An adjustable low-oxygen, constant temperature, constant humidity training room

Abstract

The application discloses an adjustable low-oxygen, constant-temperature and constant-humidity training room and relates to the technical field of training room control, which comprises an identity recognition module, an environment initialization module, a physiological monitoring module, a data analysis module, a training load adjustment module and a report generation module; the identity recognition module is used for setting target oxygen concentration, target temperature and target humidity as initial environment control parameters according to the training target type selected by a user on a man-machine interface and preset environment parameter templates, so as to exert specific low-oxygen, constant-temperature and constant-humidity combined training load on the user; the environment initialization module is used for calculating and setting initial oxygen concentration, temperature and humidity parameters according to the training target selected by the user on the man-machine interface and historical data; the physiological monitoring module is used for collecting physiological parameters of the heart rate, blood oxygen saturation, breathing frequency and muscle fatigue degree of the athlete and transmitting the data to the data analysis module for processing; and the data analysis module is used for receiving data from the physiological monitoring module.

Description

Technical Field

[0001] This invention relates to the field of training room control technology, and in particular to an adjustable low-oxygen, constant-temperature, and constant-humidity training room. Background Technology

[0002] Training room control technology refers to the technology of precisely controlling environmental parameters within a training room through a series of advanced sensors, controllers, and algorithms. Its purpose is to simulate specific environmental conditions to provide a controllable training or recovery environment for athletes, military personnel, or medical rehabilitation patients. The existing technology CN202210876543.2 describes a hypoxia training room control system that uses a fixed oxygen concentration threshold for regulation. This system does not dynamically adjust the system based on the athlete's real-time heart rate and blood oxygen data. This can easily lead to insufficient load or excessive hypoxia during endurance training. Furthermore, it lacks a predictive model, making it impossible to proactively mitigate the risk of physiological overload. In traditional training rooms, the adjustment of key environmental parameters such as oxygen concentration, temperature, and humidity typically relies on fixed thresholds or simple proportional control algorithms. These methods lack flexibility and cannot be dynamically adjusted according to the athlete's specific physical condition and training needs. Moreover, traditional training rooms often lack tools for real-time monitoring and analysis of the athlete's physiological state, making it impossible to scientifically quantify training effects and develop effective personalized training plans. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides an adjustable low-oxygen, constant-temperature, and constant-humidity training room control system to solve the problem that in traditional training rooms, the adjustment of key environmental parameters such as oxygen concentration, temperature, and humidity usually relies on fixed thresholds or simple proportional control algorithms. These methods lack flexibility and cannot be dynamically adjusted according to the athlete's specific physical condition and training needs. Furthermore, traditional training rooms often lack tools for real-time monitoring and analysis of the athlete's physiological state, resulting in the inability to scientifically quantify training effects and the difficulty in developing effective personalized training plans.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an adjustable low-oxygen, constant-temperature, and constant-humidity training room, comprising: The system includes an identity recognition module, an environment initialization module, a physiological monitoring module, a data analysis module, a training load adjustment module, and a report generation module. The identity recognition module is used to associate the user's personal health record and historical training data based on the user's identity, and to match the corresponding exclusive environmental parameter template from the preset environmental parameter template library. The preset environmental parameter template library includes three types of templates: endurance improvement, high altitude adaptation, and fat loss training. The environment initialization module is used to receive the user's historical training data output by the identity recognition module, and calculate the individualized initial oxygen concentration, initial temperature and initial humidity as the initial set values ​​for environmental regulation based on the training target selected by the user on the human-machine interface through a quantitative formula. The physiological monitoring module is used to collect physiological parameters of athletes, such as heart rate, blood oxygen saturation, respiratory rate, and muscle fatigue, and transmit the data to the data analysis module for processing. The data analysis module is used to receive physiological parameter data from the physiological monitoring module, use a long short-term memory network model to perform fusion analysis on multidimensional physiological parameters, establish a model of the athlete's current physical state, predict physiological change trends, and output the predicted state value and physiological load index for a future preset time period. The training load adjustment module is used to compare the prediction results with the preset safety threshold to obtain the difference value, generate individualized training load adjustment instructions based on the difference value, and prompt the user through the human-machine interface or control the individualized micro-environment device for local adjustment. The report generation module is used to save all data after training, evaluate the training effect using a long-term memory AI model, and generate a detailed report.

[0006] As a preferred embodiment of the adjustable low-oxygen, constant-temperature, and constant-humidity training room control system of the present invention, the identity recognition module includes the following steps: Fingerprint recognition technology is used to scan the texture of the user's finger surface to generate a fingerprint image; The fingerprint image is matched with fingerprint templates in the database to confirm the user's identity; Based on the confirmed identity information, the user's personal health record and historical training data are extracted from the database.

[0007] As a preferred embodiment of the adjustable low-oxygen, constant-temperature, and constant-humidity training room described in this invention, the environment initialization module includes the following steps: The system receives user historical training data output by the identity recognition module, the historical training data including historical temperature adaptation range. Adaptation range to historical humidity ; The training target parameters are received by the user through a human-computer interface. The training target parameters include training type, training duration and expected training load level. Based on the training type and load level, a corresponding initial environmental parameter combination is matched from a preset environmental parameter template library. The initial environmental parameter combination includes target oxygen concentration, target temperature, and target humidity. The environmental parameter template library is a standardized environmental configuration pre-set for different training purposes, used to apply controllable composite environmental stimuli to users. The different training objectives include endurance improvement, high altitude acclimatization, and fat loss training; The matched target oxygen concentration, target temperature, and target humidity are sent as initial settings to the training load adjustment module to initiate the environmental adjustment process. Calculate the initial oxygen concentration based on the training intensity level in the training objective parameters. The calculation formula is: ; in, Training intensity level This represents the volume concentration of oxygen in the air under standard atmospheric pressure, which is 20.9%. Let be the intensity attenuation coefficient, and ; The initial temperature was set based on the historical temperature adaptation range and training duration. for: ; in, Let be the time decay coefficient, and >0, For training duration; Set the initial humidity based on historical humidity adaptation range and training type. for: ; in, This is a correction factor for the type of exercise, and ; The calculated , , Send to the training load adjustment module to perform initialization settings.

[0008] As a preferred embodiment of the adjustable hypoxia, constant temperature, and constant humidity training room described in this invention, the physiological monitoring module includes the following steps: Real-time collection of user heart rate data via wearable devices And noise is removed using a filtering algorithm; Blood oxygen saturation is periodically measured using a fingertip pulse oximeter. And take the average of multiple measurements; Respiratory rate is collected using a respiratory sensor. And calculate the moving average of the number of breaths per minute; Electromyographic signals of the target muscle group were acquired using a surface electromyography sensor. Extract the root mean square value As an indicator of fatigue, the calculation formula is as follows: ; in, The number of sampling points within the preset time window; Will , , , The data is packaged into timestamped data frames and transmitted to the data analysis module at fixed time intervals.

[0009] As a preferred embodiment of the adjustable low-oxygen, constant-temperature, and constant-humidity training room described in this invention, the data analysis module includes the following steps: Receive data frames transmitted from the physiological monitoring module and construct multidimensional feature vectors according to the time series. ; The feature vectors are input into a pre-trained LSTM neural network model for analysis and prediction. The LSTM model outputs predicted state values ​​for a preset time period in the future, and the current physiological workload index (PLIPLI) is calculated using the following formula: ; ; in, , , , These are weighting coefficients; when When the preset threshold is exceeded, an early warning signal is triggered and... and Send to the training load adjustment module.

[0010] As a preferred embodiment of the adjustable low-oxygen, constant-temperature, and constant-humidity training room described in this invention, the training load adjustment module includes the following steps: Receive predicted state values ​​and physiological load index Compare with the preset safety threshold; like If the training load approaches or exceeds the safety threshold, the environmental parameters will be dynamically adjusted to reduce the training load. Based on the predicted downward trend in blood oxygen saturation, the oxygen concentration should be appropriately increased; Based on the predicted upward trend in heart rate, appropriately lower the temperature or increase ventilation; Based on the predicted trend of worsening muscle fatigue, humidity should be appropriately reduced to improve physical comfort. like If the environmental parameters remain stable within the target range, the current environmental parameters will be maintained or the environmental challenges will be gradually increased according to the preset procedure. The adjustment command is sent to the actuator, which controls the oxygen generator, air conditioner and humidifier to adjust the environmental parameters respectively.

[0011] As a preferred embodiment of the adjustable low-oxygen, constant-temperature, and constant-humidity training room described in this invention, the report generation module includes the following steps: Store time-series data of the entire training process, including environmental parameter sequences. and physiological parameter sequences ; Training effectiveness indicators were calculated based on normalized values ​​of heart rate and blood oxygen saturation. The calculation formula is: ; in, ; By comparing this time With history The mean is used to generate an adaptation score; Generate a training report that includes trend charts, key parameter comparison tables, and safety warning records.

[0012] As a preferred embodiment of the adjustable low-oxygen, constant-temperature, and constant-humidity training room described in this invention, the binding method of the RFID wristband in the identification module includes: The wristband has a built-in RFID chip with a unique identification code (UID) that is mapped to the user's health record. RFID readers are deployed at the access control point of the training room, with a set reading distance. When a UID is detected, a query request is sent to the server to retrieve the corresponding file data.

[0013] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the adjustable low-oxygen, constant-temperature, and constant-humidity training chamber control system as described in the first aspect of the present invention.

[0014] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the adjustable low-oxygen, constant-temperature, and constant-humidity training chamber control system as described in the first aspect of the present invention.

[0015] The beneficial effects of this invention are as follows: By using fingerprint recognition technology to scan the surface texture of the user's finger to generate a fingerprint image, and matching it with fingerprint templates in the database to confirm the user's identity, accurate identity verification is achieved. Through a high-precision identity verification mechanism, the system can accurately extract the user's personal health records and historical training data from the database, providing reliable data support for subsequent personalized training environment settings, thereby improving the effectiveness and security of training. By receiving data from the physiological monitoring module and using a deep neural network model for fusion analysis, a current physical state model is established and future trends are predicted, enabling a scientific evaluation of training effects. The application of the predictive model allows the training room to anticipate potential problems, such as fatigue accumulation or insufficient recovery, and take preventive measures. In addition, long-term data accumulation and analysis help reveal individualized training response patterns and further optimize the design of training programs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the adjustable low-oxygen, constant-temperature, and constant-humidity training chamber control system in Example 1. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example, refer to Figure 1 As an embodiment of the present invention, this embodiment provides an adjustable low-oxygen, constant-temperature, and constant-humidity training room control system, comprising: The system includes an identity recognition module, an environment initialization module, a physiological monitoring module, a data analysis module, a training load adjustment module, and a report generation module. The identity recognition module is used to associate the user's personal health record and historical training data based on the user's identity, and to match the corresponding exclusive environmental parameter template from the preset environmental parameter template library. The preset environmental parameter template library includes three types of templates: endurance improvement, high altitude adaptation, and fat loss training. Furthermore, fingerprint recognition technology is used to scan the texture of the user's finger surface to generate a fingerprint image; The fingerprint image is matched with fingerprint templates in the database to confirm the user's identity; Based on the confirmed identity information, the user's personal health record and historical training data are extracted from the database to provide individualized basic information for subsequent steps; The binding methods for RFID wristbands in the identity recognition module include: The wristband has a built-in RFID chip with a unique identification code (UID) that is mapped to the user's health record. RFID readers are deployed at the access control point of the training room, with a set reading distance. When a UID is detected, a query request is sent to the server to retrieve the file data corresponding to the ID; It should be noted that by using multiple identity verification methods such as fingerprint recognition, facial recognition, or RFID wristbands, the accuracy and convenience of user identification are ensured. The RFID wristband binding method enables rapid retrieval of user information, avoids manual input errors, and supports automatic identification and data matching in multi-person training scenarios, thereby improving the system's intelligent management level.

[0022] The environment initialization module is used to receive the user's historical training data output by the identity recognition module, and calculate the individualized initial oxygen concentration, initial temperature and initial humidity according to the training target selected by the user on the human-machine interface, as the initial set values ​​for environmental regulation. Furthermore, the training target parameters input by the user are received through the human-computer interface, and the training target parameters include training type, training duration and expected training load level; Based on the training type and load level, a corresponding initial environmental parameter combination is matched from a preset environmental parameter template library. The initial environmental parameter combination includes target oxygen concentration, target temperature, and target humidity. The environmental parameter template library is a standardized environmental configuration pre-set for different training purposes, used to apply controllable composite environmental stimuli to users. The different training objectives include endurance improvement, high altitude acclimatization, and fat loss training; The matched target oxygen concentration, target temperature, and target humidity are sent as initial settings to the training load adjustment module to initiate the environmental adjustment process. Calculate the initial oxygen concentration based on the training intensity level in the training objective parameters. The calculation formula is: ; in, The intensity attenuation coefficient, Training intensity level; The initial temperature was set based on the historical temperature adaptation range and training duration. for: ; in, The time decay coefficient, For training duration; Set the initial humidity based on historical humidity adaptation range and training type. for: ; in, For sports type correction coefficient; The calculated , , Send to the training load adjustment module to perform initialization settings; It should be noted that by intelligently matching the user-selected training target parameters (including training type, duration, and expected load level) with a pre-set standardized environmental parameter template library, the system achieves precise and rapid configuration of the training environment. This template library is built based on different training objectives (endurance improvement, altitude adaptation, and fat loss training), integrating research findings in exercise physiology and a large amount of experimental data. This ensures that the set initial oxygen concentration, temperature, and humidity combination can effectively apply the expected complex environmental stimulation. At the same time, the system further introduces an individualized correction mechanism, combining the user's historical adaptation data and using intensity decay coefficient, time decay coefficient, and exercise type correction coefficient to fine-tune the initial parameters. This ensures both the scientific and universal nature of the training program and takes into account individual differences, improving the safety and effectiveness of training and laying a precise starting point for subsequent dynamic control.

[0023] The physiological monitoring module is used to collect physiological parameters of athletes, such as heart rate, blood oxygen saturation, respiratory rate, and muscle fatigue, and transmit the data to the data analysis module for processing. Furthermore, wearable devices can collect users' heart rate data in real time. And noise is removed using a filtering algorithm; Blood oxygen saturation is periodically measured using a fingertip pulse oximeter. And take the average of multiple measurements; Respiratory rate is collected using a respiratory sensor. And calculate the moving average of the number of breaths per minute; Electromyographic signals of the target muscle group were acquired using a surface electromyography sensor. Extract the root mean square value As an indicator of fatigue, the calculation formula is as follows: ; in, The number of sampling points within the preset time window; Will , , , Packed into timestamped data frames, they are transmitted to the data analysis module at fixed time intervals; Among them, the wearable device uses Bluetooth 5.0 protocol to transmit heart rate data, with a sampling frequency of 1Hz; the fingertip pulse oximeter uses pulse oximetry, with a sampling interval of 30 seconds, and takes the average of 3 measurements to reduce noise; the surface electromyography sensor has a sampling frequency of 1kHz and a preset time window of N=1024 sampling points. It should be noted that this invention uses a variety of high-precision sensors to collect key physiological indicators in real time, and introduces processing methods such as filtering algorithms and moving averages, which significantly improves the stability and reliability of the data. The extracted multidimensional physiological data is not only used for immediate status assessment, but also provides high-quality data support for subsequent predictive analysis, enhancing the system's ability to perceive and respond to the athlete's physical condition.

[0024] The data analysis module receives physiological parameter data from the physiological monitoring module, uses a long short-term memory network model to fuse and analyze multidimensional physiological parameters, establishes a model of the athlete's current physical state, predicts physiological change trends, and outputs predicted values ​​of the state and physiological load index for a future preset time period. Furthermore, the system receives data frames transmitted from the physiological monitoring module and constructs multi-dimensional feature vectors based on time series. ; The feature vectors are input into a pre-trained LSTM neural network model for analysis and prediction. The LSTM model outputs predicted state values ​​for a preset time period in the future, and the current physiological workload index (PLIPLI) is calculated using the following formula: ; ; in, , , , These are weighting coefficients; when When the preset threshold is exceeded, an early warning signal is triggered and... and Send to the training load adjustment module; The LSTM model training dataset contains physiological data from 120 athletes (aged 18-35, covering endurance, strength, and fat loss training scenarios). Each dataset contains time-series data in four dimensions: HR, SpO2, BR, and RMSEMG (duration 60 minutes). The model structure is 'input layer (4-dimensional) - hidden layer 1 (64 units) - hidden layer 2 (32 units) - output layer (4-dimensional)', using the Adam optimizer with a learning rate of 0.001, 50 training epochs, and mean squared error (MSE) as the loss function. It should be noted that this invention uses an LSTM neural network model to perform deep learning modeling on physiological data, which can capture complex change patterns in long-term sequences, thereby achieving accurate prediction of future physical condition. The introduction of the physiological load index (PLI) enables the system to quantitatively assess the degree of fatigue and provide timely warnings before danger occurs, demonstrating the system's proactive intervention and intelligent decision-making capabilities.

[0025] The training load adjustment module is used to compare the prediction results with the preset safety threshold to obtain the difference value, generate individualized training load adjustment instructions based on the difference value, and prompt the user through the human-machine interface or control the individualized micro-environment device for local adjustment. Furthermore, receive predicted state values ​​and physiological load indices. Compare with the preset safety threshold; like If the training load approaches or exceeds the safety threshold, the environmental parameters will be dynamically adjusted to reduce the training load. Based on the predicted downward trend in blood oxygen saturation, the oxygen concentration should be appropriately increased; Based on the predicted upward trend in heart rate, appropriately lower the temperature or increase ventilation; Based on the predicted trend of worsening muscle fatigue, humidity should be appropriately reduced to improve physical comfort. like If the environmental parameters remain stable within the target range, the current environmental parameters will be maintained or the environmental challenges will be gradually increased according to the preset procedure. The adjustment command is sent to the actuator to control the oxygen generator, air conditioner and humidifier to adjust the environmental parameters respectively; Among them, the oxygen concentration adjustment range is 12%-20.9%, with a control error of ±0.5%; the air conditioner uses frequency conversion temperature control, with a temperature adjustment range of 18-30℃ and a control error of ±0.3℃; the humidifier uses ultrasonic atomization, with a humidity adjustment range of 30%-70% and a control error of ±2%.

[0026] It should be noted that a prediction-comparison-response closed-loop control mechanism is adopted. Based on the physiological state prediction values ​​and PLI index output by the data analysis module, the safety and suitability of the current environmental load are assessed in real time. When the system determines that the physiological indicators are close to or exceed the safety threshold, it can actively adjust environmental parameters such as oxygen concentration, temperature and humidity to implement precise load reduction (increasing oxygen content and reducing temperature and humidity) to prevent athletes from experiencing risks such as hypoxia, overheating or excessive fatigue. When the physiological state is stable within the target range, the system can also gradually increase the environmental challenge (slowly reducing oxygen) according to a predetermined program to achieve progressive adaptation training. This dynamic regulation strategy takes into account both training effectiveness and safety, and demonstrates the system's ability to make autonomous decisions and intelligently adjust in complex environments. It achieves the closed-loop intelligent training control goal of controlling the load with the environment and promoting adaptation with data.

[0027] The report generation module is used to save all data after training, evaluate the training effect using a long-term memory AI model, and generate a detailed report. Furthermore, it stores time-series data of the entire training process, including environmental parameter sequences. and physiological parameter sequences ; The TEITEI (Training Effectiveness Index) is calculated based on normalized values ​​of heart rate and blood oxygen saturation. The formula is as follows: ; in, ; By comparing this time With history The mean is used to generate an adaptation score; Generate a training report that includes trend charts, key parameter comparison tables, and safety warning records; It should be noted that this invention automatically generates a training report containing trend charts, comparison tables, and warning records after training, which facilitates users to review the training process and assists in formulating the next stage of training plan. The application of long-term memory AI models enables the systematic accumulation of training effects each time, which helps to continuously optimize training strategies and improve the scientific and personalized level of overall training.

[0028] This embodiment also provides a computer device suitable for an adjustable low-oxygen, constant-temperature, and constant-humidity training room control system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as proposed in the above embodiment.

[0029] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0030] This embodiment also provides a storage medium on which a computer program is stored. When executed by a processor, the program implements the adjustable low-oxygen, constant-temperature, and constant-humidity training chamber control system as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0031] In summary, this invention achieves accurate identity verification by scanning the surface texture of a user's finger with fingerprint recognition technology to generate a fingerprint image and matching it with fingerprint templates in a database. Through this high-precision identity verification mechanism, the system can accurately extract the user's personal health records and historical training data from the database, providing reliable data support for subsequent personalized training environment settings. This improves the effectiveness and security of training. By receiving data from the physiological monitoring module and using a deep neural network model for fusion analysis, the system establishes a current physical state model and predicts future trends, enabling a scientific evaluation of training effectiveness. The application of the predictive model allows the training room to proactively address potential problems, such as fatigue accumulation or insufficient recovery, and take preventative measures. Furthermore, long-term data accumulation and analysis help reveal individualized training response patterns, further optimizing the design of training programs.

[0032] Example 2 is a second embodiment of the present invention, which provides endurance enhancement training based on an adjustable hypoxia, constant temperature, and constant humidity training chamber control system, including: Initial parameter settings: 30-year-old male athlete, historical training data ; Training objective: Altitude acclimatization training (training duration 90 minutes, intensity level I=4). Initial parameter calculation: oxygen concentration : ; in, =0.12, =20.9%; temperature : ; in, =0.03, =90 minutes; humidity : ; in, =0.7; Physiological monitoring data after 45 minutes of training: Heart rate =170 times / minute times / minute, normalized value ); blood oxygen saturation (normalized value) ); respiratory rate =22 breaths / min (assuming maximum respiratory rate) times / minute, normalized value ); Muscle fatigue =0.6 (assuming maximum fatigue) Normalized value ); Physiological load index calculate: ; in, =0.8, =0.1, =0.05, =0.05, substitute the value: ; because If the value is less than 0.85, the system will maintain the current environmental challenge according to the preset program. ; Training effectiveness index (TEI) calculation: ; in, and This is the normalized weighting coefficient, and its value is assumed to be 48 obtained through integration. This time (48) Higher than the historical average (42), with a score of 88 (excellent), it is recommended to increase the training intensity level to 5 next time.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adjustable low-oxygen, constant-temperature, and constant-humidity training room, characterized in that: include: The system includes an identity recognition module, an environment initialization module, a physiological monitoring module, a data analysis module, a training load adjustment module, and a report generation module. The identity recognition module is used to associate the user's personal health record and historical training data based on the user's identity, and to match the corresponding exclusive environmental parameter template from the preset environmental parameter template library. The preset environmental parameter template library includes three types of templates: endurance improvement, high altitude adaptation, and fat loss training. The environment initialization module is used to receive the user's historical training data output by the identity recognition module, and calculate the individualized initial oxygen concentration, initial temperature and initial humidity as the initial set values ​​for environmental regulation based on the training target selected by the user on the human-machine interface through a quantitative formula. The physiological monitoring module is used to collect physiological parameters of athletes, such as heart rate, blood oxygen saturation, respiratory rate, and muscle fatigue, and transmit the data to the data analysis module for processing. The data analysis module is used to receive physiological parameter data from the physiological monitoring module, use a long short-term memory network model to perform fusion analysis on multidimensional physiological parameters, establish a model of the athlete's current physical state, predict physiological change trends, and output the predicted state value and physiological load index for a future preset time period. The training load adjustment module is used to compare the prediction results with the preset safety threshold to obtain the difference value, generate individualized training load adjustment instructions based on the difference value, and prompt the user through the human-machine interface or control the individualized micro-environment device for local adjustment. The report generation module is used to save all data after training, evaluate the training effect using a long-term memory AI model, and generate a detailed report.

2. The adjustable low-oxygen, constant-temperature, and constant-humidity training room as described in claim 1, characterized in that: The identity recognition module includes the following steps: Fingerprint recognition technology is used to scan the texture of the user's finger surface to generate a fingerprint image; The fingerprint image is matched with fingerprint templates in the database to confirm the user's identity; Based on the confirmed identity information, the user's personal health record and historical training data are extracted from the database.

3. The adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as described in claim 2, characterized in that: The environment initialization module includes the following steps: The system receives user historical training data output by the identity recognition module, the historical training data including historical temperature adaptation range. and historical humidity adaptation range ; The training target parameters are received by the user through a human-computer interface. The training target parameters include training type, training duration and expected training load level. Based on the training type and load level, a corresponding initial environmental parameter combination is matched from a preset environmental parameter template library. The initial environmental parameter combination includes target oxygen concentration, target temperature, and target humidity. The environmental parameter template library is a standardized environmental configuration pre-set for different training purposes, used to apply controllable composite environmental stimuli to users. The different training objectives include endurance improvement, high altitude acclimatization, and fat loss training; The matched target oxygen concentration, target temperature, and target humidity are sent as initial settings to the training load adjustment module to initiate the environmental adjustment process. Calculate the initial oxygen concentration based on the training intensity level in the training objective parameters. The calculation formula is: ； in, Training intensity level, This represents the volume concentration of oxygen in the air under standard atmospheric pressure, which is 20.9%. Let be the intensity attenuation coefficient, and ; The initial temperature was set based on the historical temperature adaptation range and training duration. for: ； in, Let be the time decay coefficient, and >0, For training duration; Set the initial humidity based on historical humidity adaptation range and training type. for: ； in, This is a correction factor for the type of exercise, and ; The calculated , , Send to the training load adjustment module to perform initialization settings.

4. The adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as described in claim 3, characterized in that: The physiological monitoring module includes the following steps: Real-time collection of user heart rate data via wearable devices And noise is removed using a filtering algorithm; Blood oxygen saturation is periodically measured using a fingertip pulse oximeter. And take the average of multiple measurements; Respiratory rate is collected using a respiratory sensor. And calculate the moving average of the number of breaths per minute; Electromyographic signals of the target muscle group were acquired using a surface electromyography sensor. Extract the root mean square value As an indicator of fatigue, the calculation formula is as follows: ； in, The number of sampling points within the preset time window; Will , , , The data is packaged into timestamped data frames and transmitted to the data analysis module at fixed time intervals.

5. The adjustable low-oxygen, constant-temperature, and constant-humidity training room as described in claim 4, characterized in that: The data analysis module includes the following steps: Receive data frames transmitted from the physiological monitoring module and construct multidimensional feature vectors according to the time series. ; The feature vectors are input into a pre-trained LSTM neural network model for analysis and prediction. The LSTM model outputs predicted state values ​​for a preset time period in the future, and the current physiological workload index (PLIPLI) is calculated using the following formula: ； ； in, , , , These are weighting coefficients; when When the preset threshold is exceeded, an early warning signal is triggered and... and Send to the training load adjustment module.

6. The adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as described in claim 5, characterized in that: The training load adjustment module includes the following steps: Receive predicted state values ​​and physiological load index Compare with the preset safety threshold; like If the training load approaches or exceeds the safety threshold, the environmental parameters will be dynamically adjusted to reduce the training load. Based on the predicted downward trend in blood oxygen saturation, the oxygen concentration should be appropriately increased; Based on the predicted upward trend in heart rate, appropriately lower the temperature or increase ventilation; Based on the predicted trend of worsening muscle fatigue, humidity should be appropriately reduced to improve physical comfort. like If the environmental parameters remain stable within the target range, the current environmental parameters will be maintained or the environmental challenges will be gradually increased according to the preset procedure. The adjustment command is sent to the actuator, which controls the oxygen generator, air conditioner and humidifier to adjust the environmental parameters respectively.

7. The adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as described in claim 6, characterized in that: The report generation module includes the following steps: Store time-series data of the entire training process, including environmental parameter sequences. and physiological parameter sequences ; Training effectiveness indicators were calculated based on normalized values ​​of heart rate and blood oxygen saturation. The calculation formula is: ； in, ; By comparing this time With history The mean is used to generate an adaptation score; Generate a training report that includes trend charts, key parameter comparison tables, and safety warning records.

8. The adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as described in claim 7, characterized in that: The binding method of the RFID wristband in the identity recognition module includes: The wristband has a built-in RFID chip with a unique identification code (UID) that is mapped to the user's health record. RFID readers are deployed at the access control point of the training room, with a set reading distance. When a UID is detected, a query request is sent to the server to retrieve the corresponding file data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the adjustable low-oxygen, constant-temperature, and constant-humidity training room control system as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the adjustable low-oxygen, constant-temperature, and constant-humidity training chamber control system as described in any one of claims 1 to 8.

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