An intelligent scooter with user motion monitoring function and monitoring method

Abstract

An embodiment of the present invention discloses an intelligent scooter with a user motion monitoring function and a monitoring method. The intelligent scooter includes: a frame, a runner rotatably arranged under the frame, a motor, and a steering wheel connected to the front end of the frame through a rotating shaft. The frame is a hollow structure and includes an upper shell and a lower shell. After the upper shell and the lower shell are closed, an accommodation cavity is formed. The intelligent scooter automatically matches the operation mode suitable for the current user through an intelligent judgment module, improving the safety and intelligent flexibility of the user in operating the intelligent scooter.

Description

Technical Field

[0001] The present invention relates to the technical field of scooters, and in particular to an intelligent scooter with a user movement monitoring function and a monitoring method. Background Art

[0002] As a popular entertainment and sports tool for children and adults, scooters are deeply loved by the market because of their simple operation and strong fun. However, traditional scooters have relatively single functions and operation modes, only providing basic sliding functions, lacking safety monitoring of the user's movement process, and their safety needs to be improved. In addition, with the increasing attention of people to healthy sports and the wide application of technology in daily life, users' demand for the intelligence of sports equipment is also growing. Currently, there is no intelligent scooter on the market that can simultaneously meet the functions of movement monitoring and safety warning. Summary of the Invention

[0003] Aiming at the defects in the prior art, the present invention provides an intelligent scooter with a user movement monitoring function, which improves the safety and intelligence of children users operating the scooter. To achieve the above object, the present invention provides the following technical solutions:

[0004] An intelligent scooter with a user movement monitoring function includes a frame, a runner rotatably arranged below the frame, a motor, and a steering wheel connected to the front end of the frame through a rotating shaft; wherein the frame is a hollow structure and includes an upper shell and a lower shell, and an accommodation cavity is formed after the upper shell and the lower shell are covered. It is characterized in that the intelligent scooter further includes:

[0005] A weighing sensor for collecting the weight information of the current user;

[0006] A central control module for receiving the guardian user instruction A or the instruction B output by the intelligent judgment module, and executing a movement operation mode adapted to the current user based on the instruction A or the instruction B; wherein the guardian user instruction A is sent by the guardian's mobile APP, and the priority of the guardian user instruction A is higher than the instruction B output by the intelligent judgment module;

[0007] A GPS positioning module for real-time collecting the current position information of the intelligent scooter and sending it to the central control module and the guardian user's mobile phone;

[0008] A movement data collection module for collecting the attitude information of the current user operating the intelligent scooter, and the attitude information at least includes speed information, acceleration information, and angular velocity information;

[0009] An intelligent judgment module, which is used to intelligently determine an appropriate operation mode according to the weight information of the current user and the posture information of the current user operating the intelligent scooter, and generate a corresponding instruction B to be sent to the central control module.

[0010] Furthermore, the central control module, the GPS positioning module, the motion data acquisition module, and the intelligent judgment module are respectively arranged in the accommodation cavity; the weighing sensor is arranged on the upper shell.

[0011] Furthermore, the motion operation mode is divided into two levels of safety operation mode and normal operation mode;

[0012] Among them, in the first-level safety operation mode, the central control module controls the motor to achieve the first speed limit, the first motion duration limit, and the first area limit processing; the first area is a safety area preset by the guardian user, and the range of the safety area is a circular area centered on the real-time mobile phone positioning position of the guardian user and with a radius of the first safety distance threshold;

[0013] In the second-level safety operation mode, the central control module controls the motor to achieve the second speed limit and the second motion duration limit processing.

[0014] Furthermore, when the intelligent scooter exceeds the range of the safety area, the central control module sends an alarm indication message to the guardian user's mobile phone, and temporarily adjusts the speed limit value to half of the first speed limit to improve safety, and then adjusts it to the first speed limit after the intelligent scooter is within the safety area.

[0015] Furthermore, the intelligent judgment module includes a deep learning model, a user data storage module, and an instruction generation module.

[0016] Furthermore, the intelligent judgment module receives the weight information of the current user output by the weighing sensor and the posture information of the current user operating the intelligent scooter output by the motion acquisition module;

[0017] When the user weight information exceeds the first weight threshold, the instruction generation module of the intelligent judgment module directly generates a normal operation mode instruction;

[0018] When the user weight information is lower than the first weight threshold, the intelligent judgment module identifies the current user through the deep learning model; when the deep learning model identifies a specific target user, matches the associated motion operation mode, determines the current motion operation mode based on the associated motion operation mode, generates a corresponding instruction B, and sends the instruction B to the central control module;

[0019] Among them, the motion operation mode associated with the specific target user is the motion operation mode previously used by the specific target user. When there are multiple previously used motion operation modes, the motion operation mode with the most usage times or the most recently used motion operation mode is selected as the current motion operation mode;

[0020] When the deep learning model fails to identify the specific target user, a default motion operation mode instruction is generated based on the current user weight information, where the first type of user is default matched with the first-level safety operation mode, and the second type of user is default matched with the second-level safety operation mode.

[0021] Furthermore, the intelligent judgment module selects the corresponding data sub-library based on the current user's weight information to train the deep learning model; the attitude information of the current user operating the intelligent scooter is input into the trained deep learning model for intelligent judgment to identify the specific target user;

[0022] The deep learning model includes a user feature preprocessing module, a graph convolutional network GCN processing module, an LSTM network processing module, and a feature fusion and classification module;

[0023] Among them, the user feature preprocessing module is used to construct a time series matrix of attitude information. Each row of the time series matrix of attitude information represents the attitude information within a time step, and each column represents a feature, which are speed information, acceleration information, and angular velocity information respectively;

[0024] The graph convolutional network GCN processing module is used to regard the attitude information within each time step as a node, construct a graph structure, and aggregate and update the node features through the graph convolutional network GCN to output an updated node feature matrix H GCN ;

[0025] The LSTM network processing module is used to input the time series matrix of the attitude information into the LSTM network to capture the time series features and output the hidden state of the LSTM; H LSTM ;

[0026] The feature fusion and classification module is used to receive the output feature matrices of the GCN and the LSTM H GCN and H LSTM to generate fusion features and output the user identification based on the softmax classifier.

[0027] Further, the user data storage module consists of two data sub - libraries, namely the first data sub - library for storing the historical movement data of the first type of users, and the second data sub - library for storing the historical movement data of the second type of users. The weights of the first - type users and the second - type users increase in sequence. The above - mentioned historical movement data includes the user posture information of multiple users operating the intelligent scooter.

[0028] Further, the instruction generation module is also used to generate a movement operation mode instruction that matches the specific target user identification when the user identification output by the softmax classifier is the specific target user identification.

[0029] Another object of the present invention is to provide a monitoring method for an intelligent scooter with a user movement monitoring function. The method includes, when a user operates the above - mentioned intelligent scooter, sending the warning information that the current user exceeds the safe range, the current user location information, and the information about the continuous duration of the current user operating the scooter to the guardian user's mobile phone.

[0030] Combined with all the above - mentioned technical solutions, the present invention has the following advantages compared with the prior art:

[0031] (1) Multiple safety operation modes are designed for different users. Among them, the first - level safety operation mode performs speed limit and area limit processing, improving the safety performance of young children using the scooter. Sending warning information when exceeding the safe area facilitates the guardian to perform safety guardianship. The second - level safety operation mode relaxes the restrictions for slightly older children, increasing the convenience of operation while ensuring safety. The normal operation mode has no restrictions for older children and adults.

[0032] (2) An intelligent judgment module based on deep learning is set up to identify the current user, accurately and quickly matching the appropriate operation mode corresponding to the user, improving the intelligence of the scooter.

[0033] (3) The deep - learning model is implemented based on GCN and LSTM, integrating the features of the two neural networks, achieving the accuracy and speed of specific target user identification. On the one hand, GCN is good at processing graph - structured data and can capture the correlation information between each node; while LSTM is good at processing sequence data and can capture the long - term dependencies in the time series. Combining the two can utilize both graph - structure and time - series information simultaneously, improving the performance of the deep - learning model in identifying user identities.

[0034] (4) In practical applications, the deep - learning model uses the data in different databases for corresponding model training according to the different weights of users, further improving the pertinence, accuracy, and recognition speed of the deep - learning model in identifying user identities.

[0035] (5) Users can customize the relevant parameters in each operation mode through the mobile phone APP, improving the flexibility control of the intelligent scooter.

[0036] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Brief Description of the Drawings

[0037] By describing the embodiments of the present invention in more detail in combination with the accompanying drawings, the above and other objects, features and advantages of the present invention will become more obvious. The accompanying drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0038] Figure 1 is a schematic structural diagram of an intelligent scooter provided by an exemplary embodiment of the present invention. Detailed Embodiments

[0039] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0040] It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions and numerical values of the components and steps described in these embodiments do not limit the scope of the present invention.

[0041] Those skilled in the art can understand that the terms "first", "second", etc. in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them.

[0042] It should also be understood that in the embodiments of the present invention, "a plurality" may refer to two or more, and "at least one" may refer to one, two or more.

[0043] It should also be understood that for any component, data or structure mentioned in the embodiments of the present invention, unless clearly defined or given a contrary indication in the context, it can generally be understood as one or more.

[0044] The schematic structural diagram of the intelligent scooter with a user motion monitoring function is shown in Figure 1 . As Figure 1As shown in the figure, the present invention provides an intelligent scooter with a user movement monitoring function, which includes a frame 1, a runner 2 rotatably arranged under the frame, a motor 3, and a steering wheel 4 connected to the front end of the frame through a rotating shaft. Among them, the motor 3 includes a magnet and a Hall sensor installed on the runner. The frame is a hollow structure and includes an upper shell 5 and a lower shell 6. After the upper shell and the lower shell are closed, an accommodation cavity 7 is formed; the intelligent scooter further includes:

[0045] A weighing sensor for collecting the weight information of the current user;

[0046] Preferably, the weighing sensor is arranged on the upper shell; with this arrangement, when the user rides the scooter, they directly contact the upper shell, which can improve the weighing accuracy.

[0047] A central control module for receiving the guardian user instruction A or the instruction B output by the intelligent judgment module, and performing a motion operation mode adapted to the current user based on the instruction A or the instruction B; among them, the guardian user instruction A is sent by the guardian's mobile phone APP, and the priority of the guardian user instruction A is higher than the instruction B output by the intelligent judgment module.

[0048] Preferably, the motion operation mode is divided into two levels of safety operation mode and normal operation mode.

[0049] In the first-level safety operation mode, the central control module controls the motor to achieve the first speed limit, the first motion duration limit, and the first area limit processing; the first area is a safety area pre-set by the guardian user. The range of the safety area is a circular area centered on the real-time mobile phone positioning position of the guardian user and with a radius of the first safety distance threshold R1 (R1 supports user-defined settings, and the default setting is 20M); when the intelligent scooter exceeds the range of the safety area, the central control module sends an alarm indication message to the guardian user's mobile phone, and temporarily adjusts the speed limit value to half of the first speed limit to improve safety. After the intelligent scooter is located in the safety area, it is adjusted back to the first speed limit. When the guardian user receives the alarm indication message, they can approach the direction of the intelligent scooter for easy guardianship to improve safety. As an example, the first speed limit defaults to 5KM / h and supports user-defined settings; the first motion duration defaults to half an hour and supports user-defined settings; R1 defaults to 20M and supports user-defined settings.

[0050] In the second-level safety operation mode, the central control module controls the motor to achieve the second speed limit and the second motion duration limit processing. As an example, the second speed limit defaults to 10KM / h and supports user-defined settings; the second motion duration defaults to 1 hour and supports user-defined settings. The second speed limit is greater than the first speed limit, and the second motion duration is greater than the first motion duration.

[0051] It should be noted that when the first exercise duration or the second exercise duration limit is reached, the central control module sends an out-of-duration warning indication message to the guardian user's mobile phone, and at the same time implements the braking process. The current user cannot use the intelligent scooter again within a certain period of time (for example, within the default half hour or the default one hour). In this case, the guardian user needs to resend instruction A or replace it with a new operating user to use the intelligent scooter to lift the above restrictions.

[0052] It should be noted that in the normal operation mode, the use function of the intelligent scooter is not restricted.

[0053] The GPS positioning module is used to collect the current position information of the intelligent scooter in real time and send it to the central control module and the guardian user's mobile phone.

[0054] It should be noted that such a design can facilitate the central control module to judge whether the intelligent scooter is in a safe area based on the current position information of the intelligent scooter and the current positioning information of the guardian user's mobile phone in the first-level safety operation mode.

[0055] The motion data acquisition module is used to collect the attitude information of the current user operating the intelligent scooter, and the attitude information at least includes speed information, acceleration information, and angular velocity information;

[0056] Preferably, the motion data acquisition module consists of a Hall sensor, an acceleration sensor, and an angular velocity sensor, which are respectively responsible for collecting the speed information, acceleration information, and angular velocity information of the intelligent scooter in real time.

[0057] The intelligent judgment module is used to intelligently determine the appropriate operation mode according to the weight information of the current user and the attitude information of the current user operating the intelligent scooter, and generate a corresponding instruction B and send it to the central control module.

[0058] Among them, the intelligent judgment module includes a deep learning model, a user data storage module, and an instruction generation module.

[0059] The specific implementation process is as follows: The intelligent judgment module receives the weight information of the current user output by the weighing sensor and the attitude information of the current user operating the intelligent scooter output by the motion acquisition module;

[0060] When the user weight information exceeds the first weight threshold (for example, the default is 20KG, and the user is supported to customize. At this time, it can be considered that the current user is an older child or an adult and can operate the scooter normally), the instruction generation module of the intelligent judgment module directly generates a normal operation mode instruction;

[0061] When the user weight information is lower than the first weight threshold, the intelligent judgment module identifies the current user through a deep learning model; when the deep learning model identifies a specific target user, it matches the associated motion operation mode, determines the current motion operation mode based on the associated motion operation mode, generates a corresponding instruction B, and sends instruction B to the central control module;

[0062] Among them, the motion operation mode associated with the specific target user is the motion operation mode previously used by the specific target user. When there are multiple previously used motion operation modes, the motion operation mode with the most usage times or the most recently used motion operation mode is selected as the current motion operation mode;

[0063] When the deep learning model does not identify a specific target user, a default motion operation mode instruction is generated based on the current user weight information. Among them, the first type of user is default matched with the first-level safety operation mode, and the second type of user is default matched with the second-level safety operation mode.

[0064] Preferably, when the user weight information is lower than the first weight threshold, the intelligent judgment module selects the corresponding data sub-library based on the current user's weight information to train the deep learning model;

[0065] It should be noted that when the current user belongs to the first type of user, the corresponding data sub-library is the first data sub-library; when the current user belongs to the second type of user, the corresponding data sub-library is the second data sub-library. Such a design can greatly improve the matching speed and accuracy of the intelligent judgment module, and at the same time reduce the calculation amount.

[0066] The attitude information of the current user operating the intelligent scooter is input into the trained deep learning model for intelligent judgment to identify a specific target user.

[0067] As an example, the deep learning model includes a user feature preprocessing module, a graph convolutional network GCN processing module, an LSTM network processing module, and a feature fusion and classification module;

[0068] Among them, the user feature preprocessing module is used to construct a time series matrix of attitude information. Each row of the time series matrix of attitude information represents the attitude information within a time step (for example, 1 second), and each column represents a feature, which are speed information, acceleration information, and angular velocity information respectively; preferably, before constructing the above time series matrix of attitude information, the feature information of each attitude information is normalized.

[0069] The graph convolutional network GCN processing module is used to regard the attitude information within each time step as a node, construct a graph structure, and aggregate and update the node features through the graph convolutional network GCN to output an updated node feature matrixH GCN 。

[0070] As an example, the GCN propagation rule is as follows: , where: is the node feature matrix of the l th layer, is the adjacency matrix plus the identity matrix, D is the degree matrix, is the ReLU function, is the weight matrix.

[0071] The LSTM network processing module is used to input the time series matrix of the pose information into the LSTM network, capture the time series features, and output the hidden state of the LSTM H LSTM 。

[0072] As an example, the LSTM network includes a forget gate, an input gate, and an output gate. The relevant processing update formulas for the three parts are as follows:

[0073]

[0074] Among them, is the time series matrix of the pose information, is the forget gate, is the input gate, is the output gate, represents the candidate state to be added to the cell at the current moment created by the tanh layer, represents the current state of the cell, represents the state of the cell at the previous moment, represents the output of the current cell, and represents the output of the cell at the previous moment, sigmoid and tanh are activation functions, is the weight vector, is the bias vector.

[0075] The feature fusion and classification module is used to receive the output feature matrices of the GCN and the LSTM H GCN and H LSTM , generate the fused features, and output the user identification based on the softmax classifier.

[0076] Preferably, the user data storage module consists of two data sub-libraries, namely the first data sub-library for storing the historical movement data of the first type of users, and the second data sub-library for storing the historical movement data of the second type of users. The weights of the first type of users and the second type of users increase in sequence. The above historical movement data includes the user posture information of multiple users' historical operations on the intelligent scooter. As an example, in specific implementation, the weight ranges of the first type of users and the second type of users can be specifically set and adjusted according to needs. For example, the default factory setting is that the weight of the first type of users is less than 15KG, corresponding to children under 3 years old, and the weight of the second type of users is greater than or equal to 15KG and less than 20KG, corresponding to children aged 3 - 6 years old. When the user's weight is greater than 20KG, it can be considered corresponding to children over 6 years old or adults.

[0077] Preferably, the instruction generation module is further configured to generate a movement operation mode instruction matching the specific target user identifier when the user identifier output by the softmax classifier is the specific target user identifier.

[0078] It should be noted that, as a preferred method, the central control module, the GPS positioning module, the movement data acquisition module, and the intelligent judgment module are respectively arranged in the accommodation cavity.

[0079] Correspondingly, the present invention also provides a monitoring method for an intelligent scooter with a user movement monitoring function. The method includes that when a user operates the above intelligent scooter, sending the warning information that the current user exceeds the safe range, the current user positioning information, and the information on the continuous duration of the current user's operation of the scooter to the guardian user's mobile phone. The information on the continuous duration of the current user's operation of the scooter can be sent in real time, regularly, or at the moment when the limited duration arrives, and there is no limitation here.

[0080] The basic principles of the present disclosure have been described above in combination with specific embodiments. However, it should be pointed out that the advantages, advantages, effects, etc. mentioned in the present disclosure are only examples and not limitations. It cannot be considered that these advantages, advantages, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above specific details disclosed are only for the purposes of illustration and easy understanding, rather than limitations. The above details do not limit the present disclosure to necessarily adopt the above specific details for implementation.

[0081] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the method embodiments, since they basically correspond to the product embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the product embodiments.

[0082] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended terms, meaning "including but not limited to", and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or", and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with each other.

[0083] The methods and apparatuses of the present disclosure can be implemented in many ways. For example, the methods and apparatuses of the present disclosure can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of the steps for the methods is for illustration only, and the steps of the methods of the present disclosure are not limited to the specific order described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure can also be implemented as a program recorded on a recording medium, and these programs include machine-readable instructions for implementing the methods according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the methods according to the present disclosure.

[0084] It should also be noted that in the apparatuses, equipment, and methods of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present disclosure. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0085] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof. For example, scooters, children's bikes, children's balance bikes, yo-yos, sliders, and other similar vehicles and their variations are all within the protection scope of the intelligent twist bike of the present invention.

Claims

1. An intelligent twisting car with a user motion monitoring function, comprising a frame, a rotating wheel rotatably arranged under the frame, a motor, and a steering wheel connected to the front end of the frame through a rotating shaft; The frame is a hollow structure and includes an upper shell and a lower shell, and the upper shell and the lower shell are covered to form a receiving cavity; the characteristic is that: The smart twist car also includes: Weighing sensor, used to collect the weight information of the current user; The central control module is used to receive the guardian user instruction A or the instruction B output by the intelligent judgment module, and execute the motion operation mode adapted to the current user based on the instruction A or the instruction B; wherein the guardian user instruction A is issued by the guardian mobile phone APP, and the guardian user instruction A has a higher priority than the instruction B output by the intelligent judgment module; The GPS positioning module is used to collect the current location information of the smart car in real time and send it to the central control module and the guardian user's mobile phone; A motion data collection module is used to collect the posture information of the current user operating the smart twisting car, and the posture information at least includes speed information, acceleration information, and angular velocity information; The intelligent judgment module is used to intelligently determine the adaptive motion operation mode according to the weight information of the current user and the posture information of the current user operating the intelligent twisting car, and generate a corresponding instruction B to send to the central control module.

2. The smart twisting car with user motion monitoring function as claimed in claim 1, characterized in that: The central control module, GPS positioning module, motion data acquisition module and intelligent judgment module are respectively arranged in the accommodating cavity; the weighing sensor is arranged on the upper shell.

3. The smart twisting car with user motion monitoring function as claimed in claim 1, characterized in that: The motion operation mode is divided into two levels of safety operation mode and normal operation mode; Among them, in the first level safety operation mode, the central control module controls the motor to achieve the first speed limit, the first exercise time limit and the first area restriction processing; the first area is the safety area set in advance by the guardian user, and the range of the safety area is a circular area with the guardian user's real-time mobile phone positioning position as the center and the first safety distance threshold as the radius; In the second level safety operation mode, the central control module controls the motor to achieve the second speed limit and the second movement duration limit processing.

4. The smart twisting car with user motion monitoring function as claimed in claim 3, characterized in that: When the smart car goes beyond the safety zone, the central control module sends an alarm to the guardian user's mobile phone and temporarily adjusts the speed limit to half of the first speed limit to improve safety. The speed limit will be adjusted to the first speed limit after the smart car is in the safety zone.

5. The smart twisting car with user motion monitoring function as claimed in claim 1, characterized in that: The intelligent judgment module includes a deep learning model, a user data storage module and an instruction generation module.

6. The smart twisting car with user motion monitoring function as claimed in claim 5, characterized in that: The intelligent judgment module receives the weight information of the current user output by the weighing sensor and the posture information of the current user operating the intelligent twisting car output by the motion acquisition module; When the user weight information exceeds the first weight threshold, the instruction generation module of the intelligent judgment module directly generates a normal operation mode instruction; When the user weight information is lower than the first weight threshold, the intelligent judgment module identifies the current user through the deep learning model; when the deep learning model identifies the specific target user, the motion operation mode associated with the specific target user is matched, the current motion operation mode is determined based on the associated motion operation mode, and the corresponding instruction B is generated, and the instruction B is sent to the central control module; The motion operation mode associated with the specific target user is a motion operation mode previously used by the specific target user. When there are multiple motion operation modes previously used, the motion operation mode with the most number of uses or the motion operation mode used most recently is selected as the current motion operation mode. When the deep learning model fails to identify a specific target user, a default motion operation mode instruction is generated based on the current user weight information, wherein the first type of user matches the first level safety operation mode by default, and the second type of user matches the second level safety operation mode by default.

7. The smart twisting car with user motion monitoring function as claimed in claim 6, characterized in that: The intelligent judgment module selects the corresponding data sub-library based on the weight information of the current user to train the deep learning model; the posture information of the current user operating the smart twisting car is input into the trained deep learning model for intelligent judgment to identify the specific target user; The deep learning model includes a user feature preprocessing module, a graph convolutional network (GCN) processing module, an LSTM network processing module, and a feature fusion and classification module; The user feature preprocessing module is used to construct a time series matrix of posture information, wherein each row of the time series matrix of posture information represents posture information within a time step, and each column represents a feature, which are velocity information, acceleration information and angular velocity information respectively; The graph convolutional network (GCN) processing module is used to treat the posture information in each time step as a node, build a graph structure, aggregate and update the node features through the graph convolutional network (GCN), and output the updated node feature matrix. H GCN ; LSTM network processing module, used to input the time series matrix of the posture information into the LSTM network, capture the time series features, and output: the hidden state of the LSTM H LSTM ; Feature fusion and classification module, used to receive the output feature matrix of GCN and LSTM H GCN and H LSTM , generate fusion features, and output user identification based on the softmax classifier.

8. The smart twisting car with user motion monitoring function as claimed in claim 5, characterized in that: The user data storage module is composed of two data sub-libraries, namely, a first data sub-library for storing historical motion data of first-category users; and a second data sub-library for storing historical motion data of second-category users. The weights of the first-category users and the second-category users increase in sequence. The historical motion data includes user posture information of multiple users operating the smart twisting car in history.

9. The smart twisting car with user motion monitoring function as claimed in claim 7, characterized in that: The instruction generation module is further used to generate a motion operation mode instruction matching a specific target user identifier when the user identifier output by the softmax classifier is a specific target user identifier.

10. A monitoring method for a smart twisting car with a user motion monitoring function, characterized in that: When a user operates the smart twisting car as described in any one of claims 1 to 9, the warning information that the current user exceeds the safety range generated during the operation, the current user location information, and the continuous time information of the current user operating the twisting car are sent to the guardian user's mobile phone.

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