracking data preprocessing;

[0033] S532. Calculate fixation entropy and saccade velocity: Fixation entropy H eye =-∑p j logp j Scanning speed

[0034] Where p j This represents the percentage of fixation time in the j-th region. Indicates a change in the scanning angle;

[0035] S533, Calculate cognitive load D eye D eye =ω H ×H eye +ω V ×v saccade , where ω H The weight ω represents the gaze entropy. V The weight representing the scanning speed.

[0036] The aforementioned method for adjusting a human living environment based on comfort perception is characterized in that: the specific method for "assessing the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load" in step S6 includes:

[0037] According to formula CI actual =ω1·N(C EEG )+ω2·N(T EMG )+ω3·N(D eye Calculate the comfort index (CI) actual , where ω1 represents the weight of attention concentration, v2 represents the weight of muscle tension, ω3 represents the weight of cognitive load, and N(·) represents the normalization function.

[0038] The aforementioned method for adjusting a human living environment based on comfort perception is characterized in that: the specific method for "adjusting environmental parameters according to user feedback and comfort index" in step S8 includes: when the user feedback indicates a comfortable feeling,

[0039] a. Update the weights using gradient descent: Where Loss = (CI) target -CI actual ) 2 CI target This indicates the threshold for the comfort index. Let h represent the h-th weight in the t-th iteration, and η represent the learning rate, which controls the step size of each weight update.

[0040] b. Convert the changes in the comfort index into environmental parameter adjustment quantities, which are then used to adjust the environmental parameters.

[0041] The aforementioned method for adjusting the human living environment based on comfort perception is characterized by the following specific method for converting changes in the comfort index into environmental parameter adjustment quantities:

[0042] According to the formula Calculate the environmental parameter adjustment amount, where ΔT represents the temperature adjustment amount, and k T ΔHU represents the temperature regulation coefficient, ΔHU represents the humidity regulation amount, and k HU The humidity regulation coefficient is represented by ΔI, the light regulation amount is represented by k. I This represents the illumination adjustment coefficient.

[0043] The aforementioned method for adjusting a human living environment based on comfort perception is characterized in that: in step S4, while adjusting the lighting and temperature to a soothing mode, the weight ω of attention concentration is increased. EEG .

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] 1. The present invention has a simple structure, reasonable design, and is convenient to implement and use.

[0046] 2. This invention utilizes physiological signal sensing technology to measure the user's comfort index of the environment, while also adding user feedback to meet the user's personalized needs, resulting in good performance.

[0047] 3. This invention obtains attention concentration based on EEG data, muscle tension based on EMG data, and cognitive load based on eye movement data; it assesses the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load; by comprehensively evaluating these three dimensions, it can more comprehensively and accurately reflect the user's overall feelings and state in the environment, and avoid the bias of subjective judgment.

[0048] 4. This invention integrates user feedback and establishes a mechanism for associating and calling user preference datasets in order to obtain users' comfort levels in specific environments and respond in a timely manner, providing users with a more comfortable and healthy user environment.

[0049] In summary, this invention has a simple structure and reasonable design. It assesses the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load, comprehensively and accurately reflecting the user's overall feelings and state in the environment. It also increases user feedback, meets the user's personalized needs, and provides users with a more comfortable and healthy user environment.

[0050] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method of the present invention.

[0052] Figure 2 This is a flowchart of the method for calculating the comfort index according to the present invention. Detailed Implementation

[0053] The method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0054] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0055] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0058] like Figure 1 and Figure 2 As shown, the present invention provides a method for adjusting the human living environment based on comfort perception, comprising the following steps:

[0059] S1: Collect environmental parameters through the first data acquisition module and output the real-time environmental parameters;

[0060] S2: Collect user physiological data through the second data acquisition module; user physiological data includes at least electroencephalogram (EEG) data, electromyography (EMG) data, and eye movement data.

[0061] S3: Determine if the EEG data shows high pressure. If yes, proceed to step S4; otherwise, proceed to step S5. If C β ≥40% indicates high stress levels in the EEG data, C β This indicates the energy proportion of the beta wave.

[0062] S4: Adjust lighting and temperature to a soothing mode; simultaneously, increase the weight of attention concentration ω. EEG .

[0063] S5: Process the collected user physiological data, obtain attention concentration based on EEG data, obtain muscle tension based on EMG data, and obtain cognitive load based on eye movement data;

[0064] The specific methods for "obtaining attention concentration based on EEG data" in step S5 include:

[0065] S51, Electroencephalogram (EEG) Data Processing:

[0066] S511, According to formula S EEG =Butterworth(S raw f low f high ) Filter the EEG data; where S EEG S represents the filtered EEG data. raw This represents the raw EEG data, specifically the raw EEG data S. raw A Butterworth filter is used for filtering. The low-pass and high-pass parameters of the Butterworth filter are f0 and f1, respectively. low and f high In one possible embodiment, f low =0.5HZ, f high =50Hz.

[0067] S512, Extract the filtered EEG data S EEG The frequency band energy;

[0068] S513, Calculate attention concentration C EEG C EEG =C α ×ω α +C β ×ω β , where C α ω represents the energy proportion of the alpha wave. α C represents the weight of the alpha wave. β ω represents the energy proportion of the beta wave. β This represents the weight of the β wave. In one possible embodiment, ω α =0.7, ω β =0.3.

[0069] It should be noted that the frequency bands include delta waves (0.5-4 Hz), theta waves (4-8 Hz), alpha waves (8-13 Hz), beta waves (13-30 Hz), and gamma waves (30-50 Hz). First, calculate the power spectral density of each frequency band, then calculate the energy percentage.

[0070] Alpha waves typically range from 8 to 13 Hz and are most prominent when a person is relaxed, awake, and has their eyes closed. The brain produces more alpha waves when people are relaxed. Alpha wave energy decreases when attention is highly focused on a specific task. Beta waves generally range from 13 to 30 Hz. The brain produces more beta waves when people are tense, thinking, solving problems, or highly focused on a task. A higher proportion of beta wave energy usually indicates a state of high concentration, enabling the brain to actively perceive, analyze, and process external stimuli.

[0071] Taking into account the energy proportions of both factors, the attention concentration C is calculated based on a weighted average of their energy proportions. EEG This allows for a more comprehensive understanding of the brain's balance between relaxation and excitement, thus enabling a more accurate assessment of the level of concentration.

[0072] The specific methods for "obtaining muscle tension based on electromyographic data" in step S5 include:

[0073] S52, Electromyography Data Processing:

[0074] S521, According to formula S EMG =abs(S raw )*LPF(f cutoff Processing raw electromyographic signals S raw Obtain the envelope signal S EMG ;

[0075] Use the abs() function to analyze the raw electromyographic signal S raw Full-wave rectification involves flipping the negative half-cycle of the signal to the positive half-cycle, so that the signal is entirely located on the non-negative half-axis of the time axis. This converts all fluctuations of the electromyographic signal into positive values, which allows for better analysis of the signal's amplitude and other characteristics, preparing for envelope extraction.

[0076] The full-wave rectified signal abs(S) is processed using a 5Hz low-pass filter LPF. raw The envelope is extracted by processing the data. cutoff Indicates the cutoff frequency. f cutoff =5Hz.

[0077] S522. Calculate the root mean square value and the integral electromyographic value respectively: Where RMS represents the root mean square value of the electromyography (EMG) data, N represents the number of EMG data sampling points, and S... EMGi Let i represent the electromyography (EMG) data at the i-th sampling point, iEMG represent the integrated EMG value, t1 and t2 represent the integration interval, and S represent the integral EMG value. EMG (t) represents the expression for the change of electromyographic data over time.

[0078] The root mean square (RMS) value of electromyography (EMG) data is the square root of the average of the squares of the EMG signal over a period of time. It mainly reflects the average power or intensity of the EMG signal, and its magnitude is related to the number of motor units recruited and the firing frequency during muscle contraction. The RMS value increases with increasing training intensity, thus providing a direct understanding of changes in muscle contraction intensity.

[0079] Integrated electromyography (iEMG) data is the integral of electromyographic signals over a period of time. It reflects the total amount of muscle activity during that time period and is of great significance for assessing muscle fatigue and endurance. For example, as exercise time increases, iEMG values ​​gradually increase, indicating the accumulation of muscle fatigue.

[0080] S523, Calculating muscle tension T EMG T EMG =ω RMS ×RMS+ω iEMG ×iEMG, where ω RMS ω represents the weight of the root mean square value. iEMG This represents the weight of the integrated electromyographic value. In one possible embodiment, v RMS =0.6, ω iEMG =0.4.

[0081] By weighting the root mean square (RMS) and integral electromyography (iEMG) values ​​of electromyography (EMG) data, we can not only comprehensively utilize the advantages of these two indicators to fully and accurately assess muscle tension, but also adjust the weights of the RMS and iEMG values ​​according to individual differences in muscle strength, thereby achieving personalized muscle state monitoring with good results.

[0082] The specific methods for "obtaining cognitive load based on eye movement data" in step S5 include:

[0083] S53, Eye-tracking data processing:

[0084] S531, Eye-tracking data preprocessing;

[0085] S532. Calculate fixation entropy and saccade velocity: Fixation entropy H eye =-∑p j logp j Scanning speed

[0086] Where p j This represents the percentage of fixation time in the j-th region. Indicates a change in the scanning angle;

[0087] gaze entropy H eyeIt measures the dispersion of fixation point distribution. Fixation point entropy H eye It can reflect an individual's attention allocation during information search and processing, reflecting the brain's range and degree of focus on different types of information. When the cognitive load is low, an individual's attention tends to be more concentrated, and they may focus more on key information areas, resulting in lower fixation entropy; conversely, the opposite is true.

[0088] Sagging speed v saccade Salivation speed (v) refers to the speed at which the eyes move rapidly between different points of fixation. saccade It can reflect an individual's rhythm and efficiency in the information acquisition process, reflecting the brain's processing speed and demand for information. When the cognitive load is low, individuals process information more easily, and their saccade speed may be relatively stable and moderate; while when the cognitive load is high, individuals may need to switch quickly between different pieces of information, and their saccade speed will increase, or the saccade speed may become unstable due to the complexity of the information.

[0089] S533, Calculate cognitive load D eye D eye =ω H ×H eye +ω V ×v saccade , where ω H The weight ω represents the gaze entropy. V The weight representing the scanning speed.

[0090] By analyzing the gaze entropy H eye And scan speed v saccade Perform weighted calculation of cognitive load D eye Cognitive Load D eye This tool reflects a user's attention allocation and information processing efficiency in cognitive tasks. It comprehensively considers both attention allocation and information acquisition rhythm during the cognitive process, providing a more complete and accurate reflection of an individual's cognitive load (D). eye .

[0091] S6: Assess the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load;

[0092] The specific methods for "assessing the user's comfort index in the current environment based on attention concentration, muscle tension, and cognitive load" in step S6 include:

[0093] According to formula CI actual =ω1·N(C EEG )+ω2·N(T EMG )+ω3·N(D eye Calculate the comfort index (CI) actualWhere ω1 represents the weight of attention concentration, ω2 represents the weight of muscle tension, ω3 represents the weight of cognitive load, and N(·) represents the normalization function. In one possible embodiment, ω1 = 0.5, ω2 = 0.3, and ω3 = 0.2.

[0094] Attention level is obtained from EEG data, muscle tension from EMG data, and cognitive load from eye movement data. The user's comfort index in the current environment is assessed based on attention level, muscle tension, and cognitive load. The comprehensive evaluation of these three dimensions can more fully and accurately reflect the user's overall feelings and state in the environment, and avoid the bias of subjective judgment.

[0095] S7: If the comfort index is less than the comfort index threshold, then interact with the user to obtain user feedback, which is the user's feeling of comfort or desired environmental parameters; in one possible embodiment, the comfort index threshold CI target =0.7. By utilizing physiological signal sensing technology to measure user comfort levels in the environment, and adding user feedback to meet personalized user needs, the effect is good.

[0096] S8: Adjust environmental parameters based on user feedback and comfort index.

[0097] The specific method for "adjusting environmental parameters based on user feedback and comfort index" in step S8 includes: when user feedback indicates a comfortable feeling,

[0098] a. Update the weights using gradient descent: Where Loss = (CI) target -CI actual ) 2 CI target This indicates the threshold for the comfort index. This represents the h-th weight in the t-th iteration, where η represents the learning rate, controlling the step size for each weight update; this is used to update the weights participating in the comfort index CI. actual The weights ω1, ω2, and ω3 in the calculation formula are used.

[0099] b. Convert the changes in the comfort index into environmental parameter adjustment quantities, which are then used to adjust the environmental parameters.

[0100] It should be noted that after the weights are updated, the changes in the comfort index are converted into environmental parameter adjustments using a linear mapping formula.

[0101] Specifically, according to the formula Calculate the environmental parameter adjustment amount, where ΔT represents the temperature adjustment amount, and k T ΔHU represents the temperature regulation coefficient, ΔHU represents the humidity regulation amount, and k HUThe humidity regulation coefficient is represented by ΔI, the light regulation amount is represented by k. I This represents the illumination adjustment coefficient.

[0102] In one possible embodiment, k T =0.5, k HU =2,k I =50.

[0103] In one possible embodiment, the user feedback is the desired environmental parameters, which may include vague instructions such as a slightly higher temperature or slightly lower light. For vague instructions, the parameters are adjusted up or down according to a preset percentage or step size.

[0104] In one possible embodiment, the user feedback is the desired environmental parameters, which may include explicit instructions such as setting the temperature to 25°C. For explicit instructions, the commands are executed directly.

[0105] After adjusting the environmental parameters in step S8, the system returns to step S2 to re-collect user physiological data and iteratively calculate the user's comfort index in the current environment until the comfort index reaches the comfort index threshold or the user reports comfort. This closed-loop control enables precise adjustment of environmental parameters.

[0106] This embodiment also includes:

[0107] Integrate user feedback to form a user preference dataset;

[0108] The user preference dataset is correlated with the environmental parameter dataset and the user physiological dataset;

[0109] A user preference dataset retrieval mechanism is set to trigger based on specific conditions, which is used to adjust environment parameters.

[0110] The triggering conditions are: all monitored user physiological data fall within the same user physiological data range, or a certain user physiological data falls within a specific range, or the labels obtained from the monitored user physiological data are the same as the labels of a certain user physiological dataset. For example, the labels are "stress" and "studying".

[0111] In one possible implementation, the upper and lower limits of the user physiological dataset are widened to obtain a user physiological data range. Real-time monitored user physiological data is then matched against this range. If all monitored user physiological data falls within the same range, or if a single data point falls within a specific range, the condition is considered met, satisfying the trigger for invoking the user preference dataset. In this case, the user preference dataset corresponding to the physiological dataset is invoked to adjust environmental parameters. This allows for a more precise understanding of users' environmental preferences under different physiological states, thereby providing users with an environment highly tailored to their individual needs.

[0112] The above description is merely an embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for regulating the human living environment based on comfort perception, characterized in that: Includes the following steps: S1: Collect environmental parameters through the first data acquisition module and output the real-time environmental parameters; S2: Collect user physiological data through the second data acquisition module; user physiological data includes at least electroencephalogram (EEG) data, electromyography (EMG) data, and eye movement data. S3: Determine if the EEG data shows high pressure. If yes, proceed to step S4; otherwise, proceed to step S5. S4: Adjust the light and temperature to a soothing mode; S5: Process the collected user physiological data, obtain attention concentration based on EEG data, obtain muscle tension based on EMG data, and obtain cognitive load based on eye movement data; The specific methods for "obtaining attention concentration based on EEG data" in step S5 include: S51. Brainwave Data Processing: S511, According to the formula Filtering of EEG data; among which This represents the filtered EEG data. This represents raw electroencephalogram (EEG) data. This indicates the lower cutoff frequency of the bandpass filter. This indicates the upper cutoff frequency of the bandpass filter; S512. Extract the filtered EEG data. The frequency band energy; S513, Calculate attention concentration , ,in, This indicates the energy proportion of the alpha wave. Indicates the weight of the alpha wave. This indicates the energy proportion of the beta wave. Indicates the weight of the beta wave; The specific methods for "obtaining muscle tension based on electromyographic data" in step S5 include: S52, Electromyography Data Processing: S521, According to the formula Processing raw electromyographic signals Obtain the envelope signal ; S522. Calculate the root mean square value and the integral electromyographic value respectively: , ,in The root mean square value of the electromyography (EMG) data is represented by N, where N represents the number of EMG data sampling points. This represents the electromyography data at the i-th sampling point. The integral electromyography (EMG) value represents the EMG data. and Indicates the integration interval. An expression representing the change of electromyographic data over time; S523, Calculating muscle tension , ,in The weights represent the root mean square values. Indicates the weight of the integrated electromyographic value; The specific methods for "obtaining cognitive load based on eye movement data" in step S5 include: S53, Eye-tracking data processing: S531, Eye-tracking data preprocessing; S532. Calculate fixation entropy and saccade velocity: Fixation entropy Scanning speed ,in This represents the percentage of fixation time in the j-th region. Indicates a change in the scanning angle; S533, Calculate cognitive load , ,in The weights representing the gaze entropy Weights representing the speed of saccades; S6: Normalize and weight attention concentration, muscle tension and cognitive load respectively to evaluate the user's comfort index in the current environment; S7: If the comfort index is less than the comfort index threshold, then interact with the user to obtain user feedback, which includes feelings of comfort or desired environmental parameters. S8: Adjust environmental parameters based on user feedback and comfort index.

2. The method for adjusting the human living environment based on comfort perception according to claim 1, characterized in that: After adjusting the environmental parameters in step S8, return to step S2, recollect user physiological data, and iteratively calculate the user's comfort index in the current environment until the comfort index reaches the comfort index threshold or the user reports that the user is comfortable.

3. A method for adjusting a human living environment based on comfort perception according to claim 1 or 2, characterized in that: Also includes: Integrate user feedback to form a user preference dataset; The user preference dataset is correlated with the environmental parameter dataset and the user physiological dataset; A user preference dataset retrieval mechanism is set to trigger based on specific conditions, which is used to adjust environment parameters.

4. A method for adjusting a human living environment based on comfort perception as described in claim 1, characterized in that: The specific method for "normalizing and weighting attention concentration, muscle tension, and cognitive load to assess the user's comfort index in the current environment" in step S6 includes: According to the formula Calculate the comfort index ,in Weights representing the degree of attention concentration The weights representing muscle tension The weights representing cognitive load This represents the normalization function.

5. A method for adjusting a human living environment based on comfort perception according to claim 4, characterized in that: The specific method for "adjusting environmental parameters based on user feedback and comfort index" in step S8 includes: when user feedback indicates a comfortable feeling, a. Update weights using gradient descent: ,in , This indicates the threshold for the comfort index. This represents the h-th weight in the t-th iteration. This represents the learning rate, which controls the step size for each weight update. b. Convert the changes in the comfort index into environmental parameter adjustment quantities, which are then used to adjust the environmental parameters.

6. A method for adjusting a human living environment based on comfort perception according to claim 5, characterized in that: The specific method for converting changes in the comfort index into adjustments to environmental parameters is as follows: According to the formula Calculate the adjustment amount of environmental parameters, where Indicates the amount of temperature adjustment. Indicates the temperature regulation coefficient. Indicates the humidity adjustment amount. Indicates the humidity regulation coefficient. Indicates the amount of light regulation. This represents the illumination adjustment coefficient.

7. A method for adjusting a human living environment based on comfort perception according to claim 1, characterized in that: In step S4, while adjusting the lighting and temperature to a soothing mode, the weight of attention concentration is increased. .

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