Learning behavior monitoring method and device, intelligent table lamp and storage medium

Abstract

The invention discloses a learning behavior monitoring method and device, an intelligent table lamp and a storage medium, and relates to the technical field of intelligent equipment, the method is applied to the intelligent table lamp, and the intelligent table lamp comprises a camera module and a position detection module; the method comprises the following steps: acquiring a scene video through a camera module, and identifying at least one target object in the scene video; acquiring position information corresponding to the target object through a position detection module; identifying behavior information corresponding to the target object according to the position information and the scene video; and outputting preset prompt information through a prompt module under the condition that the behavior information accords with the preset abnormal information. According to the method and the device, bad learning habits or abnormal behaviors of students can be detected in real time and reminded, the students can be helped to realize own behavior problems in the first time and immediately correct the behavior problems, one or more student objects can be identified and monitored, and the specific learning condition of each student can be more accurately known.

Description

Technical Field

[0001] This application relates to the field of smart device technology, and in particular to a learning behavior monitoring method, device, smart desk lamp and storage medium. Background Technology

[0002] Students often find it difficult to maintain a good sitting posture for extended periods while studying. Prolonged poor posture and excessive use of the eyes can easily lead to health problems such as cervical spondylosis and decreased vision, which can have a negative impact on the growth of teenagers.

[0003] Currently, there are some health monitoring devices and applications on the market that can record users' physical activity data, such as steps and sleep duration. However, most of these devices and applications are designed for adult health monitoring and lack professional monitoring and reminder functions for adolescents' learning behaviors. Furthermore, although some electronic devices have built-in screen time tracking functions, they are not integrated with posture monitoring, failing to form a comprehensive learning and health management system.

[0004] Therefore, how to develop technologies to monitor adolescents' learning behaviors in real time, including posture and screen time, so as to provide timely reminders when undesirable behaviors are detected and thus protect adolescents' learning health, has become an urgent problem to be solved in this field.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a method, device, smart desk lamp and storage medium for monitoring learning behavior, aiming to solve the technical problem of how to monitor the learning behavior of teenagers in real time.

[0007] To achieve the above objectives, this application proposes a learning behavior monitoring method, which is applied to a smart desk lamp, the smart desk lamp including: a camera module and a position detection module;

[0008] The method includes:

[0009] The camera module acquires scene video and identifies at least one target object in the scene video;

[0010] The location information corresponding to the target object is obtained through the location detection module.

[0011] Identify the behavioral information corresponding to the target object based on the location information and the scene video;

[0012] If the behavior information matches the preset abnormal information, the preset prompt information is output through the prompt module.

[0013] In one embodiment, the step of identifying at least one target object in the scene video includes:

[0014] Identify at least one object to be confirmed in the scene video;

[0015] Obtain the action video corresponding to the object to be confirmed;

[0016] The action video is input into a preset machine learning model to obtain the target identity type of the object to be identified. The machine learning model is trained using the action video as a sample and the identity information as a label. The target identity type includes: the target to be monitored and the general target.

[0017] If there is at least one target to be monitored, the target to be monitored shall be designated as the target object.

[0018] In one embodiment, the target identity type further includes: a guardian target; after the step of obtaining the target identity type of the object to be confirmed, the method further includes:

[0019] If at least one guardian is identified, the identification of behavioral information corresponding to that guardian shall cease.

[0020] In one embodiment, the behavioral information includes: learning posture and learning duration; the step of identifying the behavioral information corresponding to the target object based on the location information and the scene video includes:

[0021] Obtain the key location information of each key point corresponding to the target object in the location information;

[0022] Identify the learning posture corresponding to the target object based on the key location information described above;

[0023] Determine whether the target object is in a learning state based on the scene video;

[0024] When the target object is detected to have transitioned from a resting state to a learning state, the timing of the learning duration begins.

[0025] When the target object is detected to have changed from the learning state to the resting state, the timing of the learning duration is stopped, and the learning duration is obtained.

[0026] In one embodiment, when there are multiple target objects, the step of outputting preset prompt information through the prompt module when the behavior information matches preset abnormal information includes:

[0027] If the behavior information of any abnormal object among the target objects matches the preset abnormal information, the target prompt information corresponding to the abnormal object is determined from the preset prompt information;

[0028] The target prompt information is output through the prompt module.

[0029] In one embodiment, after the step of obtaining the location information corresponding to the target object through the location detection module, the method further includes:

[0030] The position detection module determines whether the smart desk lamp has moved.

[0031] When the smart desk lamp moves, the motion data of the smart desk lamp is acquired by the position detection module;

[0032] The displacement data of the smart desk lamp is calculated based on the motion data;

[0033] The position information corresponding to the target object is updated based on the displacement data.

[0034] In one embodiment, the smart desk lamp further includes: a light-emitting module and a visible light detection module;

[0035] The method further includes:

[0036] The ambient light properties of ambient light are detected by the visible light detection module.

[0037] The output parameters of the light-emitting module and the acquisition parameters of the camera module are adjusted according to the ambient light properties.

[0038] Furthermore, to achieve the above objectives, this application also proposes a learning behavior monitoring device, which is installed in a smart desk lamp, the smart desk lamp including: a camera module and a position detection module; the device includes:

[0039] The acquisition module is used to acquire scene video through the camera module and identify at least one target object in the scene video;

[0040] The positioning module is used to obtain the location information corresponding to the target object through the location detection module;

[0041] The behavior recognition module is used to recognize the behavior information corresponding to the target object based on the location information and the scene video;

[0042] An anomaly detection module is used to output preset prompt information through a prompting module when the behavior information matches preset anomaly information.

[0043] In addition, to achieve the above objectives, this application also proposes an intelligent desk lamp, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the learning behavior monitoring method described above.

[0044] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the learning behavior monitoring method described above.

[0045] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the learning behavior monitoring method described above.

[0046] This application provides a learning behavior monitoring method. This application uses a camera module to monitor the student's learning scene in real time, identifies one or more student objects (i.e., target objects) that need to be monitored in the current learning scene, and uses a position detection module to detect the position information of the target objects. Then, it uses the position information and the monitored scene video to detect the behavior information of the target objects. If the behavior information matches the preset abnormal information, the prompting module outputs prompt information, thereby prompting students to pay attention when abnormal situations such as incorrect posture or long study time occur.

[0047] In summary, this application uses a camera module to identify whether there are students in a learning environment and how many students there are. It then performs location detection on the students and uses the location information and monitored video footage to identify their behavior. This allows for the immediate detection and alerting of students' poor study habits or abnormal behaviors. This instant feedback mechanism helps students recognize their behavioral problems immediately and correct them, effectively preventing long-term health issues or decreased learning efficiency caused by bad habits. Furthermore, the ability to identify and monitor one or more students allows for a more precise understanding of each student's specific learning situation, providing more personalized guidance and support. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating an embodiment of the learning behavior monitoring method of this application.

[0051] Figure 2 This is a flowchart illustrating the single-person mode of the learning behavior monitoring method provided in Embodiment 1 of this application.

[0052] Figure 3 This is a flowchart illustrating the multi-user mode of the learning behavior monitoring method provided in Embodiment 1 of this application.

[0053] Figure 4 This is a schematic diagram of the module structure of the learning behavior monitoring device according to an embodiment of this application;

[0054] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the learning behavior monitoring method in the embodiments of this application.

[0055] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0057] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0058] The main solution of this application embodiment is: to acquire scene video through the camera module and identify at least one target object in the scene video; to acquire the location information corresponding to the target object through the location detection module; to identify the behavior information corresponding to the target object based on the location information and the scene video; and to output preset prompt information through the prompt module when the behavior information matches preset abnormal information.

[0059] Students often find it difficult to maintain a good sitting posture for extended periods while studying. Prolonged poor posture and excessive use of the eyes can easily lead to health problems such as cervical spondylosis and decreased vision, which can have a negative impact on the growth of teenagers.

[0060] Currently, there are some health monitoring devices and applications on the market that can record users' physical activity data, such as steps and sleep duration. However, most of these devices and applications are designed for adult health monitoring and lack professional monitoring and reminder functions for adolescents' learning behaviors. Furthermore, although some electronic devices have built-in screen time tracking functions, they are not integrated with posture monitoring, failing to form a comprehensive learning and health management system.

[0061] Therefore, how to develop technologies to monitor adolescents' learning behaviors in real time, including posture and screen time, so as to provide timely reminders when undesirable behaviors are detected and thus protect adolescents' learning health, has become an urgent problem to be solved in this field.

[0062] To address the aforementioned issues, this application provides a learning behavior monitoring method. This application uses a camera module to monitor students' learning scenarios in real time, identifies one or more student objects (i.e., target objects) that need to be monitored in the current learning scenario, and uses a position detection module to detect the position information of the target objects. Then, it uses the position information and the monitored scene video to detect the behavioral information of the target objects. If the behavioral information matches the preset abnormal information, the prompting module outputs prompt information, thereby prompting students to pay attention when abnormal situations such as incorrect posture or excessive study time occur.

[0063] In summary, this application uses a camera module to identify whether there are students in a learning environment and how many students there are. It then performs location detection on the students and uses the location information and monitored video footage to identify their behavior. This allows for the immediate detection and alerting of students' poor study habits or abnormal behaviors. This instant feedback mechanism helps students recognize their behavioral problems immediately and correct them, effectively preventing long-term health issues or decreased learning efficiency caused by bad habits. Furthermore, the ability to identify and monitor one or more students allows for a more precise understanding of each student's specific learning situation, providing more personalized guidance and support.

[0064] In this embodiment, for ease of description, the following description uses a smart desk lamp as the execution subject.

[0065] Based on this, embodiments of this application provide a method for monitoring learning behavior, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the learning behavior monitoring method of this application.

[0066] In this embodiment, the method is applied to a smart desk lamp, which includes a camera module and a position detection module; the learning behavior monitoring method includes steps S10 to S40:

[0067] Step S10: Acquire scene video through the camera module and identify at least one target object in the scene video;

[0068] It should be noted that in this embodiment, the smart desk lamp incorporates various modules and control systems into a conventional desk lamp design to achieve the technical effect of monitoring students' learning behavior. The smart desk lamp can have multiple modes: pure desk lamp mode, nighttime wake-up mode, indoor monitoring mode, learning monitoring mode (which can be divided into single-person, multi-person, and mixed modes), rehabilitation training monitoring mode (e.g., eye exercises), and rest mode. The smart desk lamp connects to the network and can upload monitoring and evaluation data to the cloud in real time or at scheduled intervals. Users can access the relevant data through a mobile app.

[0069] It should be noted that, in this embodiment, the hardware of the smart desk lamp mainly includes: an MCU (Microcontroller Unit), a detection module, an interaction module, and a power supply module. The MCU is the main control component of the entire system and can use mature solutions from mainstream IC manufacturers in the smart wearable product industry.

[0070] The power supply module is responsible for supplying power to the system, and mainly includes but is not limited to 5V, 3.3V, 2V8, 1.8V, 1.2V, 0.9V, etc., including power management chips and power-on timing control chips.

[0071] The detection module includes a position detection module, which further includes a light position detection module and a person position detection module. The light position detection module is mainly used for light positioning and orientation. The person position detection module mainly detects the presence, number, and location of people. The above modules are only the main functional categories; some components can be reused in multiple functional modules in different scenarios.

[0072] The interaction module is responsible for human-computer interaction actions, including the camera module. After the camera module captures images or video images, it processes them through SOC (System on Chip) algorithms to provide a basis for subsequent human-computer interaction.

[0073] In this embodiment, based on the aforementioned hardware device, during learning monitoring, the camera module is first activated, which is responsible for capturing high-definition video of the surrounding environment. Through a built-in image recognition algorithm, the device can analyze and identify at least one target object in the scene video. These target objects are typically students within the learning space. The recognition process includes various techniques such as color recognition, shape matching, and facial recognition to ensure accurate capture of all key targets.

[0074] In addition, before identifying target objects through the camera module, the infrared sensor in the position detection module can be used to collect the presence, number, and approximate location of people, so as to make a preliminary judgment on whether there are target objects in the current learning scene and how many there are.

[0075] Furthermore, in step S10 above, the step of identifying at least one target object in the scene video may include steps S11 to S14:

[0076] Step S11: Identify at least one object to be confirmed in the scene video;

[0077] In this embodiment, all possible objects to be identified are used in the scene video captured by the camera module using basic image recognition technology. These objects to be identified can be people, animals, or any objects related to the learning behavior; at this stage, they are collectively considered as potential monitoring targets.

[0078] Step S12: Obtain the action video corresponding to the object to be confirmed;

[0079] In this embodiment, for each object to be verified, its relevant motion video is extracted. The motion video contains key information such as the object's motion trajectory, posture changes, and interactions with other objects, which is crucial for subsequent identity verification.

[0080] Step S13: Input the action video into a preset machine learning model to obtain the target identity type of the object to be confirmed. The machine learning model is trained using the action video as a sample and the identity information as a label. The target identity type includes: the target to be monitored and the general target.

[0081] In this embodiment, the motion video of each object to be identified is then input into a preset machine learning model. This machine learning model is trained based on a large number of motion video samples and corresponding identity information labels, and can accurately identify the identity type of the object in the motion video. In this model, the identity type is divided into two categories: the target to be monitored and general targets. The target to be monitored is usually a person directly related to the learning behavior, such as a student; while general targets may be people or objects that are not directly related to the learning behavior.

[0082] Step S14: If there is at least one target to be monitored, the target to be monitored is taken as the target object.

[0083] In this embodiment, based on the output of the machine learning model, all objects identified as targets to be monitored are selected and used as target objects in subsequent steps. If multiple targets exist, the device will simultaneously track and record their behavioral information.

[0084] In the steps described above, the application of machine learning models enables the device to automatically distinguish between monitored targets and general targets, thereby reducing the possibility of false alarms and missed alarms. This not only improves the accuracy of the monitoring system but also allows the system to focus more on objects directly related to the learned behavior, providing more reliable data support for subsequent behavior analysis and anomaly detection. Furthermore, this method also possesses a degree of versatility and scalability, adapting to the needs of different learning scenarios and objects.

[0085] Furthermore, in one feasible implementation, the target identity type further includes: guardian target; after step S13 above, the method may further include step S15:

[0086] Step S15: If there is at least one guardian target, stop identifying the behavioral information corresponding to the target object.

[0087] In this embodiment, a machine learning model can be trained to identify the guardian in the target by collecting a large amount of information on the actions, faces, and expressions of the guardians during supervision. When at least one guardian is identified, it means that the student is being supervised and no further monitoring is required. To save energy, the algorithm for identifying behavioral information can be stopped directly.

[0088] When the device is a smart desk lamp, you can switch the operating mode of the smart desk lamp to pure desk lamp mode to save energy.

[0089] Step S20: Obtain the location information corresponding to the target object through the location detection module;

[0090] In this embodiment, after identifying one or more target objects, depth information is collected using a depth camera in the position detection module to further obtain the specific location information of each identified target object. The position detection module can be implemented using infrared sensors, depth camera detection, RFID (Radio Frequency Identification) tag readers, or computer vision technology. These technologies can track the movement trajectory of target objects in real time and accurately calculate their specific positions in space, providing crucial data for subsequent behavior analysis.

[0091] Step S30: Identify the behavioral information corresponding to the target object based on the location information and the scene video;

[0092] In this embodiment, location information and scene video are combined, and a behavior recognition algorithm is used to analyze the behavioral information of the target object. Behavioral information may include posture during learning, learning duration, monitoring of learning content, etc. Analyzing learning behavior requires frame-by-frame analysis of the video, as well as extraction and comparison of features such as the target object's posture, movements, and speed. By comparing against a preset behavior pattern library, the device can identify whether the target object is engaging in normal learning activities, such as reading, writing, or group discussions, or engaging in behaviors that may be considered abnormal, such as leaving their seat, using their phone, or dozing off. It can also remind students to take breaks by recording their learning time.

[0093] Furthermore, in one feasible implementation, the behavioral information includes: learning posture and learning duration; the above step S30 may include steps S31 to S35:

[0094] Step S31: Obtain the key location information of each key point corresponding to the target object in the location information;

[0095] In this embodiment, the key position information of each key point corresponding to the target object is first obtained from the location information. These key points include the head, shoulders, hands, feet, etc., and their position information can reflect the overall posture of the target object. Through high-precision human posture recognition technology, the device can capture and record the position changes of these key points in real time.

[0096] Step S32: Identify the learning posture corresponding to the target object based on the key location information.

[0097] In this embodiment, a preset posture recognition algorithm is used to identify the target object's learned posture based on the positional information of each key point. This includes processes such as analyzing the positional relationships of key points, extracting posture features, and comparing them with a learned posture database. Through posture recognition, the device can determine whether the target object maintains a correct sitting, standing, or writing posture, etc.

[0098] Step S33: Determine whether the target object is in a learning state based on the scene video;

[0099] In this embodiment, while recognizing the learning posture, the device also determines whether the target object is in a learning state based on the scene video. This includes analyzing the target object's activities, such as whether they are reading, writing, or using electronic devices to learn. By combining video content and posture information, the device can more accurately determine the target object's learning state.

[0100] Step S34: When the target object is detected to have changed from the resting state to the learning state, the timing of the learning duration begins;

[0101] In this embodiment, once the device detects that the target object has transitioned from a resting state to a learning state, it immediately begins timing the learning duration. This step is triggered based on the result of the learning state identification, as well as the assessment of the stability and duration of the target object's posture. The start of the timing process marks the formal commencement of the learning activity.

[0102] Step S35: When the target object is detected to have changed from the learning state to the resting state, the timing of the learning duration is stopped, and the learning duration is obtained.

[0103] In this embodiment, when the device detects that the target object has transitioned from a learning state to a resting state, it stops timing the learning period and records the final learning duration. This step also relies on the results of the learning state recognition, as well as changes in the target object's posture and activity. By stopping the timing, the device can accurately calculate the total duration of the target object's learning activity.

[0104] Through the steps described above, this application achieves greater accuracy and comprehensiveness in identifying and analyzing the learning posture and duration of target learners. This method not only enhances the monitoring system's ability to identify learning behaviors but also provides richer data support for subsequent behavior analysis and anomaly detection. Furthermore, by accurately recording learning duration, the device can provide learners with valuable feedback on their learning efficiency and habits, helping them to better adjust and optimize their learning behaviors.

[0105] Step S40: If the behavior information matches the preset abnormal information, the preset prompt information is output through the prompt module.

[0106] It should be noted that, in this embodiment, the prompt module may include a speaker, which can be used not only to play prompt sound information, but also to play system audio files and remote voice calls, etc.

[0107] In addition, the device also includes a microphone, which can be used to collect user information in order to complete voice calls or voice control of lamps.

[0108] In this embodiment, once the device detects that the target object's behavioral information matches the preset abnormal information, indicating that the object may have incorrect posture or be studying for too long, the device will immediately output preset prompts through the prompt module. These prompts may be presented in the form of sound, flashing lights, screen display, etc., aiming to promptly remind the target object to adjust its behavior, while also providing a basis for teaching administrators to monitor and intervene.

[0109] Furthermore, in a feasible implementation, when there are multiple target objects, step S40 above may include steps S41 to S42:

[0110] Step S41: If the behavior information of any abnormal object among the target objects matches the preset abnormal information, determine the target prompt information corresponding to the abnormal object from the preset prompt information.

[0111] Step S42: Output the target prompt information through the prompt module.

[0112] In this embodiment, when there are multiple target objects, the system monitors each target object separately and stores the behavioral information of each target object. During anomaly detection, the system also detects each target object based on its respective behavioral information. If the behavioral information of one of the target objects matches a preset anomaly, the device determines the target prompt information corresponding to the anomaly. Then, the corresponding target prompt information is output through the prompt module.

[0113] As an example, when there are three students, A, B, and C, the device monitors and records the behavior information of A, B, and C respectively, and stores it as (A, x), (B, y), and (C, z), where x, y, and z are the behavior information of A, B, and C respectively. When it is detected that C's study time exceeds the preset time, a prompt tone is output: "Please take a break, student C".

[0114] Specifically, when there are multiple students and student identification is required, in one example, the step of identifying at least one target object in the scene video in step S10 above may further include:

[0115] Obtain the facial information of each of the target objects;

[0116] Search for the identity information corresponding to the facial information in a preset identity information database;

[0117] Each of the aforementioned identity information is associated with and stored in relation to the corresponding facial information.

[0118] For example, student identity information can be pre-registered during student registration and associated with facial information. When multiple students are identified, facial recognition can be used to identify each student's identity information separately, and the identified behavioral information can be associated with the identity information of the corresponding target. When the behavioral information of a target matches abnormal information, a prompt message is output based on the identity information corresponding to the target. For example, if two students are identified as "Wang Yi" and "Wang Er" through facial recognition, and "Wang Yi" is detected to have an incorrect posture, the system can output "Please correct your posture, Wang Yi" through a speaker.

[0119] In another example, target objects can be labeled using the acquired location information, following a preset directional order (e.g., from left to right, from front to back). When a target object's behavior information matches an anomaly, a prompt message is output based on the target object's corresponding location label. For example, if three students are identified from left to right based on their location, and the leftmost student's posture is detected as incorrect, the speaker will output "Please correct your posture, leftmost student." Or, if multiple students are present and the second student from the left has exceeded their study time, the speaker will output "Please take a break, second student from the left."

[0120] In another example, the step of identifying at least one target object in the scene video in step S10 above may further include:

[0121] The external features of each target object are identified using an image recognition algorithm.

[0122] Each of the external features is associated with and stored in relation to each of the target objects.

[0123] For example, target objects can also be labeled using external features. These features can include clothing (wearing red clothes), appearance (whether or not glasses are worn), and body features (long hair). Each target object's features must be unique, meaning no other target object has the same feature. After labeling each target object's features, the detected behavioral information is stored accordingly. When a target object's behavioral information is detected to match an anomaly, a prompt is output based on the target object's corresponding feature label. For instance, if three students are wearing blue, red, and black shirts respectively, and the student wearing the blue shirt is detected to have incorrect posture, the system will output through a speaker, "Please correct your posture, student wearing the blue shirt."

[0124] For example, to help understand the implementation process of the learning behavior monitoring method obtained by combining this embodiment with the first embodiment described above, please refer to... Figure 2 , Figure 2 A simplified single-person mode flowchart of a learning behavior monitoring method is provided, specifically:

[0125] When the smart desk lamp starts working in learning monitoring mode, it first detects the number of people using a human infrared sensor, and then takes real-time photos using a camera (RGBCAM). If multiple people are detected in the image, it directly switches to the multi-person mode control flow. If only a single person is in the image, it takes real-time photos using both a camera and a depth camera (TOFCAM) ​​to detect the student's behavior. If incorrect posture is detected, it corrects the posture with voice prompts. Once the student's posture is correct, it checks if the student's study time has exceeded the limit. If so, it switches to the rehabilitation training monitoring mode flow. After starting the rehabilitation training monitoring mode, it prompts the student via voice that eye exercises are about to begin, then plays the eye exercise music. Simultaneously, it takes photos with the camera to determine if the student is performing the eye exercises. If not, it prompts the student to perform the eye exercises via voice prompts. After the eye exercises are completed, it switches to the rest mode flow. After starting work in rest mode, a voice prompt indicates that rest mode is about to begin, and then a pre-set audio file is played to soothe the students' mood. After the predetermined rest time has elapsed, a voice prompt indicates that rest mode is about to end, and then the camera identifies whether the student is ready to study. If the student is ready to study, the process switches to study monitoring mode. If the student is not ready to study and there is ambient light in the room, it means that the student needs to rest, so a voice prompt is given and the desk lamp is turned off. If there is no ambient light in the room and it is nighttime rest time, the process switches to nighttime wake-up mode, turning on the desk lamp when student activity is detected; otherwise, it directly switches to pure desk lamp mode.

[0126] For example, please refer to Figure 3 , Figure 3 A simplified multi-user mode flowchart of a learning behavior monitoring method is provided, specifically:

[0127] After starting work in multi-person mode, the camera takes pictures. If multiple people are studying, the camera and depth camera detect the posture and study time of each person. If the posture is incorrect, voice prompts are used to correct it. If someone exceeds their study time limit, a voice prompt reminds them to take a break. In non-multi-person mode, if it is a single person studying, it switches to single-person mode. If it is a single person studying with a guardian, or if no one is studying, it switches to pure desk lamp mode.

[0128] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Furthermore, after step S20 described above, the method may further include steps A10 to A40:

[0129] Step A10: Determine whether the smart desk lamp has moved using the position detection module;

[0130] It should be noted that, in this embodiment, the position detection module may also include an inertial sensor and a geomagnetic sensor to collect motion data of the device, and then use a machine learning model to estimate the current and past motion speed.

[0131] In this embodiment, the device first determines whether it has moved using a position detection module. The data output by the position detection module is analyzed to detect any significant changes in position or signs of movement.

[0132] Step A20: When the smart desk lamp moves, the motion data of the smart desk lamp is obtained through the position detection module;

[0133] In this embodiment, after confirming that the device has moved, the device further acquires its own motion data through the position detection module. This motion data includes the device's speed, acceleration, direction, etc., which can describe the device's movement trajectory and state in detail.

[0134] Step A30: Calculate the displacement data of the smart desk lamp based on the motion data;

[0135] In this embodiment, the acquired motion data is used to calculate the device's displacement data. The displacement data reflects the change in straight-line distance and direction of the device from its initial position to its current position.

[0136] Step A40: Update the position information corresponding to the target object based on the displacement data.

[0137] In this embodiment, the position information of the target object is updated based on the calculated displacement data. Since movement of the device may cause changes in the scene captured by the camera module, updating the position information is crucial for maintaining the accuracy and continuity of the monitoring data. The device will recalculate the target object's information relative to the new position to ensure that subsequent behavior analysis and anomaly detection can be based on accurate data.

[0138] Through the steps described above, this application can more accurately handle the impact of its own movement on monitoring data. This method not only improves the stability and reliability of the monitoring system but also ensures the continuous provision of accurate target object location information even when the device is moving. This is crucial for maintaining the continuity and consistency of monitoring data and contributes to the accuracy of subsequent behavior analysis and anomaly detection.

[0139] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to the first and / or second embodiments described above can be referred to the above description and will not be repeated hereafter. In addition, the smart desk lamp further includes: a light-emitting module and a visible light detection module;

[0140] The method may further include steps B10 to B20:

[0141] Step B10: Detect the ambient light properties of the ambient light using the visible light detection module;

[0142] In this embodiment, a visible light detection module is used to detect the light properties of the current environment. These light properties include light intensity, color temperature, and illumination uniformity. The visible light detection module can capture and analyze changes in ambient light in real time, providing accurate data support for subsequent parameter adjustments.

[0143] Step B20: Adjust the output parameters of the light-emitting module and the acquisition parameters of the camera module according to the ambient light properties.

[0144] In this embodiment, after acquiring the ambient light properties, the device adjusts the output parameters of the light-emitting module and the acquisition parameters of the camera module based on this data. For the light-emitting module, the device adjusts the brightness or color temperature of the LEDs according to the intensity of the ambient light to ensure that the camera module can capture a clear and bright image. Simultaneously, to avoid damage to the camera module or impact on acquisition performance caused by excessively strong or weak light, the device also adjusts the camera module's exposure time, gain, and other acquisition parameters based on the ambient light properties.

[0145] In addition, the light-emitting module also includes an indicator light that can display multiple colors. Different colors will be displayed for different operating modes, making it easier for users to identify the current operating mode of the device.

[0146] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the learning behavior monitoring method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0147] This application also provides a learning behavior monitoring device, please refer to... Figure 4 The device is installed in a smart desk lamp, which includes a camera module and a position detection module; the learning behavior monitoring device includes:

[0148] The acquisition module 10 is used to acquire scene video through the camera module and identify at least one target object in the scene video;

[0149] The positioning module 20 is used to obtain the location information corresponding to the target object through the position detection module;

[0150] The behavior recognition module 30 is used to recognize the behavior information corresponding to the target object based on the location information and the scene video;

[0151] The anomaly detection module 40 is used to output preset prompt information through the prompt module when the behavior information matches preset anomaly information.

[0152] Optionally, the acquisition module 10 is also used for:

[0153] Identify at least one object to be confirmed in the scene video;

[0154] Obtain the action video corresponding to the object to be confirmed;

[0155] The action video is input into a preset machine learning model to obtain the target identity type of the object to be identified. The machine learning model is trained using the action video as a sample and the identity information as a label. The target identity type includes: the target to be monitored and the general target.

[0156] If there is at least one target to be monitored, the target to be monitored shall be designated as the target object.

[0157] Optionally, the target identity type also includes: guardian target; the learning behavior monitoring device is also used for:

[0158] If at least one guardian is identified, the identification of behavioral information corresponding to that guardian shall cease.

[0159] Optionally, the behavioral information includes: learning posture and learning duration; the behavior recognition module 30 is further used for:

[0160] Obtain the key location information of each key point corresponding to the target object in the location information;

[0161] Identify the learning posture corresponding to the target object based on the key location information described above;

[0162] Determine whether the target object is in a learning state based on the scene video;

[0163] When the target object is detected to have transitioned from a resting state to a learning state, the timing of the learning duration begins.

[0164] When the target object is detected to have changed from the learning state to the resting state, the timing of the learning duration is stopped, and the learning duration is obtained.

[0165] Optionally, when there are multiple target objects, the anomaly detection module 40 is further configured to:

[0166] If the behavior information of any abnormal object among the target objects matches the preset abnormal information, the target prompt information corresponding to the abnormal object is determined from the preset prompt information;

[0167] The target prompt information is output through the prompt module.

[0168] Optionally, the learning behavior monitoring device is further used for:

[0169] The position detection module determines whether the smart desk lamp has moved.

[0170] When the smart desk lamp moves, the motion data of the smart desk lamp is acquired by the position detection module;

[0171] The displacement data of the smart desk lamp is calculated based on the motion data;

[0172] The position information corresponding to the target object is updated based on the displacement data.

[0173] Optionally, the smart desk lamp also includes: a light-emitting module and a visible light detection module; the learning behavior monitoring device is also used for:

[0174] The ambient light properties of ambient light are detected by the visible light detection module.

[0175] The output parameters of the light-emitting module and the acquisition parameters of the camera module are adjusted according to the ambient light properties.

[0176] The learning behavior monitoring device provided in this application, employing the learning behavior monitoring method in the above embodiments, can solve the technical problem of how to monitor the learning behavior of adolescents in real time. Compared with the prior art, the beneficial effects of the learning behavior monitoring device provided in this application are the same as those of the learning behavior monitoring method provided in the above embodiments, and other technical features in the learning behavior monitoring device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0177] This application provides a smart desk lamp, which 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, which are executed by the at least one processor to enable the at least one processor to perform the learning behavior monitoring method in Embodiment 1 above.

[0178] The following is for reference. Figure 5The diagram illustrates a structural schematic suitable for implementing the smart desk lamp of the embodiments of this application. The smart desk lamp in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and smart desk lamps, as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The smart desk lamp shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0179] like Figure 5 As shown, the smart desk lamp may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the smart desk lamp. The processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the smart lamp to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows a smart lamp with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0180] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0181] The smart desk lamp provided in this application, employing the learning behavior monitoring method described in the above embodiments, can solve the technical problem of how to monitor the learning behavior of teenagers in real time. Compared with the prior art, the beneficial effects of the smart desk lamp provided in this application are the same as those of the learning behavior monitoring method provided in the above embodiments, and other technical features of this smart desk lamp are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0182] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0183] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0184] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the learning behavior monitoring method in the above embodiments.

[0185] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0186] The aforementioned computer-readable storage medium may be included in the smart desk lamp; or it may exist independently and not be assembled into the smart desk lamp.

[0187] The aforementioned computer-readable storage medium carries one or more programs. When the one or more programs are executed by the smart desk lamp, the smart desk lamp causes the following: to acquire scene video through the camera module and identify at least one target object in the scene video; to acquire the location information corresponding to the target object through the location detection module; to identify the behavior information corresponding to the target object based on the location information and the scene video; and to output preset prompt information through the prompt module when the behavior information matches preset abnormal information.

[0188] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0189] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0190] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0191] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described learning behavior monitoring method, thereby solving the technical problem of how to monitor the learning behavior of adolescents in real time. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the learning behavior monitoring method provided in the above embodiments, and will not be repeated here.

[0192] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the learning behavior monitoring method described above.

[0193] The computer program product provided in this application can solve the technical problem of how to monitor the learning behavior of teenagers in real time. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the learning behavior monitoring method provided in the above embodiments, and will not be repeated here.

[0194] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for monitoring learning behavior, characterized in that, The method is applied to a smart desk lamp, which includes a camera module and a position detection module. The method includes: The camera module acquires scene video and identifies at least one target object in the scene video. The location information corresponding to the target object is obtained through the location detection module. Identify the behavioral information corresponding to the target object based on the location information and the scene video; If the behavior information matches the preset abnormal information, the preset prompt information is output through the prompt module.

2. The learning behavior monitoring method as described in claim 1, characterized in that, The step of identifying at least one target object in the scene video includes: Identify at least one object to be confirmed in the scene video; Obtain the action video corresponding to the object to be confirmed; The action video is input into a preset machine learning model to obtain the target identity type of the object to be identified. The machine learning model is trained using the action video as a sample and the identity information as a label. The target identity type includes: the target to be monitored and the general target. If there is at least one target to be monitored, the target to be monitored shall be designated as the target object.

3. The learning behavior monitoring method as described in claim 2, characterized in that, The target identity type further includes: guardian target; after the step of obtaining the target identity type of the object to be confirmed, the method further includes: If at least one guardian is identified, the identification of behavioral information corresponding to that guardian shall cease.

4. The learning behavior monitoring method as described in claim 1, characterized in that, The behavioral information includes: learning posture and learning duration; the step of identifying the behavioral information corresponding to the target object based on the location information and the scene video includes: Obtain the key location information of each key point corresponding to the target object in the location information; Identify the learning posture corresponding to the target object based on the key location information described above; Determine whether the target object is in a learning state based on the scene video; When the target object is detected to have transitioned from a resting state to a learning state, the timing of the learning duration begins. When the target object is detected to have changed from the learning state to the resting state, the timing of the learning duration is stopped, and the learning duration is obtained.

5. The learning behavior monitoring method as described in claim 1, characterized in that, When there are multiple target objects, the step of outputting preset prompt information through the prompt module when the behavior information matches preset abnormal information includes: If the behavior information of any abnormal object among the target objects matches the preset abnormal information, the target prompt information corresponding to the abnormal object is determined from the preset prompt information; The target prompt information is output through the prompt module.

6. The learning behavior monitoring method as described in claim 1, characterized in that, After the step of obtaining the location information corresponding to the target object through the location detection module, the method further includes: The position detection module determines whether the smart desk lamp has moved. When the smart desk lamp moves, the motion data of the smart desk lamp is acquired by the position detection module; The displacement data of the smart desk lamp is calculated based on the motion data; The position information corresponding to the target object is updated based on the displacement data.

7. The learning behavior monitoring method as described in any one of claims 1 to 6, characterized in that, The smart desk lamp also includes: a light-emitting module and a visible light detection module; The method further includes: The ambient light properties of ambient light are detected by the visible light detection module. The output parameters of the light-emitting module and the acquisition parameters of the camera module are adjusted according to the ambient light properties.

8. A learning behavior monitoring device, characterized in that, The device is installed in a smart desk lamp, the smart desk lamp including: a camera module and a position detection module; the device includes: The acquisition module is used to acquire scene video through the camera module and identify at least one target object in the scene video; The positioning module is used to obtain the location information corresponding to the target object through the location detection module; The behavior recognition module is used to recognize the behavior information corresponding to the target object based on the location information and the scene video; An anomaly detection module is used to output preset prompt information through a prompting module when the behavior information matches preset anomaly information.

9. A smart desk lamp, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the learning behavior monitoring method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the learning behavior monitoring method as described in any one of claims 1 to 7.

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