Reminding system based on deep learning

Abstract

The invention belongs to the technical field of online learning, and particularly relates to a reminding system based on deep learning, which comprises a login module, a camera module, an identification module and a reminding module, and is characterized in that the login module is linearly connected with the camera module, the camera module is linearly connected with the identification module, and the identification module is linearly connected with the reminding module. The login module is used for login of a user, and the camera module is used for recording changes of the user. According to the invention, through the arrangement of the identification module and the reminding module, when a user uses online learning, the user can be identified through information input of the login module, then the learning time of the user is monitored, and when the learning time is too long, the user can be reminded, so that the situation that the eyes of the user are injured due to long-time online learning of the user is avoided, and the user experience is improved. The user is reminded to pay attention to rest, and the phenomena of blurred vision, eyestrain and the like of the user are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of online learning, and in particular is a reminder system based on deep learning. Background Art

[0002] Network learning refers to a kind of learning activity conducted through computer networks, which is mainly carried out in the form of autonomous learning and negotiated learning. Compared with traditional learning activities, network learning has the following three characteristics: first, it shares rich network learning resources; second, it takes individual autonomous learning and collaborative learning as the main forms; third, it breaks through the time and space limitations of traditional learning.

[0003] As the existing Internet is relatively common, online learning has gradually become a way to improve personal cultivation. However, online learning requires users to concentrate for a long time, which will cause damage to the user's eyes and cause blurred vision, eye fatigue and other phenomena.

[0004] Therefore, a deep learning-based reminder system is needed to solve the above-mentioned problems. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a reminder system based on deep learning, which effectively solves the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: a reminder system based on deep learning, comprising a login module, a camera module, a recognition module and a reminder module, wherein the login module is linearly connected to the camera module, the camera module is linearly connected to the recognition module, the recognition module is linearly connected to the reminder module, the login module is used for user login, and the camera module is used for recording user changes;

[0007] The recognition module includes a facial recognition unit and a glasses recognition unit. The facial recognition unit is used to record changes in the user's face and to identify the user's identity. A timer is set in the facial recognition unit. When the user switches faces, the timer will automatically reset. The timing period of the timer is set manually.

[0008] Furthermore, the login module includes an information login unit and a registration unit, the information login unit supports facial login and identity information input, and the registration unit is used to enter the user's identity.

[0009] Furthermore, the camera module includes a facial camera unit and an eye tracking unit, wherein the facial camera unit is used to record the state of the user's face, and the eye tracking unit is used to track and record the movement trajectory of the user's eyes.

[0010] Furthermore, the facial recognition unit can recognize the facial features of the user and mark the facial features. Users with or without hair are marked as set A, users with hair are marked as AY, and users without hair are marked as AN. Users with single or double eyelids are marked as set B, users with single eyelids are marked as BY, and users with double eyelids are marked as BN. Whether the user's nose is complete is marked as set C, users with complete noses are marked as CY, and users with incomplete noses are marked as CN. Whether the users' ears are complete is marked as set D, users with complete ears are marked as DY, and users with incomplete ears are marked as DN. Whether the lips are complete is marked as set E, users with complete lips are marked as EY, and users with incomplete lips are marked as EN.

[0011] Furthermore, the timer in the facial recognition unit will record the length of time the user appears in the video. When the user moves out of the shooting of the facial camera unit, the timer will re-record the time and retain the original time. When the user changes, the facial recognition unit will quickly identify the user and match the user's information to identify the user's information. The timer will then reset. When the facial camera unit does not capture the user within thirty minutes, the login module will automatically close, requiring the user to log in again.

[0012] Furthermore, when the eye tracking unit records the movement of the eyeballs of the same user for one hour, the reminder unit will be triggered. At the same time, when the eye tracking unit records that the eyeballs of the same user do not move for ten minutes, the reminder unit will also be triggered.

[0013] Furthermore, the reminder module includes a light reminder unit and a sound playing unit. The light reminder unit is used to control the flashing of the light, and the color of the light can be set by oneself. The sound playing unit is used to control the emission of the sound, and the sound played can be set by oneself.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. The present invention, through the setting of the identification module and the reminder module, can identify the user by entering the information of the login module when the user uses online learning, and then monitor the user's learning time. When the learning time is too long, the user can be reminded to avoid damage to the user's eyes caused by long-term online learning, remind the user to pay attention to rest, and avoid blurred vision, eye fatigue and the like.

[0016] 2. The present invention can quickly identify the user and make a quick comparison through the user's marks by the facial recognition unit and the glasses recognition unit, which is beneficial to the operation of the reminder system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0018] Figure 1 It is a structural schematic diagram of the present invention; DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Embodiment 1, by Figure 1 The present invention discloses a reminder system based on deep learning, including a login module, a camera module, a recognition module and a reminder module, wherein the login module is linearly connected to the camera module, the camera module is linearly connected to the recognition module, the recognition module is linearly connected to the reminder module, the login module is used for user login, and the camera module is used for recording user changes;

[0021] The login module includes an information login unit and a registration unit. The information login unit supports face login and identity information input, and the registration unit is used to enter the user's identity;

[0022] The camera module includes a facial camera unit and an eye tracking unit. The facial camera unit is used to record the state of the user's face, and the eye tracking unit is used to track and record the movement trajectory of the user's eyes.

[0023] The recognition module includes a facial recognition unit and a glasses recognition unit. The facial recognition unit is used to record changes in the user's face and to identify the user's identity. A timer is set in the facial recognition unit. When the user switches faces, the timer will automatically reset. The timing period of the timer is set manually. The facial recognition unit can recognize the user's facial features and mark the facial features. Users with or without hair are marked as set A, users with hair are marked as AY, users without hair are marked as AN, users with single or double eyelids are marked as set B, users with single eyelids are marked as BY, and users with double eyelids are marked as BN. , the users whose noses are complete are marked as C set, the users whose noses are complete are marked as CY, the users whose noses are incomplete are marked as CN, the users whose ears are complete are marked as D set, the users whose ears are complete are marked as DY, the users whose ears are incomplete are marked as DN, the users whose lips are complete are marked as E set, the users whose lips are complete are marked as EY, and the users whose lips are incomplete are marked as EN; after the eye tracking unit records the movement of the eyeballs of the same user for one hour, the reminder unit will be triggered, and at the same time, the reminder unit will be triggered if the eye tracking unit records that the eyeballs of the same user do not move for ten minutes;

[0024] The reminder module includes a light reminder unit and a sound playback unit. The light reminder unit is used to control the flashing of the light, and the color of the light can be set by yourself. The sound playback unit is used to control the sound emission, and the sound played can be set by yourself.

[0025] The timer in the facial recognition unit will record the length of time the user appears in the video. When the user moves out of the facial camera unit's shooting, the timer will re-record the time and retain the original time. When the user changes, the facial recognition unit will quickly identify the user and match the user's information. After identifying the user's information, the timer will reset. When the facial camera unit does not capture the user within thirty minutes, the login module will automatically close and the user will be required to log in again.

[0026] Embodiment 2, by Figure 1 Given, taking a normal person as an example, the facial features are normal, the eyelids are double-fold, and there is no missing facial features;

[0027] The present invention discloses a reminder system based on deep learning, comprising a login module, a camera module, a recognition module and a reminder module, wherein the login module is linearly connected to the camera module, the camera module is linearly connected to the recognition module, the recognition module is linearly connected to the reminder module, the login module is used for user login, and the camera module is used for recording user changes;

[0028] The login module includes an information login unit and a registration unit. The information login unit supports face login and identity information input, and the registration unit is used to enter the user's identity;

[0029] The camera module includes a facial camera unit and an eye tracking unit. The facial camera unit is used to record the state of the user's face, and the eye tracking unit is used to track and record the movement trajectory of the user's eyes.

[0030] The recognition module includes a facial recognition unit and a glasses recognition unit. The facial recognition unit is used to record the changes in the user's face and to identify the user's identity. A timer is set in the facial recognition unit. When the user switches faces, the timer will automatically reset. The timing cycle of the timer is set manually. The facial recognition unit can recognize the user's facial features and mark the facial features. Users with hair are marked as AY, users with double eyelids are marked as BN, users with complete noses are marked as CY, users with complete ears are marked as DY, and users with complete lips are marked as EY.

[0031] When the eye tracking unit records the movement of the eyeballs of the same user for one hour, the reminder unit will be triggered. At the same time, when the eye tracking unit records the same user's eyeballs not moving for ten minutes, the reminder unit will also be triggered.

[0032] The reminder module includes a light reminder unit and a sound playback unit. The light reminder unit is used to control the flashing of the light, and the color of the light can be set by yourself. The sound playback unit is used to control the sound emission, and the sound played can be set by yourself.

[0033] The timer in the facial recognition unit will record the length of time the user appears in the video. When the user moves out of the facial camera unit's shooting, the timer will re-record the time and retain the original time. When the user changes, the facial recognition unit will quickly identify the user and match the user's information. After identifying the user's information, the timer will reset. When the facial camera unit does not capture the user within thirty minutes, the login module will automatically close and the user will be required to log in again.

[0034] By using the facial recognition unit and the glasses recognition unit to identify the user, the user can be quickly identified and quickly compared, which is beneficial to the operation of the reminder system.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A reminder system based on deep learning, comprising a login module, a camera module, a recognition module and a reminder module, characterized in that: The login module is linearly connected to the camera module, the camera module is linearly connected to the identification module, the identification module is linearly connected to the reminder module, the login module is used for user login, and the camera module is used to record user changes; The recognition module includes a facial recognition unit and a glasses recognition unit. The facial recognition unit is used to record changes in the user's face and to identify the user's identity. A timer is set in the facial recognition unit. When the user switches faces, the timer will automatically reset. The timing period of the timer is set manually.

2. A deep learning-based reminder system according to claim 1, characterized in that: The login module includes an information login unit and a registration unit. The information login unit supports face login and identity information input, and the registration unit is used to enter the user's identity.

3. A deep learning-based reminder system according to claim 2, characterized in that: The camera module includes a facial camera unit and an eye tracking unit. The facial camera unit is used to record the state of the user's face, and the eye tracking unit is used to track and record the movement trajectory of the user's eyes.

4. A deep learning-based reminder system according to claim 3, characterized in that: The facial recognition unit can identify the facial features of the user and mark them. Users with or without hair are marked as set A, users with hair are marked as AY, and users without hair are marked as AN. Users with single or double eyelids are marked as set B, users with single eyelids are marked as BY, and users with double eyelids are marked as BN. Whether the user's nose is complete is marked as set C, users with complete noses are marked as CY, and users with incomplete noses are marked as CN. Whether the user's ears are complete is marked as set D, users with complete ears are marked as DY, and users with incomplete ears are marked as DN. Whether the lips are complete is marked as set E, users with complete lips are marked as EY, and users with incomplete lips are marked as EN.

5. A deep learning-based reminder system according to claim 4, characterized in that: The timer in the facial recognition unit will record the length of time the user appears in the video. When the user moves out of the shooting of the facial camera unit, the timer will re-record the time and retain the original time. When the user changes, the facial recognition unit will quickly identify the user and match the user's information to identify the user's information. The timer will then reset. When the facial camera unit does not capture the user within thirty minutes, the login module will automatically close and the user will be required to log in again.

6. A deep learning-based reminder system according to claim 5, characterized in that: When the eye tracking unit records the movement of the eyeballs of the same user for one hour, the reminder unit will be triggered. At the same time, when the eye tracking unit records that the eyeballs of the same user do not move for ten minutes, the reminder unit will also be triggered.

7. A deep learning-based reminder system according to claim 6, characterized in that: The reminder module includes a light reminder unit and a sound playing unit. The light reminder unit is used to control the flashing of the light, and the color of the light can be set by oneself. The sound playing unit is used to control the emission of the sound, and the sound played can be set by oneself.

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