Cold radiation panel refrigeration control method based on vital signs

Abstract

The invention relates to the technical field of vital signs, in particular to a cold radiation panel refrigeration control method based on vital signs, which comprises the following steps: acquiring heart rate skin conductivity shell temperature and combining with circadian rhythm segmentation to obtain rhythm grades, calculating heart rate variability indexes, inputting the heart rate variability indexes into an SVM (Support Vector Machine) model to judge a metabolic state, the method comprises the steps of analyzing the heat dissipation requirement by combining the change rate of the electric conductivity and the change direction of the body surface temperature, deriving the power requirement through cross logic operation, and predicting a rhythm inflection point to correct a refrigeration control instruction, and comprises the steps of optimizing processing links, improving the accuracy and adaptability of a scheme, combining the heart rate, the skin electric conductivity and the body surface temperature data, and improving the accuracy and adaptability of the scheme. The physiological state is divided by means of the circadian rhythm, the metabolism active state and the heat dissipation requirement are accurately judged, complex parameter dependence is reduced, the heat dissipation requirement judgment sensitivity and control precision are improved, the power requirement is accurately adjusted, and the refrigeration effect is optimized.

Description

Technical Field

[0001] This invention relates to the field of biometrics technology, and in particular to a method for controlling the cooling of a cold radiation panel based on biometrics. Background Technology

[0002] The field of vital signs technology encompasses the acquisition, identification, and application of human physiological signals. Its core content involves monitoring vital signs such as heart rate, respiratory rate, and body surface temperature using sensors, and converting these signals into data usable by control systems. The system comprehensively covers sensor deployment and signal acquisition, data analysis and feature recognition, and the establishment and execution of control logic, and is widely applied in areas such as environmental regulation, medical health monitoring, and personalized comfort control.

[0003] One method for controlling the cooling of a radiant heat treatment panel based on vital signs involves using human vital sign signals as input for cooling control during the panel's cooling process. The technical aspects include collecting signals such as body surface temperature and heart rate, analyzing the data to obtain controllable parameters, and directly influencing the cooling control logic of the radiant heat treatment panel to adjust its operating state. The method involves using vital sign sensors for data collection, extracting human characteristic parameters using signal analysis methods, and inputting these parameters into the panel's control system to drive corresponding cooling control actions.

[0004] While existing technologies possess basic metabolic state monitoring and cooling control functions, they exhibit significant shortcomings in real-time data processing and system responsiveness. Due to the low correlation between heat dissipation requirements and metabolic state, current technologies often suffer from lag in derivation results, failing to respond quickly to real-time changes. For example, in determining metabolic activity and heat dissipation needs, existing technologies rely heavily on pre-set models and standardized calculations, making it difficult to handle sudden or subtle physiological changes. This results in sluggish system response, particularly under high loads, potentially failing to adjust power demands promptly, leading to suboptimal temperature control and even impacting user comfort and system energy efficiency. Furthermore, existing technologies often process multi-dimensional data relatively independently, failing to fully integrate the interactions of multiple signals. Consequently, they cannot provide precise control commands in complex physiological environments, reducing the overall system's adaptability and accuracy. These shortcomings may lead to lower user satisfaction in practical applications and hinder efficient energy consumption management, leaving room for optimization. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a cold radiation panel cooling control method based on biological characteristics, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a cold radiation panel cooling control method based on life characteristics, comprising the following steps: S1: Collect the user's heart rate signal, skin conductivity signal and body surface temperature value, and combine them with the circadian rhythm time axis to divide the time into the morning wakefulness period, the afternoon trough period and the nighttime deep sleep period to generate a circadian rhythm classification result; S2: Based on the user's heart rate signal in the rhythm classification results, calculate the heart rate variability index and input it into the support vector machine model for trend classification, determine the metabolically active state and attach an identifier, and generate a metabolically active state identifier. S3: Based on the metabolic activity status identifier, obtain the real-time skin conductivity signal and body surface temperature value, calculate the skin conductivity change rate, analyze the consistency with the direction of body surface temperature change, and combine the metabolic activity status level to determine the heat dissipation demand and generate heat dissipation demand results. S4: Based on the heat dissipation demand result, the metabolic activity status identifier and the rhythm classification result, perform cross-logic operation. If the metabolic and heat dissipation directions are consistent, deduce an increase in power demand. If both are weakened, reduce power demand. If the directions are inconsistent, determine it as an intermediate power demand and generate a cooling control command. S5: Based on the cooling control command and the rhythm classification result, the body surface temperature is acquired in real time and time series trend analysis is performed to predict the rhythm inflection point position. When it is determined that the inflection point is approaching, the cooling control command is offset and adjusted to generate a corrected cooling control command.

[0006] As a further aspect of the present invention, the rhythm classification results include the morning awakening period, the afternoon trough period, and the nighttime deep sleep period; the metabolic activity status identifiers include metabolic trend classification, heart rate variability level, and activity level identifiers; the heat dissipation demand results include skin conductivity change rate, consistency of body surface temperature change direction, and heat dissipation demand level; the cooling control commands include increasing power demand, decreasing power demand, and intermediate power demand; and the corrected cooling control commands include time series trend, rhythm inflection point position, and offset adjustment amount.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Collects heart rate signals, skin conductivity signals and body surface temperature values, aggregates multiple collected data according to a unified timestamp, and arranges the data points of each type of physiological signal in time sequence in the aggregated sequence to generate a physiological signal sequence. S102: Based on the physiological signal sequence, call the circadian rhythm time axis, map the timestamps in the sequence to the circadian rhythm time periods, form independent segments from the sets of values ​​in the same rhythm time period, and record the range of physiological parameters of multiple segments to obtain a set of rhythm time period segments. S103: Based on the set of rhythmic time segments, the diurnal rhythm time axis is divided into the morning wakefulness period, the afternoon trough period, and the nighttime deep sleep period. After classifying and labeling the physiological parameters of multiple segments, they are bound to the corresponding time segments to obtain the rhythm classification results.

[0008] As a further aspect of the present invention, the circadian rhythm time axis is set by a continuous sequence of physiological signal acquisition time, a time period division benchmark for the circadian cycle, and rhythm segmentation rules.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the user's heart rate signal in the rhythm classification results, extract the difference between adjacent heartbeat intervals for the continuous heart rate interval sequence, calculate the standard deviation and root mean square value of the difference sequence, and aggregate the obtained statistical values ​​into a heart rate variability index. S202: Call the heart rate variability index, input multiple index values ​​into the classification input space of the support vector machine, calculate the sample projection distance on the decision boundary function of the support vector machine, determine the classification region based on the positive or negative value of the projection distance, and obtain the trend classification result; S203: Based on the trend classification results, a unique identifier code is added to the metabolically active state intervals, and the classification labels are mapped to the identifier codes to obtain the metabolically active state identifier.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the metabolically active state identifier, collect real-time skin conductivity signals and body surface temperature values, calculate the rate of change by dividing the difference between adjacent sampling points of skin conductivity in the continuous time series by the time interval, and record them in chronological order to generate a skin conductivity rate of change sequence. S302: Call the skin conductivity change rate sequence, compare the direction of change with the direction of increase or decrease of the body surface temperature value at the corresponding time. If the two change directions are consistent, record it as positive consistency; otherwise, record it as negative consistency, and obtain the change consistency judgment set. S303: Based on the change consistency judgment set and the activity level represented in the metabolic activity status identifier, a combined judgment is made on the consistency category and the activity level, and the judgment output is mapped to a range to obtain the heat dissipation demand result.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the heat dissipation demand result, the metabolic activity status identifier and the rhythm classification result, align them by timestamp, and encode the heat dissipation demand direction, metabolic level direction and rhythm classification direction into discrete logic symbol sequences and perform logical operations to generate cross-logic sequences. S402: Call the cross logic sequence. When both the heat dissipation direction and the metabolism direction are enhanced, record the increased power demand. When both are weakened, record the reduced power demand. When the directions are inconsistent, record it as an intermediate power demand. Then encode the recording results uniformly to obtain the power demand judgment set. S403: Based on the power demand determination set, establish corresponding control parameter codes for the power increase category, power decrease category, and intermediate power category, and map the coding results to the equipment control parameter table to obtain the cooling control command.

[0012] As a further aspect of the present invention, the equipment control parameter table is composed of control parameter codes, corresponding equipment execution gear values, and operating constraints.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the cooling control command and the rhythm classification result, the body surface temperature data is acquired in real time and arranged in chronological order to establish a temperature change sequence curve over time. Then, the temperature curve is used to perform trend retrieval to extract the direction of temperature change and generate a body surface temperature trend sequence. S502: Call the body surface temperature trend sequence, combine it with the rhythm segment time points in the rhythm classification result, calculate the rate of change of multiple time periods in the sequence, record candidate inflection points at the positions where the signs of the rate of change change change and filter them to obtain the predicted position of the rhythm inflection point; S503: Based on the predicted position of the rhythm inflection point, compare the execution timing of the cooling control command. If it is determined that the control timing is close to the inflection point position, adjust the amplitude of the original control command within the corresponding execution interval, and re-encode the adjusted control parameters to obtain the corrected cooling control command.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention improves the accuracy and adaptability of the overall solution by optimizing technical means and operating modes in multiple processing stages. By combining data such as heart rate signals, skin conductivity, and body surface temperature, and using the circadian rhythm timeline to divide different physiological states, it achieves accurate judgment of metabolic activity and heat dissipation needs. Especially in the logical correlation between metabolic state and heat dissipation needs, the innovative solution is not only more refined but also derives more accurate power demand derivations through precise calculation and comparison of real-time data, making the system more flexible and effective in response. For example, when analyzing the consistency of changes in skin conductivity and body surface temperature, combining real-time data processing of metabolic activity significantly reduces reliance on complex parameters, making the derivation more accurate and effectively improving the sensitivity of heat dissipation demand judgment and enhancing control precision. Through this comprehensive data analysis and cross-logic operation optimization, the overall solution is more adaptable, has a shorter response time, and can adjust power demands according to different metabolic states, thus achieving more precise cooling control. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a method for controlling the cooling of a cold radiation panel based on biological characteristics, comprising the following steps: S1: Collect the user's heart rate signal, skin conductivity signal and body surface temperature value, and combine them with the circadian rhythm time axis to divide the time into the morning wakefulness period, the afternoon trough period and the nighttime deep sleep period to generate a circadian rhythm classification result; S2: Based on the user's heart rate signal in the rhythm classification results, calculate the heart rate variability index and input it into the support vector machine model for trend classification, determine the metabolically active state and attach a label, and generate a metabolically active state label. S3: Based on the metabolic activity status identifier, obtain real-time skin conductivity signals and body surface temperature values, calculate the skin conductivity change rate, analyze the consistency with the direction of body surface temperature change, and combine the metabolic activity status level to determine heat dissipation demand and generate heat dissipation demand results. S4: Based on the heat dissipation demand results, metabolic activity status indicators and rhythm classification results, perform cross-logic operations. If the metabolic and heat dissipation directions are consistent, deduce an increase in power demand; if both are weakened, reduce power demand; if the directions are inconsistent, determine it as an intermediate power demand and generate a cooling control command. S5: Based on the cooling control command and rhythm classification results, the body surface temperature is acquired in real time and time series trend analysis is performed to predict the rhythm inflection point. When the inflection point is determined to be close, the cooling control command is offset and adjusted to generate a corrected cooling control command.

[0023] The circadian rhythm classification results include the morning wakefulness period, the afternoon trough period, and the nighttime deep sleep period. The metabolic activity status indicators include metabolic trend classification, heart rate variability level, and activity level indicators. The heat dissipation demand results include the skin conductivity change rate, the consistency of the direction of body surface temperature change, and the heat dissipation demand level. The cooling control commands include increasing power demand, decreasing power demand, and intermediate power demand. The corrected cooling control commands include time series trend, circadian rhythm inflection point position, and offset adjustment amount.

[0024] Please see Figure 2 The specific steps of S1 are as follows: S101: Collects heart rate signals, skin conductivity signals and body surface temperature values, aggregates multiple collected data according to a unified timestamp, and arranges the data points of each type of physiological signal in time sequence in the aggregated sequence to generate a physiological signal sequence. Heart rate signals were acquired using a photoplethysmography (PPG) sensor, fixed to the user's wrist. The sensor continuously monitored changes in radial artery blood flow volume at a sampling frequency of 100Hz, generating a raw PPG data stream. Skin conductivity signals were acquired using two Ag / AgCl dry electrodes on the inside of the wristband. A constant voltage of 0.5V was applied between the electrodes, and changes in skin surface conductivity were sampled at a frequency of 20Hz, obtaining a raw skin conductivity (EDA) data stream. Body surface temperature was measured using an NTC thermistor sensor, which was in close contact with the skin and recorded a temperature reading every 10 seconds in degrees Celsius (°C), forming a raw body surface temperature (BST) data stream. Each raw data point, regardless of type, was appended with a Unix timestamp accurate to milliseconds. For example, the collected independent data points might be: {timestamp: 1695600000100, PPG value: 1024}, {timestamp: 1695600000125, EDA value: 0.85μS}, {timestamp: 1695600000000, BST value: 36.5℃}. To aggregate multiple collected data points, a fixed aggregation window of 5 seconds (5000 milliseconds) is set. The data processing unit processes the raw data in the buffer periodically using this window. For heart rate signals, there are a total of 500 PPG sample points within a single 5-second window. Cardiac events are identified by finding peaks in the sample points, and the time interval between adjacent peaks is calculated and converted into instantaneous heart rate. If six heart rate peaks are identified within a window, corresponding to instantaneous heart rates of 70, 72, 73, 71, 72, and 72 beats per minute, the aggregated heart rate value for the window is the arithmetic mean of these six instantaneous heart rates, i.e., (70+72+73+71+72+72) / 6 = 71.67 beats per minute. For skin conductivity signals, a total of 100 EDA sample points are collected within a 5-second window, and the arithmetic mean of these 100 sample points is calculated. If the sum of these 100 sample values ​​is 52.5 μS, then the aggregated skin conductivity value is 52.5 / 100 = 0.525 μS. For body surface temperature, since the 10-second sampling interval is longer than the 5-second aggregation window, there is at most one sampling point within a window. If a sampling point exists within the window, that value is used directly; otherwise, the temperature value from the previous aggregation window is used. All aggregated data use the start timestamp of the aggregation window as the unified timestamp. For example, for a window starting at timestamp 1695600000000, the generated aggregated data points are {Uniform timestamp: 1695600000000, Heart rate: 71.67, Skin conductivity: 0.525, Body surface temperature: 36.5}. This process is continuous, and the generated aggregated data points are arranged in chronological order according to their uniform timestamps, thus generating a physiological signal sequence.

[0025] S102: Based on the physiological signal sequence, call the circadian rhythm time axis, map the timestamps in the sequence to the circadian rhythm time periods, form independent segments from the sets of values ​​in the same rhythm time period, and then record the range of physiological parameters of multiple segments to obtain the set of rhythm time period segments; Based on the generated physiological signal sequence, a pre-defined circadian rhythm timeline is invoked. The timeline divides a 24-hour day into eight fixed time periods: deep sleep at night (00:00-04:00), morning wake-up preparation period (04:00-06:00), morning wake-up transition period (06:00-09:00), morning high-efficiency work period (09:00-12:00), afternoon trough preparation period (12:00-14:00), afternoon trough period (14:00-17:00), evening activity transition period (17:00-21:00), and nighttime rest preparation period (21:00-00:00). The processing unit iterates through each aggregated data point in the physiological signal sequence, extracts a unified timestamp, and converts it from Unix timestamp format to the local time's "hour:minute" format. Subsequently, the converted time is compared with the boundaries of each time period on the circadian rhythm timeline to determine the circadian rhythm time period to which the data point belongs. For example, a data point with a unified timestamp of 1695628800000 has a local time of 10:00:00. This time point falls within the interval (09:00, 12:00), and is therefore mapped to the "morning high-efficiency work period." All data points mapped to the same circadian rhythm time period constitute an independent segment. In a complete 24-hour monitoring cycle, this mapping process generates eight data segments corresponding to different circadian rhythm time periods. After all data points are mapped, the range of physiological parameters within each independent segment is recorded. This recording process specifically involves calculating the minimum, maximum, arithmetic mean, and standard deviation of the three physiological parameters—heart rate, skin conductivity, and body surface temperature—for all data points within the segment. Taking the "nighttime deep sleep period" (00:00-04:00) segment as an example, this segment is 4 hours long and contains (4 hours * 3600 seconds / hour) / 5 seconds / point = 2880 data points. The processor iterates through these 2880 data points and performs statistical analysis on the heart rate values. The calculated minimum heart rate is 50.5 beats / minute, and the maximum is 65.1 beats / minute. The sum of all heart rate values ​​is 167616, so the arithmetic mean is 167616 / 2880 = 58.2 beats / minute, and the standard deviation is 3.5 beats / minute. Similarly, the same calculations are performed on skin conductivity and body surface temperature to obtain their respective statistical indicators. The statistical results of all eight segments are then structured and stored to obtain a rhythmic time segment set.

[0026] S103: Based on the set of circadian rhythm time segments, the diurnal rhythm time axis is divided into the morning wakefulness period, the afternoon trough period, and the nighttime deep sleep period. After classifying and labeling the physiological parameters of multiple segments, they are bound to the corresponding time segments to obtain the circadian rhythm classification results. Based on the set of rhythmic time segments, the key periods of the diurnal rhythm timeline were divided. The morning wakefulness period consisted of data from the "morning wakefulness preparation period" and the "morning wakefulness transition period," while the afternoon trough period directly used data from the "afternoon trough period," and the nighttime deep sleep period directly used data from the "nighttime deep sleep period." Subsequently, physiological parameters of multiple segments within these three key periods were graded and labeled. The thresholds used for grading were derived from an independent benchmark experiment. The experiment recruited 100 healthy adults aged 25 to 40 years who wore the same monitoring device for 72 consecutive hours, combined with portable electroencephalography (EEG) for sleep stage monitoring. After the experiment, heart rate data from all participants during objectively assessed "N3 sleep" (slow-wave sleep) were summarized and statistically analyzed. A total of 85,430 valid heart rate data points were collected and sorted in ascending order. The 80th percentile was used as the dividing line between level 1 and level 2, with a calculated value of 55 beats per minute; the 50th percentile (median) was used as the dividing line between level 2 and level 3, with a calculated value of 62 beats per minute; and the 20th percentile was used as the dividing line between level 3 and level 4, with a calculated value of 68 beats per minute. This was based on the established heart rate grading standard for deep sleep at night. Other physiological parameters and grading thresholds for other periods were also set using this method.

[0027] Table 1. Physiological Parameter Grading Threshold Setting Table

[0028] As shown in Table 1, this table lists the grading thresholds for physiological parameters during key periods. When grading, the average values ​​of physiological parameters for each segment calculated in S102 are compared with the thresholds in Table 1. Taking the aforementioned "deep sleep period at night" segment as an example, the average heart rate is 58.2 beats / minute. This value is compared with the heart rate threshold for deep sleep at night; since 55 ≤ 58.2 < 62, the heart rate for this period is graded as level 2. Similarly, the average skin conductivity of the segment is 0.32 μS; since 0.20 ≤ 0.32 < 0.40, it is graded as level 2; the average body surface temperature is 36.3℃; since 36.2 ≤ 36.3 < 36.6, it is also graded as level 2. The grading results are then linked to the "deep sleep period at night." The same grading process is performed for the "afternoon trough period" and the "morning awakening period." After completing the grading and labeling of all key periods, the results are integrated to obtain the rhythm grading results, in the following format: Nighttime deep sleep: {Heart rate grade: 2, Skin conductivity grade: 2, Body surface temperature grade: 2}, Afternoon trough: {Heart rate grade: 2}, Morning wakefulness: {Heart rate variability grade: 3}. Morning wakefulness: {Heart rate variability grade: 3}.

[0029] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the user's heart rate signal in the rhythm classification results, extract the difference between adjacent heartbeat intervals for the continuous heart rate interval sequence, calculate the standard deviation and root mean square value of the difference sequence, and aggregate the obtained statistical values ​​into a heart rate variability index. Based on the acquired rhythm classification results, the user's heart rate signal within a specific rhythm time period is retrieved. This retrieval does not use the classified "level" value, but rather locates the original physiological signal segment upon which the level assessment is based. Taking a continuous 5-minute (300-second) data segment from the "afternoon trough preparation period" (12:00-14:00) as an example, the processing unit first extracts the raw waveform data collected by the PPG sensor within this time period from memory. By performing peak detection on the waveform data, the time point of each cardiac event is identified, and the time interval between adjacent cardiac events is calculated, thus obtaining a heart rate interval (RR interval) sequence in milliseconds (ms). A segment containing 10 consecutive heartbeats is set as: {810, 835, 820, 790, 815, 840, 825, 800, 830, 805}. Next, the difference between adjacent heartbeat intervals is extracted from the sequence, that is, the difference between the (i+1)th RR interval and the ith RR interval in the sequence is calculated. For the above sequence, the calculated difference sequence is: {25, -15, -30, 25, 25, -15, -25, 30, -25}. Subsequently, the standard deviation (SDSD) and root mean square (RMSSD) of this difference sequence are calculated. The arithmetic mean is calculated, i.e., (-5 / 9) ≈ -0.56 ms. (25.56)^2+(-14.44)^2+(-29.44)^2+25.56^2+25.56^2+(-14.44)^2+(-24.44)^2+30.56^2+(-24.44)^2≈5372.2 ms^2. Dividing the sum of squared deviations by the sample size of 9 yields a variance of approximately 596.91 ms². Finally, taking the square root of the variance gives a standard deviation of approximately 24.43 ms. The root mean square (RMS) value is calculated as follows: First, the sum of squares of all terms in the difference sequence is calculated: 625 + 225 + 900 + 625 + 625 + 225 + 625 + 900 + 625 = 5375 ms². Dividing this sum by the sample size of 9 yields a mean square of approximately 597.22 ms². Finally, taking the square root of the mean gives a root mean square (RMS) value of approximately 24.44 ms. The calculated standard deviation and RMS value are then combined into a data pair {24.43, 24.44} to form a heart rate variability index for 5-minute segments.

[0030] S202: Call the heart rate variability index, input multiple index values ​​into the classification input space of the support vector machine, calculate the sample projection distance on the decision boundary function of the support vector machine, determine the classification region based on the positive and negative values ​​of the projection distance, and obtain the trend classification result; Each generated heart rate variability index is used, with its standard deviation (SDSD) and root mean square value (RMSSD) as two-dimensional input samples, fed into a pre-defined support vector machine (SVM) classification function for processing. The specific parameters of the classification function, namely the weight vector and bias term, are obtained through an independent training experiment. The experiment recruited 50 healthy subjects and collected physiological data under controlled conditions in two states: one was a "metabolically inactive" state, where subjects rested in a quiet environment for 30 minutes; the other was a "metabolically active" state, where subjects performed low-intensity exercise 30 to 60 minutes after consuming a standard meal. All 5-minute heart rate variability index data points collected in the experiment were labeled "-1" (metabolically inactive) and "+1" (metabolically active), respectively. The parameters of the decision boundary function were determined by analyzing the labeled data. In this embodiment, the two components of the weight vector were set to 0.21 and 0.18, respectively, and the bias term was set to -10.5. Therefore, the decision boundary function is: function value = 0.21 × SDSD value + 0.18 × RMSSD value - 10.5. When a new heart rate variability index is input, the processing unit substitutes the two values ​​of the index into the above decision boundary function to calculate the projected distance from the sample to the decision boundary. Taking the heart rate variability index {SDSD: 24.43, RMSSD: 24.44} calculated in the previous step as an example, the calculation process after substituting into the function is: projected distance = 0.21 × 24.43 + 0.18 × 24.44 - 10.5 = 5.1303 + 4.3992 - 10.5 = -0.9705. After the calculation is completed, the classification region to which the sample belongs is determined according to the positive or negative value of the projected distance. The discrimination rule is: if the projected distance is positive, the sample is classified as "metabolically inactive"; if the projected distance is negative or zero, it is classified as "metabolically active". Since the calculated projection distance is -0.9705, which is a negative value, the physiological state of the 5-minute time segment is classified as "metabolic activity". This process is executed continuously throughout the data stream, generating a classification label for each 5-minute segment, thus obtaining the trend classification result.

[0031] S203: Based on the trend classification results, a unique identifier code is added to the metabolically active state intervals, and the classification labels are mapped to the identifier codes to obtain the metabolically active state identifiers. Based on the output trend classification result sequence, the processing unit traverses each 5-minute state interval. Only when an interval is classified as "metabolically active" is the additional process for generating a unique identifier initiated. This identifier consists of three concatenated parts: a static prefix "MA" representing the "metabolically active" event, a numeric code "01" representing the "metabolically active" category, and a Unix timestamp (accurate to the second) of the starting point of the state interval. The generation rules are fixed and deterministic. For a 5-minute segment classified as "metabolically active" in a previous step, the start time is set to 1:00 PM on September 25, 2025, with a corresponding Unix timestamp of 1695646800. According to the generation rules, the prefix "MA," the code "01," and the timestamp "1695646800" are connected with a hyphen "-" to generate the unique identifier for the interval: "MA01-1695646800." If a subsequent 5-minute segment is classified as "metabolically inactive," the processing unit will skip the segment and not generate any identifier. After generating a unique identifier, this identifier is mapped to the corresponding category label to create a structured data record. The record contains complete time information for the state interval, a clear category label, and the generated unique identifier. For the example above, the created mapping record is: {State interval start timestamp: 1695646800, State interval end timestamp: 1695647100, Category label: Metabolic Active, Unique identifier: MA01-1695646800}. This mapping process transforms continuous classification results into a series of discrete event records with unique indices. These records are stored sequentially, forming a temporally ordered list of events. By attaching a unique identifier to the state interval identified as metabolically active and mapping it as described above, the metabolically active state identifier is obtained.

[0032] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the metabolic activity status identifier, real-time skin conductivity signals and body surface temperature values ​​are collected. The rate of change is calculated by dividing the difference between adjacent sampling points of skin conductivity in a continuous time series by the time interval, and recorded in chronological order to generate a skin conductivity change rate sequence. Based on the acquired metabolic activity status identifier, such as "MA01-1695646800", real-time skin conductivity signals and body surface temperature values ​​are continuously collected at a high frequency within the corresponding 5-minute time interval (starting from timestamp 1695646800). The skin conductivity signal is collected at a frequency of 20Hz, i.e., one sampling point is acquired every 0.05 seconds; the body surface temperature is collected at a frequency of 0.1Hz, i.e., one value is acquired every 10 seconds. The collected data is stored in a short-term buffer for calculating the rate of change. The difference between adjacent skin conductivity sampling points in the continuous time series is calculated, and the difference is divided by a fixed time interval between the two sampling points to obtain the instantaneous skin conductivity change rate. This time interval is determined by the sampling frequency, which is 0.05 seconds in this embodiment. The specific calculation process is as follows: At time point t1, the collected skin conductivity value is 1.25 microsiemens (μS), and the timestamp is 1695646800.000. At the next sampling time point t2 (i.e., t1 + 0.05 seconds), the collected skin conductivity value is 1.28 μS, with a timestamp of 1695646800.050. First, the difference between these two adjacent sampling points is calculated: Skin conductivity difference = 1.28 μS - 1.25 μS = 0.03 μS. Then, the difference is divided by the time interval: Rate of change = 0.03 μS / 0.05 s = 0.6 μS / s. The calculation result is assigned a timestamp, typically the end timestamp of the calculation interval, i.e., 1695646800.050. This calculation process is performed continuously throughout the entire metabolically active state. For example, if the skin conductivity value collected at time point t3 (1695646800.100) is 1.27 μS, then the rate of change between t2 and t3 is (1.27 - 1.28) / 0.05 = -0.2 μS / s. All the calculated rate of change values ​​in chronological order are recorded to form a time series with the same sampling rate as the original signal (excluding the first point), namely the skin conductivity rate of change series. A portion of the series is: {timestamp: 1695646800.050, rate of change: 0.6}, {timestamp: 1695646800.100, rate of change: -0.2}, {timestamp: 1695646800.150, rate of change: 0.4}.

[0033] S302: Call the skin conductivity change rate sequence, compare the direction of change trend with the direction of increase or decrease of the body surface temperature value at the corresponding time. If the two change directions are consistent, it is recorded as positive consistency; otherwise, it is recorded as negative consistency, and the change consistency judgment set is obtained. The generated skin conductivity change rate sequence is invoked, and the corresponding body surface temperature value sequence is acquired simultaneously. Since the sampling interval for body surface temperature (10 seconds) is much longer than the calculation interval for the skin conductivity change rate (0.05 seconds), the sampling point of body surface temperature is used as the baseline time point when comparing the direction of change. First, the direction of change of body surface temperature is determined. At any body surface temperature sampling time point Tn, its temperature value is compared with the temperature value at the previous sampling time point Tn-1 (i.e., 10 seconds ago). If the temperature value of Tn is greater than the temperature value of Tn-1, the direction of change of body surface temperature is determined to be "rising". If it is less, it is "falling". If they are equal, it is "stable". For example, at time point 1695646810, the body surface temperature is 37.1℃, while at time point 1695646800 10 seconds ago, the body surface temperature is 37.0℃. Since 37.1 > 37.0, the direction of change of body surface temperature within the 10-second interval is determined to be "rising". Next, the trend direction of the skin conductivity change rate is determined. Within the same 10-second interval (from 1695646800 to 1695646810), there are 200 skin conductivity change rate values. The arithmetic mean of these 200 values ​​is calculated. If the average is positive, the overall trend direction of skin conductivity change within that interval is determined to be "increasing"; if it is negative, it is "decreasing"; if it is zero, it is "stable". Setting the 10-second interval, the sum of the 200 change rate values ​​is 45.6 μS / s, so the average change rate is 45.6 / 200 = 0.228 μS / s. Since 0.228 is a positive number, the trend direction of skin conductivity change is determined to be "increasing". Finally, these two trends are compared. In this example, the trend direction of body surface temperature change is "increasing", and the trend direction of skin conductivity change is also "increasing". Because the changes in both occur in the same direction, the comparison results for a 10-second time interval are recorded as "positive consistency". If, in a subsequent 10-second interval, the surface temperature trend is "rising" while the skin conductivity trend is "falling", it is recorded as "negative consistency". This comparison process is carried out in 10-second units throughout the entire metabolically active state interval, resulting in a set of change consistency judgments composed of "positive consistency" and "negative consistency" labels.

[0034] S303: Based on the consistency judgment set and the activity level represented in the metabolic activity status identifier, the consistency category and activity level are combined for judgment, and the judgment output is mapped to a range to obtain the heat dissipation demand result. Based on the generated consistency judgment set, a combined judgment is made by combining the activity level implied in the metabolic activity status identifier. The activity level is not an independent input parameter, but is quantified by calling the projection distance of the support vector machine decision boundary function calculated in S202. The larger the absolute value of the projection distance, the farther the sample point is from the classification boundary, and the more clearly defined its state. In this embodiment, the negative range of the projection distance is divided into three intervals to define the activity level: a projection distance in the (0, -1.5) interval is defined as "low activity" (level 1); in the (-1.5, -3.0) interval, it is defined as "moderate activity" (level 2); and in the (-3.0, -∞) interval, it is defined as "high activity" (level 3). For example, in the example of S202, the obtained projection distance is -0.9705, which falls in the (0, -1.5) interval, therefore the activity level of the current 5-minute segment is determined to be "low activity" (level 1). Next, the consistency category (positive consistency / negative consistency) is combined with the activity level (level 1 / 2 / 3) for judgment. The judgment is based on a pre-defined mapping rule table, which maps different combinations to an intermediate judgment value ranging from 0 to 100. The mapping rule was established through an independent calibration experiment. The experiment recruited 30 subjects who engaged in activities at three different exercise intensities (corresponding to low, medium, and high metabolic levels, respectively). Skin conductivity and surface temperature were monitored simultaneously, and the heat dissipation power per unit area of ​​skin (W / m^2) was objectively measured using a heat flux sensor as the "gold standard" for heat dissipation demand. By analyzing the average heat dissipation power corresponding to different activity levels and consistency combinations, the following mapping relationship was established. Taking the aforementioned example, the current activity level is "low activity" (level 1), and the consistency judgment is "positive consistency." The intermediate judgment value corresponding to the combination is 30. Finally, the intermediate judgment value was mapped to intervals: 0-20 corresponds to "no significant heat dissipation demand"; 21-50 corresponds to "low heat dissipation demand"; 51-80 corresponds to "moderate heat dissipation demand"; and 81-100 corresponds to "high heat dissipation demand." Since the intermediate judgment value 30 falls within the interval, the final judgment output is "low heat dissipation requirement". (21, 50)

[0035] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the heat dissipation demand results, metabolic activity status identifiers and rhythm classification results, the heat dissipation demand direction, metabolic level direction and rhythm classification direction are aligned by timestamp, and the heat dissipation demand direction, metabolic level direction and rhythm classification direction are encoded into discrete logic symbol sequences and logical operations are performed to generate cross-logic sequences. Based on the acquired heat dissipation demand results, metabolic activity status indicators, and rhythm classification results, timestamp alignment and data integration are performed using a fixed 5-minute time window as the processing unit. For a continuous 25-minute period starting from timestamp 1695646800, five consecutive 5-minute windows are processed one by one. In the first window (starting from 1695646800), the heat dissipation demand result "low heat dissipation demand" generated by S303 is called, the metabolic activity status indicator of S203 is called and the projection distance -0.9705 is extracted, and the rhythm classification result of S102 is called to determine that the current period is "afternoon trough". After completing the data alignment, these three inputs are encoded into discrete logical symbols. The encoding of the heat dissipation demand direction is achieved by comparing the quantization values ​​of the current window and the previous window (no obvious = 0, low = 1, medium = 2, high = 3). If the previous window is set to "no obvious heat dissipation demand" (0) and the current window is set to "low" (1), then the direction is enhanced and encoded as +1. The encoding of the metabolic level direction is achieved by comparing the current activity level with that of the previous window (determined by the projection distance range). The current projection distance of -0.9705 is level 1. Setting the previous window's projection distance of -0.8500 to level 1 also results in a stationary direction, encoded as 0. The rhythm grading direction is encoded as 0 based on the static characteristics of the current "afternoon trough." This encoding process is executed continuously in the subsequent four windows. In the second window (starting at 1695647100), the heat dissipation demand changes to "moderate heat dissipation demand" (quantization value 2), and the projection distance becomes -1.6500 (level 2). The heat dissipation demand direction is 2-1=1, encoded as +1. The metabolic level direction is 2-1=1, encoded as +1. The rhythm grading remains "afternoon trough," encoded as 0. In the third window (starting at 1695647400), the heat dissipation demand remains "moderate" (quantization value 2), and the projection distance becomes -1.2000 (level 1). The direction of heat dissipation demand is 2-2=0, coded as 0. The direction of metabolic level is 1-2=-1, coded as -1. The circadian rhythm classification code remains 0. In the fourth window (starting from 1695647700), the heat dissipation demand decreases to "low" (quantization value 1), the projection distance becomes -0.9800 (level 1), but the corresponding circadian rhythm time period enters the latter half of the "afternoon trough period", and the static bias is adjusted to -1. The direction of heat dissipation demand is 1-2=-1, coded as -1. The direction of metabolic level is 1-1=0, coded as 0. The circadian rhythm classification code remains -1. In the fifth window (starting from 1695648000), the heat dissipation demand remains "low" (quantization value 1), and the projection distance is -0.9500 (level 1). The direction of heat dissipation demand is 1-1=0, coded as 0. The direction of metabolic level is 1-1=0, coded as 0. The circadian rhythm classification code remains -1.Finally, the three logic symbols encoded for each window are combined in the order of (heat dissipation demand direction, metabolic level direction, and rhythm level direction) to generate a cross-logic sequence containing five elements: (+1, 0, 0), (+1, +1, 0), (0, -1, 0), (-1, 0, -1), (0, 0, -1).

[0036] S402: Call the cross logic sequence. When both the heat dissipation direction and the metabolism direction are increasing, record the increased power demand. When both are decreasing, record the decreased power demand. When the directions are inconsistent, record the intermediate power demand. Then encode the recording results uniformly to obtain the power demand judgment set. The generated cross-logic sequence (+1, 0, 0), (+1, +1, 0), (0, -1, 0), (-1, 0, -1), (0, 0, -1) is invoked. The processing unit parses and judges each triplet element in the sequence one by one. This process strictly follows a fixed set of judgment rules, which only focuses on the first two elements of the triplet, namely the direction of heat dissipation demand and the direction of metabolic level. Rule 1: If and only if the direction of heat dissipation demand is encoded as +1 and the direction of metabolic level is encoded as +1, the demand of the time window is recorded as "increased power demand". Rule 2: If and only if the direction of heat dissipation demand is encoded as -1 and the direction of metabolic level is encoded as -1, the demand of the time window is recorded as "decreased power demand". Rule 3: All other combinations that do not satisfy Rule 1 and Rule 2 are recorded as "intermediate power demand". The processing unit processes the elements in the sequence sequentially. For the first element (+1, 0, 0), the direction of heat dissipation demand +1 and the direction of metabolic level 0 are extracted. The combination does not satisfy Rule 1 (requires two +1s) nor Rule 2 (requires two -1s), therefore, according to Rule 3, it will be recorded as "Intermediate Power Requirement". For the second element (+1, +1, 0), extract +1 and +1. This combination satisfies the condition of Rule 1, therefore it will be recorded as "Increased Power Requirement". For the third element (0, -1, 0), extract 0 and -1. This combination does not satisfy Rule 1 and Rule 2, therefore it will be recorded as "Intermediate Power Requirement". For the fourth element (-1, 0, -1), extract -1 and 0. This combination also does not satisfy Rule 1 and Rule 2, therefore it will be recorded as "Intermediate Power Requirement". For the fifth element (0, 0, -1), extract 0 and 0. This combination still does not satisfy Rule 1 and Rule 2, therefore it will be recorded as "Intermediate Power Requirement". After completing the judgment of all elements in the sequence, a temporary record sequence is obtained: {"Intermediate Power Requirement", "Increased Power Requirement", "Intermediate Power Requirement", "Intermediate Power Requirement", "Intermediate Power Requirement"}. After completing the judgment record, this text record sequence is uniformly numerically encoded for subsequent processing. The encoding rule is as follows: "increase power demand" is encoded as the value 2, "intermediate power demand" is encoded as the value 1, and "decrease power demand" is encoded as the value 0. This rule is applied to the above temporary record sequence to obtain the final power demand judgment set. The judgment set is an integer sequence containing five elements, with the content: {1, 2, 1, 1, 1}.

[0037] S403: Based on the power demand determination set, establish corresponding control parameter codes for power increase category, power decrease category and intermediate power category respectively, and map the coding results to the equipment control parameter table to obtain the cooling control command; Based on the output power demand determination set {1, 2, 1, 1, 1}, each category code in the sequence is parsed, and a corresponding control parameter code is established. The purpose of this step is to convert the numerical power demand categories into instructions recognizable by the equipment control layer. The conversion follows a fixed encoding system: the power increase category (value 2) is converted to the string code "CMD-INC"; the intermediate power category (value 1) is converted to "CMD-HOLD"; and the power decrease category (value 0) is converted to "CMD-DEC". This conversion operation is performed on each element of the power demand determination set. For the sequence {1, 2, 1, 1, 1}, the resulting control parameter code sequence is: {"CMD-HOLD", "CMD-INC", "CMD-HOLD", "CMD-HOLD", "CMD-HOLD"}. Subsequently, each code in this control parameter code sequence is mapped to a fixed equipment control parameter table to obtain specific, executable cooling control instructions. The parameter table defines the corresponding thermoelectric cooler (TEC) drive voltage adjustment and cooling fan pulse width modulation (PWM) duty cycle adjustment for each instruction. The values ​​in this table are based on experimental calibration of a specific model of refrigeration equipment.

[0038] Table 2 Equipment Control Parameter Mapping Table

[0039] As shown in Table 2, the control parameter encoding sequence is processed sequentially. For the first encoding "CMD-HOLD", the TEC drive voltage adjustment is found to be 0V and the fan PWM duty cycle adjustment is 0%. These two values ​​are packaged into the first cooling control instruction. For the second encoding "CMD-INC", the voltage adjustment is found to be +0.2V and the PWM duty cycle adjustment is +10%, and the second instruction is generated. For the third, fourth, and fifth encodings "CMD-HOLD", instructions with an adjustment value of 0 are generated. An instruction sequence is generated, and these cooling control instructions containing specific parameter adjustment values ​​are sent sequentially to the device's underlying hardware controller for execution at a frequency of once every 5 minutes.

[0040] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the cooling control command and rhythm classification results, it acquires body surface temperature data in real time and arranges it in chronological order to establish a temperature change sequence curve over time. Then, it performs trend retrieval on the temperature curve to extract the direction of temperature change and generate a body surface temperature trend sequence. Based on the generated cooling control command sequence {“CMD-HOLD”, “CMD-INC”, “CMD-HOLD”, “CMD-HOLD”, “CMD-HOLD”} and the rhythm classification results (e.g., the entire 25-minute period is in the “afternoon trough”), temperature data is acquired in real time via a body surface temperature sensor at a fixed frequency of 0.1Hz. Each acquired data point is accompanied by a precise timestamp and stored in a cache in chronological order to establish a continuous temperature-time sequence curve. During the execution period of the first 5-minute instruction "CMD-HOLD" (timestamps 1695646800 to 1695647100), since there was no active cooling intervention, the body surface temperature was mainly affected by basal metabolism. The 30 data points collected showed a stable or slightly upward trend. The sequence start part is {timestamp: 1695646800, temperature: 34.50℃}, {timestamp: 1695646810, temperature: 34.50℃}, {timestamp: 1695646820, temperature: 34.51℃}, ..., {timestamp: 1695647090, temperature: 34.52℃}. Entering the execution interval of the second 5-minute instruction "CMD-INC" (1695647100 to 1695647400), the power of the cooling chip increased, and the 30 collected data points showed a clear downward trend, with the sequence {timestamp: 1695647100, temperature: 34.52℃}, {timestamp: 1695647110, temperature: 34.51℃}, {timestamp: 1695647120, temperature: 34.49℃}, ..., {timestamp: 1695647390, temperature: 34.35℃}. This data acquisition and recording process continued in subsequent instruction intervals, forming a temperature time series consisting of 150 data points covering the entire time period. Subsequently, trend retrieval was performed on this established temperature curve to extract the discrete temperature change direction. This process used a sliding time window with a fixed length of 60 seconds (i.e., containing 6 consecutive temperature data points). Within each window, the processing unit extracts the temperature value of the last data point in the window and the temperature value of the first data point, calculating the difference between them. This difference is compared to a preset temperature change threshold to determine the dominant temperature change direction within the 60-second time period. The judgment rule is set as follows: if the temperature difference is greater than +0.02℃, the direction of change in the window is judged as "rising" and encoded as the value +1. If the temperature difference is less than -0.02℃, it is judged as "falling" and encoded as the value -1. If the difference is between -0.02℃ and +0.02℃ (inclusive), it is judged as "stable" and encoded as the value 0. The threshold of 0.02℃ here was determined through an independent baseline fluctuation calibration experiment.The experiment recruited 100 healthy subjects who wore the device for one hour at rest under constant environmental conditions to collect body surface temperature data. By analyzing the temperature data of all subjects, the standard deviation of natural fluctuations under no external intervention was calculated. Three times the standard deviation (covering 99.7% of the natural fluctuation range) was used as the threshold, calculated to be 0.02℃. Taking a window starting at timestamp 1695647110 as an example, the first temperature value was 34.51℃, and the sixth temperature value (timestamp 1695647160) was 34.45℃, a difference of -0.06℃. Since -0.06℃ is less than -0.02℃, the trend of the window was encoded as -1. The sliding window moved across the entire temperature time series in 10-second increments (one data point), generating a trend code with each movement, resulting in a body surface temperature trend sequence consisting of 145 coded values.

[0041] S502: Call the body surface temperature trend sequence, combine it with the rhythm segment time points in the rhythm classification results, calculate the rate of change for multiple time periods in the sequence, record candidate inflection points at the positions where the signs of the rate of change change change and filter them to obtain the predicted positions of rhythm inflection points; The generated body surface temperature trend sequence is invoked and combined with a standard rhythm transition timetable based on large-scale population physiological data statistics contained in the rhythm classification results, which shows the typical transition time points of physiological rhythm stages. For example, the table specifies that the standard end time of the "afternoon trough" is 15:00:00 (corresponding to Unix timestamp 1695651600), followed by the "early evening activity stage". First, the acquired body surface temperature trend sequence (a sequence composed of +1, 0, -1) is processed to calculate its rate of change between adjacent time points. The rate of change calculation here is specifically implemented by performing a first-order difference operation on the trend sequence. That is, at each time point in the sequence (starting from the second point), the trend code value of the current point is subtracted from the trend code value of the previous time point. For example, if there is a segment {..., +1, -1, ...} in the trend sequence, at the time point where -1 is located, the first-order difference calculation result is (-1)-(+1)=-2. When the result of the first-order difference calculation is not equal to zero, it indicates that the direction of temperature change (rising, falling, or stabilizing) has changed. The timestamp of the point where the change occurs is recorded as a candidate inflection point. After recording all candidate inflection points, a screening process is initiated. The sole criterion for screening is whether the timestamp of the candidate inflection point falls within a predefined "rhythm warning window." The window is strictly defined as 45 minutes before the standard rhythm transition point. This 45-minute window duration was determined through a three-month longitudinal tracking study of 200 users. The study continuously monitored multiple physiological indicators of users and, through statistical analysis, found that 95% of users' physiological states began to significantly deviate from the steady-state characteristics of the current rhythm stage and showed the starting point of transitioning to the characteristics of the next rhythm stage within the interval of 30 to 60 minutes before the standard rhythm transition point. Therefore, 45 minutes (i.e., (30+60) / 2) was chosen as the duration of the warning window. Taking the "afternoon trough" ending at 15:00:00 (timestamp 1695651600) as an example, its corresponding rhythm warning window is from 14:15:00 to 15:00:00 (timestamp interval). When processing the body surface temperature trend sequence, at timestamp 1695649290 (i.e., 14:21:30), a first-order difference result of -2 (changing from +1 to -1) is detected, and this timestamp is recorded as a candidate inflection point. Subsequently, this timestamp 1695649290 is compared with the warning window. Since 1695648900≤1695649290≤1695651600, this candidate inflection point passes the screening. This timestamp 1695649290 is output as the final prediction result, yielding the predicted position of the rhythm inflection point. If the timestamp of another candidate inflection point is 1695648800 (14:13:20), it will be discarded directly because it is not within the warning window.

[0042] S503: Based on the predicted position of the rhythm inflection point, compare the execution timing of the cooling control command. If it is determined that the control timing is close to the inflection point, the original control command will be adjusted in amplitude offset within the corresponding execution interval, and the adjusted control parameters will be re-encoded to obtain the corrected cooling control command. Based on the predicted position of the output rhythm inflection point, i.e., timestamp 1695649290 (14:21:30), this time point is compared with the execution timing of the original cooling control command sequence generated by S403. The original command sequence is divided and issued according to fixed 5-minute time intervals. First, the predicted inflection point is located within which 5-minute execution interval is calculated. Since the start time of the command sequence is 14:00:00 (timestamp 1695648000), and each interval is 300 seconds long, the predicted inflection point (1695649290) falls within the fifth execution interval, the specific time range of which is 14:20:00 to 14:25:00 (timestamps 1695649200 to 1695649500). Then, the command corresponding to this interval is extracted from the original command sequence and set as "CMD-DEC". Next, it is determined whether the control timing meets the condition of being "close" to the inflection point position. Here, "approaching" is quantified as follows: the predicted rhythm inflection point appears in the "first half" of its corresponding control command execution interval. The "first half" is precisely defined as the first 40% after the start of the interval. For a 300-second execution interval, the "first half" is 120 seconds. This 40% threshold was determined through a human factors engineering experiment. In the experiment, environmental interventions with varying lead times were applied to subjects just before they entered a physiological state transition period, and their acceptability was assessed using physiological indicators and subjective questionnaires. The results showed that when the intervention occurred within 2 minutes before the transition (i.e., 40% of the 5-minute interval), the user's physiological adaptability and subjective comfort scores were the highest. In this example, the timestamp of the predicted inflection point is 1695649290, and the start timestamp of its interval is 1695649200, a time difference of 90 seconds. Since 90 seconds is less than the 120-second threshold, the control timing is determined to be "approaching" the inflection point, thus triggering the subsequent command adjustment mechanism. After the adjustment is triggered, the amplitude offset of the original control command "CMD-DEC" will be adjusted. This adjustment is achieved through a preset "rhythm transition offset coefficient," which is fixed at 0.5. This coefficient value is based on a comfort modeling study, which found that during physiological rhythm transitions, reducing the intensity of environmental control intervention to 50% of the original plan resulted in the highest overall user comfort score. The specific adjustment process is as follows: First, the original command "CMD-DEC" is mapped back to the corresponding specific parameter adjustment amounts, namely {TEC drive voltage adjustment: -0.2V, cooling fan PWM duty cycle adjustment: -10%}. Then, each value in this set of parameter adjustment amounts is multiplied by the offset coefficient 0.5. The calculation process is: {-0.2V*0.5, -10%*0.5}, resulting in a new parameter adjustment amount of {-0.1V, -5%}. This set of adjusted parameter values ​​is then re-encoded.Since there is no corresponding standard code in the parameter combination, a temporary correction command code, such as "CMD-MOD-DEC-01", is generated and associated with the parameter values ​​{-0.1V, -5%}. Finally, this corrected cooling control command "CMD-MOD-DEC-01" is output to replace the original "CMD-DEC" command during the time interval from 14:20:00 to 14:25:00.

[0043] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling the cooling of a cold radiant panel based on biological characteristics, characterized in that, Includes the following steps: S1: Collect the user's heart rate signal, skin conductivity signal and body surface temperature value, and combine them with the circadian rhythm time axis to divide the time into the morning wakefulness period, the afternoon trough period and the nighttime deep sleep period to generate a circadian rhythm classification result; S2: Based on the user's heart rate signal in the rhythm classification results, calculate the heart rate variability index and input it into the support vector machine model for trend classification, determine the metabolically active state and attach an identifier, and generate a metabolically active state identifier. S3: Based on the metabolic activity status identifier, obtain the real-time skin conductivity signal and body surface temperature value, calculate the skin conductivity change rate, analyze the consistency with the direction of body surface temperature change, and combine the metabolic activity status level to determine the heat dissipation demand and generate heat dissipation demand results. S4: Based on the heat dissipation demand result, the metabolic activity status identifier and the rhythm classification result, perform cross-logic operations. If the metabolic and heat dissipation directions are consistent, deduce an increase in power demand. If both are weakened, reduce power demand. If the directions are inconsistent, determine it as an intermediate power demand and generate a cooling control command.

2. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The circadian rhythm classification results include the morning wakefulness period, the afternoon trough period, and the nighttime deep sleep period. The metabolic activity status indicators include metabolic trend classification, heart rate variability level, and activity level indicators. The heat dissipation demand results include skin conductivity change rate, consistency of body surface temperature change direction, and heat dissipation demand level. The cooling control commands include increasing power demand, decreasing power demand, and intermediate power demand.

3. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collects heart rate signals, skin conductivity signals and body surface temperature values, aggregates multiple collected data according to a unified timestamp, and arranges the data points of each type of physiological signal in time sequence in the aggregated sequence to generate a physiological signal sequence. S102: Based on the physiological signal sequence, call the circadian rhythm time axis, map the timestamps in the sequence to the circadian rhythm time periods, form independent segments from the sets of values ​​in the same rhythm time period, and record the range of physiological parameters of multiple segments to obtain a set of rhythm time period segments. S103: Based on the set of rhythmic time segments, the diurnal rhythm time axis is divided into the morning wakefulness period, the afternoon trough period, and the nighttime deep sleep period. After classifying and labeling the physiological parameters of multiple segments, they are bound to the corresponding time segments to obtain the rhythm classification results.

4. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 3, characterized in that, The circadian rhythm timeline is set by a continuous sequence of physiological signal acquisition time, a time period division benchmark for the circadian cycle, and rhythm segmentation rules.

5. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the user's heart rate signal in the rhythm classification results, extract the difference between adjacent heartbeat intervals for the continuous heart rate interval sequence, calculate the standard deviation and root mean square value of the difference sequence, and aggregate the obtained statistical values ​​into a heart rate variability index. S202: Call the heart rate variability index, input multiple index values ​​into the classification input space of the support vector machine, calculate the sample projection distance on the decision boundary function of the support vector machine, determine the classification region based on the positive or negative value of the projection distance, and obtain the trend classification result; S203: Based on the trend classification results, a unique identifier code is added to the metabolically active state intervals, and the classification labels are mapped to the identifier codes to obtain the metabolically active state identifier.

6. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the metabolically active state identifier, collect real-time skin conductivity signals and body surface temperature values, calculate the rate of change by dividing the difference between adjacent sampling points of skin conductivity in the continuous time series by the time interval, and record them in chronological order to generate a skin conductivity rate of change sequence. S302: Call the skin conductivity change rate sequence, compare the direction of change with the direction of increase or decrease of the body surface temperature value at the corresponding time. If the two change directions are consistent, record it as positive consistency; otherwise, record it as negative consistency, and obtain the change consistency judgment set. S303: Based on the change consistency judgment set and the activity level represented in the metabolic activity status identifier, a combined judgment is made on the consistency category and the activity level, and the judgment output is mapped to a range to obtain the heat dissipation demand result.

7. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the heat dissipation demand result, the metabolic activity status identifier and the rhythm classification result, align them by timestamp, and encode the heat dissipation demand direction, metabolic level direction and rhythm classification direction into discrete logic symbol sequences and perform logical operations to generate cross-logic sequences. S402: Call the cross logic sequence. When both the heat dissipation direction and the metabolism direction are enhanced, record the increased power demand. When both are weakened, record the reduced power demand. When the directions are inconsistent, record it as an intermediate power demand. Then encode the recording results uniformly to obtain the power demand judgment set. S403: Based on the power demand determination set, establish corresponding control parameter codes for the power increase category, power decrease category, and intermediate power category, and map the coding results to the equipment control parameter table to obtain the cooling control command.

8. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 7, characterized in that, The equipment control parameter table consists of control parameter codes, corresponding equipment execution gear values, and operating constraints.

9. The method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The method further includes: S5: Based on the cooling control command and the rhythm classification result, the body surface temperature is acquired in real time and time series trend analysis is performed to predict the rhythm inflection point position. When it is determined that the inflection point is close, the cooling control command is offset and adjusted to generate a corrected cooling control command. The revised cooling control command includes time series trend, rhythm inflection point position, and offset adjustment amount.

10. A method for controlling the cooling of a cold radiation panel based on biological characteristics according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the cooling control command and the rhythm classification result, the body surface temperature data is acquired in real time and arranged in chronological order to establish a temperature change sequence curve over time. Then, the temperature curve is used to perform trend retrieval to extract the direction of temperature change and generate a body surface temperature trend sequence. S502: Call the body surface temperature trend sequence, combine it with the rhythm segment time points in the rhythm classification result, calculate the rate of change of multiple time periods in the sequence, record candidate inflection points at the positions where the signs of the rate of change change change and filter them to obtain the predicted position of the rhythm inflection point; S503: Based on the predicted position of the rhythm inflection point, compare the execution timing of the cooling control command. If it is determined that the control timing is close to the inflection point position, adjust the amplitude of the original control command within the corresponding execution interval, and re-encode the adjusted control parameters to obtain the corrected cooling control command.

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