Plateau tourism bedside intelligent interaction microenvironment adjusting system and method

Abstract

The invention discloses a plateau tourism bedside intelligent interaction microenvironment adjusting system and method, and the system comprises a signal collection and transmission unit which is responsible for collecting bedside signals, carrying out the filtering processing of the bedside signals through an MCU, and transmitting the bedside signals to a cloud platform; the intelligent eyeshade data receiving unit is responsible for receiving the electro-oculogram, the eye movement and the blood oxygen collected by the intelligent eyeshade, transmitting the electro-oculogram, the eye movement and the blood oxygen to the MCU for processing, and transmitting the processed electro-oculogram, the cloud platform is responsible for receiving data from the signal acquisition and transmission unit and the intelligent eyeshade data receiving unit, and storing, analyzing and processing the data; the artificial intelligence unit is responsible for mining the data received by the cloud platform and identifying the physiological status, sleep quality and behavior habits of the tourists through a health behavioral theory and an artificial intelligence sleep staging algorithm; the regulation and control unit is responsible for analyzing an analysis result of the cloud platform and an instruction of a user; and the terminal equipment is responsible for providing a user interface.

Description

Technical Field

[0001] The present invention relates to the technical field of plateau sleeping environment regulation, and in particular to a bedside intelligent interactive microenvironment regulation system and method for plateau tourism. Background Art

[0002] The plateau area attracts a large number of tourists with its unique natural scenery and cultural landscape. However, the plateau environment also has its own particularities, such as low oxygen, large temperature difference, and strong ultraviolet rays. These environmental factors have a significant impact on the physiological and psychological state of tourists. Therefore, the comfort and health of the accommodation environment for plateau tourism have become the focus of tourists. In recent years, the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence has provided strong support for the research and development of intelligent interactive microenvironment adjustment systems for bedside plateau tourism. These technologies enable the system to collect and analyze tourists' physiological parameters and environmental parameters in real time, and accurately control them according to the analysis results, so as to meet tourists' needs for comfort and health of the accommodation environment. At the same time, the trend of intelligence also enables the system to provide more convenient and efficient services and enhance tourists' accommodation experience.

[0003] Traditional plateau tourism accommodation environments often lack precise micro-environment regulation methods, which makes it difficult for tourists to obtain the best accommodation when facing the particularities of the plateau climate, such as low oxygen and large temperature differences. In addition, different tourists have different needs for accommodation environments. Therefore, an intelligent interactive bedside micro-environment regulation system and method for plateau tourism is proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing technology lacks precise microenvironment regulation means, which makes it difficult for tourists to obtain the best accommodation when facing the particularities of the plateau climate, such as low oxygen and large temperature differences. In addition, different tourists have different needs for the accommodation environment. A plateau tourism bedside intelligent interactive microenvironment regulation system and method are proposed.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The plateau tourism bedside intelligent interactive micro-environment adjustment system includes:

[0007] Signal acquisition and transmission unit: responsible for collecting bedside signals and filtering them through MCU (STM32F2), and then transmitting the bedside signals to the cloud platform. The bedside signals include EEG, body movement and body temperature.

[0008] Smart eye mask data receiving unit: responsible for receiving the eye electroencephalogram, eye movement and blood oxygen collected by the smart eye mask, and transmitting it to the MCU (STM32F2) for processing through a specific method (such as Bluetooth or Wi-Fi), and then passing it to the cloud platform after processing;

[0009] Cloud platform: responsible for receiving the bedside signals from the signal acquisition and transmission unit and the electrooculogram, eye movement and blood oxygen from the smart eye mask data receiving unit, and storing, analyzing and processing them. The cloud platform interacts with the artificial intelligence unit and uses health behavior theory and artificial intelligence sleep staging algorithm to deeply mine and analyze the data. The cloud platform interacts with the control unit and terminal equipment to realize remote control and data display functions;

[0010] Artificial Intelligence Unit: responsible for mining the data received from the cloud platform, identifying tourists’ physiological status, sleep quality and behavioral habits through health behavior theory and artificial intelligence sleep staging algorithm;

[0011] Control unit: responsible for regulating the flow sensor, pressure reducing valve and pressure switch in terms of oxygen concentration according to the analysis results of the cloud platform and the user's instructions, so as to adjust the oxygen concentration and temperature in the microenvironment;

[0012] Terminal device: responsible for providing the user interface, viewing real-time data, historical data and analysis reports, and performing remote control.

[0013] The above technical solution further includes:

[0014] Furthermore, the signal acquisition and transmission unit includes a sensor subunit, a signal conditioning subunit, an ADC (analog-to-digital converter) subunit, an MCU (STM32F2) and a wireless communication subunit. The sensor subunit is responsible for collecting bedside signals. The sensor subunit transmits the collected bedside signals to the signal conditioning subunit for preprocessing. The signal conditioning subunit performs preliminary conditioning on the bedside signals collected by the sensor, such as amplification, attenuation, filtering, etc., to improve the signal-to-noise ratio and anti-interference ability of the signal. The preliminary conditioned signal is transmitted to the ADC (analog-to-digital converter) subunit for digital processing. The ADC (analog-to-digital converter) subunit converts the conditioned analog signal into a digital signal for subsequent digital signal processing and analysis. The digital signal is transmitted to the MCU (STM32F2) for filtering and data processing. The MCU (STM32F2) is the core processor of the signal acquisition and transmission unit, which is responsible for receiving the digital signal transmitted by the ADC subunit, and performing filtering, data compression and format conversion. At the same time, the MCU (STM32F2) transmits the processed data to the cloud platform through wireless communication technology. The wireless communication subunit is responsible for data communication between the MCU (STM32F2) and the cloud platform, ensuring the real-time and integrity of the data, receiving the processed data transmitted by the MCU (STM32F2), and sending it to the cloud platform. At the same time, it receives instructions or configuration information sent by the cloud platform, and transmits it to the MCU (STM32F2) for processing.

[0015] Furthermore, the smart eye mask data receiving unit transmits the received data to the MCU (STM32F2) for processing via wireless communication technologies such as Bluetooth or Wi-Fi.

[0016] Further, the cloud platform includes a data receiving subunit, a data storage subunit, a data processing subunit, a data display subunit and a remote control subunit. The data receiving subunit is responsible for receiving data from the signal acquisition and transmission unit and the smart eye mask data receiving unit. After receiving the data, the data is passed to the data storage subunit for storage and to the data processing subunit for processing. The data storage subunit is responsible for storing the received data in the cloud database, and MySQL is selected as the persistence layer database for storing various types of data required by the system, such as user information, device status, environmental parameters, etc. MySQL has the advantages of high performance, scalability and ease of use. The data storage subunit provides a data access interface to the data processing subunit for subsequent data analysis. The data processing subunit interacts with the artificial intelligence unit, and uses the health behavior theory and artificial intelligence sleep staging algorithm to mine and analyze the data. The analyzed data is passed to the data display subunit for display, and is also passed to the remote control subunit for guiding control. The data display subunit is responsible for displaying the results of data analysis and the status information of the control unit to the user. The remote control subunit is responsible for interacting with the control unit to realize the remote control function.

[0017] Furthermore, the artificial intelligence unit includes a data mining subunit, a physiological state recognition subunit, a sleep quality analysis subunit, a behavior habit analysis subunit and a personalized suggestion generation subunit. The data mining subunit is responsible for preprocessing, cleaning and feature extraction of data transmitted by the cloud platform, and deep mining of the data to discover potential health information and sleep patterns. The physiological state recognition subunit receives the feature data extracted by the data mining subunit, applies health behavior theory and a pre-trained physiological state recognition model to determine the physiological state of tourists. The health behavior theory can help understand the relationship between an individual's sleep habits, behavior patterns and physiological health. The physiological state recognition subunit will conduct a preliminary analysis of the data based on these theories. The sleep quality analysis subunit uses an artificial intelligence sleep staging algorithm to stage the sleep data of tourists, analyze the duration, proportion and conversion pattern of different sleep stages, and evaluate the sleep quality of tourists. The behavioral habit analysis subunit collects and analyzes the daily behavioral habit data of tourists, and evaluates the relationship between the sleep quality of tourists and their daily behavioral habits. The personalized suggestion generation subunit integrates the results of data mining, physiological state recognition, sleep quality analysis and behavioral habit analysis, and generates personalized sleep improvement suggestions and microenvironment adjustment plans based on the individual differences and needs of tourists.

[0018] Furthermore, the control unit includes an instruction receiving subunit, a device control subunit, a device status monitoring subunit, a fault handling subunit and a safety protection subunit. The instruction receiving subunit is responsible for receiving the control instructions sent by the cloud platform and the direct instructions of the user. The instruction receiving subunit interacts with the data processing subunit and the user interface of the cloud platform, receives the instructions and performs preliminary analysis. The device control subunit performs specific control on the devices in the microenvironment according to the instructions transmitted by the instruction receiving subunit. The device control subunit interacts with the instruction receiving subunit and the device status monitoring subunit, receives the control instructions and sends control signals to the devices. The subunit monitors the operating status of the equipment in real time, including the switch status, working parameters, etc. of the equipment. The equipment status monitoring subunit interacts with the equipment control subunit and the fault handling subunit to provide equipment status information and trigger the fault handling process when a fault is found. When a fault occurs in the equipment, the fault handling subunit takes corresponding handling measures according to the type and severity of the fault, such as alarm and shutdown. During the equipment control process, the safety protection subunit ensures the safe operation of the equipment to avoid safety hazards such as overload and short circuit. The safety protection subunit interacts with the equipment control subunit and the safety monitoring sensor to monitor the safety status of the equipment and take protective measures when safety hazards are found.

[0019] Furthermore, the terminal device includes a user interface subunit, a data processing subunit, a remote control subunit, a user setting subunit and an alarm prompt subunit. The user interface subunit is responsible for displaying data, receiving user input and providing operation feedback. The user interface subunit interacts with the data processing subunit, the remote control subunit and the user setting subunit. The data processing subunit obtains data from the cloud platform and performs format conversion, screening and display preparation. The data processing subunit interacts with the data display subunit and the user interface subunit of the cloud platform. The remote control subunit receives the user's control instructions and sends them to the cloud platform for execution. The remote control subunit interacts with the user interface subunit and the remote control subunit of the cloud platform. The user setting subunit manages user settings, including data display format, alarm threshold, account information, etc. The alarm prompt subunit detects abnormal data or equipment failure and generates alarm information. The alarm prompt subunit interacts with the data processing subunit and the user interface subunit, receives the alarm information and displays it to the user.

[0020] Furthermore, the physiological state recognition subunit receives the feature data extracted by the data mining subunit, applies health behavior theory and the pre-trained physiological state recognition model to determine the physiological state of the tourist, specifically in the following steps:

[0021] Data reception and preprocessing: The physiological state recognition subunit receives the feature data extracted by the data mining subunit, and selects features that have an important impact on physiological state recognition from the feature data. The features include statistical quantities (such as mean, standard deviation, maximum value, minimum value, etc.) or transformed features (such as spectrum analysis, wavelet transform, etc.) of physiological parameters such as heart rate, blood oxygen saturation, and electrooculogram signals;

[0022] Model loading and initialization:

[0023] Loading a physiological state recognition model based on a support vector machine, wherein the physiological state recognition model based on the support vector machine has been trained using a large amount of labeled heart rate and blood oxygen saturation data for identifying different physiological states;

[0024] Model Reasoning:

[0025] Inputting the selected characteristic data into a physiological state recognition model, wherein the physiological state recognition model calculates scores representing different physiological states of the tourist based on the input characteristic data and applying health behavior theory;

[0026] Result output and explanation:

[0027] The model outputs the recognition results of tourists’ physiological states, and the results are interpreted or visualized as needed for better understanding by users or systems.

[0028] Furthermore, the sleep quality analysis subunit uses an artificial intelligence sleep staging algorithm to process the sleep data of tourists in stages, specifically in the following steps:

[0029] Feature extraction:

[0030] Extract features reflecting eye movements from electrooculogram (EOG) signals, such as the rapid eye movement (REM) period, extract features reflecting different sleep stages from electroencephalogram (EEG) signals, such as the slow-wave sleep (N3) period, and extract features of blood oxygen saturation changes from blood oxygen data to assess breathing conditions during sleep;

[0031] Model training:

[0032] Use labeled sleep staging data (such as polysomnography PSG data) to train random forests to learn the mapping relationship between sleep features and sleep periods;

[0033] Sleep stages:

[0034] The preprocessed data is input into the trained model to perform sleep staging, which includes wake (W), rapid eye movement (REM), non-rapid eye movement 1 (N1), non-rapid eye movement 2 (N2), and non-rapid eye movement 3 (N3, also known as slow-wave sleep or deep sleep);

[0035] Results evaluation and optimization:

[0036] Evaluate the accuracy of the model's staging results, such as using confusion matrix, accuracy, recall rate and other indicators, and optimize and adjust the model based on the evaluation results to improve staging accuracy;

[0037] Correlation analysis: Analyze the correlation between different sleep stages and physiological parameters (such as blood oxygen saturation, heart rate, etc.);

[0038] Trend analysis: Analyze long-term changes in an individual’s sleep quality to identify potential sleep disorders or health problems.

[0039] The method of the bedside intelligent interactive microenvironment adjustment system for plateau tourism comprises the following steps:

[0040] Start the device: Turn on the power of the sensor and the smart eye mask to ensure that they are in working condition. The MCU will automatically start collecting data from the sensor and the smart eye mask.

[0041] Data filtering and transmission: The MCU filters the data collected from the sensor and the smart eye mask to reduce noise and interference, and then transmits the filtered data to the cloud platform through the network interface router;

[0042] Data reception and storage: The cloud platform receives data from sensors and smart eye masks and stores them in a database for subsequent analysis and processing;

[0043] Data analysis and mining: The cloud platform uses artificial intelligence units, combined with health behavior theory and artificial intelligence sleep staging algorithms, to mine and analyze the received data, which helps to identify the changing trends of tourists' physiological states and environmental parameters;

[0044] Remote control: According to the analysis results of the cloud platform and the user's instructions, the cloud platform will send control instructions to the control unit, and the control unit will control the oxygen outlet, flow sensor, pressure reducing valve, pressure switch and other parameter control devices according to these instructions to adjust the oxygen concentration, temperature and other parameters in the microenvironment;

[0045] Real-time feedback: The cloud platform feeds back the control results and real-time data to terminal devices (such as computers, mobile phones, etc.). Users can view real-time data, historical data and analysis reports through terminal devices and perform remote control as needed.

[0046] The present invention has the following beneficial effects:

[0047] 1. In the present invention, by real-time collection and analysis of tourists' physiological parameters and environmental parameters, precise regulation of the microenvironment can be achieved, which significantly improves the comfort of the accommodation environment. This regulation is not only based on current real-time data, but also combines health behavior theory and artificial intelligence sleep staging algorithm, so as to provide more personalized services and monitor tourists' health status in real time. Once abnormal data is found, such as too fast heart rate, decreased blood oxygen saturation, etc., the early warning mechanism will be triggered immediately to notify relevant personnel or institutions for timely processing. This function is particularly important for tourists in plateau tourism.

[0048] 2. In the present invention, users can remotely access the cloud platform through terminal devices such as computers and mobile phones, view real-time data, historical data and analysis reports, and perform remote control, which not only improves the convenience of the system, but also enables users to monitor and control the system at any time and any place. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a system block diagram of the bedside intelligent interactive microenvironment adjustment system and method for plateau tourism proposed by the present invention;

[0050] Figure 2 This is a flow chart of the bedside intelligent interactive microenvironment adjustment system and method for plateau tourism proposed by the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1-Figure 2 As shown, the present invention is a bedside intelligent interactive microenvironment adjustment system for plateau tourism, comprising:

[0053] Signal acquisition and transmission unit: responsible for collecting bedside signals and filtering them through MCU (STM32F2), and then transmitting the bedside signals to the cloud platform. The bedside signals include EEG, body movement and body temperature;

[0054] Smart eye mask data receiving unit: responsible for receiving the eye electroencephalogram, eye movement and blood oxygen collected by the smart eye mask, and transmitting it to the MCU (STM32F2) for processing through a specific method (such as Bluetooth or Wi-Fi), and then passing it to the cloud platform after processing;

[0055] Cloud platform: Responsible for receiving bedside signals from the signal acquisition and transmission unit and the electrooculogram, eye movement and blood oxygen from the smart eye mask data receiving unit, and storing, analyzing and processing them. The cloud platform interacts with the artificial intelligence unit and uses health behavior theory and artificial intelligence sleep staging algorithm to conduct in-depth data mining and analysis. The cloud platform interacts with the control unit and terminal devices to realize remote control and data display functions;

[0056] Artificial Intelligence Unit: responsible for mining the data received from the cloud platform, identifying tourists’ physiological status, sleep quality and behavioral habits through health behavior theory and artificial intelligence sleep staging algorithm;

[0057] Control unit: responsible for regulating the flow sensor, pressure reducing valve and pressure switch in terms of oxygen concentration according to the analysis results of the cloud platform and the user's instructions, so as to adjust the oxygen concentration and temperature in the microenvironment;

[0058] Terminal device: responsible for providing the user interface, viewing real-time data, historical data and analysis reports, and performing remote control.

[0059] In one embodiment, for the above-mentioned signal acquisition and transmission unit, the signal acquisition and transmission unit includes a sensor subunit, a signal conditioning subunit, an ADC (analog-to-digital converter) subunit, an MCU (STM32F2) and a wireless communication subunit. The sensor subunit is responsible for collecting bedside signals. The sensor subunit transmits the collected bedside signals to the signal conditioning subunit for preprocessing. The signal conditioning subunit performs preliminary conditioning on the bedside signals collected by the sensor, such as amplification, attenuation, filtering, etc., to improve the signal-to-noise ratio and anti-interference ability of the signal. The signal after preliminary conditioning is transmitted to the ADC (analog-to-digital converter) subunit for digital processing. The ADC (analog-to-digital converter) subunit converts the conditioned analog signal into a digital signal for subsequent digital signal processing. The digital signal is processed and analyzed, and the digital signal is transmitted to the MCU (STM32F2) for filtering and data processing. The MCU (STM32F2) is the core processor of the signal acquisition and transmission unit, which is responsible for receiving the digital signal transmitted by the ADC subunit, and performing filtering, data compression and format conversion. At the same time, the MCU (STM32F2) transmits the processed data to the cloud platform through wireless communication technology. The wireless communication subunit is responsible for data communication between the MCU (STM32F2) and the cloud platform to ensure the real-time and integrity of the data, receive the processed data transmitted by the MCU (STM32F2), and send it to the cloud platform. At the same time, it receives the instructions or configuration information sent by the cloud platform and transmits it to the MCU (STM32F2) for processing.

[0060] In one embodiment, for the above-mentioned smart eye mask data receiving unit, the smart eye mask data receiving unit transmits the received data to the MCU (STM32F2) for processing via wireless communication technologies such as Bluetooth or Wi-Fi.

[0061] In one embodiment, for the above cloud platform, the cloud platform includes a data receiving subunit, a data storage subunit, a data processing subunit, a data display subunit and a remote control subunit. The data receiving subunit is responsible for receiving data from the signal acquisition and transmission unit and the smart eye mask data receiving unit. After receiving the data, the data is passed to the data storage subunit for storage and to the data processing subunit for processing. The data storage subunit is responsible for storing the received data in the cloud database, and MySQL is selected as the persistence layer database for storing various types of data required by the system, such as user information, device status, environmental parameters, etc. MySQL has the advantages of high performance, scalability and ease of use. The data storage subunit provides a data access interface to the data processing subunit for subsequent data analysis. The data processing subunit interacts with the artificial intelligence unit and mines and analyzes the data using health behavior theory and artificial intelligence sleep staging algorithm. The analyzed data is passed to the data display subunit for display and to the remote control subunit for guiding control. The data display subunit is responsible for displaying the results of data analysis and the status information of the control unit to the user. The remote control subunit is responsible for interacting with the control unit to realize the remote control function.

[0062] In one embodiment, for the above-mentioned artificial intelligence unit, the artificial intelligence unit includes a data mining subunit, a physiological state recognition subunit, a sleep quality analysis subunit, a behavior habit analysis subunit and a personalized suggestion generation subunit. The data mining subunit is responsible for preprocessing, cleaning and feature extraction of data transmitted by the cloud platform, and deep mining of the data to discover potential health information and sleep patterns. The physiological state recognition subunit receives the feature data extracted by the data mining subunit, applies health behavior theory and pre-trained physiological state recognition model to judge the physiological state of tourists. Health behavior theory can help understand the individual's sleep habits, behavior patterns and physiological The physiological state recognition subunit will conduct a preliminary analysis of the data based on these theories. The sleep quality analysis subunit uses the artificial intelligence sleep staging algorithm to stage the tourists' sleep data, analyze the duration, proportion and conversion pattern of different sleep stages, and evaluate the tourists' sleep quality. The behavioral habit analysis subunit collects and analyzes the tourists' daily behavioral habit data, and evaluates the relationship between the tourists' sleep quality and daily behavioral habits. The personalized suggestion generation subunit integrates the results of data mining, physiological state recognition, sleep quality analysis and behavioral habit analysis, and generates personalized sleep improvement suggestions and microenvironment adjustment plans according to the individual differences and needs of tourists.

[0063] In one embodiment, for the above-mentioned control unit, the control unit includes an instruction receiving subunit, a device control subunit, a device status monitoring subunit, a fault handling subunit and a safety protection subunit. The instruction receiving subunit is responsible for receiving the control instructions sent by the cloud platform and the direct instructions of the user. The instruction receiving subunit interacts with the data processing subunit and the user interface of the cloud platform to receive the instructions and perform preliminary analysis. The device control subunit performs specific control on the devices in the microenvironment according to the instructions transmitted by the instruction receiving subunit. The device control subunit interacts with the instruction receiving subunit and the device status monitoring subunit to receive the control instructions and send control signals to the devices. The status monitoring subunit monitors the operating status of the equipment in real time, including the switch status, working parameters, etc. of the equipment. The equipment status monitoring subunit interacts with the equipment control subunit and the fault handling subunit to provide equipment status information and trigger the fault handling process when a fault is found. When a fault occurs in the equipment, the fault handling subunit takes corresponding handling measures according to the type and severity of the fault, such as alarm and shutdown. During the equipment control process, the safety protection subunit ensures the safe operation of the equipment to avoid safety hazards such as overload and short circuit. The safety protection subunit interacts with the equipment control subunit and the safety monitoring sensor to monitor the safety status of the equipment and take protective measures when safety hazards are found.

[0064] In one embodiment, for the above-mentioned terminal device, the terminal device includes a user interface subunit, a data processing subunit, a remote control subunit, a user setting subunit and an alarm prompt subunit. The user interface subunit is responsible for displaying data, receiving user input and providing operation feedback. The user interface subunit interacts with the data processing subunit, the remote control subunit and the user setting subunit. The data processing subunit obtains data from the cloud platform and performs format conversion, screening and display preparation. The data processing subunit interacts with the data display subunit and the user interface subunit of the cloud platform. The remote control subunit receives the user's control instructions and sends them to the cloud platform for execution. The remote control subunit interacts with the user interface subunit and the remote control subunit of the cloud platform. The user setting subunit manages user settings, including data display format, alarm threshold, account information, etc. The alarm prompt subunit detects abnormal data or equipment failure and generates alarm information. The alarm prompt subunit interacts with the data processing subunit and the user interface subunit, receives alarm information and displays it to the user.

[0065] In one embodiment, for the physiological state recognition subunit, the physiological state recognition subunit receives the feature data extracted by the data mining subunit, applies health behavior theory and the pre-trained physiological state recognition model, and determines the physiological state of the tourist. The specific steps are:

[0066] Data reception and preprocessing: The physiological state recognition subunit receives the feature data extracted by the data mining subunit, and selects features that have an important impact on physiological state recognition from the feature data. The features include statistical quantities (such as mean, standard deviation, maximum value, minimum value, etc.) or transformed features (such as spectrum analysis, wavelet transform, etc.) of physiological parameters such as heart rate, blood oxygen saturation, and electrooculogram signals;

[0067] Model loading and initialization:

[0068] Load a physiological state recognition model based on support vector machine. The physiological state recognition model based on support vector machine has been trained with a large amount of labeled heart rate and blood oxygen saturation data to identify different physiological states.

[0069] Model Reasoning:

[0070] The selected characteristic data are input into the physiological state recognition model, and the physiological state recognition model calculates the scores representing different physiological states of the tourists according to the input characteristic data and the health behavior theory;

[0071] Result output and explanation:

[0072] The model outputs the recognition results of tourists’ physiological states, and the results are interpreted or visualized as needed for better understanding by users or systems;

[0073] Suppose we have a pre-trained physiological state recognition model that identifies the fatigue level of tourists based on heart rate and blood oxygen saturation data:

[0074] Data reception and preprocessing:

[0075] Assume that the received data is a two-dimensional array containing heart rate and blood oxygen saturation, where each element represents the data at a time point. We clean the data, remove outliers, and normalize it.

[0076] Feature selection and extraction:

[0077] The mean and standard deviation of heart rate and blood oxygen saturation are selected as features. These features can reflect the fluctuation of tourists' physiological state within a certain period of time.

[0078] Model loading and initialization:

[0079] Load a physiological state recognition model based on support vector machine. The model has been trained with a large amount of labeled heart rate and blood oxygen saturation data and can recognize different levels of fatigue.

[0080] Model Reasoning:

[0081] The extracted feature data is input into the SVM model, which calculates a score representing the degree of fatigue based on the input feature data.

[0082] Result output and explanation:

[0083] According to the score, the tourists' physiological state is divided into different fatigue levels (such as mild fatigue, moderate fatigue, severe fatigue), and the results are output to the user or system so that corresponding measures can be taken.

[0084] In one embodiment, for the above-mentioned sleep quality analysis subunit, the sleep quality analysis subunit uses an artificial intelligence sleep staging algorithm to process the sleep data of tourists in stages. The specific steps are:

[0085] Feature extraction:

[0086] Extract features reflecting eye movements from electrooculogram (EOG) signals, such as the rapid eye movement (REM) period, extract features reflecting different sleep stages from electroencephalogram (EEG) signals, such as the slow-wave sleep (N3) period, and extract features of blood oxygen saturation changes from blood oxygen data to assess breathing conditions during sleep;

[0087] Model training:

[0088] Use labeled sleep staging data (such as polysomnography PSG data) to train random forests to learn the mapping relationship between sleep features and sleep periods;

[0089] Sleep stages:

[0090] The preprocessed data is input into the trained model to perform sleep staging, which includes wake (W), rapid eye movement (REM), non-rapid eye movement 1 (N1), non-rapid eye movement 2 (N2), and non-rapid eye movement 3 (N3, also known as slow-wave sleep or deep sleep);

[0091] Results evaluation and optimization:

[0092] Evaluate the accuracy of the model's staging results, such as using confusion matrix, accuracy, recall rate and other indicators, and optimize and adjust the model based on the evaluation results to improve staging accuracy;

[0093] Correlation analysis: Analyze the correlation between different sleep stages and physiological parameters (such as blood oxygen saturation, heart rate, etc.);

[0094] Trend analysis: Analyze the long-term changes in an individual's sleep quality to identify potential sleep disorders or health problems;

[0095] Suppose an individual's blood oxygen saturation data during sleep is shown in the following table:

[0096]

[0097]

[0098] The cloud platform will preprocess and extract features from these data, and then input them into the trained sleep staging model. The model may output the following staging results:

[0099]

[0100] Next, the cloud platform will conduct in-depth mining and analysis and find that the individual's blood oxygen saturation is low in stage N3 (deep sleep), which may be related to breathing problems. Based on this finding, the cloud platform can provide the individual with personalized sleep improvement suggestions, such as adjusting sleeping posture and using breathing assistance equipment.

[0101] The method of the bedside intelligent interactive microenvironment adjustment system for plateau tourism comprises the following steps:

[0102] Start the device: Turn on the power of the sensor and smart eye mask to ensure that they are in working condition. The MCU will automatically start collecting data from the sensor and smart eye mask.

[0103] Data filtering and transmission: The MCU filters the data collected from the sensor and smart eye mask to reduce noise and interference, and then transmits the filtered data to the cloud platform through the network interface router;

[0104] Data reception and storage: The cloud platform receives data from sensors and smart eye masks and stores them in the database for subsequent analysis and processing;

[0105] Data analysis and mining: The cloud platform uses artificial intelligence units, combined with health behavior theory and artificial intelligence sleep staging algorithms, to mine and analyze the received data, which helps to identify the changing trends of tourists' physiological states and environmental parameters;

[0106] Remote control: According to the analysis results of the cloud platform and the user's instructions, the cloud platform will send control instructions to the control unit, and the control unit will control the oxygen outlet, flow sensor, pressure reducing valve, pressure switch and other parameter control equipment according to these instructions to adjust the oxygen concentration, temperature and other parameters in the microenvironment;

[0107] Real-time feedback: The cloud platform feeds back the control results and real-time data to terminal devices (such as computers, mobile phones, etc.). Users can view real-time data, historical data and analysis reports through terminal devices and perform remote control as needed.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The bedside intelligent interactive microenvironment adjustment system for plateau tourism is characterized by: include: Signal acquisition and transmission unit: responsible for collecting bedside signals and filtering them through the MCU, and then transmitting the bedside signals to the cloud platform. The bedside signals include EEG, body movement and body temperature; Smart eye mask data receiving unit: responsible for receiving the electrooculogram, eye movement and blood oxygen collected by the smart eye mask, transmitting them to the MCU for processing, and then transmitting them to the cloud platform after processing; Cloud platform: responsible for receiving bedside signals from the signal acquisition and transmission unit and electrooculogram, eye movement and blood oxygen from the smart eye mask data receiving unit, and storing, analyzing and processing them. The cloud platform interacts with the artificial intelligence unit, and uses health behavior theory and artificial intelligence sleep staging algorithm to deeply mine and analyze the data. The cloud platform interacts with the control unit and the terminal device; Artificial Intelligence Unit: responsible for mining the data received from the cloud platform, identifying tourists’ physiological status, sleep quality and behavioral habits through health behavior theory and artificial intelligence sleep staging algorithm; Control unit: responsible for adjusting the oxygen concentration and temperature in the microenvironment according to the analysis results of the cloud platform and the user's instructions; Terminal device: responsible for providing the user interface, viewing real-time data, historical data and analysis reports, and performing remote control.

2. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 1 is characterized in that: The signal acquisition and transmission unit includes a sensor subunit, a signal conditioning subunit, an ADC subunit, an MCU and a wireless communication subunit. The sensor subunit is responsible for collecting bedside signals. The sensor subunit transmits the collected bedside signals to the signal conditioning subunit for preprocessing. The signal conditioning subunit performs preliminary conditioning on the bedside signals collected by the sensor. The preliminary conditioned signals are transmitted to the ADC subunit for digital processing. The ADC subunit converts the conditioned analog signals into digital signals, which are transmitted to the MCU for filtering and data processing. The MCU is responsible for receiving the digital signals transmitted by the ADC subunit, and performing filtering, data compression and format conversion. At the same time, the MCU transmits the processed data to the cloud platform through wireless communication technology. The wireless communication subunit is responsible for data communication between the MCU and the cloud platform, receives the processed data transmitted by the MCU, and sends it to the cloud platform. At the same time, it receives instructions or configuration information sent by the cloud platform and transmits them to the MCU for processing.

3. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 2 is characterized in that: The smart eye mask data receiving unit transmits the received data to the MCU for processing via wireless communication technology.

4. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 1 is characterized in that: The cloud platform includes a data receiving subunit, a data storage subunit, a data processing subunit, a data display subunit and a remote control subunit. The data receiving subunit is responsible for receiving data from the signal acquisition and transmission unit and the smart eye mask data receiving unit. After receiving the data, the data is passed to the data storage subunit for storage, and at the same time to the data processing subunit for processing. The data storage subunit is responsible for storing the received data in a database in the cloud. The data storage subunit provides a data access interface to the data processing subunit. The data processing subunit interacts with the artificial intelligence unit and mines and analyzes the data using health behavior theory and artificial intelligence sleep staging algorithm. The analyzed data is passed to the data display subunit for display, and at the same time is passed to the remote control subunit for guiding control. The data display subunit is responsible for displaying the results of data analysis and the status information of the control unit to the user. The remote control subunit is responsible for interacting with the control unit to realize the remote control function.

5. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 4 is characterized in that: The artificial intelligence unit includes a data mining subunit, a physiological state recognition subunit, a sleep quality analysis subunit, a behavior habit analysis subunit and a personalized suggestion generation subunit. The data mining subunit is responsible for preprocessing, cleaning and feature extraction of data transmitted by the cloud platform, and deeply mining the data to discover potential health information and sleep patterns. The physiological state recognition subunit receives the feature data extracted by the data mining subunit, applies health behavior theory and a pre-trained physiological state recognition model to determine the physiological state of tourists. The sleep quality analysis subunit uses an artificial intelligence sleep staging algorithm to stage the sleep data of tourists, analyzes the duration, proportion and conversion mode of different sleep stages, and evaluates the sleep quality of tourists. The behavior habit analysis subunit collects and analyzes the daily behavior habit data of tourists, and evaluates the relationship between the sleep quality of tourists and their daily behavior habits. The personalized suggestion generation subunit integrates the results of data mining, physiological state recognition, sleep quality analysis and behavior habit analysis, and generates personalized sleep improvement suggestions and microenvironment adjustment plans according to the individual differences and needs of tourists.

6. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 4 is characterized in that: The control unit includes an instruction receiving subunit, a device control subunit, a device state monitoring subunit, a fault processing subunit and a safety protection subunit. The instruction receiving subunit is responsible for receiving the control instructions sent by the cloud platform and the direct instructions of the user. The instruction receiving subunit interacts with the data processing subunit and the user interface of the cloud platform to receive and parse the instructions. The device control subunit specifically controls the devices in the microenvironment according to the instructions transmitted by the instruction receiving subunit. The device control subunit interacts with the instruction receiving subunit and the device state monitoring subunit to receive the control instructions and send control signals to the devices. The device state monitoring subunit monitors the running status of the device in real time. The device state monitoring subunit interacts with the device control subunit and the fault processing subunit to provide device status information and trigger the fault processing process when a fault is found. When a fault occurs in the device, the fault processing subunit takes corresponding processing measures according to the type and severity of the fault. During the device control process, the safety protection subunit ensures the safe operation of the device. The safety protection subunit interacts with the device control subunit and the safety monitoring sensor to monitor the safety status of the device and take protective measures when potential safety hazards are found.

7. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 4 is characterized in that: The terminal device includes a user interface subunit, a data processing subunit, a remote control subunit, a user setting subunit and an alarm prompt subunit. The user interface subunit is responsible for displaying data, receiving user input and providing operation feedback. The user interface subunit interacts with the data processing subunit, the remote control subunit and the user setting subunit. The data processing subunit obtains data from the cloud platform and performs format conversion, screening and display preparation. The data processing subunit interacts with the data display subunit and the user interface subunit of the cloud platform. The remote control subunit receives the user's control instructions and sends them to the cloud platform for execution. The remote control subunit interacts with the user interface subunit and the remote control subunit of the cloud platform. The user setting subunit manages user settings. The alarm prompt subunit detects abnormal data or equipment failure and generates alarm information. The alarm prompt subunit interacts with the data processing subunit and the user interface subunit, receives the alarm information and displays it to the user.

8. The plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 5 is characterized in that: The physiological state recognition subunit receives the feature data extracted by the data mining subunit, applies health behavior theory and the pre-trained physiological state recognition model to determine the physiological state of the tourist, specifically in the following steps: Data reception and preprocessing: The physiological state recognition subunit receives the feature data extracted by the data mining subunit, and selects features that have an impact on physiological state recognition from the feature data; Model loading and initialization: Loading a physiological state recognition model based on a support vector machine, wherein the physiological state recognition model based on the support vector machine has been trained using a large amount of labeled heart rate and blood oxygen saturation data for identifying different physiological states; Model Reasoning: Inputting the selected characteristic data into a physiological state recognition model, wherein the physiological state recognition model calculates scores representing different physiological states of the tourist based on the input characteristic data and applying health behavior theory; Result output and explanation: The model outputs the tourists’ physiological state recognition results, which can be interpreted or visualized as needed.

9. The plateau tourism bedside intelligent interactive micro-environment adjustment system according to claim 5 is characterized in that: The sleep quality analysis subunit uses an artificial intelligence sleep staging algorithm to process the sleep data of tourists in stages. The specific steps are: Feature extraction: Extract features reflecting eye movement from electrooculogram signals, extract features reflecting different sleep stages from EEG signals, and extract features reflecting changes in blood oxygen saturation from blood oxygen data; Model training: Use the labeled sleep stage data to train the random forest to learn the mapping relationship between sleep features and sleep periods; Sleep stages: The preprocessed data is input into the trained model to perform sleep staging, which includes wakefulness, REM, NREM 1, NREM 2, and NREM 3. Results evaluation and optimization: Evaluate the accuracy of the model's staging results, and optimize and adjust the model based on the evaluation results; Correlation analysis: Analyze the correlation between different sleep stages and physiological parameters; Trend analysis: Analyze the long-term change trend of individual sleep quality.

10. The method of the plateau tourism bedside intelligent interactive microenvironment adjustment system according to claim 1 is characterized in that: The following steps are involved: Start the device: Turn on the power of the sensor and the smart eye mask, and the MCU will automatically start collecting data from the sensor and the smart eye mask; Data filtering and transmission: The MCU filters the data collected from the sensor and the smart eye mask, and then transmits the filtered data to the cloud platform through the network interface router; Data reception and storage: The cloud platform receives data from sensors and smart eye masks and stores them in a database; Data analysis and mining: The cloud platform uses artificial intelligence units, combined with health behavior theory and artificial intelligence sleep staging algorithms, to mine and analyze the received data; Remote control: According to the analysis results of the cloud platform and the user's instructions, the cloud platform will send control instructions to the control unit, and the control unit will control the parameter control device according to these instructions to adjust the parameters in the microenvironment; Real-time feedback: The cloud platform feeds back the control results and real-time data to the terminal device. The user can view the real-time data, historical data and analysis reports through the terminal device and perform remote control as needed.

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