Myopia Prevention and Control Method, Device, System, Storage Medium and Equipment

Abstract

The present disclosure relates to a myopia prevention and control method, device, system, storage medium, and equipment. The method includes: a first sensor module transmits the first attitude information collected by it and a second sensor module transmits the second attitude information collected by it to a first control module; the first control module inputs the first attitude information and the second attitude information into a preset feature extraction network model for feature extraction to determine a first feature, and inputs the first feature into an attention model for encoding to determine an attention feature; performs feature fusion on the attention feature to determine a second feature; determines a classification result according to the second feature, and transmits the classification result to an evaluation module; the evaluation module determines whether the sitting posture of the human body is qualified according to the classification result, and when the sitting posture of the human body is unqualified, reminds the user to correct the sitting posture. The present disclosure can monitor and evaluate the sitting posture and physical condition of the user at any time to ensure the physical health of the user.

Description

Technical Field

[0001] The present disclosure relates to the field of mechatronics technology, and particularly to a myopia prevention and control method, device, system, storage medium, and equipment. Background Art

[0002] The existing eye usage monitoring system can collect information using wearable devices and the information input by users to provide eye usage suggestions. However, it only collects data through the eyes themselves, and uses a single threshold to judge the collected data. The judgment method is simple and rough, and manually inputting data is too cumbersome for users. Moreover, it cannot observe the overall sitting posture state of users in real time, so as to give a more comprehensive evaluation. Summary of the Invention

[0003] To solve at least one of the above-mentioned technical problems, the present disclosure provides a myopia prevention and control method, a monitoring terminal, a myopia prevention and control system, a storage medium, and an electronic device.

[0004] According to one aspect of the present disclosure, there is provided a myopia prevention and control method, which is applied to a myopia prevention and control system. The myopia prevention and control system includes a monitoring terminal and a wearable device. The monitoring terminal includes a first sensor module, a first control module, and an evaluation module. The wearable device includes a second sensor module, and the method includes:

[0005] The first sensor module transmits the collected first posture information to the first control module, and the second sensor module transmits the collected second posture information to the first control module. The first posture information includes a human sitting posture image, a desk distance, and a physiological parameter value. The second posture information includes a line-of-sight distance, a head posture information, and a reading time;

[0006] The first control module inputs the first posture information and the second posture information into a preset feature extraction network model for feature extraction to determine a first feature. The first feature includes a spatial feature and a micro-motion information feature, and inputs the first feature into an attention model for encoding to determine an attention feature; performs feature fusion on the attention feature to determine a second feature; determines a classification result according to the second feature, and transmits the classification result to the evaluation module;

[0007] The evaluation module determines whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, the user is reminded to correct the sitting posture.

[0008] In some possible embodiments, the preset feature extraction network model includes a first feature extraction network model and a second feature extraction network model. The first control module inputs the first posture information and the second posture information into the preset feature extraction network model for feature extraction to determine the third feature, including:

[0009] The first control module inputs the first posture information and the second posture information into the first feature extraction network model to determine the spatial feature;

[0010] The first control module inputs the first posture information and the second posture information into the second feature extraction network model to determine the micro-motion information feature.

[0011] In some possible embodiments, the method further includes:

[0012] The first control module obtains the reading time, determines whether the reading time is greater than a preset time, and if it is greater than the preset time, reminds the user through the voice prompt unit.

[0013] According to a second aspect of the present disclosure, there is provided a myopia prevention and control device applied to a myopia prevention and control system. The myopia prevention and control system includes a monitoring terminal and a wearable device. The monitoring terminal includes a first sensor module, a first control module, and an evaluation module. The wearable device includes a second sensor module, including:

[0014] An information acquisition module, configured to transmit the first posture information collected by the first sensor module to the first control module, and transmit the second posture information collected by the second sensor module to the first control module. The first posture information includes a human sitting posture image, a desk distance, and a physiological parameter value. The second posture information includes a line-of-sight distance, a head posture information, and a reading time;

[0015] A classification result determination module, configured to input the first posture information and the second posture information into a preset feature extraction network model by the first control module for feature extraction to determine a first feature. Among them, the first feature includes a spatial feature and a micro-motion information feature, and input the first feature into an attention model for encoding to determine an attention feature; perform feature fusion on the attention feature to determine a second feature; determine a classification result according to the second feature, and transmit the classification result to the evaluation module;

[0016] A sitting posture evaluation module, configured to determine whether the human sitting posture is qualified by the evaluation module according to the classification result. If the human sitting posture is unqualified, remind the user to correct the sitting posture.

[0017] According to a third aspect of the present disclosure, a myopia prevention and control system is provided. The system includes a monitoring terminal, a wearable device, and a cloud;

[0018] The monitoring terminal includes a first sensor module, a first control module, and an evaluation module. The first sensor module is configured to collect first posture information, where the first posture information includes a human sitting posture image, a desk distance, and physiological parameter values. The first control module is configured to input the first posture information and second posture information into a preset feature extraction network model for feature extraction to determine a first feature, where the first feature includes a spatial feature and a micro-motion information feature, and input the first feature into an attention model for encoding to determine an attention feature; perform feature fusion on the attention feature to determine a second feature; determine a classification result according to the second feature, and transmit the classification result to the evaluation module. The evaluation module is configured to determine whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, the user is reminded to correct the sitting posture;

[0019] The wearable device is communicatively connected to the monitoring terminal, and the monitoring terminal is communicatively connected to the cloud;

[0020] The wearable device includes a second sensor module and a second control module. The second sensor module is configured to collect the second posture information and transmit the second posture information to the first control module and the second control module. The second posture information includes a line-of-sight distance, a head posture information, a reading tool image, and a reading time. The second control module is configured to control the reading time and distinguish reading tools;

[0021] The cloud module includes an expert engine and a public account module. The expert engine is configured to classify the fatigue state of the user, and the public account module is configured to push information on the improvement degree of fatigue.

[0022] In some possible implementation manners, the first sensor module includes a millimeter-wave radar, and the millimeter-wave radar is configured to obtain the human sitting posture image. The first control module includes a first Bluetooth communication unit, a voice prompt unit, a display screen display unit, and a network unit. The first Bluetooth communication unit is configured to communicate with the wearable device. The voice prompt unit is configured to give a voice reminder to the user when the user's sitting posture is incorrect or the reading time exceeds a preset time. The display screen unit is configured to display the reading time and the fatigue state.

[0023] In some possible implementation manners, the second sensor module includes a ranging sensor, a gyroscope sensor, and a camera;

[0024] The ranging sensor is configured to obtain the line-of-sight distance, where the line-of-sight distance is the distance between the user's eyes and the reading tool;

[0025] The gyroscope sensor is used to obtain the head posture information;

[0026] The camera is used to obtain the image of the reading tool.

[0027] In some possible implementation manners, the second control module includes a second Bluetooth communication unit and a vibration control unit; the second Bluetooth communication unit is used for communication connection with the monitoring terminal; the vibration control unit is used for controlling the wearable device to vibrate when the reading time reaches a preset time to remind the user.

[0028] According to a fourth aspect of the present disclosure, there is provided an electronic device including at least one processor and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the at least one processor realizes the myopia prevention and control method according to any one of the first aspects or the face recognition method according to any one of the second aspects by executing the instructions stored in the memory.

[0029] According to a fifth aspect of the present disclosure, there is provided a computer-readable storage medium storing at least one instruction or at least one segment of program, and the at least one instruction or at least one segment of program is loaded and executed by a processor to realize the myopia prevention and control method according to any one of the first aspects or the face recognition method according to any one of the second aspects.

[0030] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure.

[0031] Implementing the present disclosure has the following beneficial effects:

[0032] The sensor units of the wearable device and the monitoring terminal of the present disclosure collect human body or reading-related data such as human body sitting posture images, desk distances, physiological parameter values, head posture information, or reading time, and the controller processes the human body or reading-related data, so as to obtain the classification result of the human body sitting posture and evaluate the human body sitting posture to remind the user to pay attention to sitting postures, such as the distance between the eyes and the reading tool, the distance between the body and the desk, etc., and monitor the physiological condition of the human body to ensure the user's healthy eye use and reasonable learning and reading.

[0033] According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear. Description of the Drawings

[0034] To more clearly illustrate the technical solutions and advantages in the embodiments of this specification or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of this specification. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0035] Figure 1 A flowchart showing a myopia prevention and control method according to an embodiment of the present disclosure;

[0036] Figure 2 A schematic structural diagram of a neural network of a feature extraction network model according to an embodiment of the present disclosure;

[0037] Figure 3 A schematic structural diagram of the overall feature extraction network model according to an embodiment of the present disclosure;

[0038] Figure 4 A schematic structural diagram of a myopia prevention and control device according to an embodiment of the present disclosure;

[0039] Figure 5 A schematic structural diagram of a myopia prevention and control system according to an embodiment of the present disclosure;

[0040] Figure 6 A schematic structural diagram of a myopia prevention and control system according to another embodiment of the present disclosure;

[0041] Figure 7 A block diagram showing an electronic device according to an embodiment of the present disclosure;

[0042] Figure 8 A block diagram showing another electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[0043] The following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only some embodiments of this specification, rather than all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0044] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0045] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0046] The term "exemplary" used herein means "serving as an example, embodiment or illustration". Any embodiment described herein as "exemplary" does not have to be construed as superior to or better than other embodiments.

[0047] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of multiple or any combination of at least two of multiple. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set composed of A, B, and C.

[0048] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some instances, methods, means, elements and circuits well known to those skilled in the art are not described in detail in order to highlight the gist of the present disclosure.

[0049] Figure 1 A flowchart showing a myopia prevention and control method according to an embodiment of the present disclosure, which is applied to a myopia prevention and control system 100. The myopia prevention and control system 100 includes a monitoring terminal 10 and a wearable device 20. The monitoring terminal 10 includes a first sensor module 11, a first control module 12 and an evaluation module 13. The wearable device 20 includes a second sensor module 21, as Figure 1 shown, the above method includes:

[0050] S101. The first sensor module 11 transmits the first attitude information collected to the first control module 12, and the second sensor module 21 transmits the second attitude information collected to the first control module 12. The first attitude information includes the human sitting posture image, the desk distance, and the physiological parameter value. The second attitude information includes the line-of-sight distance, the head attitude information, and the reading time.

[0051] The myopia prevention and control system 100 includes a monitoring terminal 10 and a wearable device 20. The monitoring terminal 10 can be installed on the desk. Through the transmission and reception of 77GHz millimeter-wave multi-antennas, based on the Doppler effect, the multi-point distances and azimuths of the human body are obtained, and then the human body dot matrix image is depicted. The distance value is used to judge the distance between the chest and the desk in the sitting posture. The monitoring terminal 10 includes a first sensor module 11, a first control module 12, and an evaluation module 13. The wearable device 20 can be a head wearable device or an eye wearable device, such as smart glasses. The wearable device 20 includes a second sensor module 21. The second attitude information is collected through the second sensor module 21. The second attitude information includes the line-of-sight distance, the head attitude information, and the reading time. The second sensor module 21 includes an infrared rangefinder, a gyroscope, a camera, and a timer. The infrared rangefinder can be used to measure the straight-line distance between the eyes of the user's head and the reading tool, that is, the line-of-sight distance. The gyroscope is used to obtain the head attitude information of the user. The camera is used to obtain the image of the user's reading tool to distinguish the type of the reading tool. The timer is used to time the user's eye use time. The second sensor module 21 transmits the obtained second attitude information to the first control module 12 of the monitoring terminal 10 through the Bluetooth communication module.

[0052] The first sensor module 11 includes a millimeter-wave radar. The human sitting posture image is obtained through the millimeter-wave radar. At the same time, the horizontal distance between the user's body and the desk is measured as the desk distance. At the same time, the physiological parameter values such as the human heart rate and respiration can also be monitored through the millimeter-wave radar. The first sensor module 11 obtains the first attitude information and transmits the first attitude information to the first control module 12.

[0053] S102. The first control module 12 inputs the first attitude information and the second attitude information into a preset feature extraction network model for feature extraction to determine the first feature. Among them, the first feature includes the spatial feature and the micro-motion information feature, and inputs the first feature into the attention model for encoding to determine the attention feature; performs feature fusion on the attention feature to determine the second feature; determines the classification result according to the second feature, and transmits the classification result to the evaluation module 13.

[0054] Extract the three-dimensional point map of the human body and perform image preprocessing based on the first pose information and the second pose information. Respectively, use the feature extraction network model to extract features from the original human point cloud image to determine the first feature. The first feature obtains spatial features, micro-motion Doppler features, according to the dynamic time warping algorithm (DTW), Euclidean distance, or Mahalanobis distance. Please refer to Figure 2 , the feature extraction network model used in this disclosure includes 4 convolutional neural network model (CNN) layers, 2 pooling layers (Pooling), 2 fully connected layers, and the activation function softmax. The convolutional neural network includes, but is not limited to, the residual network. Please refer to Figure 3 , input the first feature into the attention encoding model for encoding to determine the attention feature, and perform feature fusion on the attention feature. Feature fusion is the part where the image information feature and the radar information feature parameters in the attention feature overlap, such as speed information and angle information. This part of the information is calibrated and matched with the real information feature label by respectively calibrating and matching the two parts of the information with the real information feature label, and a weight vector (confidence vector) is set for matching to determine the second feature, and the classification result is determined using the second feature.

[0055] S103. The evaluation module 13 determines whether the sitting posture of the human body is qualified according to the classification result. If the sitting posture of the human body is unqualified, the user is reminded to correct the sitting posture.

[0056] Compare the classification result with the standard sitting posture of the human body. If it is unqualified, the user is reminded to pay attention to the sitting posture through voice, and the correct sitting posture is displayed on the display screen to guide the user to correct the sitting posture.

[0057] Through the monitoring terminal 10 of the present disclosure, human-related data such as human sitting posture images, desk distances, and physiological parameter values are collected by sensors, and the human-related data is processed by the controller to obtain the classification result of the human sitting posture, and the human sitting posture is evaluated to remind the user to pay attention to sitting postures, such as the distance between the eyes and the reading tool, the distance between the body and the desk, etc., and monitor the physiological condition of the human body to ensure the user's healthy eye use and reasonable learning and reading.

[0058] In some embodiments, the preset feature extraction network model includes a first feature extraction network model and a second feature extraction network model. The first control module 12 inputs the first pose information and the second pose information into the preset feature extraction network model for feature extraction to determine the third feature, including:

[0059] The first control module 12 inputs the first pose information and the second pose information into the first feature extraction network model to determine the spatial feature;

[0060] The first control module 12 inputs the first attitude information and the second attitude information into the second feature extraction network model to determine the micro-motion information features.

[0061] In some embodiments, the method further includes:

[0062] The first control module 12 obtains the reading time, determines whether the reading time is greater than a preset time, and if it is greater than the preset time, reminds the user through the voice prompt unit.

[0063] The reading time of the user is monitored by the wearable device 20 and transmitted to the first control module 12 of the monitoring terminal 10. After the first control module 12 obtains the reading time of the user, it determines whether the reading time of the user is within the preset time. If it is greater than the preset time, it reminds the user to pay attention to the eye-using time through the voice prompt unit, and displays the reading time on the display screen of the display unit, so that the user can timely understand their own reading time.

[0064] Please refer to Figure 4 , in the second aspect of the present disclosure, a myopia prevention and control device is provided, which is applied to a myopia prevention and control system 100. The myopia prevention and control system 100 includes a monitoring terminal 10 and a wearable device 20. The monitoring terminal 20 includes a first sensor module 11, a first control module 12, and an evaluation module 13. The wearable device 20 includes a second sensor module 21, and includes:

[0065] An information acquisition module 40, configured to enable the first sensor module 11 to transmit the collected first attitude information to the first control module 12, and the second sensor module 21 to transmit the collected second attitude information to the first control module 12. The first attitude information includes a human sitting posture image, a desk distance, and a physiological parameter value, and the second attitude information includes a line-of-sight distance, a head attitude information, and a reading time;

[0066] A classification result determination module 50, configured to enable the first control module 12 to input the first attitude information and the second attitude information into a preset feature extraction network model for feature extraction to determine a first feature, where the first feature includes a spatial feature and a micro-motion information feature, and input the first feature into an attention model for encoding to determine an attention feature; perform feature fusion on the attention feature to determine a second feature; determine a classification result according to the second feature, and transmit the classification result to the evaluation module 13;

[0067] A sitting posture evaluation module 60, configured to enable the evaluation module 13 to determine whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, remind the user to correct the sitting posture.

[0068] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0069] Please refer to Figure 5 , according to the third aspect of the present disclosure, a myopia prevention and control system 100 is provided. The system includes a monitoring terminal 10, a wearable device 20, and a cloud 30;

[0070] The monitoring terminal 10 includes a first sensor module 11, a first control module 12, and an evaluation module 13. The first sensor module 11 is used to collect first posture information, and the first posture information includes a human sitting posture image, a desk distance, and a physiological parameter value. The first control module 12 is used to input the first posture information into a preset feature extraction network model for feature extraction to determine a first feature. Among them, the first feature includes a spatial feature and a micro-motion information feature, and input the first feature into an attention model for encoding to determine an attention feature; perform feature fusion on the attention feature to determine a second feature; determine a classification result according to the second feature, and transmit the classification result to the evaluation module 13; the evaluation module 13 is used to judge whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, the user is reminded to correct the sitting posture;

[0071] The wearable device 20 is communicatively connected to the monitoring terminal 10, and the monitoring terminal 10 is communicatively connected to the cloud 30;

[0072] The wearable device 20 includes a second sensor module 21 and a second control module. The second sensor module 21 is used to collect second posture information and transmit the second posture information to the first control module 12 and the second control module 22. The second posture information includes a line-of-sight distance, a head posture information, a reading tool image, and a reading time. The second control module 22 is used to control the reading time and distinguish reading tools;

[0073] The cloud 30 module includes an expert engine 31 and a public account module 32. The expert engine 31 is used to classify the fatigue state of the user, and the public account module 32 is used to push information on the improvement degree of fatigue.

[0074] Please refer to Figure 6, in some embodiments, the myopia prevention and control system 100 includes a wearable device 20, a monitoring terminal 10, and a cloud 30. The wearable device 20 can be worn by a user on the head, and the monitoring terminal 10 can be set on a desktop; the monitoring terminal 10 includes a first sensor module 11, a first control module 12, and an evaluation module 13. The first sensor module 11 includes a millimeter-wave radar, the first control module 12 includes a terminal processor, and the evaluation module 13 includes a distance calculation unit, an attitude analysis unit, a threshold warning unit, and a reading duration monitoring unit. The monitoring terminal 10 also includes a voice prompt unit, a display screen display unit, a Bluetooth communication module, and a network module; the monitoring terminal 10 is communicatively connected to the wearable device 20 through the Bluetooth communication module; the head wearable device 20 includes a second sensor module 21, a second control module 22, a vibration feedback module, and a Bluetooth communication module. The second sensor module 21 includes an infrared rangefinder, a gyroscope, and a camera. The infrared rangefinder can be used to measure the straight-line distance between the eyes of the user's head and the reading tool. The gyroscope is used to obtain the head attitude of the user, and the camera is used by the user to obtain an image of the user's reading tool to distinguish the type of the reading tool, and transmit the image of the reading tool to the monitoring terminal 10 through the Bluetooth communication module. The monitoring terminal 10 can analyze the type of the reading tool according to the image, and thus control the user's eye usage time according to the different reading times of different reading tools to protect the user's eyes. For example, the reading time threshold for electronic devices can be shorter, and the reading time threshold for books can be longer. In addition, through the vibration feedback module, the user is reminded. For example, when the reading time reaches the preset threshold, the vibration is controlled to remind the user to get up and move around for a while. The cloud 30 module includes a storage unit, an expert engine 31, and a WeChat public account module 32. The storage unit is used to store information of the wearable device 20 and the monitoring terminal 10 of multiple users, and can also store information and data related to the expert engine 31. The expert engine 31 can be any type of expert system, including a rule-based system or a model-based system. For example, the expert engine 31 can use a neural network system, a partial least squares method model, a regression model, etc. Using these models, the expert engine 31 can perform further analysis on the monitoring data transmitted by the wearable device 20 and the monitoring terminal 10 to obtain more accurate analysis results, and finally push the analysis results to the user through the WeChat public account module 32 so that the user can better understand their reading habits, the problems existing in their reading habits, and the directions for correction, etc.

[0075] In some embodiments, the first sensor module 11 includes a millimeter-wave radar, which is used to acquire the human sitting posture image; the first control module 12 includes a first Bluetooth communication unit, a voice prompt unit, a display screen display unit, and a network unit; the first Bluetooth communication unit is used to communicate with the wearable device 20; the voice prompt unit is used to give a voice reminder to the user when the user's sitting posture is incorrect or the reading time exceeds a preset time; the display screen unit is used to display the reading time and the fatigue state.

[0076] In some embodiments, the second sensor module 21 includes a distance measuring sensor, a gyroscope sensor, and a camera;

[0077] The distance measuring sensor is used to acquire the line-of-sight distance, which is the distance between the user's eyes and the reading tool;

[0078] The gyroscope sensor is used to acquire the head posture information;

[0079] The camera is used to acquire the reading tool image.

[0080] In some embodiments, the second control module 22 includes a second Bluetooth communication unit and a vibration control unit; the second Bluetooth communication unit is used to communicate with the monitoring terminal 10; the vibration control unit is used to control the wearable device 20 to vibrate when the reading time reaches the preset time to remind the user.

[0081] The embodiments of the present disclosure also propose a computer-readable storage medium, in which at least one instruction or at least one program segment is stored, and when the at least one instruction or at least one program segment is loaded and executed by a processor, the above method is implemented. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0082] The embodiments of the present disclosure also propose an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to implement the above method.

[0083] The electronic device can be provided as a terminal, a server, or other forms of devices.

[0084] Figure 7 The block diagram of an electronic device according to an embodiment of the present disclosure is shown. For example, the electronic device 800 can be a terminal such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0085] Refer to Figure 7, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0086] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above methods. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0087] The memory 804 is configured to store various types of data to support the operation of the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0088] The power component 806 provides power to various components of the electronic device 800. The power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0089] The multimedia component 808 includes a screen that provides an output interface between the above-mentioned electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The above-mentioned touch sensors can not only sense the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the above-mentioned touch or swipe operations. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0090] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0091] The I / O interface 812 provides an interface between the processing component 802 and a peripheral interface module, and the above-mentioned peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0092] The sensor component 814 includes one or more sensors for providing status evaluations of various aspects of the electronic device 800. For example, the sensor component 814 can detect the on / off state of the electronic device 800, the relative positioning of components, such as the display and keypad of the above-mentioned components for the electronic device 800. The sensor component 814 can also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor component 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 814 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0093] The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices in a wired or wireless manner. The electronic device 800 can access a communication standard-based wireless network, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0094] In an exemplary embodiment, the electronic device 800 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0095] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, and the computer program instructions can be executed by the processor 820 of the electronic device 800 to complete the above method.

[0096] Figure 8 A block diagram of another electronic device according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 can be provided as a server. Referring to Figure 8 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 can include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0097] The electronic device 1900 may further include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSD TM, or the like.

[0098] In an exemplary embodiment, a non - volatile computer - readable storage medium is also provided, such as a memory 1932 including computer program instructions, and the above - mentioned computer program instructions can be executed by a processing component 1922 of the electronic device 1900 to complete the above - mentioned method.

[0099] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer - readable storage medium having thereon computer - readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0100] A computer - readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer - readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non - exhaustive list) of the computer - readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read - only memory (ROM), an erasable programmable read - only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read - only memory (CD - ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer - readable storage medium used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0101] The computer - readable program instructions described herein can be downloaded from a computer - readable storage medium to respective computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter or network interface in each computing / processing device receives the computer - readable program instructions from the network and forwards the computer - readable program instructions for storage in a computer - readable storage medium in each computing / processing device.

[0102] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via an Internet service provider through the Internet). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0103] Aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer - readable program instructions.

[0104] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data - processing apparatus, create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions includes a manufacture that includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0105] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0106] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the boxes may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions.

[0107] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A myopia prevention and control method, applied to a myopia prevention and control system, the myopia prevention and control system comprising a monitoring terminal and a wearable device, the monitoring terminal comprising a first sensor module, a first control module and an evaluation module, the wearable device comprising a second sensor module, characterized in that, Including: The first sensor module transmits the first attitude information collected to the first control module, and the second sensor module transmits the second attitude information collected to the first control module. The first attitude information includes a human sitting posture image, a desk distance, and a physiological parameter value. The second attitude information includes a line-of-sight distance, a head attitude information, and a reading time. The desk distance is used to represent the horizontal distance between the user's body and the desk, and the line-of-sight distance is used to represent the straight-line distance between the user's eyes and the reading tool; The first control module inputs the first attitude information and the second attitude information into a preset feature extraction network model for feature extraction to determine a first feature. Among them, the first feature includes a spatial feature and a micro-motion information feature, and inputs the first feature into an attention model for encoding to determine an attention feature; performs feature fusion on the attention feature to determine a second feature; determines a classification result according to the second feature, and transmits the classification result to the evaluation module; The evaluation module determines whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, it reminds the user to correct the sitting posture.

2. The method according to claim 1, wherein The preset feature extraction network model includes a first feature extraction network model and a second feature extraction network model. The first control module inputs the first attitude information and the second attitude information into the preset feature extraction network model for feature extraction to determine a first feature, including: The first control module inputs the first attitude information and the second attitude information into the first feature extraction network model to determine the spatial feature; The first control module inputs the first attitude information and the second attitude information into the second feature extraction network model to determine the micro-motion information feature.

3. The method according to claim 1, characterized in that The method further includes: The first control module obtains the reading time, determines whether the reading time is greater than a preset time. If it is greater than the preset time, it reminds the user through a voice prompt unit.

4. A myopia prevention and control device is applied to a myopia prevention and control system. The myopia prevention and control system includes a monitoring terminal and a wearable device. The monitoring terminal includes a first sensor module, a first control module, and an evaluation module. The wearable device includes a second sensor module, and is characterized in that, Including: An information acquisition module, which is used for the first sensor module to transmit the first attitude information collected to the first control module, and the second sensor module to transmit the second attitude information collected to the first control module. The first attitude information includes a human sitting posture image, a desk distance, and a physiological parameter value. The second attitude information includes a line-of-sight distance, a head attitude information, and a reading time. The desk distance is used to represent the horizontal distance between the user's body and the desk, and the line-of-sight distance is used to represent the straight-line distance between the user's eyes and the reading tool; A classification result determination module, which is used for the first control module to input the first attitude information and the second attitude information into a preset feature extraction network model for feature extraction to determine a first feature. Among them, the first feature includes a spatial feature and a micro-motion information feature, and inputs the first feature into an attention model for encoding to determine an attention feature; performs feature fusion on the attention feature to determine a second feature; determines a classification result according to the second feature, and transmits the classification result to the evaluation module; A sitting posture evaluation module, which is used for the evaluation module to judge whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, the user will be reminded to correct the sitting posture.

5. A myopia prevention and control system, characterized in that, The system includes a monitoring terminal, a wearable device and a cloud; The monitoring terminal includes a first sensor module, a first control module and an evaluation module. The first sensor module is used to collect first posture information, and the first posture information includes a human sitting posture image, a desk distance, and a physiological parameter value. The desk distance is used to represent the horizontal distance between the user's body and the table. The first control module is used to input the first posture information and the second posture information into a preset feature extraction network model for feature extraction to determine a first feature, where the first feature includes a spatial feature and a micro-motion information feature, and input the first feature into an attention model for encoding to determine an attention feature; perform feature fusion on the attention feature to determine a second feature; determine a classification result according to the second feature, and transmit the classification result to the evaluation module; the evaluation module is used to judge whether the human sitting posture is qualified according to the classification result. If the human sitting posture is unqualified, the user will be reminded to correct the sitting posture; The wearable device is communicatively connected to the monitoring terminal, and the monitoring terminal is communicatively connected to the cloud; The wearable device includes a second sensor module and a second control module. The second sensor module is used to collect the second posture information and transmit the second posture information to the first control module and the second control module. The second posture information includes a line-of-sight distance, a head posture information, a reading tool image and a reading time. The second control module is used to control the reading time and distinguish the reading tool. The line-of-sight distance is used to represent the straight-line distance between the user's eyes and the reading tool; The cloud includes an expert engine and a public account module. The expert engine is used to classify the fatigue state of the user, and the public account module is used to push information on the improvement degree of fatigue.

6. The system according to claim 5, wherein The first sensor module includes a millimeter-wave radar, and the millimeter-wave radar is used to obtain the human sitting posture image; the first control module includes a first Bluetooth communication unit, a voice prompt unit, a display screen display unit and a network unit; the first Bluetooth communication unit is used to communicate with the wearable device; the voice prompt unit is used to give a voice reminder to the user when the user's sitting posture is incorrect or the reading time exceeds a preset time; The display screen display unit is used to display the reading time and the fatigue state.

7. The system according to claim 5, wherein The second sensor module includes a ranging sensor, a gyroscope sensor and a camera; The ranging sensor is used to obtain the line-of-sight distance, and the line-of-sight distance is the distance between the user's eyes and the reading tool; The gyroscope sensor is used to obtain the head posture information; The camera is used to obtain the reading tool image.

8. The system according to claim 5, characterized in that, The second control module includes a second Bluetooth communication unit and a vibration control unit; the second Bluetooth communication unit is used for communication connection with the monitoring terminal; the vibration control unit is used for controlling the wearable device to vibrate when the reading time reaches a preset time to remind the user.

9. A computer-readable storage medium, characterized in that, At least one instruction or at least one program segment is stored in the computer-readable storage medium, and the at least one instruction or at least one program segment is loaded and executed by a processor to implement the myopia prevention and control method as described in any one of claims 1-3.

10. An electronic device, characterized in that, It includes at least one processor and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the at least one processor implements the myopia prevention and control method as described in any one of claims 1-3 by executing the instructions stored in the memory.

Images