A multi-factor environment simulation system for high-speed train passenger comfort research

Abstract

This invention discloses a multi-factor environmental simulation system for high-speed train passenger comfort research, specifically relating to the field of environmental simulation technology. It includes a system that uses a Roots blower, vacuum pump, and controllable butterfly valve to switch between positive pressure, negative pressure, and baseline balance states within the cabin based on the opening and closing sequence of these components. Pressure sensors and an overpressure protection relief valve monitor the cabin pressure in real time and automatically release pressure when it exceeds a preset upper limit. Wide-angle speakers are evenly distributed inside the simulation cabin, and a multi-channel speaker controller and control computer are loaded with a train noise database to achieve millisecond-level synchronized playback. A binaural artificial head device is used to collect and correct the cabin sound field in real time. Multiple programmable LED light strips are evenly distributed along the top and side walls of the simulation cabin, and brightness, color temperature, and dynamic change rhythm are controlled in a closed loop using DMX512 or SPI protocols. This invention helps to accurately reproduce the dynamic environment inside a train.

Description

Technical Field

[0001] This invention relates to the field of environmental simulation technology, and more specifically, to a multi-factor environmental simulation system for studying the comfort of high-speed train passengers. Background Technology

[0002] With the booming development of my country's railway industry, the railway network in plateau regions has been significantly expanded. Due to the unique geographical conditions, train lines in plateau areas generally exhibit a high tunnel-to-line ratio and a dense cluster of long tunnels. Taking the Ya'an-Linzhi section of the Sichuan-Tibet Railway as an example, this section has 72 tunnels with a total length of 851.48 km. 80% of these tunnels are located at an altitude of over 3000 meters, and 87% are over 10 kilometers long. The complex external environment leads to continuous deterioration of the interior environment, resulting in increased pressure and noise for passengers. Furthermore, the long hours of sunshine in plateau regions cause complex variations in the interior lighting environment. When trains run through plateau tunnels, the interior environment is affected by a combination of external factors: for example, the rapid fluctuations in air pressure caused by frequent tunnel entry and exit, the cumulative noise effect during continuous operation, and the contrast in lighting between the tunnel entrance and the interior under strong sunlight conditions. These factors can easily trigger a series of uncomfortable experiences for passengers, such as ear fullness, tinnitus, decreased concentration, visual fatigue, headaches, and irritability, seriously affecting their physiological state and subjective comfort. Studies show that the environmental comfort of occupants is closely related to changes in in-vehicle air pressure, sound environment, and lighting conditions. Sudden environmental changes or long-term exposure can both lead to a deterioration in occupant comfort.

[0003] However, current research on the comfort of train interiors largely relies on real-vehicle tests. This approach has significant limitations: operating conditions are restricted, environmental conditions are difficult to control, experimental repeatability is poor, and there are many interfering variables, making it difficult to meet the needs of systematic and quantitative research, especially in studying the changing patterns of single variables or the mechanisms of multi-factor interactions.

[0004] In summary, there is an urgent need to design a multi-factor simulation system for reproducing the environment of high-speed train passengers, which can realistically simulate the changes in air pressure, sound field distribution characteristics, and dynamic lighting conditions inside the train during operation under laboratory conditions, providing support for studying the mechanism by which changes in the in-train environment affect passenger comfort.

[0005] To address the two aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multi-factor environmental simulation system for high-speed train passenger comfort research, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A multi-factor environmental simulation system for high-speed train passenger comfort research includes an air pressure environment reproduction module, an in-vehicle sound field reconstruction module, a light environment simulation module, and a simulation experiment data evaluation module, with signal connections between the modules;

[0009] The air pressure environment reproduction module is used to switch between positive pressure, negative pressure and baseline balance state in the chamber based on the opening and closing sequence of the Roots blower, vacuum pump and controllable butterfly valve. It also monitors the chamber pressure in real time through pressure sensor and overpressure protection relief valve and automatically releases pressure when it exceeds the preset upper limit.

[0010] The in-vehicle sound field reconstruction module is used to achieve millisecond-level synchronous playback by evenly arranging wide-angle speakers inside the simulated cabin and loading the train noise database into the multi-channel speaker controller and control computer. It also collects and corrects the in-cabin sound field in real time based on the binaural artificial head device.

[0011] The light environment simulation module is used to uniformly deploy multiple programmable LED light strips along the top and side walls of the simulation chamber, and to perform closed-loop control of brightness, color temperature and dynamic change rhythm through DMX512 or SPI protocol.

[0012] The simulation experiment data evaluation module is used to collect multi-dimensional response data of air pressure environment, sound field environment and light environment during the transition process in the train event transition simulation experiment. By using the time information and data information of the train under the event transition, it evaluates the fit of the train event transition simulation event to the real operating conditions and determines the generation of alarm signals.

[0013] In a preferred embodiment, the time information and data information of the train during the event transition include:

[0014] The train's timing information during event transitions is represented by a synchronization time fluctuation coefficient and a delay timeliness coefficient. The train's data information during event transitions is represented by a data change anomaly coefficient. The synchronization time fluctuation coefficient, This is the timeliness coefficient for delay. This is the coefficient for data anomalies.

[0015] In a preferred embodiment, the logic for obtaining the synchronization time fluctuation coefficient is as follows:

[0016] The initial settings parameters for the current train's air pressure environment, sound field environment, and light environment were determined. Through several simulation experiments, the time series of the simulated air pressure environment, sound field environment, and light environment under event transition were obtained, and the initial response time point and cutoff response time point under the train event transition were obtained. The initial response time points of the several simulation experiments were marked as follows: The cutoff response time points of several simulation experiments are marked as follows: Where k is the number of samples in the simulation experiment, and p,s,l are the indices of the air pressure environment, sound field environment, and light environment in different simulation experiments;

[0017] Calculate the initial response time difference and the cutoff response time difference. The formula for calculating the initial response time difference is: The formula for calculating the cutoff response time difference is: ,in, The initial response time difference, The cutoff response time difference ;

[0018] The continuous probability density functions of the initial response time difference and the cutoff response time difference are determined using the kernel probability density function. The expression for the continuous probability density function of the initial response time difference is as follows: The continuous probability density function expression for the cutoff response time difference is: ;in, Let be a continuous probability density function of the initial response time difference. Let N be the continuous probability density function of the cutoff response time difference, where N is the number of experiments, h is the bandwidth, and K is the Gaussian kernel.

[0019] The formula for calculating the synchronization time fluctuation coefficient is as follows: .

[0020] In a preferred embodiment, the logic for obtaining the delay timeliness coefficient is as follows:

[0021] Based on several simulation experiments of the train under event transition, the time series of the train's simulated air pressure environment, sound field environment, and light environment under event transition were determined, and the transition duration of the train under the simulated air pressure environment, sound field environment, and light environment was obtained. The transition duration was determined by the difference between the cutoff response time and the initial response time. The transition durations under the simulated air pressure environment, sound field environment, and light environment were denoted as: ,in, ;

[0022] The average transition time of the train during the event transition is calculated using the following formula: (The formula is not provided in the original text.) ;in, The average transition time is calculated under simulated air pressure, sound field, and light environments.

[0023] Based on the changes in air pressure, sound pressure, and illumination intensity during event transitions under simulated air pressure, sound field, and light environments, and their rates of change, the simulation rate of the system under these environments is determined. The simulation rate is then determined for each experiment, and a system timeliness score is obtained for each experiment. A timeliness reduction model is constructed by combining the average transition time under the simulated air pressure, sound field, and light environments. Based on the system timeliness score, the undetermined parameters of the timeliness reduction model are optimized using a nonlinear least squares method. These undetermined parameters include the maximum value of the system timeliness score, the rate of decrease in score with increasing transition time, and the sensitivity coefficient of transition time to score reduction. The expression for the timeliness reduction model is as follows: ;in, This represents the error function of the time-decrease model under simulations of air pressure, sound field, and light environments. The system timeliness score is denoted by g, where g is the score number in the optimized timeliness reduction model.

[0024] The formula for calculating the timeliness coefficient is as follows: ;in, , , The timeliness weights are respectively for air pressure environment, sound field environment, and light environment. .

[0025] In a preferred embodiment, the logic for obtaining the data change anomaly coefficient is as follows:

[0026] The time series of air pressure, sound pressure, and light intensity under the transition of a train simulation event are obtained. The rate of change of air pressure, sound pressure, and light intensity under the transition of the train simulation event are obtained. By fitting the rate of change of air pressure, sound pressure, and light intensity with time, the change function of the rate of change of air pressure, sound pressure, and light intensity with time is determined. Threshold ranges for the rate of change of air pressure, sound pressure, and light intensity are set, and the time periods when the rate of change of air pressure, sound pressure, and light intensity deviates from the threshold range during the transition of the train simulation event are determined.

[0027] The formula for calculating the anomaly coefficient of data variation is as follows: ;in, Let the rate of change of air pressure be a function of time. The sound pressure rate is a function of time. Let be the function of the rate of change of illumination intensity over time, [a,b] be the time period during which the rate of change of air pressure deviates from the threshold range, [c,d] be the time period during which the rate of change of sound pressure deviates from the threshold range, and [e,f] be the time period during which the rate of change of illumination intensity deviates from the threshold range.

[0028] In a preferred embodiment, evaluating the fit of simulated train event transitions to real operating conditions includes:

[0029] By comprehensively analyzing the time and data information of the train during event transitions, and through weighted calculations of synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, a simulation quality assessment model is constructed, generating simulation quality assessment coefficients. The calculation formula for the simulation quality assessment coefficients is as follows: Where PG is the simulated quality assessment coefficient. , , These are the proportional coefficients for synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, respectively. , , All are greater than 0.

[0030] In a preferred embodiment, determining the generation of an alarm signal includes:

[0031] Set a threshold for the simulation quality assessment coefficient, obtain the simulation quality assessment coefficient under different event transitions, compare the simulation quality assessment coefficient under different event transitions with the threshold for the simulation quality assessment coefficient, generate an alarm signal if the simulation quality assessment coefficient is greater than the threshold for the simulation quality assessment coefficient, and do not generate an alarm signal if the simulation quality assessment coefficient is less than the threshold for the simulation quality assessment coefficient.

[0032] The technical effects and advantages of this invention are as follows:

[0033] 1. The multi-factor simulation system for reproducing the environment of high-speed train passengers proposed in this invention achieves high integration and precise control in the synchronous simulation of three typical environmental factors: air pressure, noise, and light. Addressing the environmental complexity faced by passengers in typical operating scenarios such as long tunnels in high-altitude areas, a sealed environmental simulation chamber was constructed. Relying on three independently developed simulation systems for air pressure, sound field, and light environment, the system accurately reproduces the dynamic environment inside the train.

[0034] 2. This invention comprehensively analyzes the timeliness, synchronicity, and stability of the environmental simulation system to determine whether the current simulation meets the requirements for occupant comfort simulation. In particular, it can determine the fidelity of multimodal environmental assessment under sudden change conditions. Attached Figure Description

[0035] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0036] Figure 1 This is a schematic diagram of a pressure environment reproduction system;

[0037] Figure 2 This is a pneumatic control system based on a butterfly valve.

[0038] Figure 3 Schematic diagram of the acoustic field reconstruction system for an environmental simulation cabin;

[0039] Figure 4 A flowchart for sound field reconstruction;

[0040] Figure 5 The train interior air pressure and noise reproduction curves;

[0041] Figure 6 This is a schematic diagram of the structure of the multi-factor environmental simulation system of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] Figure 6 This is a schematic diagram of the structure of a multi-factor environmental simulation system for high-speed train passenger comfort research according to the present invention, including an air pressure environment reproduction module, an in-vehicle sound field reconstruction module, a light environment simulation module, and a simulation experiment data evaluation module, with signal connections between the modules;

[0045] The air pressure environment reproduction module is used to switch between positive pressure, negative pressure and baseline balance state in the chamber based on the opening and closing sequence of the Roots blower, vacuum pump and controllable butterfly valve. It also monitors the chamber pressure in real time through pressure sensor and overpressure protection relief valve and automatically releases pressure when it exceeds the preset upper limit.

[0046] The in-vehicle sound field reconstruction module is used to achieve millisecond-level synchronous playback by evenly arranging wide-angle speakers inside the simulated cabin and loading the train noise database into the multi-channel speaker controller and control computer. It also collects and corrects the in-cabin sound field in real time based on the binaural artificial head device.

[0047] The light environment simulation module is used to uniformly deploy multiple programmable LED light strips along the top and side walls of the simulation chamber, and to perform closed-loop control of brightness, color temperature and dynamic change rhythm through DMX512 or SPI protocol.

[0048] The simulation experiment data evaluation module is used to collect multi-dimensional response data of air pressure environment, sound field environment and light environment during the transition process in the train event transition simulation experiment. By using the time information and data information of the train under the event transition, it evaluates the fit of the train event transition simulation event to the real operating conditions and determines the generation of alarm signals.

[0049] The multi-factor simulation system for reproducing the environment of high-speed train passengers developed in this invention is integrated into three parts, including modules for reproducing common train interior environments such as air pressure, noise, and light. The invention is presented as an environmental simulation chamber measuring 2m long x 1.5m wide x 2m high, providing experimental hardware for conducting comfort experiments. The implementation method is as follows:

[0050] The chamber pressure environment reproduction module based on a Roots blower comprises a Roots blower, a blower control terminal, a vacuum pump, an inlet / outlet butterfly valve control system, and a silencer system. These components work together to achieve alternating pressure changes within the chamber, such as... Figure 1 As shown, its specific implementation and functions are as follows:

[0051] Pressure control logic: The core of the pressure environment reproduction module lies in the coordinated action of the Roots blower, vacuum pump, and pressure control system, which realizes the alternating changes in pressure within the environmental chamber. The four valves of the pressure control system, such as... Figure 2 As shown, the main function of the Roots blower is to store gas and deliver it at high flow rates. The vacuum pump is responsible for pumping and releasing gas throughout the entire gas flow process. The pressure control system connects the vacuum pump and the Roots blower, simultaneously controlling the opening and closing of four butterfly valves to achieve overall pressure changes. Figure 2 As shown, the four valves are named A, B, C, and D respectively. A and C are connected to the environmental chamber, while B and D are interconnected with the vacuum pump. Figure 1 Inside, the hose connected to valves AB is connected to the gas release tank; opening valves AB releases the gas. The hose connected to valves CD is connected to the pressure storage tank; opening valves CD evacuates the gas.

[0052] Therefore, if pressurization of the environmental chamber is required, valve D is opened to extract gas from the storage tank. The gas is then transferred to the A and B valve areas via a blower. Opening valve A at this point allows gas pressure to be transferred into the chamber, increasing the chamber pressure and achieving a positive pressure state. Conversely, if gas needs to be extracted from the environmental chamber to achieve a negative pressure state, valve C is opened to extract gas. The gas is then transferred to the A and B valve areas via a blower. Opening valve B at this point releases the gas, achieving a negative pressure state. In addition to positive and negative pressure states, pressure balancing may also be required inside the chamber to maintain a baseline level. To achieve this, all valves simply need to be opened, allowing gas to circulate within the Roots blower-pressure control system-vacuum pump circulation system.

[0053] In summary, to achieve positive pressure inside the cabin, AD should be open and BC should be closed; to achieve negative pressure inside the cabin, BC should be open and AD should be closed; to achieve pressure equilibrium inside the cabin, all of ABCD should be open.

[0054] Pressure Change Parameter Control Parameter Settings: The main pressure change parameters include the pressure change rate and the pressure change amplitude. These are controlled by a computer using the operating frequency of the Roots blower and the valve switching time (hereinafter referred to as valve cut-off time). A higher operating frequency results in a larger range of pressure changes, i.e., a larger amplitude. If the Roots blower operates at a high frequency within a short valve cut-off time, the pressure change rate will be even greater. Taking a pressure amplitude of 1200 Pa and a change rate of 300 Pa / s as an example, a pressure change of 4 seconds is achieved throughout the process. To maintain this pressure, the control parameters are set to a frequency that matches 1200 Pa, and the valve cut-off time is uniformly set to 4 seconds. The opening and closing logic of the four valves is designed as AD open, BC closed. This allows the cabin pressure to continuously increase to 1200 Pa within 4 seconds. Considering the limits of the chamber's capacity and personnel safety, a set of pressure sensors and a pressure relief valve are also installed inside the environmental chamber. The control logic is adjusted so that when the pressure exceeds 2500Pa, it is considered that the pressure has exceeded the limit. The pressure relief valve will slowly achieve pressure balance inside and outside the chamber, and the air pressure inside the chamber will slowly return to the baseline state.

[0055] Air pressure change control range description: Based on the above control logic, alternating air pressure changes can be achieved. The amplitude of the change is specified by the operating frequency of the Roots blower, and the time of the air pressure change is controlled by the valve cut-off parameter. The device involved can achieve a minimum valve cut-off time of 500ms, the maximum air pressure amplitude that the pressure chamber can withstand is ±3000Pa, and the minimum time to complete one alternating air pressure change control is 1 second.

[0056] Taking the current standard of 500 Pa / s for train air pressure change as an example, to achieve alternating pressure changes, the valve switching time can be set to 500 ms, achieving two valve switchings within 1 second. That is, the positive pressure of 250 Pa is reached within the first 500 ms, and after the valve switching, the air pressure changes to a negative pressure of 250 Pa within 500 ms. Therefore, the alternating change of 500 Pa air pressure is completed within 1 second.

[0057] In summary, the air pressure environment reproduction system included in the invention can realize both unidirectional air pressure changes for a fixed duration and alternating air pressure changes within a fixed time period, thus reproducing the pressure change state inside the train on the current operating line.

[0058] It should be noted that the pressure simulation system, based on the coordinated control of a Roots blower and a vacuum pump, and in conjunction with a four-valve passage structure, can rapidly switch between three states: positive pressure, negative pressure, and pressure balance within the chamber. It also supports high-frequency, adjustable-amplitude pressure changes, with a minimum valve cut-off time of 500ms. The maximum pressure amplitude the pressure chamber can withstand is ±3000Pa, and a single pressure alternation control can be completed in as little as 1 second. The four-valve passage structure (A, B, C, D), through spatially symmetrical piping configuration and parallel control strategy, effectively reduces the ventilation path length and airflow resistance, significantly improving switching efficiency. Simultaneously, by separating the pressure release path (AB) and the extraction path (CD) and employing staggered control logic, instantaneous airflow impact during positive and negative pressure transitions can be effectively avoided, thereby enhancing airflow stability and experimental safety within the chamber. Compared to traditional two-valve or series three-way structures, this design has significant advantages in dynamic response time, airflow smoothness, and system reliability.

[0059] This module uses a main control computer to precisely control the fan frequency and valve opening / closing rhythm, achieving a control accuracy of ±5Pa. Under typical simulated conditions, such as a sudden pressure change in the cabin caused by a train entering a tunnel, the system can be set to a valve shut-off time of 500ms, achieving a complete switch from positive pressure +250Pa to negative pressure -250Pa within one second, fully replicating the pressure fluctuation experience of occupants during actual operation. Simultaneously, the system is equipped with an overpressure protection unit. When the detected pressure exceeds the 2500Pa safety threshold, it automatically triggers the pressure relief valve, gradually releasing excess gas using a slow-release control method to achieve pressure balance between the inside and outside of the cabin.

[0060] The in-vehicle sound field reconstruction module based on a speaker control array aims to simulate the typical acoustic environment inside the train cabin during operation. The module mainly consists of eight wide-angle speakers, a multi-channel speaker control device, a binaural artificial head, and two laptops for control and calibration. Figure 3As shown, eight speakers are evenly distributed in different locations within the environmental simulation chamber, forming a surround sound source layout. This allows for multi-directional, controllable frequency band and intensity sound signal output. An artificial head is positioned in the center of the chamber to receive sound signals from different directions, simulating the human ear's actual auditory perception of noise. The specific implementation is as follows:

[0061] The laptop ① connects to a multi-channel speaker controller via a USB interface to load the train noise source database and perform operations such as signal synchronization, sound pressure level setting, and audio playback timing control for the eight speakers. The integrated audio synchronization module ensures millisecond-level synchronized playback across multiple sound source channels, avoiding sound image shifts or interference caused by delay differences. The controller supports at least eight channels of parallel output and can be expanded to more speaker nodes for higher-precision three-dimensional sound field reconstruction. The artificial head device is equipped with a high-fidelity microphone array, capable of acquiring left and right ear signals separately and transmitting them to the laptop ② in real time via a USB interface. The built-in calibration software on the laptop ② performs dual frequency and time domain comparisons between the sound signals received by the artificial head and the target train sound source waveforms, outputting error spectra in each direction. During the debugging phase, by gradually adjusting the sound pressure level and frequency response curves of each speaker, the signal measured by the artificial head is made highly consistent with the original in-vehicle noise in both sound pressure (dBA) and frequency spectrum (Hz), thus achieving the required accuracy for sound field reconstruction.

[0062] like Figure 4 As shown, firstly, typical noise signals during the operation of the target train are collected through actual vehicle testing, and their sound pressure level, spectral structure, and spatial distribution characteristics are extracted. Then, speakers and an artificial head are placed in a simulated cabin according to a pre-set spatial layout, and the original train sound source data from each direction is played sequentially through a laptop control system. The binaural signals collected by the artificial head are compared with the measured data. Based on the differences, the system gradually adjusts the speaker output, adjusting the amplitude, phase, and frequency characteristics until the sound pressure level received at the artificial head matches the original train noise (usually controlled within the range of 1–2 dBA), and the spectral similarity meets the reconstruction accuracy requirements. This method can effectively reproduce diverse noise scenarios generated by trains under different operating conditions, including typical acoustic environments such as constant speed operation, acceleration phases, and entering / exiting tunnels.

[0063] The sound field reconstruction device can accurately simulate multi-directional, multi-frequency noise inside the train during operation. The system synchronously outputs sound source signals through eight-channel loudspeakers and combines this with real-time binaural sound pressure data collected by an artificial head for calibration, supporting millisecond-level audio synchronization and sound pressure adjustment in 1 dBA steps. Under typical tunnel entry and exit conditions, the system can adjust the spectral correlation coefficient at the artificial head to above 0.93 within the commissioning period, controlling the binaural sound pressure error within ±1.2 dBA, accurately reproducing the human ear's sensitive frequency range of 250–4000 Hz. The reproduction curves of the actual vehicle parameters and the air pressure and noise of the reproduction system are shown below. Figure 5 As shown.

[0064] It should be noted that the acoustic environment simulation system employs a multi-channel speaker array and an artificial head receiving system. Through dual-machine control and high-precision calibration algorithms, the reconstructed sound field closely matches real vehicle operating data in terms of sound pressure level and spectrum, achieving excellent spatial reproduction. In terms of hardware layout, the speaker array adopts a surround layout (from eight directions from the center position) for initial installation and positioning within the simulated space. Based on near-field acoustic holographic scanning technology or laser ranging positioning, the spatial coordinates and directivity of each speaker unit relative to the artificial head reference point are accurately measured, ensuring uniform coverage of the occupant area. The receiving system uses an artificial head model simulating the structure of a human head as its core, with omnidirectional high-sensitivity microphones embedded at both ears at a distance of 16 cm between the ears, ensuring accurate capture of the interaural sound difference.

[0065] In terms of calibration process design, this system adopts a two-stage calibration method of "static + dynamic". First, in the static stage, standard signals (such as 1 / 3 octave band swept sine or white noise) are used to excite each channel, and the binaural frequency domain response of the artificial head in a free field is acquired. The frequency domain response is extracted using Fast Fourier Transform. Then, in the dynamic stage, a standard dummy (such as a Hybrid III 50% dummy) is introduced, and microphones are placed near its ears to obtain sound pressure response under conditions closer to real human occlusion. The system combines a deep neural network (DNN) spectrum reconstruction algorithm for dynamic correction. The DNN structure uses multi-layer convolution and residual connections, and uses the actual vehicle sound field spectrum as the training target to adaptively optimize the transfer matrix. This method can provide real-time feedback on sound pressure differences under non-ideal acoustic conditions, automatically adjusting the driving voltage and phase of each channel, ultimately achieving high-precision reconstruction of near-field sound pressure. In typical train operating conditions (such as acceleration, constant speed, and entering tunnels), the average binaural sound pressure deviation in the 1–3 kHz frequency band can be controlled within ±2 dB.

[0066] The in-vehicle lighting environment simulation module based on adjustable LED strips constructs cabin visual perception conditions that conform to the lighting characteristics of real trains to simulate typical lighting environment changes during train operation. The module employs a multi-segment programmable LED strip as its core, and through a control system, precisely adjusts brightness, color temperature, and the rhythm of change to reproduce lighting changes within the closed simulation cabin under different operating conditions, including day / night operation, tunnel entry / exit, and temporary lighting switching. The system uses high-density LED strips, evenly distributed along the top and side walls of the simulation cabin to form a fully enclosed lighting coverage structure. Each strip supports PWM dimming and multiple color temperature channels (e.g., 2700K–6500K), and connects to the main control computer via communication protocols such as DMX512 or SPI to form a unified control network. The main control unit runs lighting control software, presets various typical train lighting scenarios, and loads corresponding parameter templates. Multiple lighting areas can be linked for coordinated control or independently adjusted through a single interface.

[0067] The system implementation process is as follows: First, illuminance data, color temperature distribution, and lighting change rhythm under different operating conditions of the actual train are collected through on-site surveys and real-vehicle tests. For example, the slope of light intensity change before and after entering and exiting tunnels, the low-brightness background light level in the middle of the tunnel, and the auxiliary lighting status during nighttime operation. Then, the installation and zoning of the light strips are completed in the simulation cabin to ensure that the light distribution in each area is consistent with that of the actual train, and the brightness and color temperature of each group of light strip channels are calibrated.

[0068] During the experimental operation, the main control system invokes specific lighting scene templates and synchronously controls each group of light strips to change according to preset parameters. For example, when simulating a train entering a tunnel, the system will rapidly reduce the overall brightness to the tunnel illuminance level (e.g., from 300 lx to 60 lx) within 0.5–1 seconds, while the color temperature transitions from cool light (6000K) to warm light (3500K), creating a natural light change experience similar to eye adaptation. In situations where stable illuminance needs to be maintained, the system can also enter a constant output mode to maintain a long-term stable light environment for long-term exposure experiments.

[0069] To ensure the repeatability and quantifiability of the light environment stimulation, an artificial head or illuminance sensor can be placed at the subject's visual angle to collect and provide feedback on the surrounding light environment in real time. The system can then perform closed-loop adjustments based on the collected data to further optimize the stability of the light field and the dynamic response.

[0070] It should be noted that the lighting simulation system incorporates an adjustable light strip structure. Based on a zoned programming control and a dual-channel adjustment strategy for color temperature and brightness, it can complete the switching of multiple lighting modes in milliseconds, realistically simulating the transitions between light and dark and color temperature drift that occur during train operation. The device uses a high-density RGBW four-channel LED light strip as its core, possessing a wide color temperature adjustment range (2700K–6500K) and high brightness output capability. The light strip is evenly distributed on the top and side walls of the simulation cabin. All light strip channels support PWM dimming and are connected to the main control computer via a dedicated LED driver chip, achieving high synchronization and anti-interference capabilities. The main control system presets various typical train lighting environment scenarios (such as daytime operation, night mode, tunnel entry and exit, parking lighting, etc.) and integrates a priori lighting change curve library, which can be called and loaded with corresponding parameter templates as needed. The system response latency is controlled within 10–20 milliseconds, meeting millisecond-level switching requirements and achieving realistic simulation of "brightness transitions" and "color temperature drifts" in dynamic scenarios.

[0071] The system proposed in this invention fully considers the multidimensional characteristics of train operating environment changes in its design. At the system implementation level, each module has independent control and parameter setting capabilities, enabling it to independently complete the output of typical operating conditions. Furthermore, the model performs time-series coupling modeling of the control logic of three environmental factors. The model can use typical events during train operation as trigger nodes, synchronizing and delaying changes in environmental factors through an environmental change timeline. Taking the train entering a tunnel as an example, the model defines this event as t0, driving the response of each factor according to the coupling relationship. After t0+xx s, the air pressure control, sound field reconstruction, and illumination adjustment modules are activated respectively. The delay time of each module's adjustment is synchronized based on parameters recorded from real-vehicle experiments, thereby meeting the needs of different tunnel designs and different operating conditions for stimulus combinations. The entire system achieves programmable reproduction of multiple environmental factors through hardware and software linkage, enabling the output of stable, controllable, and real-vehicle-compatible composite environmental stimuli in experiments. This system not only meets the high-simulation requirements of train environmental comfort experiments but also provides a unified and repeatable experimental platform for research on the physiological, psychological, and cognitive states of passengers.

[0072] Example 2

[0073] During train operation, the process of a train transitioning from one operating state or environmental state to another is called an event transition. In a multi-factor environmental simulation system, an event transition represents the switching of the actual physical scene or operating state. In order to study the changes in the physiological or psychological comfort of high-speed train passengers under different operating conditions, it is necessary to recreate a complete and continuous event transition process. During the simulation, the air pressure system, sound field system, and lighting system have response delays, hardware jitter, and asynchrony between modules during the event transition. Therefore, it is necessary to find better system parameter settings such as data acquisition frequency and device braking time to ensure the simulation accuracy, response consistency, and passenger experience fidelity of the environmental simulation system under different operating conditions.

[0074] By collecting simulation data of the train under different event transitions, the time information and data information of the train under the event transitions are collected. The time information of the train under the event transitions is represented by the synchronization time fluctuation coefficient and the delay timeliness coefficient, and the data information of the train under the event transitions is represented by the data change anomaly coefficient.

[0075] The role of the synchronization time fluctuation coefficient is as follows:

[0076] To recreate the temporal relationship of factor coupling in real working conditions, in a real train environment, events such as tunnel entrance and acceleration section will trigger various physical changes in the cabin simultaneously (or with a fixed delay). If a factor is activated too early or completed too late in the experiment, it will disrupt their synchronous perception in the real scene, leading to an increase in the deviation between the subject's physiological / subjective response and the real situation.

[0077] To avoid cross-interference, if the sound field has already switched but the air pressure is still rising, it will give the subject a misaligned experience of "sound before air," making it difficult to determine which factor is driving a certain change in comfort. Ensuring temporal consistency is essential to clearly "apply the influence of the three factors on comfort simultaneously" and reduce cross-confusion.

[0078] Enhancing the immersion of multimodal stimulation: The human perceptual system is sensitive to the time difference between different modalities (auditory, visual, pressure) in the tens of milliseconds. If the delay is too large, it will be perceived as "asynchronous" and break the immersive experience. Maintaining the synchronization between start-up and completion can enhance the subject's sense of presence in the "real train environment".

[0079] To ensure the accuracy of data alignment, strict timestamp alignment is required when analyzing the correspondence between physiological signals (such as heart rate and skin conductance) and environmental factors. If the completion time of each module is different, it will be difficult to accurately determine "which time period corresponds to which environmental state", which will affect data labeling and model training.

[0080] The logic for obtaining the synchronization time fluctuation coefficient is as follows: Determine the initial settings parameters for the current train's air pressure environment, sound field environment, and light environment. Through several simulation experiments, obtain the time series of the simulated air pressure environment, sound field environment, and light environment under event transition. Obtain the initial response time point and the cutoff response time point under the train event transition. Mark the initial response time points of the several simulation experiments as follows: The cutoff response time points of several simulation experiments are marked as follows: Where k is the number of samples in the simulation experiment, and p,s,l are the indices of the air pressure environment, sound field environment, and light environment in different simulation experiments;

[0081] It should be noted that the initial settings parameters are the air pressure environment, sound field environment, and light environment parameters of the initial event, including the air pressure, noise, and brightness inside the vehicle cabin of the initial event, which are used to recreate the initial event.

[0082] The event transition is the process of the train moving from the initial simulated scene to the next scene, including the dynamic driving process of the train entering the tunnel entrance, exiting the tunnel, accelerating, and decelerating. The settings parameters of air pressure environment, sound field environment and light environment are different under different events. Therefore, the settings parameters are gradually adjusted through the air pressure environment reproduction module, the in-vehicle sound field reconstruction module and the light environment simulation module to configure the target air pressure, sound field and light environment parameters that the system should achieve when the event is triggered.

[0083] When the system receives the event-triggered instruction, it sends instructions to the air pressure environment reproduction module, the in-vehicle sound field reconstruction module, and the light environment simulation module. The initial response time is the moment when the air pressure environment reproduction module, the in-vehicle sound field reconstruction module, and the light environment simulation module first deviate from the set parameters. The end response time is the moment when the air pressure environment reproduction module, the in-vehicle sound field reconstruction module, and the light environment simulation module first reach and stabilize at the target event.

[0084] Calculate the initial response time difference and the cutoff response time difference. The formula for calculating the initial response time difference is: The formula for calculating the cutoff response time difference is: ,in, The initial response time difference, The cutoff response time difference ;

[0085] The continuous probability density functions of the initial response time difference and the cutoff response time difference are determined using the kernel probability density function. The expression for the continuous probability density function of the initial response time difference is as follows: The continuous probability density function expression for the cutoff response time difference is: ;in, Let be a continuous probability density function of the initial response time difference. Let N be the continuous probability density function of the cutoff response time difference, where N is the number of experiments, h is the bandwidth, and K is the Gaussian kernel.

[0086] The formula for calculating the synchronization time fluctuation coefficient is as follows: ;in, This is the synchronization time fluctuation coefficient.

[0087] As can be seen from the formula, the larger the synchronization time fluctuation coefficient, the greater the uncertainty in the event transition process. This indicates that there may be a disconnect in the occupant's experience in multiple experiments. It is necessary to change parameters such as the data acquisition frequency of the air pressure environment, sound field environment, and light environment simulation, as well as the device braking time, to ensure a highly consistent multi-factor simulation output under the same event transition.

[0088] The purpose of the delay timeliness coefficient is to integrate the transition time of the three environmental modules, avoiding the tediousness of analyzing each module separately, and to reflect the differences in the impact of different environmental factors on passenger comfort. The larger the delay timeliness coefficient, the more likely passengers are to perceive the longer the lag in environmental switching. Lag will lead to the asynchrony of multi-sensory information. By using the delay timeliness coefficient, the performance of the simulated train under event transition can be determined, and the sampling rate, valve switching sequence, speaker and light strip control strategies can be adjusted in a targeted manner.

[0089] The logic for obtaining the delay timeliness coefficient is as follows: Based on several simulation experimental data of the train under event transition, the time series of the train's air pressure environment, sound field environment, and light environment simulations under event transition are determined, and the transition duration of the train under the air pressure environment, sound field environment, and light environment simulations is obtained. The transition duration is determined by the difference between the cutoff response time point and the initial response time point. The transition durations under the air pressure environment, sound field environment, and light environment simulations are marked as follows: ,in, ;

[0090] The average transition time of the train during the event transition is calculated using the following formula: (The formula is not provided in the original text.) ;in, The average transition time is calculated under simulated air pressure, sound field, and light environments.

[0091] Based on the changes in air pressure, sound pressure, and illumination intensity during event transitions under simulated air pressure, sound field, and light environments, and their rates of change, the simulation rate of the system under these environments is determined. The simulation rate is then determined for each experiment, and a system timeliness score is obtained for each experiment. A timeliness reduction model is constructed by combining the average transition time under the simulated air pressure, sound field, and light environments. Based on the system timeliness score, the undetermined parameters of the timeliness reduction model are optimized using a nonlinear least squares method. These undetermined parameters include the maximum value of the system timeliness score, the rate of decrease in score with increasing transition time, and the sensitivity coefficient of transition time to score reduction. The expression for the timeliness reduction model is as follows: ;in, This represents the error function of the time-decrease model under simulations of air pressure, sound field, and light environments. The system timeliness score is denoted by g, where g is the score number in the optimized timeliness reduction model.

[0092] The formula for calculating the timeliness coefficient is as follows: ;in, This is the timeliness coefficient for delay. , , The timeliness weights are respectively for air pressure environment, sound field environment, and light environment. .

[0093] As can be seen from the formula, the larger the delay efficiency coefficient, the longer the transition time under the simulated air pressure environment, sound field environment, and light environment will be. The smaller the system efficiency score, the more obvious the environmental switching lag will be for passengers during train operation, resulting in perceptual disconnect and decreased comfort. In other words, the system simulation time will increase, leading to a decrease in simulation effect. It is necessary to change the data acquisition frequency, device braking time, and other parameters of the simulated air pressure environment, sound field environment, and light environment to ensure comfort and the reliability of experimental data.

[0094] The anomaly coefficient is a core indicator for measuring the fidelity of environmental simulation output. The role of the anomaly coefficient is:

[0095] By calculating the rate of change of air pressure, sound pressure and light intensity in real time, and judging whether they deviate from the preset safety range or simulation target range, the data change anomaly coefficient can be used as the basis for monitoring whether the performance of the control system is stable. If the coefficient increases significantly in a certain test, it indicates that there is a large error in the current environmental simulation or the system response is slow, and the alarm mechanism can be triggered in time or the test can be stopped.

[0096] The abnormal coefficient of data change can serve as a feedback signal to guide the optimization of the system's hardware and software parameters. For example, if the coefficient is consistently high in multiple simulation experiments, it can be deduced that the sampling frequency is insufficient, the signal acquisition delay is too large, or the controller response is not timely. Technicians can gradually reduce the coefficient by optimizing the fan start-up time, the speaker excitation synchronization frequency, or the LED response delay parameters, thereby improving the overall system performance.

[0097] The simulation complexity varies under different train operation events (such as entering a tunnel, exiting a tunnel, accelerating, decelerating, etc.). The data change anomaly coefficient can be used as an indicator to quantitatively compare the simulation difficulty and system adaptability of various events. The data change anomaly coefficient can reflect the large deviation of the train during the time transition. Once a large deviation occurs in a short period of time, an alarm will be triggered immediately, prompting the system to stop for calibration or switch to safe mode.

[0098] Anomaly coefficients of data changes under different event transitions

[0099] The logic for obtaining the data change anomaly coefficient is as follows: obtain the time series of air pressure, sound pressure, and light intensity under the transition of the train simulation event, obtain the rate of change of air pressure, sound pressure, and light intensity under the transition of the train simulation event, and determine the change function of the rate of change of air pressure, sound pressure, and light intensity with time by fitting the rate of change of air pressure, sound pressure, and light intensity with time. Set the threshold range of the rate of change of air pressure, sound pressure, and light intensity, and determine the time period during the transition of the train simulation event when the rate of change of air pressure, sound pressure, and light intensity deviates from the threshold range.

[0100] The formula for calculating the anomaly coefficient of data variation is as follows: ;in, The coefficient for data anomaly. Let the rate of change of air pressure be a function of time. The sound pressure rate is a function of time. Let be the function of the rate of change of illumination intensity over time, [a,b] be the time period during which the rate of change of air pressure deviates from the threshold range, [c,d] be the time period during which the rate of change of sound pressure deviates from the threshold range, and [e,f] be the time period during which the rate of change of illumination intensity deviates from the threshold range.

[0101] As can be seen from the formula, the larger the abnormal coefficient of data change, the longer the cumulative time for the rate of change of air pressure, sound pressure or light intensity to deviate from the preset safety / simulation threshold range in the simulation experiment. This indicates a decrease in the fidelity of the simulation output. Therefore, it is necessary to change the data acquisition frequency and device braking time of the air pressure environment, sound field environment and light environment simulation.

[0102] By comprehensively analyzing the time and data information of the train during event transitions, and through weighted calculations of synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, a simulation quality assessment model is constructed, generating simulation quality assessment coefficients. The calculation formula for the simulation quality assessment coefficients is as follows: Where PG is the simulated quality assessment coefficient. , , These are the proportional coefficients for synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, respectively. , , All are greater than 0.

[0103] Set a threshold for the simulation quality evaluation coefficient to obtain the simulation quality evaluation coefficient under different event transitions. Compare the simulation quality evaluation coefficient under different event transitions with the threshold. If the simulation quality evaluation coefficient is greater than the threshold, an alarm signal is generated, indicating that the simulation results obtained through multiple experiments show that there are asynchronous system responses, excessive delays, or abnormally drastic environmental changes during the current time transition, which cannot meet the simulation requirements for the comfort of high-speed train passengers. The simulation system parameters or strategies need to be adjusted in time. If the simulation quality evaluation coefficient is less than the threshold, no alarm signal is generated.

[0104] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0105] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0106] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0110] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-factor environmental simulation system for studying passenger comfort in high-speed trains, characterized in that, It includes an air pressure environment reproduction module, an in-vehicle sound field reconstruction module, a light environment simulation module, and a simulation experiment data evaluation module, with signal connections between the modules; The air pressure environment reproduction module is used to switch between positive pressure, negative pressure and baseline balance state in the chamber based on the opening and closing sequence of the Roots blower, vacuum pump and controllable butterfly valve. It also monitors the chamber pressure in real time through pressure sensor and overpressure protection relief valve and automatically releases pressure when it exceeds the preset upper limit. The in-vehicle sound field reconstruction module is used to achieve millisecond-level synchronous playback by evenly arranging wide-angle speakers inside the simulated cabin and loading the train noise database into the multi-channel speaker controller and control computer. It also collects and corrects the in-cabin sound field in real time based on the binaural artificial head device. The light environment simulation module is used to uniformly deploy multiple programmable LED light strips along the top and side walls of the simulation chamber, and to perform closed-loop control of brightness, color temperature and dynamic change rhythm through DMX512 or SPI protocol. The simulation experiment data evaluation module is used to collect multi-dimensional response data of air pressure environment, sound field environment and light environment during the transition process in the train event transition simulation experiment. By using the time information and data information of the train under the event transition, the module evaluates the fit of the train event transition simulation event to the real operating conditions and determines the generation of alarm signals. The train's timing and data information during the event transition includes: The train's timing information during event transitions is represented by a synchronization time fluctuation coefficient and a delay timeliness coefficient. The train's data information during event transitions is represented by a data change anomaly coefficient. The synchronization time fluctuation coefficient, This is the timeliness coefficient for delay. The coefficient for data anomaly; The logic for obtaining the synchronization time fluctuation coefficient is as follows: The initial settings parameters for the current train's air pressure environment, sound field environment, and light environment were determined. Through several simulation experiments, the time series of the simulated air pressure environment, sound field environment, and light environment under event transition were obtained, and the initial response time point and cutoff response time point under the train event transition were obtained. The initial response time points of the several simulation experiments were marked as follows: The cutoff response time points of several simulation experiments are marked as follows: Where k is the number of samples in the simulation experiment, and p,s,l are the indices of the air pressure environment, sound field environment, and light environment in different simulation experiments; Calculate the initial response time difference and the cutoff response time difference. The formula for calculating the initial response time difference is: The formula for calculating the cutoff response time difference is: ,in, The initial response time difference, The cutoff response time difference ; The continuous probability density functions of the initial response time difference and the cutoff response time difference are determined using the kernel probability density function. The expression for the continuous probability density function of the initial response time difference is as follows: The continuous probability density function expression for the cutoff response time difference is: ;in, Let be a continuous probability density function of the initial response time difference. Let N be the continuous probability density function of the cutoff response time difference, where N is the number of experiments, h is the bandwidth, and K is the Gaussian kernel. The formula for calculating the synchronization time fluctuation coefficient is as follows: ; The logic for obtaining the delay timeliness coefficient is as follows: Based on several simulation experiments of the train under event transition, the time series of the train's simulated air pressure environment, sound field environment, and light environment under event transition were determined, and the transition duration of the train under the simulated air pressure environment, sound field environment, and light environment was obtained. The transition duration was determined by the difference between the cutoff response time and the initial response time. The transition durations under the simulated air pressure environment, sound field environment, and light environment were denoted as: ,in, ; The average transition time of the train during the event transition is calculated using the following formula: (The formula is not provided in the original text.) ;in, The average transition time is calculated under simulated air pressure, sound field, and light environments. Based on the changes in air pressure, sound pressure, and illumination intensity during event transitions under simulated air pressure, sound field, and light environments, and their rates of change, the simulation rate of the system under these environments is determined. The simulation rate is then determined for each experiment, and a system timeliness score is obtained for each experiment. A timeliness reduction model is constructed by combining the average transition time under the simulated air pressure, sound field, and light environments. Based on the system timeliness score, the undetermined parameters of the timeliness reduction model are optimized using a nonlinear least squares method. These undetermined parameters include the maximum value of the system timeliness score, the rate of decrease in score with increasing transition time, and the sensitivity coefficient of transition time to score reduction. The expression for the timeliness reduction model is as follows: ;in, This represents the error function of the time-decrease model under simulations of air pressure, sound field, and light environments. The system timeliness score is denoted by g, where g is the score number in the optimized timeliness reduction model. The formula for calculating the timeliness coefficient is as follows: ;in, , , The timeliness weights are respectively for air pressure environment, sound field environment, and light environment. ; The logic for obtaining the data change anomaly coefficient is as follows: The time series of air pressure, sound pressure, and light intensity under the transition of a train simulation event are obtained. The rate of change of air pressure, sound pressure, and light intensity under the transition of the train simulation event are obtained. By fitting the rate of change of air pressure, sound pressure, and light intensity with time, the change function of the rate of change of air pressure, sound pressure, and light intensity with time is determined. Threshold ranges for the rate of change of air pressure, sound pressure, and light intensity are set, and the time periods when the rate of change of air pressure, sound pressure, and light intensity deviates from the threshold range during the transition of the train simulation event are determined. The formula for calculating the anomaly coefficient of data variation is as follows: ;in, Let the rate of change of air pressure be a function of time. The sound pressure rate is a function of time. Let be the function of the rate of change of illumination intensity over time, [a,b] be the time period during which the rate of change of air pressure deviates from the threshold range, [c,d] be the time period during which the rate of change of sound pressure deviates from the threshold range, and [e,f] be the time period during which the rate of change of illumination intensity deviates from the threshold range.

2. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 1, characterized in that, Evaluate the fit of simulated train event transitions to real operating conditions, including: By comprehensively analyzing the time and data information of the train during event transitions, and through weighted calculations of synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, a simulation quality assessment model is constructed, generating simulation quality assessment coefficients. The calculation formula for the simulation quality assessment coefficients is as follows: Where PG is the simulated quality assessment coefficient. , , These are the proportional coefficients for synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, respectively. , , All are greater than 0.

3. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 2, characterized in that, Determining whether to generate an alarm signal includes: Set a threshold for the simulation quality assessment coefficient, obtain the simulation quality assessment coefficient under different event transitions, compare the simulation quality assessment coefficient under different event transitions with the threshold for the simulation quality assessment coefficient, generate an alarm signal if the simulation quality assessment coefficient is greater than the threshold for the simulation quality assessment coefficient, and do not generate an alarm signal if the simulation quality assessment coefficient is less than the threshold for the simulation quality assessment coefficient.

Images