A high-speed train passenger thermal comfort identification system and method based on peripheral physiological characteristics

Abstract

The present invention relates to the field of high-speed rail health and safety protection, and specifically to a high-speed train passenger thermal comfort identification system and method based on peripheral physiological characteristics. The system includes a physiological data and group information acquisition module, a data preprocessing and feature fusion module, a passenger thermal comfort state identification module, and a group decision-making and Internet of Things transmission module. The beneficial effect is: the high-speed train passenger thermal comfort identification system and method based on peripheral physiological characteristics proposed by the present invention, by combining intelligent sensing acquisition, intelligent preprocessing and feature fusion technology, the system effectively extracts rich feature information from thermal imaging cameras and smart watches, enhances the model's recognition ability for targets of different scales, and realizes efficient feature extraction.

Description

Technical Field

[0001] The present invention relates to the field of high-speed rail health and safety protection, and in particular to a high-speed train passenger thermal comfort identification system and method based on peripheral physiological characteristics. Background Art

[0002] The development of rail transportation has led to widespread use of high-speed rail, with hundreds of millions of passengers choosing this mode of travel. Prolonged exposure to uncomfortable heat during hours-long journeys can adversely affect the physical and mental health of high-speed rail passengers. Uncomfortable indoor temperatures can affect various aspects of human health. For example, colder temperatures increase the risk of cardiovascular and respiratory diseases and provide a more favorable environment for the survival of viruses. Higher temperatures increase the risk of acute nonspecific symptoms, such as dry eyes and respiratory problems. Inadequate indoor ventilation reduces air quality and increases the risk of infection with infectious diseases, further increasing the risk of acute and chronic diseases.

[0003] Currently, the human physiological regulatory system has the ability to actively adjust these parameters within a certain range of environmental conditions. Therefore, the body's physiological level and heat dissipation are not solely determined by environmental factors. In other words, high-speed train passengers can adapt to and maintain thermal balance based on environmental conditions through various physiological mechanisms, such as sweating or vasoconstriction. Passengers' peripheral physiological signals can provide a deeper understanding of their physiological responses and adaptation mechanisms in different thermal environments. These physiological indicators provide valuable information for understanding and evaluating thermal comfort, helping to develop effective strategies to improve the comfort and well-being of high-speed train passengers. Comfort, intelligence, and efficiency are the directions for high-quality development of high-speed rail transportation. Currently, the thermal comfort of high-speed train passengers cannot be accurately assessed, which in turn affects the flexibility and accuracy of the air supply control of the air conditioning system in the carriage thermal environment. The inability to provide a comfortable in-car thermal environment also affects energy efficiency.

[0004] Among existing technologies, the invention patent "A HVAC System Control Method Based on Multi-User Thermal Comfort Data" (Patent No. CN 107120782 B) fails to describe the data acquisition equipment and uses a unified formula for calculating thermal comfort, making this method unsuitable for various scenarios. The invention patent "Thermal Comfort Evaluation System Based on Environmental Parameters and Human Physiological Parameters" (Patent No. CN 104633866 B) uses a method for identifying human thermal comfort that has poor applicability and fails to consider the specific characteristics of railway systems.

[0005] Therefore, it is necessary to propose a high-speed train passenger thermal comfort identification system and method based on thermal imaging cameras to overcome the problems that the existing technology is not applicable to various scenarios and does not take into account the particularity of the railway system. Summary of the Invention

[0006] The purpose of the present invention is to provide a high-speed train passenger thermal comfort identification system and method based on peripheral physiological characteristics to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a high-speed train passenger thermal comfort identification system based on peripheral physiological characteristics, the system comprising a physiological data and group information acquisition module, a data preprocessing and feature fusion module, a passenger thermal comfort state identification module, and a group decision-making and IoT transmission module;

[0008] Physiological data and group information collection module, responsible for collecting occupant peripheral physiological signals from thermal imaging cameras and smart watch devices;

[0009] The data preprocessing and feature fusion module is used to receive the collected signals and perform feature preprocessing and fusion on the collected signals;

[0010] The occupant thermal comfort state recognition module receives real-time changes in the occupant's thermal comfort state;

[0011] The group decision-making and IoT transmission module stores the collected thermal physiological signals and thermal comfort status categories, aggregates them into group thermal comfort levels, and transmits them to the air-conditioning control system in the train.

[0012] Preferably, the physiological data and group information acquisition module collects group category information corresponding to the passengers from the ticket purchasing system. The physiological data and group information acquisition module includes a digital-to-analog converter, a data acquisition card, and a data transmission interface component, which is used to convert real-time scenes into digital signals for processing.

[0013] Preferably, the data preprocessing and feature fusion module is connected to the physiological data and group information collection module and the occupant thermal comfort status identification module, receives the collected signals, and performs feature preprocessing and fusion on the collected signals to facilitate classification by the thermal comfort level identification module. The data preprocessing and feature fusion module consists of a data processing unit, a cache unit, and a data wireless transmission unit.

[0014] Preferably, the occupant thermal comfort state recognition module is connected to the data preprocessing and feature fusion module and the group decision-making and Internet of Things transmission module to receive real-time changes in the occupant thermal comfort state, and accelerate the response speed and accuracy of the air conditioning air supply strategy by transmitting the information to the air conditioning control system in real time; the occupant thermal comfort state recognition module is composed of a central data processing unit, a cache unit, a data wireless transmission unit and a data transmission interface.

[0015] Preferably, the group decision-making and Internet of Things transmission module is connected to the passenger thermal comfort status identification module, and the collected thermal physiological signals and thermal comfort status categories are stored, aggregated into a group thermal comfort level, and transmitted to the air-conditioning control system in the train. The group decision-making and Internet of Things transmission module consists of a control chip, a storage controller, a data wireless transmission unit and an interface circuit.

[0016] A method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics, the method comprising the following steps:

[0017] Step S1: Constructing a train passenger thermal comfort dataset based on peripheral physiological characteristics;

[0018] Step S2: data preprocessing and feature fusion;

[0019] Step S3: Identify the thermal comfort state of the occupant, using the extracted features to accurately identify the thermal comfort level through a machine learning algorithm;

[0020] Step S4: Construct a real-time monitoring and transmission device for the thermal comfort status of passengers in the vehicle.

[0021] Preferably, the specific steps of step S1 are as follows:

[0022] Step S1.1: Under different seasons and different air conditioning system operating conditions, the subjects' peripheral physiological signals are fully collected, including skin temperature of various parts of the face, galvanic skin response (GSR), and blood volume pulse (BVP). The subjects' subjective thermal comfort questionnaire is also collected. Heart rate variability (HRV) can be extracted from the blood volume pulse (BVP). Heart rate variability (HRV) refers to the change in the number of heart beats per minute, which reflects the fluctuation of the heartbeat interval. The time domain index SDNN and nonlinear index can be calculated using the following formula. SDNN is the calculation result of the standard deviation of all normal heartbeat intervals (NN intervals), which is used to reflect the overall level of overall heart rate variability. N represents the total number of NN intervals, and NNi represents the i-th N interval. is the average value of all NN intervals; SD1 and SD2 are geometric analysis indicators based on the Poincaré scatter plot, which are used to quantify instantaneous heart rate variability and long-term heart rate variability. The Poincaré scatter plot plots each cardiac interval with its previous interval as a point on a two-dimensional plane, that is, point (NNi, NNi+1); SD1 represents the standard deviation on the short axis of the scatter plot, reflecting the instantaneous component of heart rate variability and used to evaluate parasympathetic nerve activity; SD2 represents the standard deviation on the long axis of the scatter plot, reflecting the long-term component of heart rate variability and used to evaluate the combined effects of sympathetic and parasympathetic nerves; define the standard deviation on the diagonal and vertical lines, and calculate SD1 and SD2 respectively; where Var(NNi−NNi+1) represents the variance of the differences between all consecutive NN intervals;

[0023]

[0024]

[0025]

[0026] GSR data were uniformly converted to a change from the baseline, or ΔGSR, to reduce measurement errors under different seasons and air conditioning conditions. The baseline GSR value was defined as the lowest GSR value within the first 2-5 minutes of measurement. The mean facial skin temperature (MSTF) was calculated based on the area-weighted values ​​of each facial part, where Tsk is the local facial skin temperature, dAsk is the corresponding local surface area of ​​each part, and A is the total facial area.

[0027] MSTF = = forehead local skin temperature × 0.2 + eye local skin temperature × 0.1 + cheek local skin temperature × 0.4 + nose local skin temperature × 0.1 + ear local skin temperature × 0.2;

[0028] S1.2: Group information collection. Adding differentiated categories improves the performance and accuracy of the model. Therefore, group category labels corresponding to each passenger's riding season, seat row and seat column, as well as gender, BMI, and age are collected to enhance the model's ability to recognize targets.

[0029] Preferably, the specific steps of step S2 are as follows:

[0030] S2.1: Use the LabelEncoder and StandardScaler tools to standardize each identifying factor feature. The LabelEncoder tool encodes the group category feature label using values ​​between 0 and n_classes − 1, and the StandardScaler tool standardizes the numerical peripheral physiological features.

[0031] S2.2: The StandardScaler tool is based on data standardization. It performs a linear transformation on numerical features so that each feature, that is, each attribute or column, follows a standard normal distribution with a mean of 0 and a standard deviation of 1. For a given eigenvalue X, StandardScaler standardizes each feature. The transformed eigenvalue X′ is calculated according to the following formula, where μ is the mean of feature X and σ is the standard deviation of feature X:

[0032]

[0033]

[0034] .

[0035] Preferably, the specific steps of step S3 are as follows:

[0036] S3.1: Thermal comfort identification for high-speed train passengers includes both overall and local thermal sensations. The established dataset includes passenger HRV characteristics, GSR characteristics, wrist skin temperature, skin temperature of various facial parts, and average facial skin temperature, as well as demographics, seat identification, and season.

[0037] S3.2: For global thermal sensation recognition, HRV features, GSR features, facial mean skin temperature (MSTF), and wrist skin temperature are integrated as peripheral physiological recognition features. For local thermal sensation recognition of the face, the skin temperature of the corresponding part is selected. Group category labels are used to enhance the recognition accuracy of global and local thermal sensations.

[0038] S3.3: Input the pre-processed feature dataset into the machine learning model for training. During the training process, dataset partitioning, cross-validation, and hyperparameter optimization strategies are used to optimize model performance and prevent overfitting.

[0039] S3.4: K-nearest neighbor algorithm is an instance-based non-parametric classification algorithm that calculates the distance between the sample to be classified and each sample in the training set, selects the nearest K samples, and votes based on the labels of these samples.

[0040] Preferably, the real-time monitoring and transmission device for the thermal comfort status of the occupants in step S4 includes a physiological data and group information acquisition module, a data preprocessing and feature fusion module, an occupant thermal comfort status recognition module, and a group decision-making and IoT transmission module.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The proposed system and method for identifying thermal comfort for high-speed train passengers based on peripheral physiological characteristics effectively extracts rich feature information from thermal imaging cameras and smart watches by combining intelligent sensor acquisition, intelligent preprocessing, and feature fusion technologies. This enhances the model's ability to identify targets of different scales and achieves efficient feature extraction.

[0043] By integrating peripheral physiological and group classification features with machine learning algorithms, the system can identify thermal comfort status in real time and quickly adjust to different seasons and air conditioning operating conditions, improving the accuracy and flexibility of recognition and achieving real-time and accurate identification of occupant thermal comfort status.

[0044] The system is designed with the complexity of group physiological data acquisition in mind. The number and placement of thermal imaging cameras can be flexibly deployed, making acquisition and deployment easy.

[0045] Leveraging high-performance hardware and optimized algorithm design, this system enables real-time data processing and thermal comfort status identification. This real-time performance ensures that the air conditioning control system can promptly respond and adjust air supply parameters based on the occupant's thermal comfort status, providing immediate support for improving thermal comfort and rationally allocating energy consumption. This strong real-time performance ensures that the air conditioning control system can promptly respond and adjust air supply parameters based on the occupant's thermal comfort status.

[0046] The system supports personalized and flexible management. In the group decision-making module, it allows for customized group thermal comfort decisions based on passenger age distribution, passenger number, and passenger density. This personalized management and flexible decision-making capabilities enable the system to respond more accurately to various situations, improving the relevance and effectiveness of thermal comfort control. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Flow chart of the method of the present invention;

[0048] Figure 2 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0049] In order to clearly and completely describe the objectives and technical solutions of the present invention and make the advantages more clearly understood, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, not all of them, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] For example 1, please refer to Figure 1-Figure 2 The present invention provides a technical solution: a high-speed train passenger thermal comfort identification system based on peripheral physiological characteristics, the system includes a physiological data and group information acquisition module, a data preprocessing and feature fusion module, a passenger thermal comfort state identification module and a group decision-making and Internet of Things transmission module.

[0051] The physiological data and group information acquisition module is responsible for collecting passengers' peripheral physiological signals from thermal imaging cameras and smart watch devices; the physiological data and group information acquisition module collects the group category information corresponding to the passengers from the ticket purchasing system. The physiological data and group information acquisition module includes a digital-to-analog converter, a data acquisition card, and a data transmission interface component, which is used to convert real-time scenes into digital signals for processing; among them, the PPG sensor and EDA sensor on the smart watch accurately collect the passengers' blood volume pulse (BVP) and skin electrodermal signal changes (GSR). In addition, it can also read the skin temperature around the watch. At the same time, the thermal imaging cameras arranged in the car monitor in real time to obtain the skin temperature of various parts of the passengers' faces (forehead, eyes, nose, cheeks, ears) under different environmental parameters; the physiological data and group information acquisition module is responsible for collecting the season, personal characteristics of the passengers (such as gender, age and body mass index BMI) and the passengers' seats (rows and columns).

[0052] The data preprocessing and feature fusion module is used to receive the collected signals, perform feature preprocessing and fusion on the collected signals, convert the group information features into the group category labels required by the recognition model, and extract the heart rate variability characteristics and the ΔGSR relative to the baseline change from the peripheral physiological signals, and convert the local facial skin temperature into the average facial skin temperature according to the surface area ratio weight of each part of the face; the data preprocessing and feature fusion module is connected to the physiological data and group information collection module and the occupant thermal comfort status recognition module, receives the collected signals, performs feature preprocessing and fusion on the collected signals, and facilitates classification by the thermal comfort level recognition module. The data preprocessing and feature fusion module consists of a data processing unit, a cache unit, and a data wireless transmission unit.

[0053] The occupant thermal comfort state recognition module receives real-time changes in the occupant's thermal comfort state; the occupant thermal comfort state recognition module is connected to the data preprocessing and feature fusion module and the group decision-making and Internet of Things transmission module, receives real-time changes in the occupant's thermal comfort state, and accelerates the response speed and accuracy of the air conditioning supply strategy by transmitting the information to the air conditioning control system in real time; the occupant thermal comfort state recognition module consists of a central data processing unit, a cache unit, a data wireless transmission unit and a data transmission interface.

[0054] The group decision-making and Internet of Things transmission module stores the collected thermal physiological signals and thermal comfort status categories, aggregates them into group thermal comfort levels, and transmits them to the air-conditioning control system in the train; the group decision-making and Internet of Things transmission module is connected to the passenger thermal comfort status identification module, stores the collected thermal physiological signals and thermal comfort status categories, aggregates them into group thermal comfort levels, and transmits them to the air-conditioning control system in the train. The group decision-making and Internet of Things transmission module consists of a control chip, a storage controller, a data wireless transmission unit and an interface circuit.

[0055] The high-speed train passenger thermal comfort recognition system based on peripheral physiological characteristics effectively improves the accuracy and efficiency of identifying the thermal comfort levels of high-speed train passengers under different thermal environment conditions through the integration of machine learning technology and peripheral physiological characteristics. It can achieve accurate identification of the overall and local thermal comfort of passengers in different seasons, with different personal characteristics and in different seats, so as to achieve more personalized and efficient in-vehicle thermal environment control.

[0056] Example 2, based on Example 1, provides a method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics, the method comprising the following steps:

[0057] Step S1: Constructing a train passenger thermal comfort dataset based on peripheral physiological characteristics;

[0058] Step S1.1: Under different seasons and different air conditioning system operating conditions, the subjects' peripheral physiological signals are fully collected, including skin temperature of various parts of the face, galvanic skin response (GSR), and blood volume pulse (BVP). The subjects' subjective thermal comfort questionnaire is also collected. Heart rate variability (HRV) can be extracted from the blood volume pulse (BVP). Heart rate variability (HRV) refers to the change in the number of heart beats per minute, which reflects the fluctuation of the heartbeat interval. The time domain index SDNN and nonlinear index can be calculated using the following formula. SDNN is the calculation result of the standard deviation of all normal heartbeat intervals (NN intervals), which is used to reflect the overall level of overall heart rate variability. N represents the total number of NN intervals, and NNi represents the i-th N interval. is the average value of all NN intervals; SD1 and SD2 are geometric analysis indicators based on the Poincaré scatter plot, which are used to quantify instantaneous heart rate variability and long-term heart rate variability. The Poincaré scatter plot plots each cardiac interval with its previous interval as a point on a two-dimensional plane, that is, point (NNi, NNi+1); SD1 represents the standard deviation on the short axis of the scatter plot, reflecting the instantaneous component of heart rate variability and used to evaluate parasympathetic nerve activity; SD2 represents the standard deviation on the long axis of the scatter plot, reflecting the long-term component of heart rate variability and used to evaluate the combined effects of sympathetic and parasympathetic nerves; define the standard deviation on the diagonal and vertical lines, and calculate SD1 and SD2 respectively; where Var(NNi−NNi+1) represents the variance of the differences between all consecutive NN intervals;

[0059]

[0060]

[0061]

[0062] GSR data were uniformly converted to a change from the baseline, or ΔGSR, to reduce measurement errors under different seasons and air conditioning conditions. The baseline GSR value was defined as the lowest GSR value within the first 2-5 minutes of measurement. The mean facial skin temperature (MSTF) was calculated based on the area-weighted values ​​of each facial part, where Tsk is the local facial skin temperature, dAsk is the corresponding local surface area of ​​each part, and A is the total facial area.

[0063] = forehead local skin temperature × 0.2 + eye local skin temperature × 0.1 + cheek local skin temperature × 0.4 + nose local skin temperature × 0.1 + ear local skin temperature × 0.2;

[0064] S1.2: Group information collection. Adding differentiated categories improves the performance and accuracy of the model. Therefore, group category labels corresponding to each passenger's riding season, seat row and seat column, as well as gender, BMI, and age are collected to enhance the model's ability to recognize targets.

[0065] Step S2: data preprocessing and feature fusion;

[0066] S2.1: Use the LabelEncoder and StandardScaler tools to standardize each identifying factor feature. The LabelEncoder tool encodes the group category feature label using values ​​between 0 and n_classes − 1, and the StandardScaler tool standardizes the numerical peripheral physiological features.

[0067] S2.2: The StandardScaler tool is based on data standardization. It performs a linear transformation on numerical features so that each feature, that is, each attribute or column, follows a standard normal distribution with a mean of 0 and a standard deviation of 1. For a given eigenvalue X, StandardScaler standardizes each feature. The transformed eigenvalue X′ is calculated according to the following formula, where μ is the mean of feature X and σ is the standard deviation of feature X:

[0068]

[0069]

[0070] .

[0071] Step S3: Identify the thermal comfort state of the occupant, using the extracted features to accurately identify the thermal comfort level through a machine learning algorithm;

[0072] S3.1: Thermal comfort identification for high-speed train passengers includes both overall and local thermal sensations. The established dataset includes passenger HRV characteristics, GSR characteristics, wrist skin temperature, skin temperature of various facial parts, and average facial skin temperature, as well as demographics, seat identification, and season.

[0073] S3.2: For global thermal sensation recognition, HRV features, GSR features, facial mean skin temperature (MSTF), and wrist skin temperature are integrated as peripheral physiological recognition features. For local thermal sensation recognition of the face, the skin temperature of the corresponding part is selected. Group category labels are used to enhance the recognition accuracy of global and local thermal sensations.

[0074] S3.3: Input the pre-processed feature dataset into the machine learning model for training. During the training process, dataset partitioning, cross-validation, and hyperparameter optimization strategies are used to optimize model performance and prevent overfitting.

[0075] S3.4: K-nearest neighbor algorithm is an instance-based non-parametric classification algorithm that calculates the distance between the sample to be classified and each sample in the training set, selects the nearest K samples, and votes based on the labels of these samples.

[0076] Step S4: Construct a real-time monitoring and transmission device for the thermal comfort status of vehicle occupants, including a physiological data and group information acquisition module, a data preprocessing and feature fusion module, an occupant thermal comfort status recognition module, and a group decision-making and IoT transmission module.

[0077] In Example 3, based on Example 2, the following physiological characteristic input information table is collected:

[0078]

[0079] The following environmental diagnostic inputs were collected:

[0080]

[0081] The following group information characteristics were collected:

[0082]

[0083] The following local thermal sensory outputs were collected:

[0084]

[0085] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics, characterized by: The method comprises the following steps: Step S1: Constructing a train passenger thermal comfort dataset based on peripheral physiological characteristics; Step S1.1: Under different seasons and different air-conditioning system operating conditions, the subjects' peripheral physiological signals are fully collected, including skin temperature of various parts of the face, galvanic skin response (GSR), and blood volume pulse (BVP). The subjects' subjective thermal comfort questionnaire is also collected. Heart rate variability (HRV) can be extracted from the blood volume pulse (BVP). Heart rate variability (HRV) refers to the change in the number of heart beats per minute, which reflects the fluctuation of the heartbeat interval. The time domain index SDNN and nonlinear index can be calculated by the following formula. SDNN is the calculation result of the standard deviation of all normal heartbeat intervals (NN intervals), which is used to reflect the overall level of heart rate variability. N represents the total number of NN intervals, and NN represents the total number of NN intervals. i represents the i-th N interval, is the average value of all NN intervals; SD1 and SD2 are geometric analysis indicators based on the Poincaré scatter plot, which are used to quantify instantaneous heart rate variability and long-term heart rate variability. The Poincaré scatter plot is to plot each heart beat interval and its previous interval as a point on a two-dimensional plane, that is, point (NN i , NN i+1 ); SD1 represents the standard deviation on the short axis of the scatter plot, reflecting the instantaneous component of heart rate variability, which is used to evaluate parasympathetic nerve activity; SD2 represents the standard deviation on the long axis of the scatter plot, reflecting the long-term component of heart rate variability, which is used to evaluate the combined effects of sympathetic and parasympathetic nerves; define the standard deviation on the diagonal and vertical lines, and calculate SD1 and SD2 respectively; where Var(NN i −NN i+1 ) represents the variance of all consecutive NN interval differences; For GSR data, it is uniformly converted into a change value from the baseline, namely ΔGSR, to reduce the measurement error under different air-conditioning conditions in different seasons. The baseline GSR value is defined as the lowest GSR value within the first 2-5 minutes of the measurement. The mean facial skin temperature MSTF is obtained according to the area-weighted value of each part, where T sk is the local skin temperature of the face, dA sk is the corresponding local surface area of ​​each part, and A is the total facial area; = forehead local skin temperature × 0.2 + eye local skin temperature × 0.1 + cheek local skin temperature × 0.4 + nose local skin temperature × 0.1 + ear local skin temperature × 0.2; S1.2: Group information collection: Adding differentiated categories improves model performance and accuracy. Therefore, we collect group category labels for each passenger, including their travel season, seat row and column, as well as their gender, BMI, and age, to enhance the model's ability to recognize targets. Step S2: data preprocessing and feature fusion; Step S3: Identify the thermal comfort state of the occupant, using the extracted features to accurately identify the thermal comfort level through a machine learning algorithm; Step S4: Construct a real-time monitoring and transmission device for the thermal comfort status of passengers in the vehicle.

2. The method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics according to claim 1, characterized in that: The specific steps of step S2 are as follows: S2.1: Use the LabelEncoder and StandardScaler tools to standardize each identifying factor feature. The LabelEncoder tool encodes the group category feature label using values ​​between 0 and n_classes − 1, and the StandardScaler tool standardizes the numerical peripheral physiological features. S2.2: The StandardScaler tool is based on data standardization. It performs a linear transformation on numerical features so that each feature, that is, each attribute or column, follows a standard normal distribution with a mean of 0 and a standard deviation of 1. For a given eigenvalue X, StandardScaler standardizes each feature. The transformed eigenvalue X′ is calculated according to the following formula, where μ is the mean of feature X and σ is the standard deviation of feature X: 。 3. The method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics according to claim 1, characterized in that: The specific steps of step S3 are as follows: S3.1: Thermal comfort identification for high-speed train passengers includes both overall and local thermal sensations. The established dataset includes passenger HRV characteristics, GSR characteristics, wrist skin temperature, skin temperature of various facial parts, and average facial skin temperature, as well as demographics, seat identification, and season. S3.2: For global thermal sensation recognition, HRV features, GSR features, facial mean skin temperature (MSTF), and wrist skin temperature are integrated as peripheral physiological recognition features. For local thermal sensation recognition of the face, the skin temperature of the corresponding part is selected. Group category labels are used to enhance the recognition accuracy of global and local thermal sensations. S3.3: Input the pre-processed feature dataset into the machine learning model for training. During the training process, dataset partitioning, cross-validation, and hyperparameter optimization strategies are used to optimize model performance and prevent overfitting. S3.4: K-nearest neighbor algorithm is an instance-based non-parametric classification algorithm that calculates the distance between the sample to be classified and each sample in the training set, selects the nearest K samples, and votes based on the labels of these samples.

4. The method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics according to claim 1, characterized in that: The real-time monitoring and transmission device for the thermal comfort status of the occupants in step S4 includes a physiological data and group information acquisition module, a data preprocessing and feature fusion module, an occupant thermal comfort status recognition module, and a group decision-making and IoT transmission module.

5. A system for use in the method for identifying thermal comfort of high-speed train passengers based on peripheral physiological characteristics as described in any one of claims 1 to 4, characterized in that: The system includes a physiological data and group information acquisition module, a data preprocessing and feature fusion module, an occupant thermal comfort state recognition module, and a group decision-making and IoT transmission module; Physiological data and group information collection module, responsible for collecting occupant peripheral physiological signals from thermal imaging cameras and smart watch devices; The data preprocessing and feature fusion module is used to receive the collected signals and perform feature preprocessing and fusion on the collected signals; The occupant thermal comfort state recognition module receives real-time changes in the occupant's thermal comfort state; The group decision-making and IoT transmission module stores the collected thermal physiological signals and thermal comfort status categories, aggregates them into group thermal comfort levels, and transmits them to the air-conditioning control system in the train.

6. The system according to claim 5, characterized in that: The physiological data and group information acquisition module collects group category information corresponding to the passengers from the ticket purchasing system. The physiological data and group information acquisition module includes a digital-to-analog converter, a data acquisition card, and a data transmission interface component, which is used to convert real-time scenes into digital signals for processing.

7. The system according to claim 5, characterized in that: The data preprocessing and feature fusion module is connected to the physiological data and group information collection module and the occupant thermal comfort status identification module, receives the collected signals, and performs feature preprocessing and fusion on the collected signals to facilitate classification by the thermal comfort level identification module. The data preprocessing and feature fusion module is composed of a data processing unit, a cache unit, and a data wireless transmission unit.

8. The system according to claim 5, characterized in that: The occupant thermal comfort state recognition module is connected to the data preprocessing and feature fusion module and the group decision-making and Internet of Things transmission module, receives real-time changes in the occupant's thermal comfort state, and accelerates the response speed and accuracy of the air conditioning air supply strategy by transmitting the information to the air conditioning control system in real time; the occupant thermal comfort state recognition module consists of a central data processing unit, a cache unit, a data wireless transmission unit and a data transmission interface.

9. The system according to claim 5, characterized in that: The group decision-making and Internet of Things transmission module is connected to the passenger thermal comfort status identification module, stores the collected thermal physiological signals and thermal comfort status categories, aggregates them into group thermal comfort levels, and transmits them to the air-conditioning control system in the train. The group decision-making and Internet of Things transmission module consists of a control chip, a storage controller, a data wireless transmission unit and an interface circuit.

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