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 based on face information 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 invention, bad learning habits or abnormal behaviors of students can be detected in real time and prompted, the students are helped to realize their own behavior problems and correct them immediately, one or more student objects can be identified and monitored, and the specific learning condition of each student can be known more accurately.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent devices, and in particular to a learning behavior monitoring method, device, intelligent desk lamp and storage medium. Background Art

[0002] When students are studying, it is usually difficult for them to maintain a good sitting posture for a long time. Maintaining a bad sitting posture for a long time and overusing the eyes can easily lead to health problems such as cervical spondylosis and vision loss, which will have an adverse impact on the growth of adolescents.

[0003] Currently, there are some health monitoring devices and applications on the market that can record users' physical activity data, such as number of steps, sleep duration, etc. However, most of these devices and applications are aimed at health monitoring of adults and lack professional monitoring and reminder functions for adolescent learning behavior. In addition, although some electronic devices have built-in screen time statistics functions, they are not combined with sitting posture monitoring, and cannot form a comprehensive learning health management system.

[0004] Therefore, how to monitor teenagers' learning behavior in real time, including sitting posture and eye usage time, so as to provide timely reminders when bad behavior is found, thereby ensuring the healthy learning of teenagers, has become an urgent problem to be solved in this field.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

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

[0007] To achieve the above-mentioned purpose, the present application proposes a learning behavior monitoring method, which is applied to a smart desk lamp, and the smart desk lamp includes: a camera module and a position detection module;

[0008] The method comprises:

[0009] Acquire a scene video through the camera module, and identify at least one target object in the scene video based on face information in the scene video;

[0010] Acquiring the position information corresponding to the target object through the position detection module;

[0011] Identify behavior information corresponding to the target object according to the location information and the scene video;

[0012] When the behavior information meets 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 based on face information in the scene video includes:

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

[0015] Obtaining facial information of the object to be confirmed;

[0016] Matching the user information to which the facial information belongs in a preset user information database;

[0017] If the target identity type in the user information is a target to be monitored, the object to be confirmed corresponding to the face information is taken as the target object.

[0018] In one embodiment, after the step of identifying the behavior information corresponding to the target object according to the position information and the scene video, the method further includes:

[0019] Searching for the primary guardian information corresponding to the target object in a preset user information database;

[0020] If the target face information in the primary guardian information does not exist in each of the face information, the behavior information is sent to the primary guardian according to the primary guardian information.

[0021] In one embodiment, the behavior information includes: learning posture and learning duration; the step of identifying the behavior information corresponding to the target object according to the position information and the scene video includes:

[0022] Acquire key position information of each key point corresponding to the target object in the position information;

[0023] Identify the learning posture corresponding to the target object according to each of the key position information;

[0024] Determining whether the target object is in a learning state according to the scene video;

[0025] When it is recognized that the target object is switched from the resting state to the learning state, the timing of the learning time is started;

[0026] When it is identified that the target object is converted from the learning state to the resting state, the timing of the learning time is stopped to obtain the learning time.

[0027] In one embodiment, when there are multiple target objects, when the behavior information meets the preset abnormal information, the step of outputting the preset prompt information through the prompt module includes:

[0028] In the case where the behavior information of any abnormal object among the target objects conforms to the preset abnormal information, determining the target prompt information corresponding to the abnormal object in the preset prompt information;

[0029] The target prompt information is outputted through the prompt module.

[0030] In one embodiment, after the step of acquiring the position information corresponding to the target object by the position detection module, the method further includes:

[0031] Determining whether the smart desk lamp is moved by the position detection module;

[0032] When the smart desk lamp moves, obtaining motion data of the smart desk lamp through the position detection module;

[0033] Calculating displacement data of the smart desk lamp according to the motion data;

[0034] The position information corresponding to the target object is updated according to the displacement data.

[0035] In one embodiment, the smart desk lamp further comprises: a light emitting module and a visible light detection module;

[0036] The method further comprises:

[0037] Detecting the ambient light properties of the ambient light by means of the visible light detection module;

[0038] The output parameters of the light emitting module and the acquisition parameters of the camera module are adjusted according to the ambient light properties.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a learning behavior monitoring device, which is arranged in a smart desk lamp, and the smart desk lamp comprises: a camera module and a position detection module; the device comprises:

[0040] An acquisition module, used to acquire a scene video through the camera module, and identify at least one target object in the scene video based on face information in the scene video;

[0041] A positioning module, used to obtain the position information corresponding to the target object through the position detection module;

[0042] A behavior recognition module, used to recognize behavior information corresponding to the target object according to the position information and the scene video;

[0043] The anomaly detection module is used to output preset prompt information through the prompt module when the behavior information meets the preset abnormal information.

[0044] In addition, to achieve the above-mentioned purpose, the present application also proposes a smart desk lamp, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the learning behavior monitoring method described above.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the learning behavior monitoring method described above are implemented.

[0046] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the learning behavior monitoring method described above are implemented.

[0047] The present application provides a learning behavior monitoring method, which uses a camera module to monitor students' learning scenes in real time, uses face recognition technology to identify 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 object, and then uses the position information and the monitored scene video to detect the behavior information of the target object. If the behavior information is consistent with the preset abnormal information, the prompt information is output through the prompt module, thereby prompting the students to pay attention when the students have abnormal situations such as incorrect posture or long learning time.

[0048] In summary, the present application sets up a camera module to identify whether there are students in the learning scene, how many students there are, and then performs position detection on the students. It then identifies the students' behavior information through the position information and the monitored scene video. It can instantly detect students' bad learning habits or abnormal behaviors and give reminders. This instant feedback mechanism helps students realize their behavioral problems at the first time and correct them immediately, thereby effectively avoiding health problems or decreased learning efficiency caused by long-term bad habits. In addition, it can identify and monitor one or more student objects, so as to understand each student's specific learning situation more accurately and provide more personalized guidance and support for each student. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A flowchart of the first embodiment of the learning behavior monitoring method of the present application is provided;

[0052] Figure 2 A schematic diagram of a single-player mode flow chart provided in Example 1 of the learning behavior monitoring method of this application;

[0053] Figure 3 A multi-person mode flow chart provided for the first embodiment of the learning behavior monitoring method of the present application;

[0054] Figure 4 This is a schematic diagram of the module structure of the learning behavior monitoring device according to an embodiment of the present application;

[0055] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the learning behavior monitoring method in the embodiment of the present application.

[0056] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0058] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of the embodiment of the present application is: obtaining a scene video through the camera module, and identifying at least one target object in the scene video based on the facial information in the scene video; obtaining the position information corresponding to the target object through the position detection module; identifying the behavior information corresponding to the target object based on the position information and the scene video; and outputting a preset prompt information through the prompt module when the behavior information meets the preset abnormal information.

[0060] When students are studying, it is usually difficult for them to maintain a good sitting posture for a long time. Maintaining a bad sitting posture for a long time and overusing the eyes can easily lead to health problems such as cervical spondylosis and vision loss, which will have an adverse impact on the growth of adolescents.

[0061] Currently, there are some health monitoring devices and applications on the market that can record users' physical activity data, such as number of steps, sleep duration, etc. However, most of these devices and applications are aimed at health monitoring of adults and lack professional monitoring and reminder functions for adolescent learning behavior. In addition, although some electronic devices have built-in screen time statistics functions, they are not combined with sitting posture monitoring, and cannot form a comprehensive learning health management system.

[0062] Therefore, how to monitor teenagers' learning behavior in real time, including sitting posture and eye usage time, so as to provide timely reminders when bad behavior is found, thereby ensuring the healthy learning of teenagers, has become an urgent problem to be solved in this field.

[0063] In response to the above problems, the present application provides a learning behavior monitoring method, which uses a camera module to monitor students' learning scenes in real time, uses face recognition technology to identify 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 object, and then uses the position information and the monitored scene video to detect the behavior information of the target object. If the behavior information is consistent with the preset abnormal information, the prompt information is output through the prompt module, so as to remind students to pay attention when students have abnormal situations such as incorrect posture or long learning time.

[0064] In summary, the present application sets up a camera module to identify whether there are students in the learning scene, how many students there are, and then performs position detection on the students. It then identifies the students' behavior information through the position information and the monitored scene video. It can instantly detect students' bad learning habits or abnormal behaviors and give reminders. This instant feedback mechanism helps students realize their behavioral problems at the first time and correct them immediately, thereby effectively avoiding health problems or decreased learning efficiency caused by long-term bad habits. In addition, it can identify and monitor one or more student objects, so as to understand each student's specific learning situation more accurately and provide more personalized guidance and support for each student.

[0065] In this embodiment, for the convenience of description, the following description is made taking the smart desk lamp as the execution subject.

[0066] Based on this, the present application embodiment provides a learning behavior monitoring method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the learning behavior monitoring method of the present application.

[0067] 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:

[0068] Step S10, acquiring a scene video through the camera module, and identifying at least one target object in the scene video based on face information in the scene video;

[0069] It should be noted that, in this embodiment, the smart desk lamp adds various modules and control systems to the 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, night mode, indoor monitoring mode, learning monitoring mode (which can be divided into single-person, multi-person and mixed modes), rehabilitation training monitoring mode (such as eye exercises, etc.), rest mode, etc. The smart desk lamp is connected to the network and can upload monitoring and evaluation data to the cloud in real time or at a scheduled time, and users can obtain relevant data through the mobile phone APP.

[0070] It should be noted that in this embodiment, in terms of hardware, the smart desk lamp mainly includes: MCU (MicroController Unit), detection module, interaction module and power module. MCU is the main control part of the entire system, and mature solutions of mainstream IC manufacturers of smart wearable products in the industry can be used.

[0071] The power module is responsible for supplying power to the system, mainly including 5V, 3.3V, 2V8, 1.8V, 1.2V, 0.9V, etc., including but not limited to these, including power management chip and power-on timing control chip.

[0072] The detection module includes the position detection module, which includes the light position detection module and the human position detection module. The light position state detection module is mainly used to locate the position and direction of the light. The human position detection module is mainly used to detect the presence of people, the number of people, and the position and orientation of people. The above modules are only classified for the main functions. Some devices can be reused in multiple classification function modules in different scenarios.

[0073] The interactive module is responsible for human-computer interaction actions, including the camera module. After the camera module collects pictures or video images, they are processed through the SOC (System on a Chip) algorithm to provide a basis for subsequent human-computer interaction.

[0074] It should be noted that when the smart desk lamp is turned on for the first time, the user needs to connect the smart desk lamp to WI FI, and then select the desk lamp number through the APP corresponding to the smart desk lamp, or automatically match the corresponding smart desk lamp through the same WI FI, bind the smart desk lamp with the APP, and then the user can choose to use the smart desk lamp for home or public use. Home use means use in a home environment, and public use means use in a study room or other areas with large personnel flow. In home mode, the desk lamp enters the home desk lamp learning and monitoring mode. After the desk lamp is restarted, it will no longer prompt for mode selection. In public mode, the desk lamp enters the public desk lamp learning and monitoring working mode. After the desk lamp is restarted, it needs to reconnect to WI FI and bind to the APP.

[0075] In this embodiment, based on the above hardware devices, when learning monitoring is performed, the camera module is first started, which is responsible for capturing high-definition video of the surrounding environment. When the smart desk lamp is used at home, since the flow of people in the family is limited, the number of objects detected by the smart desk lamp is also limited. Therefore, after the family members register their identity information, the identity of the objects in the current scene can be identified through face information. Through the image recognition algorithm of facial recognition, the device can analyze and identify at least one target object in the scene video, which is usually a child or a parent in the family.

[0076] In addition, before identifying the target object through the camera module, the infrared sensor in the position detection module can be used to collect the presence, number and approximate position of people, and make a preliminary judgment on whether there are and how many target objects there are in the current learning scene.

[0077] Furthermore, in the above step S10, the step of identifying at least one target object in the scene video based on the face information in the scene video may include steps S11 to S14:

[0078] Step S11, identifying at least one object to be confirmed in the scene video;

[0079] In this embodiment, in the scene video captured by the camera module, all possible objects to be confirmed are identified using basic image recognition technology. These objects to be confirmed can be people, animals or any objects related to the learning behavior, and they are all regarded as potential monitoring targets at this stage.

[0080] Step S12, obtaining the facial information of the object to be confirmed;

[0081] In this embodiment, for each object to be confirmed, its facial information is extracted, and the facial information includes but is not limited to the overall image of the face, facial contour, position information of key points such as eyes, nose, mouth, and the relative distances between these features.

[0082] Step S13, matching the user information to which the face information belongs in a preset user information database;

[0083] In this embodiment, when the smart desk lamp is used for the first time, the smart desk lamp can prompt the user to enter the user information, and then store the entered user information in a preset user information database. The user information includes the user's identity type, face information, communication method, corresponding primary guardian, etc. The identity type includes but is not limited to: target to be monitored, guardian, guest, etc. When the identity type is a target to be monitored, a guardian identity information can be selected as the primary guardian of the target to be monitored. The communication method includes but is not limited to: mobile phone number, network communication account, client, etc.

[0084] When the user subsequently uses the smart desk lamp, after detecting the facial information of the object to be confirmed in the scene, the facial information is compared with the facial information of each user information in the user information database to match the user information to which the facial information belongs.

[0085] Step S14: If the target identity type in the user information is a target to be monitored, the object to be confirmed corresponding to the face information is used as the target object.

[0086] In this embodiment, according to the result of face recognition, all objects identified as targets to be monitored are screened out and used as target objects in subsequent steps. If there are multiple targets to be monitored, the smart desk lamp will track and record their behavior information at the same time.

[0087] When identifying the target through face recognition, it can automatically distinguish between the target to be monitored and the general target, thereby reducing the possibility of false alarms and missed alarms. This not only improves the accuracy of the monitoring system, but also enables the system to focus more on objects directly related to the learning behavior, providing more reliable data support for subsequent behavior analysis and anomaly detection. In addition, since the computational complexity of face recognition matching is small, the identity can be quickly identified, the response speed is fast, and the user experience is improved.

[0088] In another embodiment, not only facial information can be used as the basis for identification, but also relevant action videos can be extracted for each object to be confirmed. The action video contains key information such as the object's motion trajectory, posture changes, and interaction with other objects. Then, the action video of each object to be confirmed is input into a preset machine learning model. This machine learning model is trained based on a large number of action video samples and corresponding identity information labels, and can accurately identify the identity type of objects in action videos. In this model, identity types are divided into two categories: targets to be monitored and general targets. According to the output results of the machine learning model, all objects identified as targets to be monitored are screened out and used as target objects in subsequent steps. If there are multiple targets to be monitored, the smart desk lamp will track and record their behavior information at the same time.

[0089] In the above steps, the application of machine learning models requires more calculations than face recognition, requires higher computing power of hardware devices, and takes longer to calculate, but it can make the smart desk lamp more accurate in recognition and the recognition results more accurate. At the same time, this method also has certain versatility and scalability, and can adapt to the needs of different learning scenarios and objects.

[0090] Step S20, obtaining the position information corresponding to the target object through the position detection module;

[0091] In this embodiment, after identifying one or more target objects, the depth camera in the position detection module is used to collect depth information 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, wireless radio frequency identification technology) 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 key data for subsequent behavior analysis.

[0092] Step S30, identifying behavior information corresponding to the target object according to the location information and the scene video;

[0093] In this embodiment, the behavior information of the target object is analyzed by combining the location information and the scene video with a behavior recognition algorithm. The behavior information may include posture during learning, learning duration, monitoring of learning content, etc. When analyzing the learning behavior, it is necessary to analyze the video frames frame by frame, as well as extract and compare the characteristics of the target object's posture, movement, speed, etc. By comparing the preset behavior pattern library, the smart desk lamp can identify whether the target object is performing normal learning activities, such as reading, writing, or group discussions, or is performing behaviors that may be considered abnormal, such as leaving the seat, playing with the phone, or dozing off. Students can also be reminded that they need to rest by recording the time they study.

[0094] Furthermore, in a feasible implementation manner, the behavior information includes: learning posture and learning duration; the above step S30 may include steps S31 to S35:

[0095] Step S31, obtaining key position information of each key point corresponding to the target object in the position information;

[0096] In this embodiment, the key position information of each key point corresponding to the target object in the position information is first obtained. 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 smart desk lamp can capture and record the position changes of these key points in real time.

[0097] Step S32, identifying the learning posture corresponding to the target object according to each of the key position information;

[0098] In this embodiment, based on the position information of each key point, a preset posture recognition algorithm is used to identify the learning posture of the target object. This includes the analysis of the position relationship of the key points, the extraction of posture features, and the comparison with the learning posture database. Through the recognition of the learning posture, the smart desk lamp can determine whether the target object maintains the correct sitting posture, standing posture or writing posture.

[0099] Step S33, judging whether the target object is in a learning state according to the scene video;

[0100] In this embodiment, while identifying the learning posture, it is also determined whether the target object is in a learning state based on the scene video. For example, the analysis of the target object's activity content, such as whether it is reading, writing, using an electronic device to learn, etc. By combining the video content and posture information, the device can more accurately determine the target object's learning state.

[0101] Step S34, when it is identified that the target object is switched from the resting state to the learning state, the timing of the learning time is started;

[0102] In this embodiment, once the device recognizes that the target object changes from the resting state to the learning state, it immediately starts timing the learning time. The triggering of this step depends on the result of the learning state recognition and the judgment of the stability and duration of the target object's posture. The start of the timing process marks the official start of the learning activity.

[0103] Step S35, when it is identified that the target object is converted from the learning state to the resting state, the timing of the learning time is stopped to obtain the learning time.

[0104] In this embodiment, when the device recognizes that the target object changes from the learning state to the resting state, it stops timing the learning time and records the final learning time. This step also depends on the result of the learning state recognition and the changes in the target object's posture and activity. By stopping the timing, the device can accurately calculate the total time the target object is in the learning activity.

[0105] Through the above steps, the present application becomes more accurate and comprehensive in identifying and analyzing the learning posture and learning time of the target object. This method not only improves the monitoring system's ability to identify learning behavior, but also provides richer data support for subsequent behavior analysis and anomaly detection. At the same time, through accurate recording of learning time, the device can provide learners with valuable feedback on their learning efficiency and habits, helping them to better adjust and optimize their learning behavior.

[0106] Furthermore, in a feasible implementation manner, after the above step S30, the method may further include steps S301 to S302:

[0107] Step S301, searching for the primary guardian information corresponding to the target object in a preset user information database;

[0108] In this embodiment, in the user information database, each object to be monitored corresponds to a primary guardian. After the behavior information of the target object is obtained, the information of the primary guardian corresponding to the target object is searched in the user information database.

[0109] Step S302: If the target face information in the primary guardian information does not exist in each of the face information, the behavior information is sent to the primary guardian according to the primary guardian information.

[0110] In this embodiment, if the target facial information in the main guardian information does not exist in the facial information of each person in the current scene, it means that the main guardian of the target object is not currently in a state of guardianship. The target object is currently studying alone or in a group or is under the guardianship of other guardians except the main guardian. Therefore, the behavior information of the target object needs to be sent to the main guardian. The sending method can be sending it in the form of text messages, sending it through online social media, or sending it to the client corresponding to the main guardian.

[0111] Step S40: when the behavior information meets the preset abnormal information, the preset prompt information is outputted through the prompt module.

[0112] It should be noted that, in this embodiment, the prompt module may include a speaker, which is used not only for playing prompt sound information but also for playing system audio files and remote voice calls.

[0113] In addition, the device also includes a microphone, which can be used to collect user information in order to complete the voice signal collection work when completing remote voice calls or voice control of the console lamp.

[0114] In this embodiment, once the device detects that the behavior information of the target object matches the preset abnormal information, it means that the object may be sitting in an incorrect posture or studying for too long, and the device will immediately output the preset prompt information through the prompt module. These prompt information may be presented in the form of sound, flashing lights, screen display, etc., in order to timely remind the target object to adjust its behavior, and also provide a basis for monitoring and intervention for teaching management personnel.

[0115] Furthermore, in a feasible implementation manner, when there are multiple target objects, the above step S40 may include steps S41 to S42:

[0116] Step S41, when the behavior information of any abnormal object among the target objects conforms to the preset abnormal information, determining the target prompt information corresponding to the abnormal object in the preset prompt information;

[0117] Step S42: outputting the target prompt information through the prompt module.

[0118] In this embodiment, when there are multiple target objects, when monitoring the target objects, the system monitors each target object separately and stores the behavior information of each target object respectively. When performing abnormality detection, detection is also performed according to the behavior information of each target object respectively. When the behavior information of an abnormal object among the target objects meets the preset abnormal information, the device determines the target prompt information corresponding to the abnormal object. Then the corresponding target prompt information is output through the prompt module.

[0119] As an example, when there are three students A, B, and C, the device monitors and records the behavioral information of A, B, and C respectively, and stores them as (A, x), (B, y), and (C, z), where x, y, and z are the behavioral information of A, B, and C respectively. When it is detected that C's study time exceeds the preset time, a prompt tone is outputted: "Please take a break, student C."

[0120] Specifically, when there are multiple students and the students are to be identified, in an example, after the above step S13, the method may further include:

[0121] Each of the identity information is associated with the corresponding face information and stored.

[0122] For example, when students register, the student identity information can be pre-registered and the identity information can be associated with the face information. When multiple students are identified, the identity information of each student can be identified through face recognition, and the identified behavior information can be associated with the identity information of the corresponding target object. When the behavior information of a target object matches the abnormal information, the prompt information is output according to the identity information corresponding to the target object. For example, through face recognition, two students are identified as "Wang Yi" and "Wang Er". When it is detected that the posture of "Wang Yi" is incorrect, the speaker outputs "Please correct your sitting posture, Wang Yi".

[0123] In another example, each target object can also be labeled by the acquired position information, and each target object can be labeled in a preset direction order (for example, from left to right, from front to back). When the behavior information of a target object matches the abnormal information, the prompt information is output according to the position label corresponding to the target object. For example, through position recognition, there are three students from left to right. When it is detected that the posture of the leftmost student is incorrect, the speaker outputs "please correct the sitting posture of the leftmost student", or when there are multiple people, the study time of the second student from the left has exceeded, and the speaker outputs "please take a rest for the second student from the left".

[0124] In another example, the step of identifying at least one target object in the scene video based on the face information in the scene video in the above step S10 may also include:

[0125] Respectively identifying the external features of each of the target objects by using an image recognition algorithm;

[0126] Each of the external features is associated with each of the target objects and stored.

[0127] Exemplarily, each target object can also be labeled by external features, which may include: clothing features (wearing red clothes), appearance features (whether wearing glasses), human body features (long hair), etc., and the features of each target object need to be unique features, that is, other target objects do not have the same features. After completing the feature labeling of each target object, the detected behavior information is stored accordingly. When the behavior information of a target object is identified to match the abnormal information, the prompt information is output according to the feature labeling corresponding to the target object. For example, three students are wearing blue, red and black tops respectively. When it is detected that the student wearing the blue top has an incorrect sitting posture, the speaker outputs "Please correct the sitting posture of the student wearing the blue top."

[0128] For example, in order to help understand the implementation process of the learning behavior monitoring method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 2 , Figure 2 A brief single-player mode flow chart of a learning behavior monitoring method is provided, specifically:

[0129] When the home smart desk lamp starts working in the learning monitoring mode, it first detects the number of people through the human infrared sensor (Sensor), and then takes pictures in real time through the camera (RGBCAM). If it is recognized that there are multiple people in the picture, it will directly enter the control process in the multi-person mode. When there is only one person in the image, it will take pictures in real time through the camera and depth camera (TOFCAM) ​​to detect the student's behavior information. When it is detected that the student's sitting posture is not correct, it will correct the sitting posture through voice prompts, and send the prompt information and the pictures on the scene to the corresponding primary guardian of the student so that the primary guardian can understand the student's learning situation. When the student's sitting posture is correct, it will determine whether the student's learning time has exceeded. If it has exceeded, it will be transferred to the rehabilitation training monitoring mode process. After the rehabilitation training monitoring mode starts working, it will prompt the student to do eye exercises through voice prompts, and then play the eye exercises music. At the same time, it will take pictures through the camera to determine whether the student is doing eye exercises. If not, the student will be prompted to do eye exercises through voice. After the eye exercises are completed, it will enter the rest mode process. After starting work in rest mode, a voice prompt will be given to inform the student that rest mode is about to begin, and then a set audio file will be played to soothe the student's mood. After the scheduled rest time is reached, a voice prompt will be given to inform the student that rest mode is about to end, and then the camera will be used to identify whether the student is ready to study. If so, the process will enter the study monitoring mode. If the student is not ready to study and there is ambient light in the room, it means the student needs a rest, and a voice prompt will be given to turn off the desk lamp. If there is no ambient light in the room and it is time to rest at night, the process will enter the night waking mode, and the desk lamp will be turned on when the student's movements are detected, otherwise it will directly enter the pure desk lamp mode.

[0130] For example, please refer to Figure 3 , Figure 3 A brief multiplayer mode flow chart of a learning behavior monitoring method is provided, specifically:

[0131] After the multiplayer mode starts working, the camera will be used to shoot. If there are multiple people studying, the camera and depth camera will be used to detect the sitting posture and study time of each target. If the sitting posture is not correct, the voice prompt will be used to correct the sitting posture. When someone's study time exceeds the limit, the voice prompt will be given to the person who has exceeded the limit to take a rest. If there are not multiple people, if it is a single person studying, it will switch to single-person mode. If it is a single person studying with a guardian, or if there is no one studying, it will switch to pure desk lamp mode.

[0132] In addition, whether it is a case of multiple people learning with single-person supervision, multiple people learning with multiple-person supervision, or multiple people learning with no supervision, as long as the primary guardian of a certain object is not in the current scene, the prompt information and on-site pictures corresponding to the object need to be sent to the corresponding primary guardian.

[0133] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction, and will not be repeated later. On this basis, after the above step S20, the method can also include steps A10 to A40:

[0134] Step A10, determining whether the smart desk lamp is moved by the position detection module;

[0135] It should be noted that, in this embodiment, the position detection module may also include an inertial sensor and a geomagnetic sensor to collect the motion data of the device, and then use a machine learning model to estimate the current and past motion speeds.

[0136] In this embodiment, the device first determines whether it has moved by using a position detection module, and analyzes the data output by the position detection module to detect any significant position change or movement signs.

[0137] Step A20, when the smart desk lamp moves, obtaining motion data of the smart desk lamp through the position detection module;

[0138] In this embodiment, after confirming the movement of the device, the device further obtains its own motion data through the position detection module. These motion data include the speed, acceleration, direction, etc. of the device, which can describe the movement trajectory and state of the device in detail.

[0139] Step A30, calculating the displacement data of the smart desk lamp according to the motion data;

[0140] In this embodiment, the acquired motion data is used to calculate the displacement data of the device. The displacement data reflects the linear distance and direction change of the device from the initial position to the current position.

[0141] Step A40: updating the position information corresponding to the target object according to the displacement data.

[0142] In this embodiment, the position information corresponding to the target object is updated according to the calculated displacement data. Since the movement of the device may cause the scene captured by the camera module to change, updating the position information is essential to maintain the accuracy and continuity of the monitoring data. The device will recalculate the information of the target object relative to the new position to ensure that subsequent behavior analysis and anomaly detection can be performed based on accurate data.

[0143] Through the above steps, the application can more accurately handle the impact of its own movement on the monitoring data. This method not only improves the stability and reliability of the monitoring system, but also ensures that accurate target object location information can be continuously provided even when the device is moving. This is crucial to maintaining the continuity and consistency of monitoring data, and helps the accuracy of subsequent behavior analysis and anomaly detection.

[0144] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment and / or the second embodiment can be referred to the above introduction, and will not be described in detail later. On this basis, the smart desk lamp further includes: a light emitting module and a visible light detection module;

[0145] The method may further include steps B10 to B20:

[0146] Step B10, detecting the ambient light attribute of the ambient light by the visible light detection module;

[0147] In this embodiment, the light properties of the current environment are detected by a visible light detection module. These light properties include light intensity, color temperature, illumination uniformity, etc. The visible light detection module can capture and analyze changes in ambient light in real time, providing accurate data support for subsequent parameter adjustments.

[0148] Step B20: adjusting the output parameters of the light emitting module and the acquisition parameters of the camera module according to the ambient light properties.

[0149] In this embodiment, after obtaining the ambient light properties, the device will adjust the output parameters of the light-emitting module and the acquisition parameters of the camera module according to these data. For the light-emitting module, the device will adjust the brightness or color temperature of the LED light according to the intensity of the ambient light to ensure that the camera module can capture clear and bright images. At the same time, in order to avoid damage to the camera module or affect the acquisition effect due to excessive or weak light, the device will also adjust the exposure time, gain and other acquisition parameters of the camera module according to the properties of the ambient light.

[0150] In addition, the light emitting module also includes a prompt 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.

[0151] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the learning behavior monitoring method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0152] This application also provides a learning behavior monitoring device, please refer to Figure 4 The device is arranged on a smart desk lamp, and the smart desk lamp comprises: a camera module and a position detection module; the learning behavior monitoring device comprises:

[0153] The acquisition module 10 is used to acquire the scene video through the camera module, and identify at least one target object in the scene video based on the face information in the scene video;

[0154] A positioning module 20, used to obtain the position information corresponding to the target object through the position detection module;

[0155] A behavior recognition module 30, configured to recognize behavior information corresponding to the target object according to the location information and the scene video;

[0156] The abnormality detection module 40 is used to output preset prompt information through the prompt module when the behavior information meets the preset abnormality information.

[0157] Optionally, the acquisition module 10 is further used for:

[0158] Identifying at least one object to be confirmed in the scene video;

[0159] Obtaining facial information of the object to be confirmed;

[0160] Matching the user information to which the facial information belongs in a preset user information database;

[0161] If the target identity type in the user information is a target to be monitored, the object to be confirmed corresponding to the face information is taken as the target object.

[0162] Optionally, the learning behavior monitoring device is also used for:

[0163] Searching for the primary guardian information corresponding to the target object in a preset user information database;

[0164] If the target face information in the primary guardian information does not exist in each of the face information, the behavior information is sent to the primary guardian according to the primary guardian information.

[0165] Optionally, the behavior information includes: learning posture and learning duration; the behavior recognition module 30 is also used for:

[0166] Acquire key position information of each key point corresponding to the target object in the position information;

[0167] Identify the learning posture corresponding to the target object according to each of the key position information;

[0168] Determining whether the target object is in a learning state according to the scene video;

[0169] When it is recognized that the target object is switched from the resting state to the learning state, the timing of the learning time is started;

[0170] When it is identified that the target object is converted from the learning state to the resting state, the timing of the learning time is stopped to obtain the learning time.

[0171] Optionally, when there are multiple target objects, the anomaly detection module 40 is further configured to:

[0172] In the case where the behavior information of any abnormal object among the target objects conforms to the preset abnormal information, determining the target prompt information corresponding to the abnormal object in the preset prompt information;

[0173] The target prompt information is outputted through the prompt module.

[0174] Optionally, the learning behavior monitoring device is also used for:

[0175] Determining whether the smart desk lamp is moved by the position detection module;

[0176] When the smart desk lamp moves, obtaining motion data of the smart desk lamp through the position detection module;

[0177] Calculating displacement data of the smart desk lamp according to the motion data;

[0178] The position information corresponding to the target object is updated according to the displacement data.

[0179] Optionally, the smart desk lamp further includes: a light emitting module and a visible light detection module; the learning behavior monitoring device is also used for:

[0180] Detecting the ambient light properties of the ambient light by means of the visible light detection module;

[0181] The output parameters of the light emitting module and the acquisition parameters of the camera module are adjusted according to the ambient light properties.

[0182] The learning behavior monitoring device provided by the present application adopts the learning behavior monitoring method in the above embodiment, which 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 learning behavior monitoring device provided by the present application are the same as the beneficial effects of the learning behavior monitoring method provided by the above embodiment, and the other technical features in the learning behavior monitoring device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0183] The present 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 that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the learning behavior monitoring method in the above-mentioned embodiment one.

[0184] Reference below Figure 5 , which shows a schematic diagram of the structure of a smart desk lamp suitable for implementing the embodiment of the present application. The smart desk lamp in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), smart desk lamps, etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The smart desk lamp shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0185] like Figure 5As shown, the smart desk lamp may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the smart desk lamp are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other 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: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the smart desk lamp to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a smart desk lamp with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0186] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0187] The smart desk lamp provided by this application adopts the learning behavior monitoring method in the above embodiment, which 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 by this application are the same as the beneficial effects of the learning behavior monitoring method provided by the above embodiment, and the other technical features of the smart desk lamp are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0188] It should be understood that the various parts disclosed in this application can be implemented by 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 one or more embodiments or examples in a suitable manner.

[0189] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0190] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the learning behavior monitoring method in the above-mentioned embodiment.

[0191] The computer-readable storage medium provided in the present 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 of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. 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 combination with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0192] The computer-readable storage medium may be included in the smart desk lamp; or may exist independently without being assembled into the smart desk lamp.

[0193] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the smart desk lamp, the smart desk lamp: obtains the scene video through the camera module, and identifies at least one target object in the scene video based on the facial information in the scene video; obtains the position information corresponding to the target object through the position detection module; identifies the behavior information corresponding to the target object based on the position information and the scene video; and outputs the preset prompt information through the prompt module when the behavior information meets the preset abnormal information.

[0194] The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "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 an independent software package, partially on the user's computer and partially on the remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can 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 can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0195] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0196] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0197] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned learning behavior monitoring method, and 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-readable storage medium provided in this application are the same as the beneficial effects of the learning behavior monitoring method provided in the above-mentioned embodiment, and will not be repeated here.

[0198] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned learning behavior monitoring method when executed by a processor.

[0199] 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 the beneficial effects of the learning behavior monitoring method provided in the above embodiment, which will not be repeated here.

[0200] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A learning behavior monitoring method, characterized in that: The method is applied to a smart desk lamp, which includes: a camera module and a position detection module; The method comprises: Acquire a scene video through the camera module, and identify at least one target object in the scene video based on face information in the scene video; Acquiring the position information corresponding to the target object through the position detection module; Identify behavior information corresponding to the target object according to the location information and the scene video; When the behavior information meets the preset abnormal information, the preset prompt information is output through the prompt module.

2. The learning behavior monitoring method according to claim 1, characterized in that: The step of identifying at least one target object in the scene video based on the face information in the scene video comprises: Identifying at least one object to be confirmed in the scene video; Obtaining facial information of the object to be confirmed; Matching the user information to which the facial information belongs in a preset user information database; If the target identity type in the user information is a target to be monitored, the object to be confirmed corresponding to the face information is taken as the target object.

3. The learning behavior monitoring method according to claim 2, characterized in that: After the step of identifying the behavior information corresponding to the target object according to the position information and the scene video, the method further includes: Searching for the primary guardian information corresponding to the target object in a preset user information database; If the target face information in the primary guardian information does not exist in each of the face information, the behavior information is sent to the primary guardian according to the primary guardian information.

4. The learning behavior monitoring method according to claim 1, characterized in that: The behavior information includes: learning posture and learning duration; the step of identifying the behavior information corresponding to the target object according to the position information and the scene video includes: Acquire key position information of each key point corresponding to the target object in the position information; Identify the learning posture corresponding to the target object according to each of the key position information; Determining whether the target object is in a learning state according to the scene video; When it is recognized that the target object is switched from the resting state to the learning state, the timing of the learning time is started; When it is identified that the target object is converted from the learning state to the resting state, the timing of the learning time is stopped to obtain the learning time.

5. The learning behavior monitoring method according to claim 1, characterized in that: In the case where there are multiple target objects, when the behavior information meets the preset abnormal information, the step of outputting the preset prompt information through the prompt module includes: In the case where the behavior information of any abnormal object among the target objects conforms to the preset abnormal information, determining the target prompt information corresponding to the abnormal object in the preset prompt information; The target prompt information is outputted through the prompt module.

6. The learning behavior monitoring method according to claim 1, characterized in that: After the step of acquiring the position information corresponding to the target object through the position detection module, the method further includes: Determining whether the smart desk lamp is moved by the position detection module; When the smart desk lamp moves, obtaining motion data of the smart desk lamp through the position detection module; Calculating displacement data of the smart desk lamp according to the motion data; The position information corresponding to the target object is updated according to the displacement data.

7. The learning behavior monitoring method according to 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 comprises: Detecting the ambient light properties of the ambient light by means of 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 arranged on a smart desk lamp, and the smart desk lamp comprises: a camera module and a position detection module; the device comprises: An acquisition module, used to acquire a scene video through the camera module, and identify at least one target object in the scene video based on face information in the scene video; A positioning module, used to obtain the position information corresponding to the target object through the position detection module; A behavior recognition module, used to recognize behavior information corresponding to the target object according to the position information and the scene video; The anomaly detection module is used to output preset prompt information through the prompt module when the behavior information meets the preset abnormal information.

9. A smart desk lamp, characterized in that: The smart desk lamp comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is 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, the steps of the learning behavior monitoring method according to any one of claims 1 to 7 are implemented.

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