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

Abstract

The invention discloses a multi-factor environment simulation system for high-speed train passenger comfort research, and particularly relates to the technical field of environment simulation, which comprises a Roots blower, a vacuum pump and a controllable butterfly valve, and realizes the switching of positive pressure, negative pressure and baseline balance states in a cabin based on the opening and closing sequence of the blower, the vacuum pump and the butterfly valve. Through a pressure sensor and an overpressure protection pressure release valve, the pressure in the cabin is monitored in real time, the pressure is automatically released when the pressure exceeds a preset upper limit, wide-angle loudspeakers are uniformly arranged in the simulation cabin, and a multi-channel loudspeaker controller and a control computer are loaded to a train noise database, so that millisecond-level synchronous playing is realized; the sound field in the cabin is collected and corrected in real time based on a double-ear artificial head device, multiple sections of programmable LED lamp strips are used and evenly distributed along the top and the side wall of the simulation cabin, closed-loop control is conducted on the brightness, the color temperature and the dynamic change rhythm through a DMX512 or SPI protocol, and accurate reproduction of the dynamic environment in the train is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental simulation, and more particularly to a multi-factor environmental simulation system for studying the comfort of high-speed train passengers. Background Art

[0002] With the booming development of my country's railway industry, the plateau railway network has expanded significantly. Due to the unique geographical conditions of the plateau, train routes in the plateau region are generally characterized by a high tunnel-to-line ratio and a dense concentration of long and extensive tunnels. For example, the Ya'an-Linzhi section of the Sichuan-Tibet Railway boasts 72 tunnels totaling 851.48 km. 80% of these tunnels are located above 3,000 meters above sea level, and 87% are ultra-long tunnels exceeding 10 kilometers. This complex external environment leads to a continuous deterioration of the interior environment, which in turn exerts stress on passengers through increased air pressure and noise levels. Furthermore, the long hours of sunshine in the plateau create complex variations in the interior light environment. When trains operate within the plateau tunnels, the interior environment is subject to the combined effects of multiple external factors: for example, rapid fluctuations in air pressure caused by frequent tunnel entry and exit, the cumulative effect of noise during continuous operation, and the varying lighting contrast between tunnel entrances and interiors under the high plateau sunlight conditions. These factors can easily cause a range of discomfort for passengers, such as ear fullness, tinnitus, decreased concentration, visual fatigue, headaches, and irritability, severely impacting their physiological state and subjective comfort. Studies have shown that passengers' environmental comfort is closely related to changes in the vehicle's air pressure, sound environment and lighting conditions. Sudden changes in the environment or long-term exposure may cause passengers' comfort to deteriorate.

[0003] However, current research on train interior comfort mostly relies on on-board testing. This approach has significant limitations: restricted operating conditions, difficult-to-control environmental conditions, poor experimental repeatability, and numerous interfering variables. This makes it difficult to meet the needs of systematic and quantitative research, especially when examining the dynamics of a single variable 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 passenger environment of high-speed trains, which can realistically simulate the air pressure changes, sound field distribution characteristics and dynamic lighting conditions inside the car during the operation of a high-speed train under laboratory conditions, and provide support for studying the mechanism by which changes in the interior environment affect passenger comfort.

[0005] In order to solve the above two defects, a technical solution is now provided. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-factor environment simulation system for studying the comfort of high-speed train passengers, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A multi-factor environmental simulation system for high-speed train passenger comfort research includes an air pressure environment reproduction module, an in-car 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 equilibrium in the cabin based on the opening and closing sequence of the Roots blower, vacuum pump, and controllable butterfly valve. It also monitors the cabin pressure in real time through a pressure sensor and overpressure protection relief valve, and automatically relieves pressure when it exceeds a preset upper limit. The in-car sound field reconstruction module is used to evenly arrange wide-angle speakers inside the simulation cabin, load the multi-channel speaker controller and control computer with the train noise database, achieve millisecond-level synchronous playback, and use the binaural artificial head device to collect and correct the in-cabin sound field in real time; The light environment simulation module uses multi-segment programmable LED light strips evenly distributed along the top and side walls of the simulation cabin, and performs closed-loop control of brightness, color temperature, and dynamic change rhythm through DMX512 or SPI protocols; The simulation experiment data evaluation module is used to collect multi-dimensional response data of the air pressure environment, sound field environment and light environment during the transition process in the train event transition simulation experiment. Through 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 actual operating conditions and determines the generation of alarm signals.

[0008] In a preferred embodiment, the time information and data information of the train under event transition include: The time information of the train under event transition is represented by the synchronization time fluctuation coefficient and the delay timeliness coefficient, and the data information of the train under event transition is represented by the data change anomaly coefficient, where: is the synchronization time fluctuation coefficient, is the delay timeliness coefficient, is the data variation anomaly coefficient.

[0009] In a preferred embodiment, the logic for obtaining the synchronization time fluctuation coefficient is: Determine the initial setting parameters of the current train air pressure environment, acoustic field environment, and light environment. Through several simulation experiments, obtain the time series of the train air pressure environment, acoustic field environment, and light environment simulation under event transition. Obtain the initial response time point and cutoff response time point under the train event transition. Mark the initial response time points of several simulation experiments as: , mark the cutoff response time points of several simulation experiments as: , where k is the number of samples in the simulation experiment, p, s, l are the indexes 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 calculation formula for the initial response time difference is: , the calculation formula of the cut-off response time difference is: ,in, is the initial response time difference, is the cut-off response time difference, ; The continuous probability density function of the initial response time difference and the continuous probability density function of the cutoff response time difference are determined by the kernel probability density function. The continuous probability density function expression of the initial response time difference is: , the continuous probability density function expression of the cut-off response time difference is: ;in, is the continuous probability density function of the initial response time difference, is the continuous probability density function of the cutoff response time difference, N is the number of experiments, h is the bandwidth, and K is the Gaussian kernel; Calculate the synchronization time fluctuation coefficient using the following formula: .

[0010] In a preferred embodiment, the acquisition logic of the delay timeliness coefficient is: Based on the data of several simulation experiments of trains under event transition, the time series of the train's air pressure environment, sound field environment, and light environment simulation under event transition were determined, and the transition duration of the train under the air pressure environment, sound field environment, and light environment simulation was obtained. The transition duration was determined by the difference between the cutoff response time point and the initial response time point. The transition duration under the air pressure environment, sound field environment, and light environment simulation was marked as: ,in, ; Calculate the average transition time of the train under the simulation of the air pressure environment, sound field environment, and light environment during the event transition process. The calculation formula is: ;in, is the average transition time under the simulation of air pressure environment, sound field environment and light environment; Based on the changes in air pressure, sound pressure and light illumination during the event transition under the simulation of air pressure environment, sound field environment and light environment, and the rates of change of air pressure, sound pressure and light illumination, the simulation rate of the system under the simulation of air pressure environment, sound field environment and light environment is determined, and the simulation rate of the system is determined in each experiment to obtain the system timeliness score in each experiment. The timeliness decline model is constructed in combination with the average transition time under the simulation of air pressure environment, sound field environment and light environment. The undetermined parameters of the timeliness decline model are optimized based on the nonlinear least squares method according to the system timeliness score. The undetermined parameters include the maximum value of the system timeliness score, the rate of decline of the score with the increase of transition time, and the sensitivity coefficient of transition time to the decline of the score. The expression of the timeliness decline model is: ;in, is the error function of the time-dependent degradation model under the simulation of air pressure environment, sound field environment and light environment, is the system timeliness score, g is the number of the system timeliness score in the optimization timeliness reduction model; Calculate the delay timeliness coefficient, the calculation formula is: ;in, 、 、 are the timeliness weights of the air pressure environment, sound field environment, and light environment, respectively. .

[0011] In a preferred embodiment, the logic for obtaining the data change abnormality coefficient is: Obtaining a time series of air pressure, sound pressure, and light brightness during a train simulation event transition, obtaining the air pressure change rate, sound pressure change rate, and light brightness change rate during the train simulation event transition, determining a function of the air pressure change rate, sound pressure change rate, and light brightness change rate over time by fitting the air pressure change rate, sound pressure change rate, and light brightness change rate with time, setting a threshold range for the air pressure change rate, sound pressure change rate, and light brightness change rate, and determining a time period during which the air pressure change rate, sound pressure change rate, and light brightness change rate deviate from the threshold range during the train simulation event transition; Calculate the data change abnormal coefficient, the calculation formula is: ;in, is the pressure change rate as a function of time, is the function of the rate of change of sound pressure over time, is the function of the change rate of light brightness over time, [a, b] is the time period when the air pressure change rate deviates from the threshold range, [c, d] is the time period when the sound pressure change rate deviates from the threshold range, and [e, f] is the time period when the light brightness change rate deviates from the threshold range.

[0012] In a preferred embodiment, evaluating the degree of fit of a train event transition simulation event to an actual operating condition includes: The time information and data information of the train under event transition are comprehensively analyzed. Through weighted calculation of the synchronization time fluctuation coefficient, delay timeliness coefficient and data change abnormality coefficient, a simulation quality assessment model is constructed to generate the simulation quality assessment coefficient. The calculation formula of the simulation quality assessment coefficient is: ; Where PG is the simulation quality assessment coefficient, 、 、 They are the proportional coefficients of synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, 、 、 Both are greater than 0.

[0013] In a preferred embodiment, determining whether to generate an alarm signal includes: Set the simulation quality assessment coefficient threshold, obtain the simulation quality assessment coefficient under different event transitions, compare the simulation quality assessment coefficient under different event transitions with the simulation quality assessment coefficient threshold, and generate an alarm signal if the simulation quality assessment coefficient is greater than the simulation quality assessment coefficient threshold; if the simulation quality assessment coefficient is less than the simulation quality assessment coefficient threshold, no alarm signal is generated.

[0014] Technical effects and advantages of the present invention: 1. The multi-factor simulation system for recreating the high-speed train passenger environment proposed in this invention achieves high integration and precise control in the simultaneous simulation of three typical environmental factors: air pressure, noise, and light. To address the environmental complexity faced by passengers in typical operating scenarios, such as long and dense tunnels on the plateau, a closed environmental simulation cabin was constructed. Relying on independently developed simulation systems for air pressure, sound field, and light environment, the system accurately reproduces the dynamic environment inside the train. 2. The present invention comprehensively analyzes the timeliness, synchronization and change stability of the environmental simulation system to determine whether the current simulation meets the requirements of occupant comfort simulation, especially under sudden change conditions, and can determine the fidelity of multimodal environmental assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the air pressure environment reproduction system; Figure 2 It is an air pressure control system based on a control butterfly valve; Figure 3 Schematic diagram of the sound field reconstruction system for the environmental simulation cabin; Figure 4 Reconstruct a flow chart for the sound field; Figure 5 is the train interior air pressure and noise reproduction curve; Figure 6 It is a structural schematic diagram of the multi-factor environmental simulation system of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on 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.

[0017] Example 1 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, which 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 equilibrium in the cabin based on the opening and closing sequence of the Roots blower, vacuum pump, and controllable butterfly valve. It also monitors the cabin pressure in real time through a pressure sensor and overpressure protection relief valve, and automatically relieves pressure when it exceeds a preset upper limit. The in-car sound field reconstruction module is used to evenly arrange wide-angle speakers inside the simulation cabin, load the multi-channel speaker controller and control computer with the train noise database, achieve millisecond-level synchronous playback, and use the binaural artificial head device to collect and correct the in-cabin sound field in real time; The light environment simulation module uses multi-segment programmable LED light strips evenly distributed along the top and side walls of the simulation cabin, and performs closed-loop control of brightness, color temperature, and dynamic change rhythm through DMX512 or SPI protocols; The simulation experiment data evaluation module is used to collect multi-dimensional response data of the air pressure environment, sound field environment and light environment during the transition process in the train event transition simulation experiment. Through 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 actual operating conditions and determines the generation of alarm signals.

[0018] The multi-factor simulation system developed by the present invention for recreating the passenger environment on high-speed trains consists of three integrated components, including modules for reproducing the common air pressure, noise, and light environments within a train interior. The system takes the form of an environmental simulation chamber measuring 2m long, 1.5m wide, and 2m high, providing experimental hardware for conducting comfort experiments. The implementation is as follows: The cabin pressure environment reproduction module based on the Roots blower includes multiple parts, such as the Roots blower, blower control terminal, vacuum pump, inlet / outlet butterfly valve control system, and silencer system, which work together to achieve the alternating changes in cabin pressure. Figure 1 As shown, its specific implementation and functions are as follows: Air pressure control logic: The core of the air pressure environment reproduction module lies in the synergy of the Roots blower, vacuum pump and air pressure control system, which realizes the alternating change of air pressure in the environmental chamber. The four valves of the air pressure control system are as follows: Figure 2 As shown in the figure, the main function of the Roots blower is to store gas and transport it at a high flow rate. The vacuum pump is responsible for pumping / discharging the gas during the entire gas flow process. The air pressure control system connects the vacuum pump and the Roots blower and controls the opening and closing of the four butterfly valves at the same time, thereby achieving changes in the overall air pressure. Figure 2 As shown, the four valves are named A, B, C, and D, among which A and C are connected to the environmental chamber, and B and D are connected to the vacuum pump. Figure 1 Inside, the hose connected to the AB valve is connected to the gas release tank, and opening the AB valve realizes the deflation function; the hose connected to the CD valve is connected to the air pressure storage tank, and opening the CD valve realizes the exhaust function.

[0019] Therefore, if the environmental chamber needs to be pressurized, valve D is opened to extract gas from the storage tank and transfer it to the A and B valve areas through the blower. At this time, valve A is opened to transmit the air pressure into the chamber, completing the increase in the chamber air pressure and achieving a positive pressure state in the chamber. If gas needs to be extracted from the environmental chamber to achieve a negative pressure state in the chamber, valve C is opened, and gas is extracted from the chamber and transferred to the A and B valve areas through the blower. At this time, valve B is opened to release the gas, thus achieving a negative pressure state in the chamber. In addition to positive and negative pressure states, the pressure inside the chamber may also need to be balanced to maintain a baseline level. To achieve this function, all valves need to be opened, and the gas will circulate in the circulation system of Roots blower-air pressure control system-vacuum pump.

[0020] To sum up, to achieve a positive pressure state in the cabin, AD is opened and BC is closed; to achieve a negative pressure state in the cabin, BC is opened and AD is closed; to achieve a pressure balance state in the cabin, ABCD are all opened.

[0021] Air pressure change parameter control parameter setting: The air pressure change parameters mainly include the air pressure change rate and the air pressure change amplitude. The operating frequency of the Roots blower and the valve switching time (hereinafter collectively referred to as the valve cut time) are controlled by a computer. The higher the operating frequency, the greater the range of air pressure change, that is, the greater the amplitude. If the Roots blower operates at a high operating frequency within a short valve cut time, the air pressure change rate will be greater. Taking an air pressure amplitude of 1200Pa and a change rate of 300Pa / s as an example, a pressure change of 4s is achieved during the entire process. The subsequent pressure needs to be maintained at this value. The control parameters are set to match the frequency parameters of 1200Pa, and the valve cut time is uniformly set to 4s. The opening and closing logic of the four valves is designed to be AD open and BC close. In this way, the cabin pressure is continuously increased to 1200Pa within 4s. Taking into account the cabin's bearing limit and personnel safety, a set of pressure sensors and pressure relief valves are also installed in the environmental cabin. The control logic is adjusted so that when the pressure exceeds 2500Pa, it is considered that the pressure exceeds the limit. The pressure relief valve will slowly achieve pressure balance inside and outside the cabin, and the air pressure in the cabin will slowly return to the baseline state.

[0022] Air pressure control range: Based on the above control logic, alternating air pressure variations are possible. The amplitude of the variation is determined by the operating frequency of the Roots blower, and the duration of the pressure change is controlled by the valve switching parameters. The device can achieve a minimum valve switching time of 500ms. The maximum pressure amplitude that the air pressure chamber can withstand is ±3000Pa, and alternating air pressure control can be completed in as little as 1s.

[0023] Taking the current standard train air pressure change rate of 500 Pa / s as an example, to achieve alternating pressure changes, the valve switching time can be set to 500 ms, achieving two valve switches within 1 second. That is, a positive pressure of 250 Pa is reached within the first 500 ms, and after the valve switch, a negative pressure change occurs, reaching a negative pressure of 250 Pa within 500 ms. Thus, the alternating pressure change of 500 Pa is completed within 1 second.

[0024] In summary, the air pressure environment reproduction system included in the invention can not only realize one-way air pressure changes of a fixed duration, but also complete alternating air pressure changes within a fixed time, and can reproduce the pressure change state inside the train on the current operating line.

[0025] It should be noted that the pressure simulation system, based on the coordinated control of a Roots blower and a vacuum pump, coupled with a four-valve circuit structure, enables rapid switching between positive, negative, and balanced pressure states within the chamber. It also supports high-frequency, adjustable pressure variations. Pressure control achieves a minimum valve switching time of 500ms, with a maximum pressure amplitude of ±3000 Pa. Pressure switching can be completed in as little as 1 second. The four-valve circuit structure (A, B, C, and D), through spatially symmetrical piping and a parallel control strategy, effectively reduces ventilation path length and airflow resistance, significantly improving switching efficiency. Furthermore, by separating the pressure release path (AB) from the air extraction path (CD) and employing interleaved control logic, transient airflow shock during positive-negative pressure transitions is effectively avoided, thereby improving airflow stability within the chamber and experimental safety. Compared to traditional two-valve or series-connected three-way structures, this design offers significant advantages in dynamic response time, airflow smoothness, and system reliability.

[0026] This module uses the main control computer to achieve precise control of the fan frequency and valve opening and closing rhythm, with a control accuracy of ±5Pa. Under typical simulation conditions, such as simulating a sudden change in cabin pressure caused by a train entering a tunnel, the system can set the valve cut-off time to 500ms, achieving a complete switching process from positive pressure +250Pa to negative pressure -250Pa within 1 second, fully replicating the pressure fluctuations experienced by passengers during actual operation. At the same time, the system is equipped with an overpressure protection unit. When the detected air pressure exceeds the safety threshold of 2500Pa, the pressure relief valve will be automatically triggered, and the excess gas will be gradually released using a slow-release control method to achieve pressure balance inside and outside the cabin.

[0027] The in-car sound field reconstruction module based on the speaker control array is designed to simulate the typical sound environment in the cabin where passengers are located during train operation. The in-car sound field reconstruction module mainly consists of eight wide-angle speakers, a set of multi-channel speaker control devices, a binaural artificial head, and two laptop computers for control and calibration. Figure 3 As shown, eight speakers are evenly arranged in different directions in the environmental simulation cabin, forming a surround sound source layout that can output sound signals in multiple directions with controllable frequency bands and intensities. The artificial head is placed in the center of the cabin to receive sound signals from different directions to simulate the actual auditory perception of noise by the human ear. The specific implementation is as follows: Laptop computer ① connects to the multi-channel speaker controller via a USB port. This computer loads the train noise source database and performs signal synchronization, sound pressure level settings, and audio playback timing control for the eight speakers. The system's integrated audio synchronization module ensures millisecond-level synchronization of multiple sound source channels, preventing image shifts or interference caused by latency differences. The controller supports at least eight channels of parallel output and can be expanded to include more speaker nodes to achieve even higher-precision three-dimensional sound field reconstruction. The artificial head device is equipped with a high-fidelity microphone array that captures left and right ear acoustic signals separately and transmits the signals in real time to laptop computer ② via a USB port. Computer ②'s built-in calibration software compares the acoustic signals received by the artificial head with the target train sound source waveform in both the frequency and time domains, outputting error maps for each direction. During the commissioning phase, the sound pressure level and frequency response curve of each speaker are gradually adjusted to ensure that the signals measured by the artificial head are highly consistent with the original interior noise in both sound pressure (dBA) and frequency spectrum (Hz), thus meeting the required accuracy for sound field reconstruction.

[0028] like Figure 4 As shown, a real-vehicle test first collects typical noise signals from the target train during operation, extracting their sound pressure level, spectral structure, and spatial distribution characteristics. Speakers and artificial heads are then placed in a pre-set spatial layout within the simulation cabin. A laptop control system sequentially plays original train sound source data from various directions. The binaural signals collected by the artificial heads are compared with the measured data. Based on the discrepancies, the system gradually adjusts the speaker output, adjusting the amplitude, phase, and frequency characteristics until the sound pressure level received at the artificial heads matches the original train noise (typically within 1–2 dBA), and the spectral similarity meets the required reconstruction accuracy. This method can effectively reproduce a variety of noise scenarios generated by trains under different operating conditions, including typical acoustic environments such as constant speed operation, acceleration, and entering and exiting tunnels.

[0029] The sound field reconstruction device can achieve accurate simulation of multi-directional and multi-band noise inside the train during operation. The system outputs the sound source signal synchronously through eight-channel speakers, and uses an artificial head to collect binaural sound pressure data in real time for calibration, supporting millisecond-level audio synchronization and sound pressure adjustment with a step size of 1 dBA. 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 debugging cycle, and control the binaural sound pressure error within ±1.2 dBA, accurately restoring the human ear sensitive frequency band within the 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 in the figure. Figure 5 shown.

[0030] It should be noted that the acoustic environment simulation system utilizes a multi-channel speaker array and an artificial head receiving system. Through dual-machine coordinated control and a high-precision calibration algorithm, the reconstructed sound field closely matches actual vehicle operating data in terms of sound pressure level and spectrum, achieving excellent spatial restoration. In terms of hardware layout, the speaker array adopts a surround layout (from eight directions at the center) and is initially installed and positioned within the simulated space. Using 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 precisely measured to ensure uniform coverage of the occupant area. The receiving system is centered around an artificial head model that simulates the structure of the human head. Omnidirectional, high-sensitivity microphones are embedded in both ears, with a distance of 16 cm between the ears, ensuring accurate capture of interaural sound differences.

[0031] The system utilizes a two-stage calibration process: static and dynamic. First, in the static stage, a standard signal (such as a 1 / 3-octave swept sine wave or white noise) is excited channel by channel to acquire the binaural frequency responses of the artificial head in a free field. The frequency responses are extracted using a fast Fourier transform. Subsequently, in the dynamic stage, a standard dummy (such as the Hybrid III 50% dummy) is introduced, and microphones are placed near its ears to obtain sound pressure responses that more closely resemble those under real-world occlusion conditions. The system incorporates a deep neural network (DNN) spectrum restoration algorithm for dynamic correction. The DNN architecture utilizes multi-layer convolution and residual connections, and uses the actual vehicle sound field spectrum as a training target to adaptively optimize the transfer matrix. This method provides 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 near-field sound pressure restoration. Under typical train operating conditions (such as acceleration, constant speed, and tunnel entry), the average binaural sound pressure deviation in the 1–3 kHz frequency band is controlled within ±2 dB.

[0032] The interior lighting environment simulation module, based on adjustable light strips, features a device designed to simulate typical lighting environment changes during train operation and create in-cabin visual perception conditions that align with the lighting characteristics of a real train. Using multi-segment programmable light strips as its core, the control system precisely adjusts brightness, color temperature, and lighting rhythm to recreate lighting changes within a closed simulation cabin under various operating conditions, including daytime / nighttime operation, tunnel entry and exit, and temporary lighting switching. The system utilizes high-density LED light strips evenly distributed along the cabin's roof and side walls, creating a fully enveloping lighting coverage structure. Each light strip supports PWM dimming and multiple color temperature channels (e.g., 2700K–6500K). It connects to a master computer via communication protocols such as DMX512 or SPI, forming a unified control network. The master computer runs lighting control software, pre-configured with corresponding parameter templates for typical train lighting scenarios, allowing for coordinated control or independent adjustment of multiple lighting zones through a single interface.

[0033] The system implementation process is as follows: First, through on-site research and real-vehicle testing, data on illumination, color temperature distribution, and lighting change rhythms under different operating conditions of actual trains are collected. For example, data on the slope of light intensity changes before and after entering and exiting tunnels, low-brightness background light levels in tunnel sections, and auxiliary lighting status during nighttime operation are collected. Then, light strips are installed and zoned within the simulation cabin to ensure that the lighting distribution in each zone is consistent with that of the actual train. Brightness and color temperature are then calibrated for each light strip channel.

[0034] During the experimental run, the main control system invokes a specific lighting scenario template and synchronously controls each light strip group according to preset parameters. For example, to simulate a train entering a tunnel, the system rapidly reduces overall brightness to tunnel illumination levels (e.g., from 300 lx to 60 lx) within 0.5–1 second. Simultaneously, the color temperature transitions from cooler light (6000K) to warmer light (3500K), creating a natural light transition experience similar to eye adaptation. In scenarios where maintaining constant illumination is crucial, the system can also enter constant output mode to maintain a stable light environment for long-term exposure experiments.

[0035] To ensure repeatability and quantification of light environment stimulation, an artificial head or illuminance sensor can be placed at the subject's viewing angle to provide real-time feedback on the light environment. Based on this collected data, the system can perform closed-loop adjustments to further optimize light field stability and dynamic response.

[0036] It should be noted that the lighting simulation system incorporates adjustable light strips. Based on zoned programmable control and a dual-channel color temperature and brightness adjustment strategy, it can switch between various lighting modes in milliseconds, realistically simulating the light-dark transitions and color temperature drift that occur during train operation. The system is centered around high-density, four-channel RGBW LED strips, offering a wide color temperature adjustment range (2700K–6500K) and high brightness output. These strips are evenly distributed across the ceiling and side walls of the simulation cabin. All strip channels support PWM dimming and are connected to the main control computer via a dedicated LED driver chip, ensuring high synchronization and interference resistance. The main control system has preset scenarios for typical train lighting environments (such as daytime operation, night mode, tunnel entry and exit, and parking lighting), and integrates a library of a priori illumination change curves, allowing for on-demand access and loading of corresponding parameter templates. The system response latency is kept within 10–20 milliseconds, meeting millisecond-level switching requirements and enabling realistic simulation of brightness transitions and color temperature drift in dynamic scenarios.

[0037] The proposed system's design fully considers the multidimensional nature of the train's operating environment. At the system implementation level, each module features independent control and parameter setting capabilities. While independently outputting typical operating conditions, the model also models the control logic of three environmental factors through temporal coupling. The model uses typical events during train operation as trigger nodes, synchronizing and delaying changes in these factors along the environmental change timeline. For example, the model defines the event t0 as the train entering a tunnel. Based on the coupling relationships, the model activates the air pressure control, sound field reconstruction, and illumination adjustment modules after t0+xx seconds. The delays for these modules are synchronized based on parameters recorded from actual vehicle experiments, thus meeting the stimulus combination requirements for different tunnel designs and operating conditions. The entire system achieves programmable reproduction of multiple environmental factors through software and hardware linkage, generating stable, controllable, and real-world composite environmental stimuli during experiments. This system not only meets the high-fidelity requirements of train environmental comfort experiments but also provides a unified and repeatable experimental platform for studying the physiological, psychological, and cognitive states of passengers.

[0038] Example 2 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 actual physical scenes or operating states. In order to study the changes in physiological or psychological comfort of high-speed train passengers under different operating conditions, it is necessary to restore the complete and continuous event transition process. During the simulation process, the air pressure system, sound field system, and lighting system have response delays, hardware jitter, and asynchrony between modules during event transitions. 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 working conditions.

[0039] Through the simulation experimental data of the train under different event transitions, the time information and data information of the train under the event transition are collected. The time information of the train under the event transition is represented by the synchronization time fluctuation coefficient and the delay timeliness coefficient, and the data information of the train under the event transition is represented by the data change anomaly coefficient.

[0040] The role of the synchronization time fluctuation coefficient is: Restore the temporal relationship of factor coupling in real operating conditions. In a real train environment, events such as tunnel entrances and acceleration sections will trigger various physical changes in the cabin simultaneously (or with a fixed delay). If a factor is initiated too early or completed too late in the experiment, the synchronous perception in the real scene will be disrupted, causing the subject's physiological / subjective reactions to deviate from the actual situation. Avoid cross-interference. If the sound field has been switched but the air pressure is still rising, the subject will experience a misalignment of "sound first, air second", making it difficult to determine which factor is driving a certain comfort change. Ensuring temporal consistency can clearly "apply" the effects of the three factors on comfort together, reducing cross-interference. Enhance the immersiveness of multimodal stimulation. The human perceptual system is sensitive to the time difference between different modalities (auditory, visual, and pressure) at the level of tens of milliseconds. If the delay is too long, it will be perceived as "out of sync", breaking the immersive experience. Maintaining the synchronization of start and completion can enhance the subject's sense of presence in the "real train environment"; To ensure the accuracy of data alignment, strict timestamp alignment is required when subsequently analyzing the correspondence between physiological signals (such as heart rate and skin conductance) and environmental factors. If the completion timing of each module is different, it will be difficult to accurately divide "which time period corresponds to which environmental state", which will affect data labeling and model training.

[0041] The logic for obtaining the synchronization time fluctuation coefficient is as follows: determine the initial setting parameters of the current train air pressure environment, sound field environment, and light environment; obtain the time series of the train air pressure environment, sound field environment, and light environment simulation under event transition through several simulation experiments; obtain the initial response time point and the cutoff response time point under the train event transition; and mark the initial response time point of several simulation experiments as: , mark the cutoff response time points of several simulation experiments as: , where k is the number of samples in the simulation experiment, p, s, l are the indexes of the air pressure environment, sound field environment and light environment in different simulation experiments; It should be noted that the initial setting parameters are the air pressure environment, sound field environment, and light environment parameters of the initial event, including the cabin air pressure, noise, and brightness of the initial event, which are used to restore the initial event; An event transition is the process of a train moving from the initial simulated scene to the next, including dynamic driving processes such as entering and exiting a tunnel, accelerating, and decelerating. The setting parameters for the air pressure environment, acoustic field environment, and light environment are different under different events. Therefore, the setting 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 an event is triggered. When the system receives an 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 point is the moment when the air pressure environment reproduction module, the in-vehicle sound field reconstruction module, and the light environment simulation module record the first deviation of the set parameters. The end response time point 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.

[0042] Calculate the initial response time difference and the cutoff response time difference. The calculation formula for the initial response time difference is: , the calculation formula of the cut-off response time difference is: ,in, is the initial response time difference, is the cut-off response time difference, ; The continuous probability density function of the initial response time difference and the continuous probability density function of the cutoff response time difference are determined by the kernel probability density function. The continuous probability density function expression of the initial response time difference is: , the continuous probability density function expression of the cut-off response time difference is: ;in, is the continuous probability density function of the initial response time difference, is the continuous probability density function of the cutoff response time difference, N is the number of experiments, h is the bandwidth, and K is the Gaussian kernel; Calculate the synchronization time fluctuation coefficient using the following formula: ;in, is the synchronization time fluctuation coefficient.

[0043] It can be seen from the formula that the larger the synchronization time fluctuation coefficient, the greater the uncertainty in the event transition process, which means that there may be a sense of disconnection between passengers in multiple experiments. It is necessary to change parameters such as the data acquisition frequency and the device braking time of the air pressure environment, sound field environment, and light environment simulation to ensure highly consistent multi-factor simulation output under the same event transition.

[0044] The role of the delay timeliness coefficient is as follows: the delay timeliness coefficient integrates the transition duration of the three environmental modules, avoiding the tediousness of analyzing each module separately, and reflects the different impacts of different environmental factors on passenger comfort. The larger the delay timeliness coefficient, the more likely passengers are to perceive a longer lag in environmental switching, and lag will cause multi-sensory information to be out of sync. The delay timeliness coefficient can be used to determine the performance of the simulated train under event transitions, and the sampling rate, valve switching timing, speaker and light strip control strategies can be adjusted in a targeted manner.

[0045] The logic for obtaining the delay timeliness coefficient is as follows: based on several simulation test data of the train under event transition, the time series of the train's air pressure environment, sound field environment, and light environment simulation under the event transition is determined, and the transition duration of the train under the air pressure environment, sound field environment, and light environment simulation is obtained. The transition duration is determined by the difference between the cutoff response time point and the initial response time point. The transition duration under the air pressure environment, sound field environment, and light environment simulation is marked as: ,in, ; Calculate the average transition time of the train under the simulation of the air pressure environment, sound field environment, and light environment during the event transition process. The calculation formula is: ;in, is the average transition time under the simulation of air pressure environment, sound field environment and light environment; Based on the changes in air pressure, sound pressure and light illumination during the event transition under the simulation of air pressure environment, sound field environment and light environment, and the rates of change of air pressure, sound pressure and light illumination, the simulation rate of the system under the simulation of air pressure environment, sound field environment and light environment is determined, and the simulation rate of the system is determined in each experiment to obtain the system timeliness score in each experiment. The timeliness decline model is constructed in combination with the average transition time under the simulation of air pressure environment, sound field environment and light environment. The undetermined parameters of the timeliness decline model are optimized based on the nonlinear least squares method according to the system timeliness score. The undetermined parameters include the maximum value of the system timeliness score, the rate of decline of the score with the increase of transition time, and the sensitivity coefficient of transition time to the decline of the score. The expression of the timeliness decline model is: ;in, is the error function of the time-dependent degradation model under the simulation of air pressure environment, sound field environment and light environment, is the system timeliness score, g is the number of the system timeliness score in the optimization timeliness reduction model; Calculate the delay timeliness coefficient, the calculation formula is: ;in, is the delay timeliness coefficient, 、 、 are the timeliness weights of the air pressure environment, sound field environment, and light environment, respectively. .

[0046] It can be seen from the formula that the larger the delay timeliness coefficient, the longer the transition time under the air pressure environment, sound field environment and light environment simulation. The smaller the system timeliness score, the more obvious the lag in environment switching will be for passengers during the train journey, resulting in perceptual fragmentation and decreased comfort. In other words, the system simulation time will increase, resulting in a decrease in simulation effect. It is necessary to change parameters such as the data collection frequency and device braking time of the air pressure environment, sound field environment and light environment simulation to ensure comfort and the reliability of experimental data.

[0047] The data variation anomaly coefficient is a core indicator for measuring the fidelity of environmental simulation output. The functions of the data variation anomaly coefficient are: 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 a basis for monitoring whether the control system performance is stable. If this 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 terminated; The data change anomaly coefficient can be used as a feedback signal to guide the optimization of the system's software and hardware parameters. For example, if the coefficient is consistently high during multiple simulations, it can be inferred that the sampling frequency is insufficient, the signal acquisition delay is too large, or the controller is not responding in a timely manner. Technicians can gradually reduce the coefficient by optimizing the fan startup time, speaker excitation synchronization frequency, or LED response delay parameters, thereby improving the overall system performance. The simulation complexity of different train operation events (such as entering and exiting a tunnel, accelerating, decelerating, etc.) is different. 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 deviations of the train during the time transition process. Once a large deviation occurs in a short period of time, an alarm will be immediately issued, prompting the need to shut down for calibration or switch to safe mode.

[0048] By changing the abnormal coefficient of data under different event transitions The logic for obtaining the data change anomaly coefficient is as follows: obtaining the time series of air pressure, sound pressure, and light brightness during the train simulation event transition, obtaining the air pressure change rate, sound pressure change rate, and light brightness change rate during the train simulation event transition, determining the time-varying functions of the air pressure change rate, sound pressure change rate, and light brightness change rate by fitting the air pressure change rate, sound pressure change rate, and light brightness change rate with time, setting threshold ranges for the air pressure change rate, sound pressure change rate, and light brightness change rate, and determining the time period during which the air pressure change rate, sound pressure change rate, and light brightness change rate deviate from the threshold range during the train simulation event transition; Calculate the data change abnormal coefficient, the calculation formula is: ;in, is the data variation anomaly coefficient, is the pressure change rate as a function of time, is the function of the rate of change of sound pressure over time, is the function of the change rate of light brightness over time, [a, b] is the time period when the air pressure change rate deviates from the threshold range, [c, d] is the time period when the sound pressure change rate deviates from the threshold range, and [e, f] is the time period when the light brightness change rate deviates from the threshold range.

[0049] It can be seen from the formula that the larger the data change anomaly coefficient is, the longer the cumulative time that the air pressure, sound pressure or light change rate leaves the preset safety / simulation threshold range in the simulation experiment, indicating that the fidelity of the simulation output of the simulation experiment decreases. Therefore, it is necessary to change parameters such as the data acquisition frequency, device braking time, etc. for the simulation of the air pressure environment, sound field environment, and light environment.

[0050] The time information and data information of the train under event transition are comprehensively analyzed. Through weighted calculation of the synchronization time fluctuation coefficient, delay timeliness coefficient and data change abnormality coefficient, a simulation quality assessment model is constructed to generate the simulation quality assessment coefficient. The calculation formula of the simulation quality assessment coefficient is: ; Where PG is the simulation quality assessment coefficient, 、 、 They are the proportional coefficients of synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, 、 、 Both are greater than 0.

[0051] Set a simulation quality assessment coefficient threshold, obtain the simulation quality assessment coefficient under different event transitions, and compare the simulation quality assessment coefficient under different event transitions with the simulation quality assessment coefficient threshold. If the simulation quality assessment coefficient is greater than the simulation quality assessment coefficient threshold, an alarm signal is generated, indicating that through multiple experimental simulations, there are situations in which the system response is asynchronous, the delay is too long, or the environmental changes are abnormally drastic in the current time transition, which cannot meet the simulation requirements for high-speed train passenger comfort. It is necessary to adjust the simulation system parameters or strategies in time. If the simulation quality assessment coefficient is less than the simulation quality assessment coefficient threshold, no alarm signal is generated.

[0052] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. 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 can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0054] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0055] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0056] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0057] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0058] If the functions are implemented in the form of 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 the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0059] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A multi-factor environmental simulation system for high-speed train passenger comfort research, characterized by: 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 equilibrium in the cabin based on the opening and closing sequence of the Roots blower, vacuum pump, and controllable butterfly valve. It also monitors the cabin pressure in real time through a pressure sensor and overpressure protection relief valve, and automatically relieves pressure when it exceeds a preset upper limit. The in-car sound field reconstruction module is used to evenly arrange wide-angle speakers inside the simulation cabin, load the multi-channel speaker controller and control computer with the train noise database, achieve millisecond-level synchronous playback, and use the binaural artificial head device to collect and correct the in-cabin sound field in real time; The light environment simulation module uses multi-segment programmable LED light strips evenly distributed along the top and side walls of the simulation cabin, and performs closed-loop control of brightness, color temperature, and dynamic change rhythm through DMX512 or SPI protocols; The simulation experiment data evaluation module is used to collect multi-dimensional response data of the air pressure environment, sound field environment and light environment during the transition process in the train event transition simulation experiment. Through 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 actual operating conditions and determines the generation of alarm signals.

2. A multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 1, characterized in that: The train's time and data information during event transitions include: The time information of the train under event transition is represented by the synchronization time fluctuation coefficient and the delay timeliness coefficient, and the data information of the train under event transition is represented by the data change anomaly coefficient, where: is the synchronization time fluctuation coefficient, is the delay timeliness coefficient, is the data variation anomaly coefficient.

3. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 2 is characterized in that: The acquisition logic of the synchronization time fluctuation coefficient is: Determine the initial setting parameters of the current train air pressure environment, acoustic field environment, and light environment. Through several simulation experiments, obtain the time series of the train air pressure environment, acoustic field environment, and light environment simulation under event transition. Obtain the initial response time point and cutoff response time point under the train event transition. Mark the initial response time points of several simulation experiments as: , mark the cutoff response time points of several simulation experiments as: , where k is the number of samples in the simulation experiment, p, s, l are the indexes 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 calculation formula for the initial response time difference is: , the calculation formula of the cut-off response time difference is: ,in, is the initial response time difference, is the cut-off response time difference, ; The continuous probability density function of the initial response time difference and the continuous probability density function of the cutoff response time difference are determined by the kernel probability density function. The continuous probability density function expression of the initial response time difference is: , the continuous probability density function expression of the cut-off response time difference is: ;in, is the continuous probability density function of the initial response time difference, is the continuous probability density function of the cutoff response time difference, N is the number of experiments, h is the bandwidth, and K is the Gaussian kernel; Calculate the synchronization time fluctuation coefficient using the following formula: .

4. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 3 is characterized in that: The acquisition logic of the delay timeliness coefficient is: Based on the data of several simulation experiments of trains under event transition, the time series of the train's air pressure environment, sound field environment, and light environment simulation under event transition were determined, and the transition duration of the train under the air pressure environment, sound field environment, and light environment simulation was obtained. The transition duration was determined by the difference between the cutoff response time point and the initial response time point. The transition duration under the air pressure environment, sound field environment, and light environment simulation was marked as: ,in, ; Calculate the average transition time of the train under the simulation of the air pressure environment, sound field environment, and light environment during the event transition process. The calculation formula is: ;in, is the average transition time under the simulation of air pressure environment, sound field environment and light environment; Based on the changes in air pressure, sound pressure and light illumination during the event transition under the simulation of air pressure environment, sound field environment and light environment, and the rates of change of air pressure, sound pressure and light illumination, the simulation rate of the system under the simulation of air pressure environment, sound field environment and light environment is determined, and the simulation rate of the system is determined in each experiment to obtain the system timeliness score in each experiment. The timeliness decline model is constructed in combination with the average transition time under the simulation of air pressure environment, sound field environment and light environment. The undetermined parameters of the timeliness decline model are optimized based on the nonlinear least squares method according to the system timeliness score. The undetermined parameters include the maximum value of the system timeliness score, the rate of decline of the score with the increase of transition time, and the sensitivity coefficient of transition time to the decline of the score. The expression of the timeliness decline model is: ;in, is the error function of the time-dependent degradation model under the simulation of air pressure environment, sound field environment and light environment, is the system timeliness score, g is the number of the system timeliness score in the optimization timeliness reduction model; Calculate the delay timeliness coefficient, the calculation formula is: ;in, 、 、 are the timeliness weights of the air pressure environment, sound field environment, and light environment, respectively. .

5. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 4 is characterized in that: The logic for obtaining the data change anomaly coefficient is: Obtaining a time series of air pressure, sound pressure, and light brightness during a train simulation event transition, obtaining the air pressure change rate, sound pressure change rate, and light brightness change rate during the train simulation event transition, determining a function of the air pressure change rate, sound pressure change rate, and light brightness change rate over time by fitting the air pressure change rate, sound pressure change rate, and light brightness change rate with time, setting a threshold range for the air pressure change rate, sound pressure change rate, and light brightness change rate, and determining a time period during which the air pressure change rate, sound pressure change rate, and light brightness change rate deviate from the threshold range during the train simulation event transition; Calculate the data change abnormal coefficient, the calculation formula is: ;in, is the pressure change rate as a function of time, is the function of the rate of change of sound pressure over time, is the function of the change rate of light brightness over time, [a, b] is the time period when the air pressure change rate deviates from the threshold range, [c, d] is the time period when the sound pressure change rate deviates from the threshold range, and [e, f] is the time period when the light brightness change rate deviates from the threshold range.

6. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 5 is characterized in that: Evaluate the fit of the train event transition simulation events to the actual operating conditions, including: The time information and data information of the train under event transition are comprehensively analyzed. Through weighted calculation of the synchronization time fluctuation coefficient, delay timeliness coefficient and data change abnormality coefficient, a simulation quality assessment model is constructed to generate the simulation quality assessment coefficient. The calculation formula of the simulation quality assessment coefficient is: ; Where PG is the simulation quality assessment coefficient, 、 、 They are the proportional coefficients of synchronization time fluctuation coefficient, delay timeliness coefficient, and data change anomaly coefficient, 、 、 Both are greater than 0.

7. The multi-factor environmental simulation system for high-speed train passenger comfort research according to claim 6 is characterized in that: Determine whether to generate an alarm signal, including: Set the simulation quality assessment coefficient threshold, obtain the simulation quality assessment coefficient under different event transitions, compare the simulation quality assessment coefficient under different event transitions with the simulation quality assessment coefficient threshold, and generate an alarm signal if the simulation quality assessment coefficient is greater than the simulation quality assessment coefficient threshold; if the simulation quality assessment coefficient is less than the simulation quality assessment coefficient threshold, no alarm signal is generated.

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