High-speed rail arrival reminder system and method integrating multi-source information

Abstract

The present invention relates to the technical field of arrival reminders, and specifically to a high-speed rail arrival reminder system and method that integrates multi-source information, wherein a positioning module is used to obtain the current position of the high-speed rail; an IMU module includes a first IMU element and a second IMU element disposed on both sides of the first IMU element, the first IMU element being used to obtain the angular velocity and acceleration of the human body in three-dimensional space, and the second IMU element being arranged in a tumbler-like manner to obtain the acceleration of the high-speed rail; an electrode patch module capable of contacting human skin for collecting and transmitting electrical signals; a voice recognition module being used to collect high-speed rail notification voice information; a voice warning module being used to issue a warning voice to remind passengers; a clock module being used to obtain time and date, and setting a clock reminder time point; and a power supply module being used to power an MCU module. The present invention can obtain the acceleration of the high-speed rail's forward direction through the IMU module, accurately judge the high-speed rail's operating status to accurately predict the high-speed rail's arrival time, and avoid waking up passengers before the advance reminder time.

Description

Technical Field

[0001] The present invention relates to the technical field of arrival reminders, and in particular to a high-speed rail arrival reminder system and method that integrates multi-source information. Background Art

[0002] High-speed rail travel has become an essential mode of transportation for modern people. The development of an arrival reminder system can provide passengers with greater peace of mind during their journeys, preventing them from missing their departure times and thus enhancing their travel experience. This is particularly true for passengers prone to falling asleep or focusing on other matters while traveling. Arrival reminder systems can effectively prevent these passengers from missing their destinations due to unawareness of arrival information, reducing the inconvenience and safety risks associated with missed stops. For railway operators, arrival reminder systems can help reduce train delays and station congestion caused by passengers missing their stops, thereby improving overall operational efficiency.

[0003] The existing high-speed rail arrival reminder systems and implementation solutions mainly include the following: (1) Reminder systems based on official railway apps or websites: users can log in to their accounts through official railway apps or websites, find the train for which they want to set a reminder, and click the "Arrival Reminder" button. The system will then send a reminder message before the time set by the user. (2) Arrival reminder functions based on smartphones / watches: add the departure and arrival times of the high-speed rail through the calendar application of the smartphone / watch, and the system will automatically send a reminder message to the watch according to the time set by the user. (3) Intelligent reminder systems based on single-chip microcomputers: This system is usually composed of single-chip microcomputers, liquid crystal display circuits, Bluetooth module circuits, clock circuits, etc. Users can update the current time and set a time point through the Bluetooth app on their mobile phones. When the set time point arrives, the system will remind the user through methods such as a vibration motor.

[0004] When a high-speed train is delayed due to factors like temperature or rainfall, a system that relies solely on arrival time will inevitably wake passengers long before their arrival. When a delayed high-speed train stops in a tunnel or other underground structure, a system that relies on location-based settings cannot accurately predict arrival times and cannot effectively wake passengers before arrival. Furthermore, existing wake-up systems rely primarily on sound, which is easily affected by the noisy environment of the high-speed train and makes it difficult to wake passengers from deep sleep. Summary of the Invention

[0005] The present invention aims to provide a high-speed rail arrival reminder system and method that integrates multi-source information, aiming to solve the problem that existing reminder systems cannot accurately predict arrival times.

[0006] The specific technical solutions are as follows:

[0007] The present invention provides a high-speed rail arrival reminder system that integrates multi-source information, including a wearable carrier, a mobile terminal, and a reminder device. The wearable carrier is worn by a passenger, and the reminder device is arranged on the wearable carrier. The reminder device is characterized in that it includes an MCU module, a positioning module, an IMU module, an electrode patch module, a voice recognition module, a voice warning module, a clock module, and a power module; the positioning module, the IMU module, the voice recognition module, and the clock module are all communicatively connected to the MCU module; the electrode patch module, the voice warning module, and the power module are electrically connected to the MCU module;

[0008] The MCU module is communicatively connected to the mobile terminal; the positioning module is used to obtain the current high-speed rail position; the IMU module includes a first IMU element and a second IMU element located on both sides of the first IMU element, the first IMU element is used to obtain the angular velocity and acceleration of the human body in three-dimensional space, and the second IMU element is arranged in a tumbler style to obtain the acceleration of the high-speed rail; the electrode patch module is arranged on a wearable carrier and can contact human skin for collecting and transmitting electrical signals; the voice recognition module is used to collect high-speed rail notification voice information; the voice warning module is used to issue a warning voice to remind passengers; the clock module is used to obtain time and date, and set the clock reminder time point; the power supply module is used to power the MCU module.

[0009] Optionally, the MCU module has built-in speech recognition program, acceleration calculation program, running speed calculation program, arrival time prediction program and sleep state evaluation program, which can predict the time when the high-speed rail arrives at the destination station.

[0010] Optionally, the high-speed rail arrival reminder system that integrates multi-source information also includes a Bluetooth module, and the MCU module is communicated with the mobile terminal through the Bluetooth module; and / or, the high-speed rail arrival reminder system that integrates multi-source information also includes a WIFI module, and the WIFI module is communicated with the MCU module, and the WIFI module is used to connect to the hotspot of the mobile terminal to update the map information built into the MCU module.

[0011] The present invention also provides a high-speed rail arrival reminder method that integrates multi-source information, which uses the high-speed rail arrival reminder system that integrates multi-source information as described above to obtain an accurate arrival time at the destination station, including the following steps:

[0012] S1: Use a mobile terminal to obtain site map information, set the destination site and advance reminder time, where the site map information includes site information along the route, arrival time, longitude and latitude corresponding to the site, and longitude and latitude of map points along the route;

[0013] S2. Turn on the power module and update the MCU module's set advance reminder time and built-in map information based on the site map information, the destination site, and the advance reminder time;

[0014] S4: Start the clock module, calculate the clock reminder time point based on the current time point and the arrival time of the destination station, and combine it with the set advance reminder time;

[0015] S5: Based on the clock reminder time point, the current high-speed rail coordinates, forward direction and running speed are obtained through the positioning module and MCU module, and the current high-speed rail running status is determined;

[0016] S6: predicting the arrival time of the high-speed train at the destination station according to the running status of the high-speed train, and updating the clock reminder time point according to the predicted arrival time;

[0017] S7: When the clock reminder time point updated in S6 is reached, the voice warning module is activated to emit a warning sound to remind the passenger that the station is about to be reached, and half of the electrode patches in the electrode patch module are simultaneously activated to simulate acupuncture stimulation of the human body;

[0018] S8: Use the first IMU element and the other half of the electrode patches in the electrode patch module to detect the current human body state of the passenger, and use the sleep state assessment program to assess whether the passenger is awake; if it is detected that the passenger is not awake, adjust the current of the electrode patch until the passenger is detected to be awake or passively shut down, and automatically turn off the power module; wherein: the maximum current of the electrode patch does not exceed 10mA.

[0019] Optionally, the S5 includes:

[0020] Start the positioning module and two second IMU elements 10 minutes before the clock reminder time;

[0021] The positioning module receives satellite radio signals through the antenna for M consecutive times, and obtains the current high-speed rail coordinates, forward direction and running speed through the running speed calculation program of the MCU module, where M is the set number of times;

[0022] Use the two second IMU elements to obtain the linear acceleration along the X, Y, and Z axes, the rotational angular velocity along the X, Y, and Z axes, and the magnetic field strength along the X, Y, and Z axes of the current IMU module, and calculate the current high-speed rail acceleration through the acceleration solver of the MCU module;

[0023] The current operating status of the high-speed rail is determined based on its operating speed and acceleration, specifically:

[0024] When the high-speed rail's running speed is 0 and the acceleration is -0.1m / s to 0.1m / s, the high-speed rail is in the temporary parking stage; when the high-speed rail's running speed is not 0 and the acceleration is -0.1m / s to 0.1m / s, the high-speed rail is in the uniform speed running stage; when the high-speed rail's running speed is not 0, the acceleration is a negative value and the absolute value is greater than 0.1m / s, the high-speed rail is in the deceleration running stage.

[0025] Optionally, the acceleration calculation procedure in S5 includes:

[0026] ① Use the magnetometer of the second IMU element to measure the component of the Earth's magnetic field on the horizontal plane [m x ,m y ], calculate the azimuth angle θ of the IMU module according to the component of the geomagnetic field on the horizontal plane imu , the specific calculation formula is as follows:

[0027]

[0028] ② The acceleration component [a x ,a y ,a z ] Estimate the pitch angle α and roll angle β of the second IMU element;

[0029] ③ According to the pitch angle α and roll angle β of the second IMU element, the azimuth angle of the second IMU element is corrected to obtain the corrected azimuth angle θ imu ′, the specific calculation formula is as follows:

[0030]

[0031] ④ Calculate the acceleration a in the forward direction of the high-speed rail based on the corrected azimuth angle. The specific calculation formula is as follows:

[0032] a=a x ·cos(Δθ)+a y sin(Δθ);

[0033] Where: Δθ is the angle between the second IMU element and the direction of the high-speed train, Δθ=θ-θ′ imu .

[0034] Optionally, in S5,

[0035] When the high-speed rail passes through an underground project, if the distance between the coordinates obtained by the positioning module before and after the underground project is at least greater than the set distance threshold, it means that the high-speed rail is running. The positioning module is kept on until M consecutive satellite signals are obtained or 1 minute before the set reminder time point is reached or 10 minutes remain before the arrival time is recognized by voice recognition. At this time, the operating status of the high-speed rail is determined by combining the IMU module and the acceleration solution program;

[0036] If the coordinates obtained by the positioning module before and after passing through the underground project are not much different, the azimuth of the IMU module is used as the running direction of the high-speed rail. When the acceleration of the high-speed rail's current forward direction obtained by the IMU module is zero, it is judged that the high-speed rail is in a stopped state, and other modules except the MCU module and the IMU module are suspended. When the acceleration of the high-speed rail's current forward direction is not zero, the high-speed rail is running. Until the positioning module can obtain M consecutive satellite signals, the calculated azimuth of the running speed solver is used as the running direction of the high-speed rail.

[0037] Optionally, between S2 and S4, the following steps are further included:

[0038] S3: Pre-training the speech recognition program in the MCU module according to the keywords to obtain a keyword recognition model, wherein the keywords include site names and key time point words;

[0039] The S6 includes:

[0040] S6.1. Calculate the distance between the current high-speed rail coordinates and the destination station based on the vectorized data of the map information built into the MCU;

[0041] S6.2. Calculate the predicted arrival time of the high-speed train at the destination station using the arrival time prediction program of the MCU module based on the distance between the high-speed train coordinates and the destination station, the heading direction, and the running speed;

[0042] S6.3. Update the clock reminder time point based on the predicted arrival time, specifically:

[0043] If the predicted arrival time is delayed by more than 10 minutes, the clock reminder time point is updated by delaying it by the time that has exceeded the delay; the positioning module is set to obtain the high-speed rail coordinates every 10 minutes, and the process returns to S6.1.

[0044] If the clock reminder time is within 10 minutes of the predicted arrival time, the voice recognition module is activated 11 minutes before the predicted arrival time, and the voice from the 11th minute to the 8th minute of the predicted arrival time is recorded in real time. The voice is transmitted to the MCU module, and the pre-trained keyword recognition model is used to identify the keyword and record the time point when the keyword appears. The remaining arrival time is then calculated based on the average of the predicted arrival time and the time point when the keyword appears, and the clock reminder time is updated based on the remaining arrival time.

[0045] Within 2 minutes before the clock reminder time, set the positioning module to obtain the high-speed rail coordinates twice every 10 seconds on average, and return to S6.1;

[0046] When the high-speed rail is in operation, if the current coordinates of the high-speed rail cannot be obtained within 1 minute before the clock reminder time, the reminder time originally set by the clock will be used as the basis; if the set reminder time is within 10 minutes of arriving at the station, the clock reminder time will be updated with the time point that appears in the keyword recognized by the voice recognition module.

[0047] 9. The high-speed rail arrival reminder method integrating multi-source information according to claim 8, wherein the arrival time prediction program includes:

[0048] ①Calculate the remaining distance from the current high-speed rail coordinates to the destination station, specifically:

[0049] Calculate the current high-speed rail coordinates and the coordinates of the nearest line network point in the direction of travel under the current high-speed rail coordinates;

[0050] Divide the high-speed rail line into multiple segments in a rasterized manner, mark the longitude and latitude coordinates of the segment points and the actual lengths of adjacent points;

[0051] Calculate the distance between the nearest network point and the destination station, and add up the distances of each section to get the remaining distance from the high-speed rail to the destination station;

[0052] ② Establish a prediction model for the remaining distance to start deceleration and a prediction model for the remaining arrival time to start deceleration, specifically:

[0053] Collect at least 100 sets of high-speed rail data at different speeds, including the average speed and acceleration in the 10 minutes before the train decelerates to enter the station, as well as the remaining distance to the station and the remaining arrival time when the train begins to decelerate.

[0054] The high-speed rail data was normalized using the minimum-maximum normalization method. Then, based on the BP neural network model, the average running speed and average acceleration were used as input parameters, and the remaining distance to the station and the remaining arrival time when the train started to decelerate were used as output parameters. This data was learned to obtain the remaining distance prediction model and the remaining arrival time prediction model when the train started to decelerate.

[0055] ③Calculate the remaining arrival time based on the current high-speed rail operation status, specifically:

[0056] If the high-speed train is temporarily stopped, all modules except the MCU and IMU modules will be suspended, and passengers will be temporarily awakened.

[0057] If the high-speed train is in a constant speed stage, the specific steps for calculating the remaining arrival time are as follows:

[0058] i. Input the average running speed and average acceleration during the phase into the remaining distance prediction model at the start of deceleration and the remaining arrival time at the start of deceleration, and output the remaining distance at the start of deceleration and the running time of the deceleration segment;

[0059] ii. Subtract the remaining distance at the start of deceleration from the remaining distance from the high-speed train to the destination to obtain the remaining distance of the uniform speed running section;

[0060] iii. Calculate the running time of the remaining uniform speed segment based on the uniform speed;

[0061] iv. Add the running time of the deceleration phase and the running time of the constant speed phase to obtain the remaining arrival time;

[0062] v. Calculate the high-speed train arrival time based on the current time of the clock module and update the clock reminder time;

[0063] vi. Recalculate i to v every 30 seconds;

[0064] If the high-speed rail is in the deceleration phase, the specific steps for calculating the remaining arrival time are as follows:

[0065] i. The running speed v measured at the i-th time point in the deceleration phase i , acceleration a i , the remaining distance d from the high-speed rail to the destination i , the theoretical remaining arrival time t can be calculated i , the specific calculation formula is as follows:

[0066]

[0067] ii. Verify whether the theoretical arrival time is consistent with the actual arrival time based on the remaining distance. If the theoretical remaining distance d i 'Greater than the actual remaining distance d i , then the actual arrival time is considered to be less than the theoretical remaining arrival time, and the acceleration and arrival time are recalculated, and the recalculated arrival time is used as the actual arrival time; if the theoretical remaining distance d i 'Equal to the actual remaining distance d i , then the calculated theoretical remaining arrival time is taken as the actual arrival time;

[0068] Where: Theoretical remaining distance d i The calculation formula of ' is as follows:

[0069]

[0070] The formulas for recalculating acceleration and arrival time are as follows:

[0071]

[0072] iii. After obtaining the arrival time point, use the clock module to recalculate the clock reminder time point.

[0073] Optionally, the sleep state assessment procedure includes:

[0074] ① Establish the IMU sleep state assessment model and the electrode patch sleep state assessment model, specifically:

[0075] Establishing the IMU sleep state assessment model includes the following steps:

[0076] i. Collect at least 300 sets of signal data from the first IMU element in different states of the human head, and use professional instruments to evaluate the human state, where different states of the human head include wakefulness, light sleep, and deep sleep;

[0077] ii. Then, the signal data of the first IMU element is subjected to low-pass filtering and denoising, and four key characteristic parameter data are extracted. The IMU data under human condition is evaluated by professional instruments, and three label data sets are established; among them, the four key characteristics include the standard deviation of acceleration a std , the root mean square of acceleration a rms , angular velocity change rate Δω and acceleration signal periodicity P;

[0078] iii. Then, a typical and reliable classification algorithm, the support vector machine algorithm, is used to learn and analyze the data set. The grid search algorithm is used to optimize the sum and penalty parameters of the support vector machine until the loss function is minimized, thereby establishing an IMU sleep state assessment model.

[0079] The establishment of a sleep state assessment model for electrode patches includes the following steps:

[0080] i. Collect at least 300 sets of electromyographic signal data from the electrode patch module in different states of the human head, and use a human sleep state measurement instrument to evaluate the human state;

[0081] ii. Then, the signal data from the electrode patch is amplified, its root mean square characteristic data is extracted, and the electromyographic signal characteristic data under the human body state is evaluated according to the human sleep state measurement instrument to establish three labeled data sets;

[0082] iii. Then, a support vector machine algorithm is used to learn and analyze the data set, and a grid search algorithm is used to optimize the sum and penalty parameters of the support vector machine until the loss function is minimized, thereby establishing a sleep state assessment model for the electrode patch;

[0083] ② Use the IMU sleep state assessment model and the electrode patch sleep state assessment model to assess the passenger's sleep state, specifically:

[0084] The electrode patch module is used to collect the passenger's electromyographic signals. Specifically, the electrode patch is placed close to the skin on the neck of the human body to collect the changes in skin surface potential and the weak electrical signals generated by muscle activity, namely the electromyographic signals. The electrode patch collects the electromyographic signals once every 2 seconds with a time window of 1 second.

[0085] The first IMU element is used to measure the signal data of the passenger's head, wherein: the first IMU element is set to detect five times every 5 seconds, and then detect again after an interval of 5 seconds;

[0086] The passenger's electromyographic signal and head signal data are transmitted to the MCU module's IMU sleep state assessment model and the electrode patch's sleep state assessment model respectively to assess the passenger's sleep state;

[0087] If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate awake, the power module is automatically turned off;

[0088] If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate a light sleep state, adjust the initial current of the electrode patch to 0.5-1 mA and the stimulation frequency to a medium-frequency, low-amplitude pulse wave of 20-50 Hz;

[0089] If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate a deep sleep state, adjust the initial current of the electrode patch to 1-2 mA and the stimulation frequency to a low-frequency, high-amplitude pulse wave of 50-100 Hz;

[0090] After 5 to 10 seconds of continuous stimulation, if the passenger is awake, the stimulation will be stopped; if the passenger is not awake, the current will be adjusted according to the changes in the passenger's electromyographic signals or small movements. Specifically, the current amplitude will be increased by 0.5 mA every 5 seconds; if there is no response after 30 to 60 seconds of stimulation, the current will be directly increased to the maximum current to wake the passenger up; the stimulation will be maintained for 5 seconds and then stopped.

[0091] The present invention has the following beneficial effects:

[0092] The second IMU element in the IMU module of the present invention adopts a tumbler-style structure. When a high-speed train is delayed and stops in a tunnel or other underground project, the IMU module can be used to obtain the acceleration in the direction of the high-speed train's forward movement, accurately determine the high-speed train's operating status, and accurately predict the high-speed train's arrival time, avoiding waking passengers before the advance reminder time.

[0093] The present invention uses an IMU module and an electrode patch module to monitor the human body state. When the initial wake-up method is not enough to wake up the passenger, it can detect that the human body is still asleep and feedback to the MCU module. The MUC module can gradually increase the strength of the electrode patch module until the passenger is awakened.

[0094] The arrival reminder system of the present invention is worn by passengers through a wearable carrier to remind passengers of their arrival time, reducing the pressure on users to remember intermediate stops and improving travel comfort;

[0095] Passengers are awakened by voice and by electric current stimulation from electrode patches, which can reduce the noise impact on surrounding people. The main reminder method is physical touch stimulation, which can help deep sleepers wake up gradually before arriving at the station and avoid abrupt external interference. It can not only effectively prevent missing the station, but also relieve travel fatigue and significantly improve user experience and practicality.

[0096] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0098] Figure 1 Schematic diagram of the structure of the wearable carrier and the reminder device in an embodiment of the present invention;

[0099] Figure 2 2 is a schematic diagram of the ring cutting of the wearable carrier and the reminder device in an embodiment of the present invention;

[0100] Figure 3 1 is a schematic diagram of the installation of the second IMU element in an embodiment of the present invention;

[0101] Figure 4 It is a flowchart of a high-speed rail arrival reminder system method integrating multi-source information in an embodiment of the present invention.

[0102] Description of Figure Numbers:

[0103] 1. Wearable carrier, 1.1. Inner ring, 1.2. Outer ring, 1.3. Shock-absorbing cotton block, 2. Reminder device, 2.1. MCU module + WIFI module + Bluetooth module, 2.2. Positioning module, 2.3. IMU module, 2.3.1. First IMU element, 2.3.2. Second IMU element, 2.3.3. Counterweight, 2.3.4. Fixing rod, 2.4. Electrode patch module, 2.5. Voice recognition module + voice warning module, 2.6. Clock module, 2.7. Power module. DETAILED DESCRIPTION

[0104] To make the objectives, features, and advantages of the present invention more readily apparent, the following detailed description of the present invention is provided with reference to the accompanying drawings. The accompanying drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein.

[0105] In the present invention, unless otherwise expressly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of such features.

[0106] In the present invention, unless otherwise expressly specified and limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Moreover, a first feature being "above," "above," and "above" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature includes the first feature being directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature. The terms "vertical," "horizontal," "left," "right," "above," "below," and similar expressions are for illustrative purposes only and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention.

[0107] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0108] Example 1

[0109] like Figure 1 and Figure 2 As shown, this embodiment provides a high-speed rail arrival reminder system that integrates multi-source information, including a wearable carrier 1, a mobile terminal and a reminder device 2. The wearable carrier 1 is worn on the passenger, and the reminder device 2 is set on the wearable carrier 1. The reminder device 2 includes an MCU module, a positioning module 2.2, an IMU module 2.3, an electrode patch module 2.4, a voice recognition module, a voice warning module, a clock module 2.6 and a power module 2.7; the positioning module 2.2, the IMU module 2.3, the voice recognition module and the clock module 2.6 are all connected to the MCU module in communication; the electrode patch module 2.4, the voice warning module and the power module 2.7 are electrically connected to the MCU module; the MCU module is connected to the mobile terminal in communication; the positioning module 2.2 is used to obtain the current The IMU module 2.3 includes a first IMU element 2.3.1 and a second IMU element 2.3.2 located on both sides of the first IMU element 2.3.1. The first IMU element 2.3.1 is used to obtain the angular velocity and acceleration of the human body in three-dimensional space. The second IMU element 2.3.2 is arranged in a tumbler-like manner to obtain the acceleration of the high-speed rail. The electrode patch module 2.4 is arranged on the wearable carrier 1 and can contact the human skin to collect and transmit electrical signals. The voice recognition module is used to collect high-speed rail notification voice information; the voice warning module is used to issue a warning voice to remind passengers; the clock module 2.6 is used to obtain the time and date, and set the clock reminder time point; the power supply module 2.7 is used to power the MCU module.

[0110] In this embodiment, the mobile carrier is a U-shaped pillow, which includes a filler, an inner ring 1.1, and an outer ring 1.2. The inner ring 1.1 forms a filling space for the filler, and the outer ring 1.2 is wrapped around the inner ring 1.1. The patches of the electrode patch module 2.4 are all set between the inner ring 1.1 and the outer ring 1.2 of the U-shaped pillow. A slot is formed on the U-shaped pillow, and the reminder device 2 is embedded in the U-shaped pillow through the slot, and the surface is covered with a shock-absorbing cotton block 1.3 to avoid causing discomfort to the human body. The reminder device 2 is arranged in a three-layer structure in the direction away from the shock-absorbing cotton block 1.3. The layer close to the shock-absorbing cotton block 1.3 is the IMU module 2.3 and the clock module 2.5 and positioning module 2.2 located at both ends of the IMU module; the middle layer is the MCU module + WIFI module + Bluetooth module 2.1, and the layer away from the shock-absorbing cotton block 1.3 is the power module 2.7; and both ends of the three-layer structure are provided with a voice recognition module + voice warning module 2.5.

[0111] See also Figure 3The first IMU element 2.3.1 in the IMU module 2.3 is equipped with three single-axis accelerometers and three single-axis gyroscopes, which can directly measure the angular velocity and acceleration of an object in three-dimensional space; the second IMU element 2.3.2 adopts a tumbler-style setting. The second IMU element 2.3.2 includes three single-axis accelerometers, three single-axis gyroscopes and a magnetometer. The IMU module 2.3 is communicated with the MCU control unit. Specifically: the second IMU element 2.3.2 is set on the counterweight block 2.3.3, and the counterweight block 2.3.3 is a teardrop-shaped structure with a light top and a middle bottom, and is rotated on the wearable carrier 1 through a fixed rod 2.3.4 to ensure that the acceleration in the z-axis direction of the second IMU element 2.3.2 must be the acceleration of gravity. When the high-speed train is delayed and stops in a tunnel or other underground project, the acceleration in the high-speed train's forward direction can be obtained through the IMU module 2.3, and the high-speed train's operating status can be accurately judged to accurately predict the high-speed train's arrival time, avoiding waking up passengers before the advance reminder time.

[0112] The IMU module 2.3 and electrode patch module 2.4 monitor the human body's condition. If the initial wake-up method is insufficient to awaken the passenger, it can detect that the person is still asleep and provide feedback to the MCU module. The MUC module can gradually increase the intensity of the electrode patch module 2.4 until the passenger wakes up. The electrode patch module 2.4 is made of conductive materials, carbon materials, etc., and can collect and transmit electrical signals. The module is attached to the U-shaped pillow through the module, thereby directly contacting the skin on the human neck. The module is distributed with at least three patches for collecting electrical signals and three patches for transmitting electrical signals, and is electrically connected to the MCU module via wiring.

[0113] The high-speed rail arrival reminder system that integrates multi-source information is worn on the passenger through the wearable carrier 1, and the passenger is reminded of the arrival time in a timely manner, which reduces the pressure of the user to remember the intermediate stations and improves the travel comfort.

[0114] Passengers are awakened by voice and by electric current stimulation from electrode patches, which can reduce the noise impact on surrounding people. The main reminder method is physical touch stimulation, which can help deep sleepers wake up gradually before arriving at the station and avoid abrupt external interference. It can not only effectively prevent missing the station, but also relieve travel fatigue and significantly improve user experience and practicality.

[0115] The voice recognition module is mainly composed of two microphones placed on the upper and lower sides of the instrument body. The sound collected by the microphones is transmitted to the MCU module. The voice recognition program of the MCU module identifies key site information and arrival time information. The voice recognition module is communicated with the MCU module; the voice warning module is a miniature buzzer component, and the voice warning module is electrically connected to the MCU module.

[0116] The MCU module has built-in speech recognition, acceleration calculation, speed calculation, arrival time prediction, and sleep state assessment programs, enabling high-speed train arrival time prediction. In this embodiment, the MCU module is a single-chip microcomputer equipped with a USB port and a Type-C port for connecting to a mobile terminal and charging, respectively. The MCU also includes embedded speech recognition, acceleration calculation, speed calculation, arrival time prediction, and sleep state assessment programs for high-speed train arrival time prediction.

[0117] Furthermore, the high-speed rail arrival reminder system, which integrates multi-source information, also includes a Bluetooth module, through which the MCU module communicates with the mobile terminal. The Bluetooth module, consisting of a Bluetooth wireless transceiver and a baseband controller, receives station information transmitted from the mobile terminal and transmits it to the MCU module, which interprets the station name, arrival time, and longitude and latitude coordinates. The Bluetooth module then communicates with the MCU module.

[0118] Furthermore, the multi-source information-integrated high-speed rail arrival reminder system also includes a Wi-Fi module, which is connected to the MCU module and is used to connect to the mobile terminal's hotspot to update the MCU's built-in map information. The Wi-Fi module consists of a Wi-Fi chip, a transceiver, a wireless local area network (WLAN) transceiver, and an antenna. It connects to the mobile phone's hotspot, allowing the MCU module to automatically update the internally stored high-speed rail route map and communicate with the MCU module.

[0119] In this embodiment, the clock module 2.6 is composed of a counter, a memory, a stable oscillator, etc., which is used to provide accurate time and date, transmit it to the MCU module, and update the time in real time with the mobile terminal; the clock module 2.6 is connected to the MCU module for communication.

[0120] The power module 2.7 is composed of a micro rechargeable lithium battery with a capacity of at least 10,000 mAh, which is electrically connected to the MCU module to transmit power to each module.

[0121] The mobile terminal can be a mobile phone / notebook. The mobile terminal app has built-in corresponding high-speed rail station reading program, map analysis program and arrival reminder program, etc. The mobile terminal is connected to the reminder system body through a Bluetooth module.

[0122] Example 2

[0123] like Figure 4As shown, a high-speed rail arrival reminder method that integrates multi-source information adopts the high-speed rail arrival reminder system that integrates multi-source information as above to obtain the accurate arrival time at the destination station. The method in this embodiment is different from the traditional high-speed rail arrival reminder method in which the IMU module 2.3 is installed on the high-speed rail. Instead, it is based on the human body and the IMU module 2.3 is installed on a U-shaped pillow. Taking into account the human-machine state, the two second IMU elements 2.3.2 for measuring the human posture in the IMU module 2.3 are arranged in a tumbler style to ensure the horizontal measurement of the IMU, avoiding the problem of the azimuth angle of the IMU module 2.3 being inconsistent with the running direction of the high-speed rail due to different sleeping postures of the human body; and the forward direction of the high-speed rail is measured by GPS, and the angle between the two directions is calculated according to the self-direction calculated by the IMU, and then the acceleration in the forward direction of the high-speed rail is accurately calculated.

[0124] On the other hand, the arrival time prediction requires obtaining the coordinates of the high-speed train and the station, and calculating the straight-line distance between the two points as the remaining distance to the high-speed train. However, high-speed train lines are not straight lines, but may be composed of arcs and straight lines. The method in this embodiment obtains the map grid points of the stations along the line and then calculates the segments of each grid point, which can obtain the remaining distance with higher accuracy.

[0125] In addition, when a train is delayed, it must run at a constant speed for a period of time and then decelerate before it can arrive. The conventional method only predicts the remaining time to the station by speed and distance, without taking into account the remaining distance required for deceleration when about to arrive at the station. The method in this embodiment establishes a remaining distance prediction model for starting deceleration and a remaining arrival time prediction model for starting deceleration; effectively improving the accuracy of arrival time prediction.

[0126] The specific steps include:

[0127] (1) Import site data and select the destination site.

[0128] S1: Use a mobile terminal to obtain site map information, set the destination site and advance reminder time, where the site map information includes site information along the route, arrival time, longitude and latitude corresponding to the site, and longitude and latitude of map points along the route;

[0129] The mobile terminal obtains the station information and arrival time along the designated high-speed rail vehicle number through the high-speed rail station reading program, obtains the longitude and latitude corresponding to the station and the longitude and latitude of the map points along the line by using the map analysis level, and sets the destination station and advance reminder time according to the arrival reminder program

[0130] (2) Update the reminder settings and map of the MCU module.

[0131] S2. Turn on the power module 2.7 and update the set advance reminder time and built-in map information of the MCU module according to the site map information, the destination site and the advance reminder time;

[0132] The MCU module is connected to the mobile terminal through the Bluetooth connection module, and the site information data, advance reminder time, map information and arrival site are imported into the MCU module. The WIFI module is connected to the wifi of the mobile terminal and the map information built into the MCU module is updated according to the site map information.

[0133] (3) Establish a keyword recognition program.

[0134] S3: Pre-train the speech recognition program in the MCU module according to the keywords to obtain a keyword recognition model, where the keywords include the site name and key time point words; the key time point words include "10 minutes", "5 minutes", "about to arrive", "reach", etc.

[0135] (4) Start the clock module 2.6 and calculate the reminder time point.

[0136] S4: Start the clock module 2.6, and calculate the clock reminder time point based on the current time point and the arrival time of the destination station, combined with the set advance reminder time;

[0137] (5) Start positioning to obtain the current coordinates and running speed of the high-speed rail; (6) Start IMU module 2.3 to determine the current running status of the high-speed rail.

[0138] S5: Based on the clock reminder time point, the current high-speed rail coordinates, forward direction and running speed are obtained through the positioning module 2.2 and the MCU module, and the current high-speed rail running status is determined;

[0139] S5 includes:

[0140] Start the positioning module 2.2 and the two second IMU elements 2.3.2 10 minutes before the clock reminder time;

[0141] The positioning module 2.2 receives satellite radio signals through the antenna M times in a row, and obtains the current high-speed rail coordinates, forward direction, and running speed through the running speed calculation program of the MCU module, where M is a set number of times. In this embodiment, M=10;

[0142] The two second IMU elements 2.3.2 are used to obtain the linear acceleration along the X, Y, and Z axes, the rotational angular velocity along the X, Y, and Z axes, and the magnetic field strength along the X, Y, and Z axes of the current IMU module 2.3, and the current high-speed rail acceleration is calculated through the acceleration solver of the MCU module;

[0143] The current operating status of the high-speed rail is determined based on its operating speed and acceleration, specifically:

[0144] When the high-speed rail's running speed is 0 and the acceleration is -0.1m / s to 0.1m / s, the high-speed rail is in the temporary parking stage; when the high-speed rail's running speed is not 0 and the acceleration is -0.1m / s to 0.1m / s, the high-speed rail is in the uniform speed running stage; when the high-speed rail's running speed is not 0, the acceleration is a negative value and the absolute value is greater than 0.1m / s, the high-speed rail is in the deceleration running stage.

[0145] The acceleration solver in S5 includes:

[0146] ① Use the magnetometer of the second IMU element 2.3.2 to measure the component of the Earth's magnetic field on the horizontal plane [m x ,m y ], calculate the azimuth angle θ of the IMU module 2.3 based on the component of the geomagnetic field on the horizontal plane imu , the specific calculation formula is as follows:

[0147]

[0148] ② The acceleration component [a x ,a y ,a z ] Estimate the pitch angle α and roll angle β of the second IMU element 2.3.2;

[0149] ③ According to the pitch angle α and roll angle β of the second IMU element 2.3.2, the azimuth angle of the second IMU element 2.3.2 is corrected to obtain

[0150] Corrected azimuth angle θ imu ′, the specific calculation formula is as follows:

[0151]

[0152] ④ Calculate the acceleration a in the forward direction of the high-speed rail based on the corrected azimuth angle. The specific calculation formula is as follows:

[0153] a=a x ·cos(Δθ)+a y sin(Δθ);

[0154] Where: Δθ is the angle between the second IMU element 2.3.2 and the direction of the high-speed train, Δθ = θ - θ′ imu .

[0155] In S5,

[0156] When the high-speed railway passes through an underground project, if the distance between the coordinates obtained by the positioning module 2.2 before and after the passage through the underground project is at least greater than the set distance threshold, it means that the high-speed railway is running, and the positioning module 2.2 is kept turned on until M consecutive satellite signals can be obtained or 1 minute before the set reminder time point or 10 minutes left before the arrival time is recognized by voice recognition. At this time, the operating status of the high-speed railway is determined in combination with the IMU module 2.3 and the acceleration solution program; in this embodiment, the distance threshold is set to the distance value calculated according to the speed obtained before the passage and the time interval between the two positioning before and after.

[0157] If the coordinates obtained by the positioning module 2.2 before and after crossing the underground project are not much different, the azimuth of the IMU module 2.3 is used as the direction of the high-speed train. When the acceleration of the high-speed train's current forward direction obtained by the IMU module 2.3 is zero, it is determined that the high-speed train is in a stopped state. All modules except the MCU module and the IMU module 2.3 are suspended, and passengers are temporarily delayed, which can effectively save power. When the acceleration of the high-speed train's current forward direction is not zero, the high-speed train is running. Until the positioning module 2.2 can obtain M consecutive satellite signals, the azimuth calculated by the running speed solver is used as the direction of the high-speed train.

[0158] (7) Predict the arrival time of high-speed trains and update reminder time points.

[0159] S6: predicting the arrival time of the high-speed train at the destination station according to the running status of the high-speed train, and updating the clock reminder time point according to the predicted arrival time;

[0160] S6 includes:

[0161] S6.1. Calculate the distance between the current high-speed rail coordinates and the destination station based on the vectorized data of the map information built into the MCU;

[0162] S6.2. Calculate the predicted arrival time of the high-speed train at the destination station using the arrival time prediction program of the MCU module based on the distance between the high-speed train coordinates and the destination station, the heading direction, and the running speed;

[0163] S6.3. Update the clock reminder time point based on the predicted arrival time, specifically:

[0164] If the predicted arrival time is delayed by more than 10 minutes, the clock reminder time point is updated by delaying it by the time that has exceeded the delay; and positioning module 2.2 is set to obtain the high-speed rail coordinates every 10 minutes, and the process returns to S6.1;

[0165] If the clock reminder time is within 10 minutes of the predicted arrival time, the voice recognition module is activated 11 minutes before the predicted arrival time, and the voice from the 11th minute to the 8th minute of the predicted arrival time is recorded in real time. The voice is transmitted to the MCU module, and the pre-trained keyword recognition model is used to identify the keyword and record the time point when the keyword appears. The remaining arrival time is then calculated based on the average of the predicted arrival time and the time point when the keyword appears, and the clock reminder time is updated based on the remaining arrival time.

[0166] Within 2 minutes before the clock reminder time, set the positioning module 2.2 to obtain the high-speed rail coordinates twice every 10 seconds on average, and return to S6.1;

[0167] When the high-speed rail is in operation, if the current coordinates of the high-speed rail cannot be obtained within 1 minute before the clock reminder time, the reminder time originally set by the clock will be used as the basis; if the set reminder time is within 10 minutes of arriving at the station, the clock reminder time will be updated with the time point that appears in the keyword recognized by the voice recognition module.

[0168] The arrival time prediction procedure includes:

[0169] ①Calculate the remaining distance from the current high-speed rail coordinates to the destination station, specifically:

[0170] Calculate the current high-speed rail coordinates and the coordinates of the nearest line network point in the direction of travel under the current high-speed rail coordinates;

[0171] Divide the high-speed rail line into multiple segments in a rasterized manner, mark the longitude and latitude coordinates of the segment points and the actual lengths of adjacent points;

[0172] Calculate the distance between the nearest network point and the destination station, and add up the distances of each section to get the remaining distance from the high-speed rail to the destination station;

[0173] ② Establish a prediction model for the remaining distance to start deceleration and a prediction model for the remaining arrival time to start deceleration, specifically:

[0174] Collect at least 100 sets of high-speed rail data at different speeds, including the average speed and acceleration in the 10 minutes before the train decelerates to enter the station, as well as the remaining distance to the station and the remaining arrival time when the train begins to decelerate.

[0175] The high-speed rail data was normalized using the minimum-maximum normalization method. Then, based on the BP neural network model, the average running speed and average acceleration were used as input parameters, and the remaining distance to the station and the remaining arrival time when the train started to decelerate were used as output parameters. This data was learned to obtain the remaining distance prediction model and the remaining arrival time prediction model when the train started to decelerate.

[0176] ③Calculate the remaining arrival time based on the current high-speed rail operation status, specifically:

[0177] If the high-speed train is temporarily stopped, all modules except the MCU module and IMU module 2.3 will be suspended, and passengers will be temporarily awakened.

[0178] If the high-speed train is in a constant speed stage, the specific steps for calculating the remaining arrival time are as follows:

[0179] i. Input the average running speed and average acceleration during the phase into the remaining distance prediction model at the start of deceleration and the remaining arrival time at the start of deceleration, and output the remaining distance at the start of deceleration and the running time of the deceleration segment;

[0180] ii. Subtract the remaining distance at the start of deceleration from the remaining distance from the high-speed train to the destination to obtain the remaining distance of the uniform speed running section;

[0181] iii. Calculate the running time of the remaining uniform speed segment based on the uniform speed;

[0182] iv. Add the running time of the deceleration phase and the running time of the constant speed phase to obtain the remaining arrival time;

[0183] v. Calculate the high-speed train arrival time based on the current time of clock module 2.6 and update the clock reminder time;

[0184] vi. Recalculate i to v every 30 seconds;

[0185] If the high-speed rail is in the deceleration phase, the specific steps for calculating the remaining arrival time are as follows:

[0186] i. The running speed v measured at the i-th time point in the deceleration phase i , acceleration a i , the remaining distance d from the high-speed rail to the destination i , the theoretical remaining arrival time t can be calculated i , the specific calculation formula is as follows:

[0187]

[0188] ii. Verify whether the theoretical arrival time is consistent with the actual arrival time based on the remaining distance. If the theoretical remaining distance d i 'Greater than the actual remaining distance d i , then the actual arrival time is considered to be less than the theoretical remaining arrival time, and the acceleration and arrival time are recalculated, and the recalculated arrival time is used as the actual arrival time; if the theoretical remaining distance d i 'Equal to the actual remaining distance d i, then the calculated theoretical remaining arrival time is taken as the actual arrival time;

[0189] Where: Theoretical remaining distance d i The calculation formula of ' is as follows:

[0190]

[0191] The formulas for recalculating acceleration and arrival time are as follows:

[0192]

[0193] iii. After obtaining the arrival time point, the clock module 2.6 is used to recalculate the clock reminder time point.

[0194] (8) Start reminder device 2.

[0195] S7: When the clock reminder time point updated in S6 is reached, the voice warning module is activated to issue a warning sound to remind passengers that they are about to arrive at the station, and half of the electrode patches in the electrode patch module 2.4 are simultaneously activated to simulate acupuncture stimulation of the human body; the warning sound is required to be less than 60 decibels.

[0196] (9) Determine the sleeping state of the human body.

[0197] S8: Utilize the first IMU element 2.3.1 and the other half of the electrode patches in the electrode patch module 2.4 to detect the current state of the passenger, and utilize the sleep state assessment program to assess whether the passenger is awake. If the passenger is not awake, adjust the current of the electrode patches until the passenger is awake or passively shuts down, and automatically shut down the power module 2.7. The maximum current of the electrode patches does not exceed 10mA.

[0198] The sleep status assessment process includes:

[0199] ① Establish the IMU sleep state assessment model and the electrode patch sleep state assessment model, specifically:

[0200] Establishing the IMU sleep state assessment model includes the following steps:

[0201] i. Collect at least 300 sets of signal data from the first IMU element 2.3.1 in different head states, and use professional equipment to evaluate the human state. Different head states include wakefulness, light sleep, and deep sleep.

[0202] Sleep state assessment mainly relies on the IMU element and electrode patches in the middle of the reminder system body.

[0203] IMU components primarily detect human motion through accelerometers and gyroscopes. Accelerometers detect changes in acceleration in all directions, primarily reflecting a person's displacement or resting state. During sleep, especially deep sleep, the body is typically relatively still, with minimal significant acceleration changes. Gyroscopes detect changes in head rotation or tilt, describing changes in a person's posture within a plane or space. While awake, the head rotates or moves significantly, while during sleep, especially deep sleep, head movement is minimal. The IMU is configured to perform five measurements every 5 seconds, followed by another measurement every 5 seconds.

[0204] ii. Then, the signal data of the first IMU element 2.3.1 is subjected to low-pass filtering and denoising, and four key characteristic parameter data are extracted. The IMU data under human condition is evaluated by professional instruments, and three label data sets are established; among them, the four key characteristics include the standard deviation of acceleration a std , the root mean square of acceleration a rms , angular velocity change rate Δω and acceleration signal periodicity P;

[0205] The three-axis data (X, Y, and Z) from the accelerometer and gyroscope record head acceleration and rotation changes in real time. The data acquisition frequency can be set to 50-100Hz to ensure sufficient accuracy. During the acquisition process, the raw data is filtered to remove high-frequency noise and external vibrations. Because the IMU element is primarily attached to the neck and is significantly affected by changes in human posture, changes in posture can cause the IMU element to receive high-frequency signals. Therefore, a low-pass filter or band-pass filter is used to remove frequency components greater than 5Hz, filtering out most of the vibration caused by high-speed rail operation.

[0206] The four key features are:

[0207] Standard deviation of acceleration a std : Used to measure the fluctuation degree of acceleration signal over a period of time. The calculation formula is as follows:

[0208]

[0209] Where n is the number of any time point after reaching the updated clock reminder time point in S6; a n is the acceleration value of the nth sampling point in the time period, N is the number of sampling points in the time window, N = 5; is the mean value of acceleration in the time window,

[0210] The root mean square of acceleration a rms: Reflects the intensity of the acceleration signal and is used to distinguish between intense motion and static state. The calculation formula is as follows:

[0211]

[0212] Angular velocity change rate Δω: used to describe the degree of change in the head rotation rate.

[0213]

[0214] Where, ω n is the angular velocity value of the nth sampling point, |ω n+1 -ω n | is the angular velocity change value of adjacent sampling points.

[0215] Acceleration signal periodicity P: reflects the periodic characteristics of the motion signal and is used to detect turning over or periodic small movements. The calculation formula is as follows:

[0216]

[0217] Where argmax|FFT(a(t))| is the main frequency of the signal; a(t) is the acceleration change curve value within the time period.

[0218] iii. Then, a typical and reliable classification algorithm, the support vector machine algorithm, is used to learn and analyze the data set. The grid search algorithm is used to optimize the sum and penalty parameters of the support vector machine until the loss function is minimized, thereby establishing an IMU sleep state assessment model.

[0219] The establishment of a sleep state assessment model for electrode patches includes the following steps:

[0220] i. Collect at least 300 sets of electromyographic signal data from the electrode patch module 2.4 in different states of the human head, and use a human sleep state measurement instrument to evaluate the human state;

[0221] ii. Then, the signal data from the electrode patch is amplified, its root mean square characteristic data is extracted, and the electromyographic signal characteristic data under the human body state is evaluated according to the human sleep state measurement instrument to establish three labeled data sets;

[0222] iii. Then, a support vector machine algorithm is used to learn and analyze the data set, and a grid search algorithm is used to optimize the sum and penalty parameters of the support vector machine until the loss function is minimized, thereby establishing a sleep state assessment model for the electrode patch;

[0223] ② Use the IMU sleep state assessment model and the electrode patch sleep state assessment model to assess the passenger's sleep state,

[0224] The two trained sleep state assessment models are burned into the MCU module. The IMU module 2.3 collects data every 5 seconds, and the electrode patch collects myoelectric signals every 2 seconds with a 1-second time window. If both parameters indicate awakeness, the passenger is considered awake.

[0225] Acupuncture is achieved through electrode patches, which transmit current to the skin's surface and deeper nerve endings. This function is commonly used in massagers. Acupuncture stimulation is simulated by applying current to the skin. For example, a low-frequency pulse generator circuit generates the stimulation signal (frequency range 10-100Hz, pulse width approximately 50-200μs), and the output voltage is controlled within a safe range (0-20V).

[0226] Specifically:

[0227] Electrode patch module 2.4 is used to collect passengers' electromyographic signals. Specifically, the electrode patch is placed close to the skin on the neck of the human body to collect changes in skin surface potential and weak electrical signals generated by muscle activity, namely electromyographic signals. Among them, the electrode patch obtains electromyographic signals once every 2 seconds with a time window of 1 second. Electromyographic activity is significantly reduced during light sleep and deep sleep, and is higher when awake.

[0228] The first IMU element 2.3.1 is used to measure the signal data of the passenger's head, wherein the first IMU element 2.3.1 is set to measure five times every 5 seconds, and then measure again after an interval of 5 seconds;

[0229] The passenger's electromyographic signal and head signal data are transmitted to the MCU module's IMU sleep state assessment model and the electrode patch's sleep state assessment model respectively to assess the passenger's sleep state;

[0230] If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate awake, the power module 2.7 is automatically turned off;

[0231] If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate a light sleep state, adjust the initial current of the electrode patch to 0.5-1 mA and the stimulation frequency to a medium-frequency, low-amplitude pulse wave of 20-50 Hz;

[0232] If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate a deep sleep state, adjust the initial current of the electrode patch to 1-2 mA and the stimulation frequency to a low-frequency, high-amplitude pulse wave of 50-100 Hz;

[0233] After 5 to 10 seconds of continuous stimulation, if the passenger is awake, the stimulation will be stopped; if the passenger is not awake, the current will be adjusted according to the changes in the passenger's electromyographic signals or small movements. Specifically, the current amplitude will be increased by 0.5 mA every 5 seconds; if there is no response after 30 to 60 seconds of stimulation, the current will be directly increased to the maximum current to wake the passenger up; the stimulation will be maintained for 5 seconds and then stopped.

[0234] In this embodiment, the language recognition program in the MCU module includes:

[0235] 1. First, select Baidu's open source speech recognition program PaddleSpeech, which is suitable for Chinese broadcast word recognition, as the basic program;

[0236] 2. Then, we collect audio data containing high-speed rail broadcast keywords and provide corresponding text labels for these audio data to ensure that each audio file contains clear keyword pronunciation. We also preprocess the audio data, including noise reduction, volume adjustment, and format conversion, to ensure that the quality and format of the audio data meet the training requirements of PaddleSpeech.

[0237] 3. Deploy the relevant programs and training sample data to the cloud computing service platform. Then, select a suitable training script from the PaddleSpeech recipes directory. Then, based on the "station name" keyword along all high-speed rail lines nationwide, as well as time keywords such as "10 minutes," "5 minutes," "about to arrive," and "arrive," configure appropriate parameters and begin training the speech recognition model. Use the test dataset to evaluate the model's performance. Adjust the training parameters and sample data until the test performance is excellent. Convert the trained PaddleSpeech model to a format suitable for offline mobile deployment. After that, connect to the cloud computing platform via the Wi-Fi module and deploy the tested program to the MCU module so that it can still be used when disconnected from the network.

[0238] 4. Secondly, the RNNoise deep learning algorithm, which is recognized for its excellent noise reduction effect, is selected as the basic algorithm for audio noise suppression. On this basis, audio data of radio station broadcasts in a noise-free state and audio data with noisy noise are prepared. The program and sample data are deployed to the cloud computing platform, and then the architecture and core parameters of RNNoise are arranged. The noise reduction model is trained and the performance of the model is evaluated using a test data set to ensure that it has good noise reduction performance. The trained model is deployed to the MCU module for easy use when the network is disconnected.

[0239] 5. Finally, select the corresponding site name so that the speech recognition model only recognizes the site name and the corresponding time-related keyword detection. After the microphone obtains the surrounding sound, the noise in the sound is removed through the noise reduction model, and then the keywords in the broadcast sound are identified, and the time point when the keywords appear is recorded.

[0240] In this embodiment, the specific process of interpreting the site data information is as follows:

[0241] The interpretation of station data information is completed on the mobile terminal app. The app has built-in 12306 website address and map latitude and longitude coordinate information as well as corresponding crawling technology. This technology logs in to your account through the URL https: / / kyfw.12306.cn, and then enters the high-speed rail vehicle number to crawl and retrieve information using Python crawler technology in developer mode. The station information structure is: {"arrive_day_str":"**","arrive_time":"**","station_train_code":"**","station_name":"**","arrive_day_diff":"**","OT":[],"start_time":"**","wz_num":"**","station_no":"**","running_time":"**"}. The data of each station and arrival time in the information are obtained by analyzing and saving them in the keyword dictionary.

[0242] The longitude and latitude coordinates of the train stations are then retrieved from the Chinese High-speed Rail and Airline Database (CRAD). An electronic map, such as the China Railway Online Map (http: / / cnrail.geogv.org / zhcn / about), is accessed using an API key. The geocoding function identifies the high-speed rail stations at the corresponding longitude and latitude coordinates, and the high-speed rail lines between the stations are crawled and divided into multiple segments using a rasterization method. The longitude and latitude coordinates of the segment nodes and the actual lengths of the adjacent nodes are marked.

[0243] After completing the above data interpretation, the data information points are transmitted to the MCU module, and the map information along the route of the MCU module is updated.

[0244] In this embodiment, the running speed calculation program refers to the running speed calculation method based on the GPS / Beidou chip, and the calculation steps are as follows:

[0245] GPS / Beidou chips generally use GNSS data settlement, calculating the position by measuring the time difference between the signal reaching the receiver. The output data generally includes key information such as latitude and longitude, elevation, speed, and positioning time. This key information is input into the MCU module. Then, the train's running speed can be obtained by calculating the distance between two positioning points. The actual distance d between two adjacent positioning points can be calculated using the Haversine formula:

[0246]

[0247] Where r is the radius of the Earth, approximately 6371000 m; Δφ = φ2 - φ1 is the latitude difference between the two positioning points, where φ2 and φ1 are the latitudes of the previous and next positioning, respectively; Δλ = λ2 - λ1 is the longitude difference between the two positioning points, where λ2 and λ1 are the longitudes of the previous and next positioning, respectively.

[0248] The time interval Δt can be calculated based on the positioning time of the two positioning points, and the speed v of the high-speed rail at this time can be calculated as follows:

[0249]

[0250] Then, based on the latitude and longitude coordinates of the two positionings, the azimuth angle θ of the high-speed rail's forward direction (relative to the north direction) can be calculated:

[0251]

[0252] This embodiment also includes a readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the high-speed rail arrival reminder method for integrating multi-source information as described above is implemented.

[0253] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0254] This embodiment also includes an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the high-speed rail arrival reminder method that integrates multi-source information as described above is implemented.

[0255] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in a memory and executed by a processor to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in an electronic device.

[0256] Electronic devices can include computing devices such as mobile phones, desktop computers, laptops, PDAs, and cloud servers. They can include, but are not limited to, processors and memory. For example, electronic devices can also include input / output devices, network access devices, and buses.

[0257] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of an electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0258] The memory can be used to store computer programs and / or modules. The processor implements the computer programs by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0259] Among them, if the module / unit integrated into the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0260] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A high-speed rail arrival reminder method integrating multi-source information, which uses a high-speed rail arrival reminder system integrating multi-source information to obtain accurate arrival time at the destination station, characterized in that: The steps include: S1: Use a mobile terminal to obtain site map information, set the destination site and advance reminder time, where the site map information includes site information along the route, arrival time, longitude and latitude corresponding to the site, and longitude and latitude of map points along the route; S2. Turn on the power module (2.7), and update the set advance reminder time and built-in map information of the MCU module according to the site map information, the destination site and the advance reminder time; S4: Start the clock module (2.6), calculate the clock reminder time point based on the current time point and the arrival time of the destination station, and combine it with the set advance reminder time; S5: Based on the clock reminder time point, the current high-speed rail coordinates, forward direction and running speed are obtained through the positioning module (2.2) and the MCU module, and the current running status of the high-speed rail is determined; The S5 includes: Start the positioning module (2.2) and the two second IMU elements (2.3.2) 10 minutes before the clock reminder time point; The positioning module (2.2) receives satellite radio signals through the antenna for M consecutive times, and obtains the current high-speed rail coordinates, forward direction and running speed through the running speed calculation program of the MCU module, where M is the set number of times; Use the two second IMU elements (2.3.2) to obtain the linear acceleration along the X, Y, and Z axes, the rotational angular velocity along the X, Y, and Z axes, and the magnetic field strength along the X, Y, and Z axes of the current IMU module (2.3), and calculate the current high-speed rail acceleration through the acceleration solver of the MCU module; The current operating status of the high-speed rail is determined based on its operating speed and acceleration, specifically: When the high-speed rail's running speed is 0 and the acceleration is between -0.1m / s and 0.1m / s, the high-speed rail is in the temporary parking stage; when the high-speed rail's running speed is not 0 and the acceleration is between -0.1m / s and 0.1m / s, the high-speed rail is in the uniform speed running stage; when the high-speed rail's running speed is not 0 and the acceleration is negative and the absolute value is greater than 0.1m / s, the high-speed rail is in the deceleration running stage; S6: predicting the arrival time of the high-speed train at the destination station according to the running status of the high-speed train, and updating the clock reminder time point according to the predicted arrival time; S7: When the updated clock reminder time point in S6 is reached, the voice warning module is activated to emit a warning sound to remind the passenger that the station is about to be reached, and half of the electrode patches in the electrode patch module (2.4) are simultaneously activated to simulate acupuncture stimulation of the human body; S8: Using the first IMU element (2.3.1) and the other half of the electrode patches in the electrode patch module (2.4) to detect the current state of the passenger, and using the sleep state assessment program to assess whether the passenger is awake; if the passenger is not awake, adjusting the current of the electrode patches until the passenger is awake or passively shuts down, and automatically shutting down the power module (2.7); wherein: the maximum current of the electrode patches does not exceed 10mA; A high-speed rail arrival reminder system integrating multi-source information comprises a wearable carrier (1), a mobile terminal and a reminder device (2), wherein the wearable carrier (1) is worn on a passenger, the reminder device (2) is arranged on the wearable carrier (1), and the reminder device (2) comprises an MCU module, a positioning module (2.2), an IMU module (2.3), an electrode patch module (2.4), a voice recognition module, a voice warning module, a clock module (2.6) and a power module (2.7); the positioning module (2.2), the IMU module (2.3), the voice recognition module and the clock module (2.6) are all communicatively connected to the MCU module; the electrode patch module (2.4), the voice warning module and the power module (2.7) are electrically connected to the MCU module; The MCU module is communicatively connected to the mobile terminal; the positioning module (2.2) is used to obtain the current high-speed rail position; the IMU module (2.3) includes a first IMU element (2.3.1) and a second IMU element (2.3.2) arranged on both sides of the first IMU element (2.3.1), the first IMU element (2.3.1) being used to obtain the angular velocity and acceleration of the human body in three-dimensional space, and the second IMU element (2.3.2) being arranged in a tumbler-like manner and being used to obtain the acceleration of the high-speed rail; the electrode patch module (2.4) is arranged on the wearable carrier (1) and is capable of contacting human skin for collecting and transmitting electrical signals; the voice recognition module is used to collect high-speed rail notification voice information; the voice warning module is used to issue a warning voice to remind passengers; the clock module (2.6) is used to obtain time and date and set a clock reminder time point; and the power supply module (2.7) is used to supply power to the MCU module.

2. The high-speed rail arrival reminder method integrating multi-source information according to claim 1 is characterized in that: The MCU module has built-in speech recognition program, acceleration calculation program, running speed calculation program, arrival time prediction program and sleep state evaluation program, which can predict the time when the high-speed rail arrives at the destination station.

3. The high-speed rail arrival reminder method integrating multi-source information according to claim 2 is characterized in that: The high-speed rail arrival reminder system that integrates multi-source information also includes a Bluetooth module, and the MCU module is communicated with the mobile terminal through the Bluetooth module; and / or, the high-speed rail arrival reminder system that integrates multi-source information also includes a WIFI module, and the WIFI module is communicated with the MCU module, and the WIFI module is used to connect to the hotspot of the mobile terminal to update the map information built into the MCU module.

4. The high-speed rail arrival reminder method integrating multi-source information according to claim 3 is characterized in that: The acceleration calculation procedure in S5 includes: ① Use the magnetometer of the second IMU element (2.3.2) to measure the components of the Earth's magnetic field [m x ,m y ], calculate the azimuth angle θ of the IMU module (2.3) based on the component of the geomagnetic field on the horizontal plane imu , the specific calculation formula is as follows: ② The acceleration component [a x ,a y ,a z ] Estimate the pitch angle α and roll angle β of the second IMU element (2.3.2); ③ According to the pitch angle α and roll angle β of the second IMU element (2.3.2), the azimuth angle of the second IMU element (2.3.2) is corrected to obtain the corrected azimuth angle θ imu ′, the specific calculation formula is as follows: ④ Calculate the acceleration a in the forward direction of the high-speed rail based on the corrected azimuth angle. The specific calculation formula is as follows: a=a x ·cos(Δθ)+a y ·sin(Δθ); Where: Δθ is the angle between the second IMU element (2.3.2) and the direction of the high-speed train, Δθ = θ - θ imu ′, θ is the azimuth of the high-speed rail's forward direction.

5. The high-speed rail arrival reminder method integrating multi-source information according to claim 4 is characterized in that: In said S5, When the high-speed railway passes through an underground project, if the distance between the coordinates obtained by the positioning module (2.2) before and after the railway passes through the underground project is at least greater than the set distance threshold, it means that the high-speed railway is running, and the positioning module (2.2) is kept on until M consecutive satellite signals are obtained or 1 minute before the set reminder time point or 10 minutes before the arrival time is recognized by voice recognition, then the running status of the high-speed railway is determined by combining the IMU module (2.3) and the acceleration solution program; If the coordinates obtained by the positioning module (2.2) before and after crossing the underground project are not much different, the azimuth of the IMU module (2.3) is used as the running direction of the high-speed rail. When the acceleration of the high-speed rail's current forward direction obtained by the IMU module (2.3) is zero, it is determined that the high-speed rail is in a stopped state, and other modules except the MCU module and the IMU module (2.3) are suspended; when the acceleration of the high-speed rail's current forward direction is not zero, the high-speed rail is running. Until the positioning module (2.2) can obtain M consecutive satellite signals, the calculated azimuth of the running speed solver is used as the running direction of the high-speed rail.

6. The high-speed rail arrival reminder method integrating multi-source information according to claim 5 is characterized in that: The S2 and S4 also include: S3: Pre-training the speech recognition program in the MCU module according to the keywords to obtain a keyword recognition model, wherein the keywords include site names and key time point words; The S6 includes: S6.

1. Calculate the distance between the current high-speed rail coordinates and the destination station based on the vectorized data of the map information built into the MCU; S6.

2. Calculate the predicted arrival time of the high-speed train at the destination station using the arrival time prediction program of the MCU module based on the distance between the high-speed train coordinates and the destination station, the heading direction, and the running speed; S6.

3. Update the clock reminder time point based on the predicted arrival time, specifically: When the predicted arrival time is delayed by more than 10 minutes, the clock reminder time point is updated by delaying the clock reminder time point by the time that has exceeded the time limit; and the positioning module (2.2) is set to obtain the high-speed rail coordinates every 10 minutes, and the process returns to S6.1; If the clock reminder time is within 10 minutes of the predicted arrival time, the voice recognition module is activated 11 minutes before the predicted arrival time, and the voice from the 11th minute to the 8th minute of the predicted arrival time is recorded in real time. The voice is transmitted to the MCU module, and the pre-trained keyword recognition model is used to identify the keyword and record the time point when the keyword appears. The remaining arrival time is then calculated based on the average of the predicted arrival time and the time point when the keyword appears, and the clock reminder time is updated based on the remaining arrival time. Within 2 minutes before the clock reminder time, set the positioning module (2.2) to obtain the high-speed rail coordinates twice every 10 seconds on average, and return to S6.1; When the high-speed rail is in operation, if the current coordinates of the high-speed rail cannot be obtained within 1 minute before the clock reminder time, the reminder time originally set by the clock will be used as the basis; if the set reminder time is within 10 minutes of arriving at the station, the clock reminder time will be updated with the time point that appears in the keyword recognized by the voice recognition module.

7. The high-speed rail arrival reminder method integrating multi-source information according to claim 6 is characterized in that: The arrival time prediction procedure includes: ①Calculate the remaining distance from the current high-speed rail coordinates to the destination station, specifically: Calculate the current high-speed rail coordinates and the coordinates of the nearest line network point in the direction of travel under the current high-speed rail coordinates; Divide the high-speed rail line into multiple segments in a rasterized manner, mark the longitude and latitude coordinates of the segment points and the actual lengths of adjacent points; Calculate the distance between the nearest network point and the destination station, and add up the distances of each section to get the remaining distance from the high-speed rail to the destination station; ② Establish a prediction model for the remaining distance to start deceleration and a prediction model for the remaining arrival time to start deceleration, specifically: Collect at least 100 sets of high-speed rail data at different speeds, including the average speed and acceleration in the 10 minutes before the train decelerates to enter the station, as well as the remaining distance to the station and the remaining arrival time when the train begins to decelerate. The high-speed rail data was normalized using the minimum-maximum normalization method. Then, based on the BP neural network model, the average running speed and average acceleration were used as input parameters, and the remaining distance to the station and the remaining arrival time when the train started to decelerate were used as output parameters. This data was learned to obtain the remaining distance prediction model and the remaining arrival time prediction model when the train started to decelerate. ③Calculate the remaining arrival time based on the current high-speed rail operation status, specifically: If the high-speed rail is temporarily stopped, all modules except the MCU module and the IMU module (2.3) will be suspended, and passengers will be temporarily awakened. If the high-speed train is in a constant speed stage, the specific steps for calculating the remaining arrival time are as follows: i. Input the average running speed and average acceleration during the phase into the remaining distance prediction model at the start of deceleration and the remaining arrival time at the start of deceleration, and output the remaining distance at the start of deceleration and the running time of the deceleration segment; ii. Subtract the remaining distance at the start of deceleration from the remaining distance from the high-speed train to the destination to obtain the remaining distance of the uniform speed running section; iii. Calculate the running time of the remaining uniform speed segment based on the uniform speed; iv. Add the running time of the deceleration phase and the running time of the constant speed phase to obtain the remaining arrival time; v. Calculate the high-speed train arrival time based on the current time of the clock module (2.6) and update the clock reminder time; vi. Recalculate i to v every 30 seconds; If the high-speed rail is in the deceleration phase, the specific steps for calculating the remaining arrival time are as follows: i. The running speed v measured at the i-th time point in the deceleration phase i , acceleration a i , the remaining distance d from the high-speed rail to the destination i , the theoretical remaining arrival time t can be calculated i , the specific calculation formula is as follows: ii. Verify whether the theoretical arrival time is consistent with the actual arrival time based on the remaining distance. If the theoretical remaining distance d' i Greater than the actual remaining distance d i , then the actual arrival time is considered to be less than the theoretical remaining arrival time, and the acceleration and arrival time are recalculated, and the recalculated arrival time is used as the actual arrival time; if the theoretical remaining distance d' i Equal to the actual remaining distance d i , then the calculated theoretical remaining arrival time is taken as the actual arrival time; Where: Theoretical remaining distance d i The calculation formula of ' is as follows: The formulas for recalculating acceleration and arrival time are as follows: iii. After obtaining the arrival time point, use the clock module (2.6) to recalculate the clock reminder time point.

8. The high-speed rail arrival reminder method integrating multi-source information according to claim 1 is characterized in that: The sleep state assessment procedure includes: ① Establish the IMU sleep state assessment model and the electrode patch sleep state assessment model, specifically: Establishing the IMU sleep state assessment model includes the following steps: i. Collect at least 300 sets of signal data from the first IMU element (2.3.1) in different head states, and use professional instruments to evaluate the human state. Different head states include wakefulness, light sleep, and deep sleep. ii. Then, the signal data of the first IMU element (2.3.1) is subjected to low-pass filtering and denoising, and four key characteristic parameter data are extracted. The IMU data under human condition is evaluated by professional instruments, and three label data sets are established; among them, the four key characteristics include the standard deviation of acceleration a std , the root mean square of acceleration a rms , angular velocity change rate Δω and acceleration signal periodicity P; iii. Then, a typical and reliable classification algorithm, the support vector machine algorithm, is used to learn and analyze the data set. The grid search algorithm is used to optimize the sum and penalty parameters of the support vector machine until the loss function is minimized, thereby establishing an IMU sleep state assessment model. The establishment of a sleep state assessment model for electrode patches includes the following steps: i. Collect at least 300 sets of electromyographic signal data of the electrode patch module (2.4) in different states of the human head, and use a human sleep state measurement instrument to evaluate the human state; ii. Then, the signal data from the electrode patch is amplified, its root mean square characteristic data is extracted, and the electromyographic signal characteristic data under the human body state is evaluated according to the human sleep state measurement instrument to establish three labeled data sets; iii. Then, a support vector machine algorithm is used to learn and analyze the data set, and a grid search algorithm is used to optimize the sum and penalty parameters of the support vector machine until the loss function is minimized, thereby establishing a sleep state assessment model for the electrode patch; ② Use the IMU sleep state assessment model and the electrode patch sleep state assessment model to assess the passenger's sleep state, specifically: The electrode patch module (2.4) is used to collect the passenger's electromyographic signal. Specifically, the electrode patch is placed close to the skin of the human neck to collect the changes in the skin surface potential and the weak electrical signals generated by muscle activity, namely the electromyographic signal. The electrode patch collects the electromyographic signal once every 2 seconds with a time window of 1 second. The first IMU element (2.3.1) is used to measure the signal data of the passenger's head, wherein: the first IMU element (2.3.1) is set to detect 5 times every 5 seconds, and then detect again after an interval of 5 seconds; The passenger's electromyographic signal and head signal data are transmitted to the MCU module's IMU sleep state assessment model and the electrode patch's sleep state assessment model respectively to assess the passenger's sleep state; If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate awake, the power module (2.7) is automatically turned off. If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate a light sleep state, adjust the initial current of the electrode patch to 0.5-1 mA and the stimulation frequency to a medium-frequency, low-amplitude pulse wave of 20-50 Hz; If the sleep state assessment model of the IMU and the sleep state assessment model of the electrode patch both indicate a deep sleep state, adjust the initial current of the electrode patch to 1-2 mA and the stimulation frequency to a low-frequency, high-amplitude pulse wave of 50-100 Hz; After 5 to 10 seconds of continuous stimulation, if the passenger is awake, the stimulation will be stopped; if the passenger is not awake, the current will be adjusted according to the changes in the passenger's electromyographic signals or small movements. Specifically, the current amplitude is increased by 0.5 mA every 5 seconds. If there is no response after 30 to 60 seconds of stimulation, the current will be directly increased to the maximum current to wake the passenger up; the stimulation will be maintained for 5 seconds and then stopped.

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