A control method of a multi-environment parameter simulation low-pressure oxygen cabin

Abstract

The application provides a control method of a low-pressure oxygen cabin simulating multiple environmental parameters, comprising: obtaining altitude data currently needed to be simulated, controlling the air pressure adjusting module to simulate the current cabin air pressure; obtaining speed data currently needed to be simulated, controlling the stepless variable speed wind power device to simulate the current cabin wind power; obtaining altitude data currently needed to be simulated, controlling the environment simulation system to simulate the current cabin temperature; identifying the motion posture of target personnel, and coupling the cabin air pressure, cabin wind power, cabin temperature and motion posture in real time to control the current posture of the six-degree-of-freedom skiing platform; the closed-loop linkage of environmental conditions can be realized, each simulation parameter is coupled and operated, and the correlation between the environment and platform action in real skiing can be reflected in real time.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-pressure oxygen cabins, and particularly relates to a control method of a low-pressure oxygen cabin for simulating multiple environmental parameters. BACKGROUND

[0002] With the popularity of ice and snow sports and the increasing demand for scientific training, simulation training equipment for extreme sports such as alpine skiing has become a research hotspot. In the current low-pressure training cabin technology, the simulation system for skiing has made certain progress in partial functions.

[0003] During the conception and implementation of the present application, the applicant has found at least the following problems: the prior art cannot meet the needs of high immersion, multiple parameter coordination, and precise matching of physiology and movement in alpine skiing simulation training, and generally has the problem of insufficient multiple parameter coordination, cannot realize closed-loop linkage of environmental conditions, each simulation parameter is independently run, and cannot reflect the correlation between the environment and platform action in real skiing. SUMMARY

[0004] In order to alleviate the above problems, the main purpose of the present application is to provide a control method of a low-pressure oxygen cabin for simulating multiple environmental parameters, which comprises a gas pressure adjusting module, an environmental simulation system, a stepless variable speed wind device, a six-degree-of-freedom skiing platform, and a central controller. The control method is applied to the central controller, and the control method comprises:

[0005] Obtaining altitude data currently required to be simulated, calling a gas pressure-altitude mapping algorithm based on an international standard atmospheric model, calculating a current target gas pressure, and controlling the gas pressure adjusting module to simulate the current cabin gas pressure;

[0006] Obtaining speed data currently required to be simulated, calling a wind speed-velocity coupling algorithm based on Bernoulli equation, calculating a current target wind speed, and controlling the stepless variable speed wind device to simulate the current cabin wind speed;

[0007] Obtaining altitude data currently required to be simulated, calling an altitude correlation algorithm based on a preset decreasing rate, calculating a current target temperature, and controlling the environmental simulation system to simulate the current cabin temperature;

[0008] Identifying the movement posture of the target person, and performing real-time linkage coupling of the cabin gas pressure, cabin wind speed, cabin temperature, and movement posture to control the current posture of the six-degree-of-freedom skiing platform.

[0009] Optionally, the process of identifying the movement posture of the target person, and performing real-time linkage coupling of the cabin gas pressure, cabin wind speed, cabin temperature, and movement posture to control the current posture of the six-degree-of-freedom skiing platform comprises:

[0010] Obtaining a terrain vertical slope currently required to be simulated in a moving direction, when the terrain vertical slope is greater than a preset angle, calling a speed drop mode to control a current posture of the six-degree-of-freedom skiing platform, and generating altitude data currently required to be simulated and speed data currently required to be simulated;

[0011] Obtaining a terrain turning radius currently required to be simulated in the moving direction, when the terrain vertical slope is in a preset radius interval, calling a turning mode to control the current posture of the six-degree-of-freedom skiing platform, and generating the altitude data currently required to be simulated and the speed data currently required to be simulated;

[0012] Obtaining a terrain undulation currently required to be simulated in the moving direction, when the terrain undulation is in a preset undulation interval, calling a cross-country mode to control the current posture of the six-degree-of-freedom skiing platform, and generating the altitude data currently required to be simulated and the speed data currently required to be simulated.

[0013] Optionally, the simulation multi-environment parameter low-pressure oxygen cabin further comprises a biomechanics monitoring module; the process of identifying the motion posture of the target person and performing real-time linkage coupling of the cabin internal pressure, the cabin internal wind, the cabin internal temperature and the motion posture to control the current posture of the six-degree-of-freedom skiing platform comprises:

[0014] Obtaining biomechanics monitoring data through the biomechanics monitoring module;

[0015] Judging the motion posture of the target person according to the biomechanics monitoring data;

[0016] According to the motion posture, calculating a center-of-gravity offset ratio of the target person, and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform;

[0017] When the center-of-gravity offset ratio exceeds a preset ratio, generating a motion posture prompt.

[0018] Optionally, the biomechanics monitoring module comprises a muscle electrical sensor and an inertial measurement unit, and the six-degree-of-freedom skiing platform comprises an embedded pressure sensor matrix; the process of calculating the center-of-gravity offset ratio of the target person according to the motion posture and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform comprises:

[0019] Real-time acquisition of pressure distribution data of the feet of the target person based on the embedded pressure sensor matrix to calculate a center-of-gravity coordinate;

[0020] Capturing angular velocity and acceleration of the torso of the target person based on the inertial measurement unit to calculate an angular velocity change rate and an acceleration change rate;

[0021] acquire the electromyographic signal of the target person based on the electromyographic sensor, and when the electromyographic signal is abnormal, increase the data weight of the pressure distribution data and the data weight of the angular velocity change rate and the acceleration change rate, and reacquire the center of gravity offset and the overall posture trend of the target person;

[0022] spatiotemporal correlation analysis is performed on the electromyographic signal, the pressure distribution data, the angular velocity change rate and the acceleration change rate, and cross verification is performed on the center of gravity offset and the overall posture trend to determine the motion posture of the target person.

[0023] Optionally, the process of acquiring the electromyographic signal of the target person based on the electromyographic sensor, and when the electromyographic signal is abnormal, increasing the data weight of the pressure distribution data and the data weight of the angular velocity change rate and the acceleration change rate, and reacquiring the center of gravity offset and the overall posture trend of the target person comprises:

[0024] real-time analysis of the data characteristics of the electromyographic signal, the data characteristics of the electromyographic signal including baseline drift characteristics and high-frequency noise proportion characteristics, and when the signal-to-noise ratio of the electromyographic signal is lower than a preset threshold, the electromyographic signal is determined to be abnormal.

[0025] Optionally, the step of generating a motion posture prompt when the center of gravity offset ratio exceeds a preset ratio comprises:

[0026] determining the motion intensity of the target person according to the angular velocity change rate and the acceleration change rate, and adaptively adjusting the judgment threshold of the center of gravity offset ratio;

[0027] when the angular velocity change rate and the acceleration change rate increase synchronously, the judgment threshold of the center of gravity offset ratio is increased;

[0028] when the angular velocity change rate and the acceleration change rate decrease synchronously, the judgment threshold of the center of gravity offset ratio is decreased.

[0029] Optionally, the process of calculating the center of gravity offset ratio of the target person according to the motion posture and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform comprises:

[0030] introducing a dynamic baseline correction mechanism to record the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value of the target person in a stationary state;

[0031] comparing the current sensor data with the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value at regular intervals during the motion, and if the electromyographic signal offset amplitude exceeds a preset offset threshold, automatically correcting the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value in combination with the posture change of the inertial measurement data.

[0032] Optionally, the process of identifying the motion posture of the target person, and coupling the cabin air pressure, cabin wind force, cabin temperature and motion posture in real time to control the current posture of the six-degree-of-freedom skiing platform further comprises:

[0033] Extracting image data of the cabin camera and extracting human body posture features of the target person;

[0034] Adding uniform timestamps to the data sources of the cabin camera, the electromyography sensor, the inertial measurement device and the pressure sensor using clock data of the central controller, and performing delay compensation on the image data;

[0035] Analyzing data features of each data source, and combining image feature hierarchical data fusion for dynamically adjusting control parameters of the simulated multi-environment parameter hypobaric chamber.

[0036] Optionally, the process of extracting image data of the cabin camera and extracting human body posture features of the target person comprises:

[0037] Using a global shutter camera, integrating an infrared fill light, and acquiring front torso, upper limbs and skiing board actions of the target person;

[0038] Capturing upper body posture, side lower limb action and overall center position of the target person, and extracting joint coordinates, posture parameters and motion trajectories of the target person through a skeletal key point detection algorithm.

[0039] Optionally, the process of adding uniform timestamps to the data sources of the cabin camera, the electromyography sensor, the inertial measurement device and the pressure sensor using clock data of the central controller, and performing delay compensation on the image data comprises:

[0040] According to the clock data of the central controller, marking the frame rate matching acquisition time for each frame of image;

[0041] Marking time stamps for the data sources of the electromyography sensor, the inertial measurement device and the pressure sensor according to the original sampling rate, and mapping non-uniform sampling data to the time axis of the camera through an interpolation method.

[0042] Optionally, the process of analyzing data features of each data source, and combining image feature hierarchical data fusion for dynamically adjusting control parameters of the simulated multi-environment parameter hypobaric chamber comprises:

[0043] Respectively extracting image data features, electromyography data features, inertial measurement data features and pressure distribution data features, and extracting key information in the data through principal component analysis model dimension reduction processing;

[0044] According to the key information, the knee bending angle is analyzed based on the image data, the quadriceps femoris activity is analyzed based on the electromyography data, and the trunk forward angle is analyzed based on the inertial measurement data, and a weighted voting mechanism is used to fuse the multi-source data to generate a posture judgment result of the target person;

[0045] According to the posture judgment result, the calculation weights of each data source are adaptively adjusted based on the scene mode of high-speed motion or fine action.

[0046] When the knee bending angle exceeds the preset maximum knee angle based on the image data, and / or the quadriceps femoris activity is greater than the preset activity threshold based on the electromyography data, and / or the trunk forward angle is greater than the preset trunk angle based on the inertial measurement data, it is determined that the center of gravity is excessively tilted, and the posture judgment result of the multi-source data is fused by using the weighted voting mechanism.

[0047] Optionally, in the process of adaptively adjusting the calculation weights of each data source based on the scene mode of high-speed motion or fine action according to the posture judgment result, the following steps are included:

[0048] When the target person's action is detected to be blocked or the light is detected to be suddenly changed, low-quality data is filtered through feature point confidence, to automatically reduce the image weight, and the image data of other perspectives is used for triangulation completion;

[0049] When the image data and other data logically conflict, secondary verification is triggered to call historical data to judge the consistency of the trend, and the posture judgment result is corrected in combination with air pressure data and / or wind power data.

[0050] The application provides a control method of a simulated multi-environment parameter low-pressure oxygen cabin, which acquires current altitude data to be simulated, calls an air pressure-altitude mapping algorithm based on an international standard atmospheric model, calculates a current target air pressure, and controls an air pressure adjusting module to simulate the current cabin air pressure; acquires current speed data to be simulated, calls a wind speed-speed coupling algorithm based on Bernoulli equation, calculates a current target wind power, and controls a stepless variable speed wind power device to simulate the current cabin wind power; acquires current altitude data to be simulated, calls an altitude correlation algorithm based on a preset decreasing rate, calculates a current target temperature, and controls an environment simulation system to simulate the current cabin temperature; the motion posture of a target person is identified, and the real-time linkage coupling of the cabin air pressure, cabin wind power, cabin temperature and motion posture is performed to control the current posture of the six-degree-of-freedom skiing platform; the needs of high immersion, multi-parameter cooperation and physiological and motion accurate matching in high mountain skiing simulation training can be met, the problem of multi-parameter cooperation is solved, the linkage of the closed loop of the environment condition is realized, the simulation parameters are coupled and operated, the correlation between the environment and the platform action in real skiing is reflected in real time, and the user experience is improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application. In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the description of the embodiments will be briefly introduced as follows. Obviously, the drawings described here only represent some embodiments of the application, and the one of ordinary skill in the art can obtain other drawings based on these drawings without any creative effort.

[0052] Figure 1 A flow chart of a control method of a simulated multi-environment parameter low-pressure oxygen cabin according to an embodiment of the application.

[0053] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. The above drawings have shown the specific embodiments of the application, and more detailed descriptions will be given in the following. These drawings and the written description are not intended to limit the scope of the concept of the application in any way, but to illustrate the concept of the application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are merely intended to explain the application, and are not intended to limit the application.

[0055] Various embodiments of the application will now be described with reference to the accompanying drawings. In the following description, the suffixes "module" and "unit" used for components are merely intended for facilitating description of the present application, and are not intended to limit the scope of the application.

[0056] First Embodiment

[0057] The application provides a control method of a simulated multi-environment parameter low-pressure oxygen cabin, the simulated multi-environment parameter low-pressure oxygen cabin comprising a gas pressure adjusting module, an environment simulation system, a stepless variable speed wind power device, a six-degree-of-freedom skiing platform and a central controller. Figure 1 A flow chart of a control method of a simulated multi-environment parameter low-pressure oxygen cabin according to an embodiment of the application.

[0058] As shown in the drawings, in an embodiment, the control method is applied to the central controller, and the control method comprises: Figure 1

[0059] S10: Obtain the current altitude data to be simulated, call a gas pressure-altitude mapping algorithm based on an international standard atmospheric model, calculate the current target gas pressure, and control the gas pressure adjusting module to simulate the current cabin gas pressure.

[0060] ​S20: Obtain the current speed data that needs to be simulated, call the wind-speed coupling algorithm based on the Bernoulli equation, calculate the current target wind force, and control the continuously variable speed wind device to simulate the current cabin wind force.

[0061] S30: Obtain the current altitude data that needs to be simulated, call the altitude-related algorithm based on the preset decreasing rate, calculate the current target temperature, and control the environmental simulation system to simulate the current cabin temperature.

[0062] S40: Identify the motion posture of the target person, and perform real-time linkage coupling of the cabin air pressure, cabin wind force, cabin temperature, and motion posture to control the current posture of the six-degree-of-freedom skiing platform.

[0063] Exemplarily, the low-pressure oxygen cabin of the embodiment is configured with an air pressure adjusting module, which can simulate the air pressure gradient change (±50 mbar accuracy) of an altitude of 1000-4000 meters. The continuously variable speed wind device adopts a variable frequency fan, is equipped with a wind direction sensor (±2° accuracy), an environmental simulation system integrated -20°C to 10°C temperature control module (±0.5°C accuracy), and a 3D surround sound effect supporting 100Hz-20kHz frequency response. The six-degree-of-freedom skiing platform is based on the Stewart parallel mechanism, which can realize the control of stroke ±150mm and angular velocity ±60° / s. The central controller adopts an ARM architecture processor, integrates the air pressure-wind force-temperature-motion coupling algorithm, and is configured according to the response time <100ms.

[0064] Exemplarily, the air pressure adjusting module in the embodiment adopts a double-loop control, wherein the main loop adopts a vacuum pump with an air extraction rate of 1000L / min; and a proportional pressure reducing valve (accuracy ±0.1% FS) is matched; the auxiliary loop adopts an oxygen injection system to realize 0-100% O2 concentration adjustment.

[0065] Exemplarily, the continuously variable speed wind device of the embodiment adopts a four-axis fan array, which is recommended to have an optimal diameter of 300mm and supports a ±45° pitch angle adjustment function; and a wind speed feedback sensor (hot film type, accuracy ±0.2m / s) is matched. The wind speed adjustment of 0-30m / s can be realized.

[0066] Exemplarily, the environmental simulation system of the embodiment includes a semiconductor refrigeration sheet matched with a PTC heating film, which can realize a refrigeration power of 2000W and operate under a power condition of 3000W.

[0067] Exemplarily, the multi-channel sound system configured in the embodiment can set 7.1 channels to realize 120dB sound pressure level environmental sound simulation. Through the combination of 8K VR and 3D sound effect, the multi-sensory immersion (such as simulating snow avalanche accompanied by low frequency sound waves) environmental immersion technology is realized by cooperating with the wind force and temperature change.

[0068] Exemplarily, the six-degree-of-freedom platform of the embodiment is driven by an electric cylinder to achieve an adjustment capacity of a stroke of ±150 mm and a thrust of 5 kN. In combination with a six-dimensional force sensor, the platform data in a range of ±500 N and ±50 N·m can be measured.

[0069] Exemplarily, the ski platform surface of the six-degree-of-freedom platform of the embodiment adopts a polymer simulated snow surface, which can achieve an adjustable friction coefficient of 0.02-0.05, and is combined with an embedded pressure sensor matrix, and a resolution of 100 kPa is recommended.

[0070] Exemplarily, the low-pressure oxygen cabin for simulating multiple environmental parameters of the embodiment adopts a double-layer cabin structure, in which the inner layer is made of 316L stainless steel, the outer layer is made of carbon fiber material, and an air-tight door is selected, and the leakage rate is required to be 0.1 mbar / h.

[0071] In the embodiment, the air pressure control technology in the field of aerospace can be applied to ski simulation to achieve dynamic air pressure adjustment with a gradient change of an altitude of 1000-4000 meters. Through a central controller, real-time linkage of air pressure, wind power, temperature and motion is realized, for example, a multi-parameter coupling control strategy of air pressure decreasing by 100 mbar and wind power increasing by 5 m / s.

[0072] Optionally, the process of identifying the motion posture of the target person and coupling the real-time linkage of the cabin air pressure, the cabin wind power, the cabin temperature and the motion posture to control the current posture of the six-degree-of-freedom ski platform includes:

[0073] Obtaining a terrain vertical slope currently required to be simulated in a motion direction, when the terrain vertical slope is greater than a preset angle, calling a speed reduction mode to control the current posture of the six-degree-of-freedom ski platform, and generating altitude data currently required to be simulated and speed data currently required to be simulated;

[0074] Obtaining a terrain rotation radius currently required to be simulated in a motion direction, when the terrain vertical slope is in a preset radius interval, calling a rotation mode to control the current posture of the six-degree-of-freedom ski platform, and generating altitude data currently required to be simulated and speed data currently required to be simulated;

[0075] Obtaining a terrain undulation currently required to be simulated in a motion direction, when the terrain undulation is in a preset undulation interval, calling a cross-country mode to control the current posture of the six-degree-of-freedom ski platform, and generating altitude data currently required to be simulated and speed data currently required to be simulated.

[0076] Exemplarily, the low-pressure oxygen cabin for simulating multiple environmental parameters in the embodiment can adopt multi-mode training. The speed-down mode can be applied to a maximum gradient of 45° snow slope terrain, the rotation mode can be applied to a rotation terrain with a radius of 5-20 m, and the cross-country mode can be applied to undulating terrain. Exemplarily, the low-pressure oxygen cabin can detect the real-time blood oxygen saturation and heart rate of the target person, and realize blood oxygen saturation detection with an accuracy of ±1% and heart rate monitoring with an accuracy of ±2bpm.

[0077] Optionally, the low-pressure oxygen cabin for simulating multiple environmental parameters further comprises a biomechanics monitoring module; the process of identifying the motion posture of the target person and coupling the cabin air pressure, cabin wind force, cabin temperature and motion posture in real time to control the current posture of the six-degree-of-freedom skiing platform comprises:

[0078] acquiring biomechanics monitoring data through the biomechanics monitoring module;

[0079] judging the motion posture of the target person according to the biomechanics monitoring data;

[0080] calculating the center of gravity offset ratio of the target person according to the motion posture and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform;

[0081] generating a motion posture prompt when the center of gravity offset ratio exceeds a preset ratio.

[0082] The embodiment detects the motion posture of the target person in real time through the biomechanics monitoring module, and timely issues a motion posture prompt when a dangerous situation is likely to occur, such as triggering a vibration prompt when the center of gravity offset is greater than 5%.

[0083] Optionally, the biomechanics monitoring module comprises an electromyography sensor and an inertial measurement unit, and the six-degree-of-freedom skiing platform comprises an embedded pressure sensor matrix; the process of calculating the center of gravity offset ratio of the target person according to the motion posture and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform comprises:

[0084] real-time acquisition of pressure distribution data of the target person's foot based on the embedded pressure sensor matrix to calculate the center of gravity coordinates;

[0085] capturing the angular velocity and acceleration of the target person's torso based on the inertial measurement unit to calculate the angular velocity change rate and the acceleration change rate;

[0086] acquiring the electromyography signal of the target person based on the electromyography sensor, so as to increase the data weight of the pressure distribution data and the data weight of the angular velocity change rate and the acceleration change rate when the electromyography signal is abnormal, and reacquire the center of gravity offset amount and the overall posture trend of the target person;

[0087] The myoelectric signal, pressure distribution data, angular velocity change rate and acceleration change rate are analyzed in space-time correlation, and the center of gravity offset and overall posture trend are cross-verified to determine the motion posture of the target person.

[0088] In this embodiment, the embedded pressure sensor matrix on the surface of the skiing platform has a resolution of 100 kPa, which can collect the pressure distribution of the target person's foot in real time, calculate the center of gravity coordinates (such as the pressure difference between the left and right feet, the pressure ratio of the front and back palms), and the precision ±0.1° inertial measurement can capture the angular velocity and acceleration change of the torso, reflecting the overall posture trend. The central controller performs space-time correlation analysis on the myoelectric signal, pressure distribution, and inertial measurement data through multi-source data fusion algorithm based on the air pressure-wind-temperature-motion coupling algorithm expansion, avoids misjudgment caused by relying only on myoelectric signal (such as false triggering of vibration prompt), and can combine myoelectric and inertial measurement data to dynamically adjust the platform motion and correct the target person's posture, thereby realizing biomechanical closed-loop control.

[0089] Optionally, the process of acquiring the myoelectric signal of the target person based on the myoelectric sensor to increase the data weight of the pressure distribution data and the data weight of the angular velocity change rate and the acceleration change rate when the myoelectric signal is abnormal, and reacquiring the center of gravity offset and the overall posture trend of the target person includes:

[0090] Real-time analysis of the data characteristics of the myoelectric signal, the data characteristics of the myoelectric signal including baseline drift characteristics and high-frequency noise proportion characteristics, when the signal-to-noise ratio of the myoelectric signal is lower than the preset threshold, it is determined that the myoelectric signal is abnormal.

[0091] Exemplarily, the central controller adopts ARM architecture with a response time <100ms. The signal-to-noise ratio (SNR) evaluation module is integrated to analyze the characteristics of the myoelectric signal in real time (such as baseline drift and high-frequency noise proportion under 1000Hz sampling rate): when the SNR is lower than the threshold (such as <15dB), it is determined that the signal distortion is caused by sensor displacement, and the data correction mechanism is triggered immediately to realize real-time signal quality monitoring.

[0092] Optionally, the step of generating a motion posture prompt when the center of gravity offset ratio exceeds the preset ratio includes:

[0093] According to the angular velocity change rate and the acceleration change rate, the motion intensity of the target person is determined to adaptively adjust the judgment threshold of the center of gravity offset ratio;

[0094] When the angular velocity change rate and the acceleration change rate increase synchronously, the judgment threshold of the center of gravity offset ratio is increased;

[0095] When the angular velocity change rate and the acceleration change rate decrease synchronously, the judgment threshold of the center of gravity offset ratio is decreased.

[0096] Exemplarily, the posture judgment threshold is adaptively adjusted by acceleration data of the inertial measurement data, such as acceleration > 5 m / s2 when rapidly turning, in combination with the motion intensity of the target person. When the motion amplitude is large, the judgment threshold of the center of gravity deviation is temporarily relaxed (such as from 5% to 8%), and the delay time of triggering the vibration prompt is prolonged (such as from immediate triggering to triggering after 200 ms deviation), to avoid false actions caused by transient signal distortion.

[0097] Optionally, the process of calculating the center of gravity deviation ratio of the target person according to the motion posture, and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform further comprises:

[0098] A dynamic baseline mechanism is introduced to record the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value of the target person in a stationary state;

[0099] During the motion, the current sensor data is compared with the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value at regular intervals, and if the electromyographic signal deviation amplitude exceeds a preset deviation threshold, the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value are automatically corrected in combination with the posture change of the inertial measurement data.

[0100] Exemplarily, the real-time processing capability of the central controller can be used to introduce a dynamic baseline calibration mechanism in the system operation. In the initialization stage, the electromyographic signal baseline, inertial measurement angle reference value and center of gravity distribution reference value of the target person in a stationary state (such as standing, basic sliding posture) are recorded. During the motion, the current sensor data is compared with the baseline every 500 ms (5 times lower than the system response time 100 ms), and if the electromyographic signal appears systematic deviation (such as amplitude deviation > 30%), the baseline is automatically corrected in combination with the posture change of the inertial measurement (such as stable state when the trunk inclination angle < ± 5°), to offset the signal drift caused by displacement.

[0101] Optionally, the process of identifying the motion posture of the target person, and coupling the cabin air pressure, cabin wind force, cabin temperature and motion posture in real time to control the current posture of the six-degree-of-freedom skiing platform further comprises:

[0102] Extracting image data of the cabin camera, and extracting human posture features of the target person;

[0103] Using the clock data of the central controller, adding a unified timestamp to the data sources of the cabin camera, electromyographic sensor, inertial measurement device and pressure sensor, and performing delay compensation on the image data;

[0104] The data characteristics of each data source are analyzed, and image feature hierarchical data fusion is combined to dynamically adjust the control parameters of the simulated multi-environment parameter low-pressure oxygen cabin.

[0105] Exemplarily, at least 3-4 cameras are arranged in the low-pressure oxygen cabin to form a dead angle-free coverage. Exemplarily, at least one camera is arranged at the front end of the cabin body to shoot the front torso, upper limbs and ski board actions of the target person, and to focus on capturing the upper body posture (such as the torso inclination angle and the arm swing amplitude); at least one camera is arranged on each side of the cabin body to shoot the lower limb actions (such as the knee joint bending angle and the ankle joint turning angle) of the target person on the side; and at least one camera is arranged at the top of the cabin body to capture the overall center of gravity position (cross-verified with the center of gravity data of the pressure sensor matrix) from a bird's eye view. Exemplarily, all the cameras are connected to the central controller through a POE switch to ensure the stability of data transmission.

[0106] Exemplarily, the high-precision clock data (synchronization accuracy ±1 ms) of the central controller is used to add a unified timestamp to all data sources such as the cameras, electromyography sensors, inertial measurement and pressure sensors. The delay (about 20-30 ms) of camera data transmission and processing is measured, and the coupling algorithm (response time <100 ms) of the central controller is used for compensation: the image feature data is "pre-synchronized" to the real-time data chain of other sensors to avoid fusion deviation caused by processing delay.

[0107] Optionally, the process of extracting the image data of the cameras in the extraction cabin and extracting the human posture features of the target person includes:

[0108] A global shutter camera is used, and an infrared fill light is integrated to obtain the front torso, upper limbs and ski board actions of the target person.

[0109] The upper body posture, side lower limb action and overall center position of the target person are captured, and the joint coordinates, posture parameters and motion trajectories of the target person are extracted through a bone key point detection algorithm.

[0110] Exemplarily, an industrial-grade high-speed camera (recommended resolution ≥1080P, frame rate ≥60fps, delay ≤20ms) is selected in this embodiment, which supports low-light environment shooting, is suitable for the possible weak light scene in the oxygen cabin, and integrates an infrared fill light to avoid strong light affecting the target person's line of sight. A global shutter camera is preferentially selected to reduce the picture distortion when moving at high speed (such as turning while skiing and jumping).

[0111] In the image preprocessing stage, Gaussian filtering is used to remove high-frequency noise generated by cabin equipment such as fans and platforms. Semantic segmentation algorithms such as U-Net are used for background segmentation to separate the target personnel body from the cabin background and ski platform, and to retain the effective target area. For fast motion scenes such as speed reduction mode, a deblurring model based on deep learning such as DeblurGAN is used to restore clear frames and correct motion blur.

[0112] Based on the preprocessed images, core features are extracted through a skeletal key point detection algorithm such as MediaPipe Pose or OpenPose. For example, for 2D / 3D joint coordinates, the spatial coordinates (accuracy ±5mm) of 18 key nodes including the head, shoulders, hips, knees, and ankles are collected. For posture parameters, the trunk pitch angle, knee flexion angle, and left-right foot spacing (cross-verified with the left-right foot pressure difference of the pressure sensor) can be calculated. The trunk pitch angle is calculated for comparison with the trunk angle measured by the inertial measurement device. The knee flexion angle is used for correlation analysis with the thigh electromyographic signal of the electromyographic sensor. The left-right foot spacing is used for cross-verification with the left-right foot pressure difference of the pressure sensor.

[0113] Further tracking of the displacement trajectory of the hip center point is performed to calculate the center of gravity movement speed and direction to assist in determining whether the center of gravity deviation exceeds the threshold value.

[0114] Optionally, the clock data of the central controller adds a uniform timestamp to the data sources of the cabin camera, electromyographic sensor, inertial measurement device, and pressure sensor, and includes the following in the process of delay compensation for the image data:

[0115] According to the clock data of the central controller, the frame rate matching mark collection time for each frame of image is performed.

[0116] The data sources of the electromyographic sensor, inertial measurement device, and pressure sensor are marked with timestamps according to the original sampling rate, and non-uniformly sampled data is mapped to the time axis of the camera through interpolation.

[0117] For example, the high-precision clock data (synchronization accuracy ±1ms) of the central controller is used to add a uniform timestamp to all data sources such as the camera, electromyographic sensor, inertial measurement, and pressure sensor. For the image data of the camera, the frame rate is matched, with 60fps corresponding to one timestamp every 16.7ms, and each frame of image is marked with the collection time. Other sensor data can be marked with timestamps according to the original sampling rate (such as 1000Hz sampling rate for electromyography and 100Hz sampling rate for inertial measurement), and non-uniformly sampled data is mapped to the time axis of the camera through interpolation to ensure that the data is aligned in the same time dimension.

[0118] Optionally, for the data characteristics of each data source, combined with image feature hierarchical data fusion, in the process of dynamically adjusting the control parameters of the simulated multi-environment parameter low-pressure oxygen cabin includes:

[0119] Respectively extract image data features, electromyographic data features, inertial measurement data features, and pressure distribution data features, and perform dimensionality reduction processing on the key information in the data through a principal component analysis model;

[0120] According to the key information, analyze the knee joint bending angle based on the image data, analyze the quadriceps femoris activity based on the electromyographic data, and analyze the trunk forward inclination angle based on the inertial measurement data, and generate a posture judgment result about the target person by using a weighted voting mechanism to fuse multi-source data;

[0121] According to the posture judgment result, adaptively adjust the calculation weight of each data source based on the scene mode of high-speed motion or fine motion.

[0122] Exemplarily, for image data features, joint angle change rate, center of gravity trajectory curvature, and limb motion symmetry can be extracted; for electromyographic data features, muscle activity root mean square (RMS) and explosive contraction duration can be extracted; for inertial measurement data features, trunk angular velocity and acceleration peak value can be extracted; and for pressure distribution data features, left and right foot pressure ratio and front and back palm pressure change rate can be extracted. The core features of each data source are extracted and associated, and dimensionality reduction is performed through principal component analysis (PCA) to retain more than 80% of the key information and reduce the amount of calculation.

[0123] Exemplarily, when the knee joint bending angle is detected to exceed the preset maximum knee joint angle based on the image data, and / or the quadriceps femoris activity is greater than the preset activity threshold based on the electromyographic data, and / or the trunk forward inclination angle is greater than the preset trunk angle based on the inertial measurement data, it is determined that the center of gravity is excessively tilted, and the posture judgment result of the multi-source data is fused by using a weighted voting mechanism. For example, when “knee joint overflexion (> 120°)” is detected based on image data + “abnormal enhancement of quadriceps femoris activity” is displayed based on electromyographic data + “trunk forward inclination angle > 15°” is displayed based on inertial measurement, it is determined that the “center of gravity is excessively tilted forward”, and a vibration prompt is triggered. When a single data source (such as electromyography due to displacement distortion) conflicts with the other three types of data, the weight of the single data source is automatically reduced (such as from 0.3 to 0.1), and the weights of the image and pressure sensor are increased (such as from 0.25 to 0.4).

[0124] Exemplarily, based on the scene mode, the weight of the image (capturing the overall posture) and the inertial measurement data (motion trend) can be increased (total ≥ 0.6) for the rapid descent mode (such as high-speed motion); the weight of the electromyography (muscle control) and the pressure sensor (foot force) can be increased (total ≥ 0.6) for the rotation mode (such as fine action).

[0125] Optionally, in the process of adaptively adjusting the calculation weight of each data source based on the scene mode of high-speed motion or fine action according to the posture judgment result, the following steps are included:

[0126] When the action of the target person is detected to be blocked or the light is suddenly changed, low-quality data is filtered through feature point confidence to automatically reduce the weight of the image, and the image data of other perspectives is used for triangulation completion;

[0127] When the image data and other data logically conflict, secondary verification is triggered to call historical data to judge the consistency of the trend, and the posture judgment result is corrected in combination with air pressure data and / or wind data.

[0128] Exemplarily, when the action of the target person is detected to be blocked (such as the arms blocking the torso) or the light is suddenly changed (such as VR scene reflection), low-quality data is filtered through feature point confidence (MediaPipe output key point confidence ≥ 0.5) to automatically reduce the weight of the image; if a camera fails, the image data quality of the camera is evaluated in real time by using the image data of other perspectives for triangulation completion (such as using the side camera and the top camera to calculate the front joint coordinates).

[0129] Exemplarily, when the image data and the traditional sensor data logically conflict (such as the image showing that the center of gravity is left-biased, but the pressure sensor shows that it is right-biased), secondary verification is triggered. For example, historical data (fusion results in the last 1s) is called to judge the consistency of the trend; the current judgment can be further corrected in combination with air pressure / wind parameters (such as passive body shift caused by high wind speed) to avoid false triggering of the prompt.

[0130] The application provides a low-pressure oxygen cabin capable of simulating multiple environmental parameters, in particular a low-pressure oxygen cabin for simulating high-mountain skiing. The cabin has the function of simulating the gradual increase in air pressure caused by sliding down from a high mountain, simulating the air pressure gradient caused by altitude change, and combining air pressure adjustment with skiing movement. The cabin has a stepless variable wind device to simulate head-on wind speed. The cabin has a temperature adjustment function to simulate high-mountain low temperature. The cabin has a six-degree-of-freedom simulated ski integrated low-pressure oxygen cabin. The cabin has a front-end scene display that is linked with air pressure and temperature to realize closed-loop linkage of air pressure, wind, temperature, and movement. The cabin has environmental sound simulation during skiing

[0131] The application provides a control method of a low-pressure oxygen cabin simulating multiple environmental parameters, which comprises the following steps: acquiring altitude data currently required to be simulated, calling an air pressure-altitude mapping algorithm based on an international standard atmospheric model, calculating a current target air pressure, and controlling an air pressure adjusting module to simulate the current cabin air pressure; acquiring speed data currently required to be simulated, calling a wind force-speed coupling algorithm based on Bernoulli equation, calculating a current target wind force, and controlling a stepless speed change wind force device to simulate the current cabin wind force; acquiring altitude data currently required to be simulated, calling an altitude correlation algorithm based on a preset decrement rate, calculating a current target temperature, and controlling an environmental simulation system to simulate the current cabin temperature; identifying a motion posture of a target person, and performing real-time linkage coupling of the cabin air pressure, cabin wind force, cabin temperature and motion posture to control a current posture of the six-degree-of-freedom skiing platform; the method can meet the requirements of high immersion, multiple parameter cooperation and physiological and motion accurate matching in high mountain skiing simulation training, solves the problem of multiple parameter cooperation, realizes closed-loop linkage of environmental conditions, coupled operation of simulation parameters, real-time reflection of the correlation between the environment in real skiing and platform action, and improves user experience.

[0132] It should be noted that in the present application, step codes such as S10, S20, etc. are used, the purpose of which is to more clearly and briefly describe the corresponding content, and does not constitute a substantial limitation on the order. Those skilled in the art may perform S20 before S10, etc. when implementing, but these should be within the scope of protection of the present application.

[0133] In the embodiments of the device and storage medium provided in the present application, any of the technical features of the above-mentioned method embodiments can be included, and the description and explanation content is basically the same as that of the above-mentioned method embodiments, which will not be repeated here.

[0134] The embodiments of the present application also provide a computer program product, which comprises computer program code, and when the computer program code runs on a computer, the computer executes the method in various possible embodiments as above.

[0135] The embodiments of the present application also provide a chip comprising a memory and a processor, the memory being used to store a computer program, and the processor being used to call and run the computer program from the memory, so that the device installed with the chip executes the method in various possible embodiments as above.

[0136] It can be understood that the above scenarios are only examples and do not constitute a limitation on the application scenarios of the technical solutions provided by the embodiments of the present application. The technical solutions of the present application can also be applied to other scenarios. For example, those skilled in the art can know that, with the evolution of device architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0137] The above sequence numbers of the embodiments of the present application are only for description, and do not represent advantages or disadvantages of the embodiments.

[0138] The steps in the method of the embodiments of the present application can be adjusted in sequence, combined and reduced according to actual needs.

[0139] The units in the device of the embodiments of the present application can be combined, divided and reduced according to actual needs.

[0140] In the present application, for the same or similar term concept, technical solution and / or application scene description, generally only the first time is described in detail, and for the sake of brevity, the repeated description is generally not repeated, and for the understanding of the technical solutions of the present application, the same or similar term concept, technical solution and / or application scene description which is not described in detail can be referred to the previous related description.

[0141] In the present application, the description of each embodiment has its own emphasis, and the part which is not described or recorded in a certain embodiment can be referred to the related description of other embodiments.

[0142] The technical features of the technical solutions of the present application can be combined arbitrarily, in order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the range of the present application.

[0143] The above is only the preferred embodiment of the present application, and does not limit the application range of the present application, any equivalent structure or equivalent process transformation by using the content of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the application protection range of the present application.

Claims

1. A control method for simulating a hypobaric chamber with multiple environmental parameters, characterized in that, The simulated multi-environmental-parameter hypobaric oxygen chamber includes a pressure regulation module, an environmental simulation system, a continuously variable wind turbine, a six-degree-of-freedom ski platform, and a central controller; the control method is applied to the central controller, and the control method includes: The system acquires the altitude data to be simulated, uses an international standard atmospheric model, calls the pressure-altitude mapping algorithm to calculate the current target air pressure, and controls the air pressure regulation module to simulate the current cabin air pressure. The system acquires the speed data to be simulated, and based on Bernoulli's equation, calls the wind-speed coupling algorithm to calculate the current target wind force, thereby controlling the continuously variable wind turbine to simulate the current wind force inside the cabin. The system acquires the altitude data to be simulated, and based on a preset descent rate, calls an altitude correlation algorithm to calculate the current target temperature in order to control the environmental simulation system to simulate the current cabin temperature. The movement posture of the target personnel is identified, and the cabin air pressure, cabin wind force, cabin temperature and movement posture are coupled in real time to control the current posture of the six-degree-of-freedom skiing platform. The process of identifying the target person's movement posture and performing real-time linkage and coupling of the cabin air pressure, cabin wind force, cabin temperature, and movement posture to control the current posture of the six-degree-of-freedom skiing platform includes: The vertical slope of the terrain to be simulated in the direction of motion is obtained. When the vertical slope of the terrain is greater than the preset angle, the downhill mode is invoked to control the current attitude of the six-degree-of-freedom ski platform and generate the altitude data and speed data to be simulated. Obtain the current terrain turning radius to be simulated in the direction of motion. When the vertical slope of the terrain is within the preset radius range, call the turning mode to control the current attitude of the six-degree-of-freedom ski platform and generate the current altitude data and current speed data to be simulated. The system obtains the terrain undulation that needs to be simulated in the direction of motion. When the terrain undulation is within a preset undulation range, the system calls the off-road mode to control the current attitude of the six-degree-of-freedom skiing platform and generates the altitude data and speed data that need to be simulated.

2. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 1, characterized in that, The simulated multi-environmental parameter hypobaric oxygen chamber also includes a biomechanical monitoring module; the process of identifying the target person's movement posture and performing real-time linkage coupling of the chamber's air pressure, wind force, temperature, and movement posture to control the current posture of the six-degree-of-freedom skiing platform includes: Biomechanical monitoring data is acquired through the biomechanical monitoring module; Based on the biomechanical monitoring data, determine the movement posture of the target person; Based on the motion posture, calculate the center of gravity offset ratio of the target person and dynamically adjust the current posture of the six-degree-of-freedom skiing platform; When the center of gravity offset ratio exceeds the preset ratio, a motion posture prompt is generated.

3. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 2, characterized in that, The biomechanical monitoring module includes an electromyography sensor and an inertial measurement unit, and the six-degree-of-freedom skiing platform includes an embedded pressure sensor matrix; the process of calculating the target person's center of gravity offset ratio based on the motion posture and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform includes: The embedded pressure sensor matrix is ​​used to collect pressure distribution data of the target person's feet in real time to calculate the center of gravity coordinates; Based on the inertial measurement unit, the angular velocity and acceleration of the target person's torso are captured, and the rate of change of angular velocity and the rate of change of acceleration are calculated; Based on the electromyography sensor, the electromyography signal of the target person is acquired. When the electromyography signal is abnormal, the data weight of the pressure distribution data and the data weight of the angular velocity change rate and acceleration change rate are increased, and the center of gravity offset and overall posture trend of the target person are reacquired. The electromyography signals, pressure distribution data, angular velocity change rate and acceleration change rate are subjected to spatiotemporal correlation analysis, and the center of gravity offset is cross-validated with the overall posture trend to determine the motion posture of the target person. The process of acquiring the electromyographic signals of the target person based on the electromyographic sensor, and increasing the data weights of the pressure distribution data, angular velocity change rate, and acceleration change rate when the electromyographic signals are abnormal, to re-acquire the target person's center of gravity offset and overall posture trend, includes: The data characteristics of electromyography (EMG) signals are analyzed in real time. The data characteristics of EMG signals include baseline drift characteristics and high-frequency noise ratio characteristics. When the signal-to-noise ratio of EMG signals is lower than a preset threshold, it is determined that the EMG signals are abnormal. And / or, Before the step of generating a motion posture prompt when the center of gravity offset ratio exceeds a preset ratio, the following steps are included: Based on the angular velocity change rate and acceleration change rate, the motion intensity of the target person is determined, so as to adaptively adjust the judgment threshold of the center of gravity offset ratio. When the rate of change of angular velocity and the rate of change of acceleration increase simultaneously, the threshold for judging the proportion of the center of gravity offset is increased. When the rate of change of angular velocity and the rate of change of acceleration decrease simultaneously, the threshold for determining the proportion of the center of gravity offset is reduced.

4. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 1, characterized in that, The process of calculating the target person's center of gravity offset ratio based on the motion posture and dynamically adjusting the current posture of the six-degree-of-freedom skiing platform also includes: A dynamic baseline calibration mechanism is introduced to record the baseline of electromyography signals, the reference value of inertial measurement angle, and the reference value of center of gravity distribution of the target person in a static state; During the exercise, the current sensor data is compared with the electromyography signal baseline, the inertial measurement angle reference value, and the center of gravity distribution reference value at regular intervals. If the electromyography signal deviation exceeds the preset deviation threshold, the electromyography signal baseline, the inertial measurement angle reference value, and the center of gravity distribution reference value are automatically corrected in combination with the posture change of the inertial measurement data.

5. A control method for a hypobaric chamber simulating multiple environmental parameters according to any one of claims 1-4, characterized in that, The process of identifying the target person's movement posture and performing real-time linkage and coupling of the cabin air pressure, cabin wind force, cabin temperature, and movement posture to control the current posture of the six-degree-of-freedom skiing platform also includes: Extract image data from the in-cabin camera and extract the human posture features of the target personnel; Using the clock data from the central controller, a unified timestamp is added to the data sources of the in-cabin camera, electromyography sensor, inertial measurement unit, and pressure sensor, and delay compensation is performed on the image data. The data characteristics of each data source are analyzed, and the data is fused in layers based on image features to dynamically adjust the control parameters of the hypobaric chamber simulating multiple environmental parameters.

6. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 5, characterized in that, The process of extracting image data from the in-cabin camera and extracting the human posture features of the target personnel includes: Using a global shutter camera with integrated infrared fill light, the camera captures the frontal torso, upper limbs, and ski movements of the target person. The system captures the upper body posture, side lower limb movements, and overall center position of the target person, and extracts the joint coordinates, posture parameters, and motion trajectory of the target person using a skeletal key point detection algorithm.

7. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 6, characterized in that, The process of adding a unified timestamp to the data sources of the in-cabin camera, electromyography sensor, inertial measurement unit, and pressure sensor using the clock data of the central controller, and performing delay compensation on the image data, includes: Based on the clock data of the central controller, the frame rate is matched and the acquisition time is marked for each frame of image; For the data sources of electromyography (EMG) sensors, inertial measurement units (IMUs), and pressure sensors, timestamps are marked according to the original sampling rate, and non-uniform sampling data is mapped to the time axis of the camera using interpolation.

8. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 7, characterized in that, The analysis of data characteristics from various data sources, combined with image feature-based hierarchical data fusion, is used in the process of dynamically adjusting the control parameters of a hypobaric chamber simulating multiple environmental parameters. Image data features, electromyography data features, inertial measurement data features, and pressure distribution data features were extracted respectively. Dimensionality reduction was performed using a principal component analysis model to extract key information from the data. Based on the key information, the knee flexion angle is analyzed based on the image data, the quadriceps activity is analyzed based on the electromyography data, and the trunk forward tilt angle is analyzed based on the inertial measurement data. A weighted voting mechanism is used to fuse multi-source data to generate a posture judgment result about the target person. Based on the posture judgment results, the calculation weights of each data source are adaptively adjusted according to the scene mode of high-speed movement or fine movements.

9. The control method for a hypobaric chamber simulating multiple environmental parameters according to claim 8, characterized in that, The process of adaptively adjusting the calculation weights of each data source based on the posture judgment result and the scene mode of high-speed movement or fine movements includes: When the target person's movement is detected to cause occlusion or a sudden change in lighting, low-quality data is filtered by feature point confidence to automatically reduce the image weight, and triangulation is performed using image data from other perspectives. When image data conflicts with other data logic, a secondary verification is triggered to call historical data to determine trend consistency, and the attitude judgment result is corrected by combining air pressure data and / or wind force data.

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